THE APPLICATION AND MODELLING POSSIBILITES OF CVT IN TRACTOR

System Modeling and SimulationSystem modeling and simulation is a critical process that helps organizations to design, develop, and test complex systems before they are implemented. It involves creating a virtual model of the system and simulating its behavior under different conditions to identify potential issues and optimize its performance. The use of system modeling and simulation has become increasingly important in various industries, including aerospace, automotive, defense, healthcare, and manufacturing. In this response, I will discuss the importance of system modeling and simulation, its benefits, challenges, and future trends.First and foremost, system modeling and simulation help organizations to reduce the risk of failure and save costs. By creating a virtual model of the system, engineers can identify potential issues and optimize its performance before it is implemented. This helps to reduce the risk of failure and minimize the cost of rework. For example, in the aerospace industry, system modeling and simulation are used to test the performance of aircraft before they are built. This helps to identify potential issues and optimize the design, which can save millions of dollars in development costs.Secondly, system modeling and simulation help organizations to improve their decision-making process. By simulating the behavior of the system under different conditions, engineers can evaluate the impact of different design choices and make informed decisions. This helps to reduce the risk of making costly mistakes and ensures that the system meets the requirements of the stakeholders. For example, in the healthcare industry, system modeling and simulation are used to evaluate the impact of different treatment options on patients. This helps doctors to make informed decisions and provide the best possible care to their patients.Thirdly, system modeling and simulation help organizations to improve their productivity and efficiency. By simulating the behavior of the system, engineers can identify potential bottlenecks and optimize the system's performance. This helps to improve productivity and reduce the time and cost of production. For example, in the manufacturing industry, system modeling and simulation are used to optimize the production process and reduce the time and cost of production.However, there are also challenges associated with system modeling and simulation. One of the biggest challenges is the complexity of the systems being modeled. As systems become more complex, it becomes increasingly difficult to create an accurate model and simulate its behavior. This can lead to inaccurate results and increase the risk of failure. Another challenge is the availability of data. In order to create an accurate model, engineers need access to a large amount of data. However, in some cases, data may not be availableor may be difficult to obtain.Looking into the future, there are several trends that are likely to shape the future of system modeling and simulation. One of the trends is the use of artificial intelligence (AI) and machine learning (ML) to improve the accuracy of models. AI and ML can help to identify patterns in data and create more accurate models. Another trend is the use of cloud computing to improve the scalability and accessibility of system modeling and simulation. Cloud computing allows engineers to access powerful computing resources and collaborate with others in real-time.In conclusion, system modeling and simulation are critical processes that help organizations to design, develop, and test complex systems before they are implemented. They help to reduce the risk of failure, improve decision-making, and improve productivity and efficiency. However, there are also challenges associated with system modeling and simulation, such as the complexity of the systems being modeled and the availability of data. Looking into the future, there are several trends that are likely to shape the future of system modeling and simulation, including the use of AI and ML and cloud computing.。

cdmp p级题库The problem at hand is related to the CDMP P-level question bank. To address this issue, it is important to consider various perspectives and provide a comprehensive response.From an educational standpoint, having a well-designed and comprehensive question bank is crucial for the success of any certification program. The CDMP P-level question bank plays a significant role in assessing the knowledge and skills of individuals seeking certification in data management. It is essential that the question bank covers a wide range of topics, including data governance, data quality, data modeling, and data integration, among others. By ensuring a diverse and comprehensive set of questions, the question bank can effectively evaluate the proficiency of candidates in various areas of data management.Moreover, the quality of the question bank is of utmost importance. The questions should be carefully crafted totest not only the candidates' theoretical knowledge but also their practical understanding and application of concepts. Each question should be clear, concise, and unambiguous to avoid any confusion or misinterpretation. Furthermore, the question bank should be regularly updated to keep up with the evolving field of data management. This can include incorporating new technologies, industry best practices, and emerging trends in data management.In addition to the educational perspective, it is crucial to consider the perspective of the candidates. The CDMP P-level certification is a significant milestone for individuals in their data management careers. Therefore, the question bank should provide a fair and accurate representation of the knowledge and skills required at this level. Candidates should feel confident that the questions are relevant, challenging, and aligned with the expectations of the industry. This will ensure that the certification holds value and serves as a credible recognition of their expertise in data management.Furthermore, it is important to address the emotionalaspect of the problem. Candidates invest significant time, effort, and resources in preparing for the CDMP P-level certification. They may experience various emotions, such as anxiety, stress, and excitement, throughout the preparation process. The question bank should be designedin a way that supports candidates' emotional well-being and motivates them to succeed. This can be achieved by providing clear instructions, offering practice questions, and providing feedback on performance. Additionally, the question bank should be accessible and user-friendly, allowing candidates to navigate through the questions easily.From a practical standpoint, the CDMP P-level question bank should be easily accessible to candidates. It should be available in various formats, such as online platforms or downloadable resources, to cater to different learning preferences. The question bank should also be accompanied by detailed explanations and references to relevant study materials. This will enable candidates to understand the rationale behind the correct answers and further enhance their knowledge in specific areas. Moreover, the questionbank should include a sufficient number of questions to adequately assess the candidates' proficiency level.In conclusion, the CDMP P-level question bank plays a crucial role in evaluating the knowledge and skills of individuals seeking certification in data management. It is essential to ensure that the question bank is comprehensive, of high quality, and regularly updated. Additionally, it should be designed to support candidates' emotional well-being and provide a fair representation of the knowledgeand skills required at the P-level. By considering these perspectives and addressing the various aspects of the problem, the CDMP P-level question bank can effectively serve its purpose and contribute to the success of data management professionals.。

什么是宏平均（macro-average）和微平均（micro-average）什么是宏平均（macro-average）和微平均（micro-average）Fri, 05/14/2010 - 14:53 — Fuller宏平均（macro-average）和微平均（micro-average）是衡量文本分类器的指标。

根据Coping with theNews: the machine learning wayWhen dealing with multiple classes there are two possible ways of averaging thesemeasures(i.e. recall, precision, F1-measure) , namely, macro-average andmicro-average. The macro-average weights equally all the classes, regardless of how manydocuments belong to it. The micro-average weights equally all the documents, thus favouringthe performance on common classes. Different classifiers will perform different in commonand rare categories. Learning algorithms are trained more often on more populated classesthus risking local over-fitting.宏平均指标相对微平均指标而言受小类别的影响更大文章《一种快速高效的文本分类方法》给出了几个文本分类性能评估的公式。

对于给定的某个类别，a 表示被正确分到该类的实例的个数，b 表示被误分到该类的实例的个数，c 表示属于该类但被误分到其它类别的实例的个数，则准确率（p）和召回率（r）和F-指标分别被定义为：r = a / (a + c), if a + c > 0; otherwise r = 1p = a / (a + b), if a + b > 0; otherwise p = 1其中参数β 用来为准确率（p）和召回率（r）赋予不同的权重，当β 取1 时，准确率和召回率被赋予相同的权重。

The application of the competency model for a project manager in aconstruction companyProject manager is the core of the construction engineering project management. Competency model can drive people pay attention to mining their deep implicit characteristics to achieve excellent performance. Therefore, the engineering project manager competency model can help enterprises better predict and select those project manager with high performance.Project managers competency modelGeneral competencyTeam building and development project manager can form the whole project management team effectively and develop the team potential.Leadership motivation skills. project managers can use reasonable means to motivate managers and workers to let them contribute an own strength until the endAnalytical thinking Able to know the cause of a incurred problems and handle themeffectively.Risk awareness project managers can analyze the various affecting factors indifferent stages and avoid them, then control the risk of theproject.Emergency strain project managers can take effective method to ensure projectquality, progress, safety when appeared situationsAchievement desire project manager has a eager for success, setting high goals for self and staff. And improve desire and motivation of performance and efficiency in order to achieve these goals.Self-awareness and introspection project manager can clearly know themselves, knowing what can do and what to do,and reflect their mistakes, then avoid the problem occurrence.responsibility consciousness project managers understand their responsibility and can take the responsibility for their mistakes.Self-confidence project managers believe they can complete the projectsuccessfully through their skills.Ability to adapt to new environment. project manager can adapt quickly and achieve high performance no matter new site or new team.Personal charm project manager has ability to let subordinate willing to contributehigher performance without increase costGood physicalqualityproject manager can be able to do the hard work of project work.information search ability. according to keen judgment, project managers can search information through various channels,analyze the project, grasp the potential risks or opportunities.Discriminating competencyControl plan project manager can control all aspects of the plan duringthe construction process, providing guarantee for thecompletion.Reserves of professional knowledge project manager master the necessary professional knowledge and apply it effectively in the process of project management.Communication skills project manager own effective communication skills andapply them in the process of project management.Arrange time reasonably, improve work efficiency project managers can allocate their time effectively, thus improve their work performance.Keep good relationship with stakeholder project manager can effectively balance the interests of all stakeholders of the project.Regard customer’s satisfaction as the central task of the project management.Be familiar with the organization's background and culture.project manager is aware of situations and agree with organization culture.Its application in recruitmentopen selection process based on the competency model:1. Planning bidding project manager job description. The construction regulations and requirements of the tender documents should be based on the investment scale, adding the corresponding competent factors;2. The formation of judges. Choose 5 person as the judges from the HR department, engineering department, general labor office, preparing for the recruitment plan; organize employees to post messages; experts and leaders are responsible for the qualification examination and candidates competency tests;3. Ask experts to calculate the importance degree for each competency factor. In the form of 0, the factor’s weight is weighted averaging and calculated by the analytic hierarchy process (ahp).4. Release recruitment information.5. requirements examination. If yes, anticipating a physical examination, and those with good physical quality can enter the competency assessment ;otherwise pass out.6. competency assessment .First determine assessment content, factors, classification criteria and the evaluation factors weight;Next combining behavioral interviews or assessment center;Then uses the fuzzy mathematics method to calculate competitors’ scores;Final evaluation results are divided into excellent, good, qualified and unqualified. Top three can enter speech link , the rest can register into the enterprise backup project manager training library.7.Making a speech. The content should include the basic information, work experience and work performance, competition advantage,position working thought, management plan. Public juries score.8.Attendees vote democratic, and the highest scored one will be appointed as project manager.Its application in the training and developmentBased on the project manager competency model, the training is designed to cultivate key quality characteristics and enhance the ability to achieve high performance, adapt to changing environment and develop competency potential.Training development steps:1. Clear corporate strategic objectives and customer/working environment, clear what project manager needs to do.2. From the strategic orientation analysis. In terms of work and task analysis, we need clear the degree a project manager should do according to the competency model and professional standard behavior;In terms of personnel and performance, we need clear that the project manager actual do according to the performance evaluation results and personal career development planning.3. Compare and get the cause of poor performance or some qualities that are not up to par.4. Make training methods and training content according to the results.5. Execute and evaluate the result of project and course.6. Feedback and guidance to the project manager.7. Make a table of name, performance gaps and classes participation, classes hours. Training mode selectionManagers belong to action learning styles, prefer to learn from the individual experience.Case analysis Video or movie Role playSand table simulationApplied lecture Thesis writing Knowledge teaching teaching program Read the article Project field inspection Topics for discussionTraining structure designActive degreeIts application in the performance managementCompetency model of the performance management process:1 make enterprise strategic plan. Establish annual management policy andmanagement plan, determine the critical success factors( CSF), host the annualmanagement review.2 Establish enterprise management goal and plan. implement performancecompletion and improvement, develop a action plan based on the CSF andshortcomings.3 Monitoring the project manager performance. Established KPI index systemunder the framework of balanced scorecard and monitor business planexecution, reacting enterprise's overall operating ability.4 Examine project manager’s performance. Evaluate the project managerperformance and assess the project manager’s qualified quality.5 Use the performance appraisal results. Tie the performance with pay andpersonnel deployment, to carry out training plan, to carry out the core talentmanagement.Objectiv e 1. help project manager to achieve high performance;2. Enables the project manager have competency to contribute;3. Strengthen enterprise continuous attention and ability development to project managersIn the implementation phase, based on the target decomposition and strategic KPI system, decision-makers can tell the project manager what is the standard of excellent performance, or what project managers can contribute to the firm, or What is his obligation, etc.When making evaluation and feedback,using competency model raises new challenges. First of all, decision makers should not only realize the deficiency of the project manager but also see their potentials; Second, decision makers should clearly know what resources and conditions needed to inspire theirs potential, including training plan, specific incentives and management measures,etc.Anyhow, For the project manager, the building model can be used in HR management module and will have a far-reaching influence to improve the enterprises’ performance. Its application in the compensation managementThe salary incentive system basing on the competency is mainly considered "compensation and post competency" matching problem, connecting competency with employees' pay directly, thus we can carry on the scientific,comprehensive and accurate evaluation to the staff's competence.For project managers, according to competency assessment results, combined with responsibilities, division of labor, and the performance result, we established the competence oriented project compensation incentive system.Establishing project managers competency compensation system, follow the steps: 1, convert competence level into the pay level 2, determination the compensation levels 3,compensation design 4,the compensation structure 5, salary adjustment.1.Broadband salary dividedcompensation grade total points of competenceevaluationA 800-1000B 600-800C 400-600D 200-400E 0-200A, B, C, D, E represent salary level. Every competency has a index weight about 5%and points grade about 50. Then competency assessment implementation team (project members and each head of the departments) evaluate. And compensation level is determined by managers' competency assessment total points, the higher the total points, the higher the compensation level.2.The determination of compensationEnterprises adopt indirect link, namely competency level partly affects wages, and wages also link to post value and petence evaluation divided into five levels, each given a different compensation.compensation grade compensation level(yuan) competency assessment totalpoints800-1000 A 8000-10000600-800 B 6000-8000400-600 C 4000-6000200-400 D 2000-40000-200 E 0-2000Floating salary part is from zero to ten thousand yuan, divided into five intervals. Then match the employee's competency level evaluation to the corresponding compensation grade and compensation level.3, compensation designDeterminate total wages. According to the average wages calculate total wages, then decide salary provision ratio.A fixed salary points and floating salary points. According to the project manager competency level, market salary level and salary strategy determine the pay points. Bonus design.The influencing factors:1. total bonus;2. pay points, represent employee's competency level and their value to the company;3. personal evaluation coefficient; 4. Months.Bonus point value * = point value* salary points * personal assessment results* months. Welfare design.Design method: (1) determine total welfare, extract the total welfare according to the certain proportion of total wages;(2) staff appraisal is consistent with personal welfare;(3) pay points, the higer competency level, the greater the amount of welfare;(4) effective months, employee welfare months subtract holidays.4, The compensation composition structure5. Competency salary adjustmentSalary adjustme nt Overallsalaryadjustmentsalary level Salary provision scaleBonus provision scaleWelfare provision scaleFixed salary fixed salary points &floating salary pointsCompetency level changessalarypointsadjustmentCompetencylevel changesCompetency hierarchiesThe proportion of competence factorsTotal salary salary BonusWelfare monthly fixed salary: determined by competency factors. equivalent of previous post salary, seniority salary and allowance.determined by competency factors.determined by competency factors. commercial insurance, travel and medical, etc.。

Flame capturing with anadvection-reaction-diffusion modelNatalia Vladimirova†,V.Gregory Weirs†,andLenya Ryzhik‡†ASC/Flash Center,Department of Astronomy&Astrophysics,The Universityof Chicago,Chicago,IL60637,USA‡Department of Mathematics,The University of Chicago,Chicago,IL60637,USAAbstract.We conduct several verification tests of the advection-reaction-diffusionflame capturing model,developed by Khokhlov(1995)for subsonicnuclear burning fronts in supernova simulations.Wefind that energy conservationis satisfied,but there is systematic error in the computedflame speed due tothermal expansion,which was neglected in the original model.We decouplethe model from the full system,determine the necessary corrections for thermalexpansion,and then demonstrate that these corrections produce the correctflamespeed.Theflame capturing model is an alternative to other popular interfacetracking techniques,and might be useful for applications beyond astrophysics.(7March2005)1.Introduction1.1.Modelling aflame as an interfaceFor simulations of systems involvingflames,several strategies for computing theflame are available.If theflame length scale is not much smaller than the computational domain,then highly detailed kinetics models,high-order discretizations,and adequately posed initial and boundary conditions can be used to probe the structural subtleties of theflame.At the other extreme areflames so much smaller than the computational domain they are impossible to resolve numerically;then the most common approach is to model theflame as a discontinuity in the thermodynamic variables.A number of techniques dealing with infinitely thin interfaces are available in the literature[12,9].Among them are front tracking methods,known for better accuracy; level set methods,which easily treat topological changes;and volume-of-fluid methods, which possess an intrinsic conservation property.All these methods do the essentially the same thing:starting with the current position of the interface and its speed of propagation,compute the position at a later time.Each method strikes a different compromise between computational cost,accuracy,development and implementation cost,and other desirable properties.However,few applications can take advantage of the commonality among the interface ers cannot easily swap interface methods in their codes as requirements change–allowing one to use the technique with the best properties for,say,the available computational power,or a new regime of phenomena they want to investigate.When an interface method is incorporated into a physical model,it is encumbered with additional restrictions.For example,the volume of incompatible fluid on both sides of the interface must remain unchanged,or thermodynamic quantities across theflame front must satisfy the Rankine-Hugoniot jump conditions, or a biological interface might set conditions on elasticity or surface charge.The challenge of building a simulation tool involving an interface technique is not in choosing or implementing the technique itself,but in coupling the interface technique with the other physics models.The biggest challenge in representing aflame as an infinitely thin interface is to correctly model the coupling between theflame speed and thermodynamic conditions on each side of theflame.The difficulty is reduced if theflame speed can be assumed to be independent of thermodynamic states and thefluid on both sides sides of the flame can be assumed incompressible.If these conditions are satisfied,the system can be modelled without reconstructing the interface(without determining the precise location of the interface as it crosses each computational cell,)which permits taking full advantage of the level set approach[11].However,if theflame speed depends on the local thermodynamics or the incompressibility assumption is invalid,then interface reconstruction is unavoidable[13].Moreover,modelling a compressiblefluid implies solving the energy equation,which introduces another constraint associated with the interface:the transformation of chemical energy to thermal energy must be consistent with the transformation of unburned mass(reactant)to burned mass(product).1.2.Original ARD modelIn1995Khokhlov developed aflame model which combines the automatic treatment of interface topology with an energy conservation mechanism[7].The easy treatment offlame topology follows from an implicit interface representation.Similar to the level set method where the interface is described by a scalar level set variable,the interface in Khokhlov’s method is described by a scalar reaction progress variable. The value of the reaction progress variable is zero in the reactant,one in the product, and monotonically varies inside a“flame region.”The width of theflame region is much larger than the thickness of the physicalflame it represents;it is set by the model to be several computational zones thick.Unlike the level set variable,which in the vicinity of theflame is interpreted as the signed distance to the interface,but far from the interface has no physical meaning, the reaction progress variable is relevant everywhere.It can be associated with the mass fraction of burned material in a cell.In this way,the reaction progress variable is similar to the volume fraction variable in the volume offluid method.But contrary to the volume fraction variable,the reaction progress variable distributes the interface over several computational cells.The evolution of the reaction progress variable is described by the advection-reaction-diffusion(ARD)equation.The reaction progress variable is advected by the flow,diffuses,and is generated in some simple source term,here called reaction.In the limit of fast reaction rate and small diffusivity(thinflame limit)the advection-reaction-diffusion equation is equivalent to the level set equation and describes the front governed by Huygens’s principle[6,10].The front’s speed of propagation with respect to theflowfield depends on diffusivity and reaction rate.In Khokhlov’s model,however,diffusivity and the reaction rate have very little to do with the physical properties of thefluid.They are artificial parameters chosen with the sole purpose of producing a desiredflame speed and front thickness.The ARD equation is added to a system of equations representing other types of physics,e.g.the Euler equations with a suitable state equation.The ARD model “gets input from physics”through the advection velocity and theflame speed,which can be determined dynamically from the local conditions.Theflame model is coupled back to theflowfield through a source term in the energy equation:the amount of burned material,and consequently the energy released,is proportional to the change in reaction progress variable.1.3.Extending the ARD modelThe original ARD model was designed to represent deflagrations in a white dwarf star,as an initial stage of a Type Ia supernova explosion.Although thefluid in the burned and unburned regions cannot be assumed incompressible because of strong gravitational stratification,the local conditions in the vicinity of theflame are almost incompressible.Near the center of the white dwarf,the thermonuclearflame is extremely thin(10−8–10−6of the star radius),highly subsonic,and is characterized by a small density decrease across theflame(about10%),and essentially no jump in pressure(less than1%)[16].To extend the model to the compressible regime,modifications are required to account for velocity variations on the scale of the interface thickness.Velocity variations come from the density jump between unburned and burnedfluids:when material crosses theflame front it expands and accelerates,resulting in normal velocity variations of the order of theflame speed.The original method was built on the implicit assumption that these velocity variations do not affect the travelling wave speed;the control parameters for the model were chosen based on the properties of the isolated ARD equation(discussed in Sec.2.)Consequently the original ARD model fails to recover the properflame speed unless thermal expansion is negligible.Our solution to this problem is based on a physical interpretation of the ARD equation,which allows us to decouple it from the mass,momentum,and energy equations.This approach is described in Sec.3.We can then study the ARD equation in isolation tofind the effect of thermal expansion and consequent velocity variation on theflame speed,as explained in Sec.4.Knowing the effects of thermal expansion,we suggest modifications to the control parameters of theflame capturing model in Sec.5. With the modifications,the model recovers the desiredflame speed,as demonstrated through verification tests in Sec.6.Section7gives suggestions regarding the model’s use and implementation and potential avenues for improvement.2.Properties of the advection-reaction-diffusion equationThe evolution of the reaction progress variableφis described by the advection-reaction-diffusion equation,1φt+v·∇φ=κ∇2φ+reaction progress variable is scaled so thatφ=0andφ=1represent pure reactant and pure product,respectively.In the absence of advection,the velocity v=0,and the ARD equation describes travelling wave solutions.For a given reaction rate,R(φ),the travelling wave speed s0is determined by the reaction timeτ,and the diffusion coefficientκ.In cases when the dependence R(φ)is simple enough,the travelling wave speed can be obtained analytically.One reaction rate for which the analytical front propagation speed is known is the Kolmogorov-Petrovskii-Piskunov(KPP)reaction rate[8,5],1R(φ)=κ/τ.The KPP reaction rate is often used as a source term in advection-reaction-diffusion models because it makes such models more accessible for rigorous analysis(see reviews[17,1]).Another reaction rate we consider is a reaction rate of“ignition”type,where reaction is impossible until the reaction progress variable reaches some critical value, i.e.R(φ)=0forφ<φ0.Ignition–type reaction terms are widely used to model combustion processes(see review[17]),in particular,for approximating the behavior of Arrhenius-type chemical reaction rates.The original ARDflame-capturing method[7] was developed with a reaction rate of ignition type,specifically,the top-hat reaction rate:R(φ)=0,φ<φ0,R(φ)=R0,φ0≤φ<1,(3)R(φ)=0,φ≥1,where R0is a constant chosen such that the travelling wave speed is s0=κ/τcan always be achieved by multiplying the reaction rate by a constant.)In Appendix1we show the analytical solution of equation(1)with the top-hat reaction rate and v=0,and derive the expression for R0.Another parameter,which can be constructed fromκandτ,has the dimension of√length.We call it the reaction length scaleδ0=Our goal is to solve the ARD equation as one member of a system of equations, in which the velocityfield is computed as part of the solution.In the fully coupled system,exothermic reaction in theflame causes a reduction in the density,i.e.thermal expansion.Mass conservation then requires acceleration of thefluid as it passes through theflame,with consequent alteration of theflame speed.In the next section,we will derive an expression for the velocity profile across the fully coupledflame as a function of the reaction progress variable.Then,using this velocity profile,we will study the ARD equation in isolation.We will show that for a given density ratio,theflame speed and theflame thickness depend on the diffusion coefficient and the reaction time,and we will determine the dependencies,s=s(κ,τ)and l=l(κ,τ).(5) Finally,a comment about terminology and notation.When the velocity profile accounts for thermal expansion,we call s the variable-densityflame speed.The spatially-constant velocityfield does not account for thermal expansion,so we can refer to s0as the isochoricflame speed.The quantities s0andδ0are not directly relevant to the variable-density case,but we still use them as reference quantities, s0≡ κτ,(6) based on diffusivity and reaction time.3.Coupling advection-reaction-diffusionflame model toflow physicsA natural way to couple the ARDflame equation to theflow physics comes from the physical interpretation of the reaction progress variable as the mass fraction of the product.If the rate of conversion from reactant to product isρ˙φ,and q is the energy released per unit mass of convertedfluid,an additional heat release term appears in the energy equation.Then the full system of governing equations becomes,∂ρ∂t +∇(ρvv)+∇P=f,∂ρE∂t+∇·(ρφv)=ρ˙φ,˙φ=κ∇2φ+12is the total energy and e=e(ρ,P)is specific thermal energy,where the functional relationship is specified by the equation of state.Note that by substitutingρ˙φfrom the equation for reaction progress variable into the energy conservation equation,we obtain the conservation of energyρE′=ρ(e−qφ)+ρvvrelease,q,are input parameters in theflame capturing model.The diffusion coefficient κand the reaction timescaleτin the ARD equation are not related to the physical diffusion or reaction,but chosen solely to produce a desiredflame speed and a specified flame width.For a givenflame speed andflame thickness,the diffusion coefficient and the reaction timescale can be found from relations(5),κ=κ(s,l)andτ=τ(s,l).(8) If all goes well,the observedflame speedˆs and thicknessˆl will be the same as the input s and l.Unfortunately,at this point relations(5),and consequently(8),are available only for an isochoricflame.Extension to the compressible regime requires the velocity profile across theflame interface as function of the reaction progress variable,which we develop next.We consider a one-dimensional laminarflame with no external force applied to thefluid.We assume the system(7)has reached a stable travelling wave solution. Then,in the reference frame of the travelling wave,the mass,momentum,and energy conservation equations have a simple algebraic form:ρv=const,ρv2+P=const,(9)v ρ(e−qφ)+P+ρv2˜α(φ),v=v u−s(˜α(φ)−1),(10)P=P u−ρu s2(˜α(φ)−1).Here˜α(φ)is the ratio of the(partially burned)fluid density to the unburnedfluid density and is a function of the reaction progress variable only.The functional relationship˜α(φ)depends on the equation of state.For instance, for the gamma-law equation of state,e=1ρ,and one can derive˜α(φ)=1+11−2ǫ1ǫ2φ ,(11)withǫ1=(γ+1)s2c2u−s2,where c u=assumes a one-dimensional flame propagating to the right,but can be generalized to the multi-dimensional case,v (φ)=v u +n φ∆v f ,where n is the unit normal to the front pointing in the direction of flame propagation.Note that no assumptions have been made on ǫ2,i.e.the parameter ǫ2≈γ−1P u need not be small.The velocity jump ∆v f is a direct consequence of thermal expansion.Thermalexpansion influences the flame speed through two competing effects.On one hand,the burned fluid moves away from the flame at a greater velocity,and this slows the flame.On the other hand,expansion thickens the reaction zone,which increases the amount of fuel burned per unit time;consequently the flame speed is increased.To show these competing effects,we integrate the one-dimensional form of the ARD equation in (7)or the equivalent isolated ARD equation (1),with the velocity profile in (10),and solve for the flame speed,obtaining s =s 0 +∞−∞R d (x/δ0)ρ(φ)dφ,where s 0and δ0are defined in (6).Compared to the isochoric case,the burning region in the variable-density case is wider,leading to a larger integral in the numerator.At the same time,the ratio ρu /ρ(φ)is always greater than one,so the integral in the denominator is also larger.The difference between s and s 0depends both on the distribution of φ(x )across the interface and the density dependence ρ(φ)on the reaction progress variable.In general,the variable-density flame speed can be larger or smaller than the isochoric flame speed.For a general equation of state,an analytic expression for ˜α(φ)might not be available,but simplifications might still be possible.In the zero Mach-number limit,kinetic energy can be neglected in (9).This implies the pressure is constant across the flame,and the energy equation reduces to a purely thermodynamic relation.Thus,the state at any stage of reaction (at any φ)can be found from any known state by solvinge (ρ,P )+P4.Numerical solution of isolated ARD equation withφ-dependent velocityNow that we have derived an expression(12)for v(φ)we return to the solution of equation(1)with an advection velocity which accounts for thermal expansion.For some simple reaction rates the variable-densityflame speed can be found analytically.The KPP reaction rate(2)has a single stable point,φ=1,and metastable point,φ=0,and is characterized by the condition that the function R(φ)is positive and convex on the interval0<φ<1.In Appendix2we show that if the velocity is as in(12),there is only one way to connect metastable and stable points on the phase diagram(φ,˙φ).The solution corresponds to theflame speed s=s0for any∆v f<0.Unfortunately,the analytical approach above does not extend to reaction rates with multiple stable and unstable points,and thus cannot be used for reaction rates of“ignition”type,such as the top-hat reaction rate(3).In this section we solve the ARD equation(1)numerically,using theφ-dependent advection velocity(12).This exercise has two purposes.First,we want to verify the independence of theflame speed on the jump∆v f for the KPP reaction rate,and to measure the effect of the jump for the top-hat reaction rate.Doing so,we will consider well-resolved numerical solutions of equation(1).Second,we want to study the effect of spatial resolution.In theflame-capturing model,the ARD equation is used at low spatial resolutions to mimic the discontinuity. Keeping the front thin reduces the effect of background velocity and thermodynamic variations,which are unavoidable in the full system and which might modify the internalflame structure.At the same time,we will keep the time step significantly smaller than both diffusive and advection CFL limits,assuming that in a compressible flow simulation the time step is set by the CFL condition based on the speed of sound. The small time step ensures that numerical errors due to temporal discretization are dominated by spatial discretization errors.We discretize equation(1)using fourth-order central differences in space and the explicit Euler method in time.Thefluid velocity profile is specified by(12).The KPP reaction rate is given in(2)and the top-hat rate in(3).We express resolution in terms of computational cells across the reacting region,b=l/∆x,where l is the width of the isochoricflame(4).The time step is typically∆t=10−3τ,which is at least two orders of magnitude smaller than the diffusive and advection CFL limits.We repeated a number of cases with different time step sizes and using the 3rd-order Adams-Bashforth time advancement algorithm and obtained essentially the same results.Tofind the effect of thermal expansion,wefirst conduct simulations for an isochoricfluid,∆v f=0,then for an expandingfluid with∆v f=−s0.To test the influence of the simulation reference frame,we consider three velocities in the unburned gas:v u=−s0,v u=0,and v u=+s0.If theflame speed were not affected by thermal expansion,then∆v f=−s0would correspond to a density ratio ofα=2, and the background velocity v u=−s0would place the expansion-independentflame stationary in the observer’s frame of reference.The second value,v u=0,corresponds to the reference frame with stationary reactant,andfinally,v u=s0,corresponds to the frame with stationary product.Of course,if theflame speed is influenced by thermal expansion,then theflame,reactant,and product will not remain stationary in the respective reference frames.0 1 23 1 2 48 16 32s / s ob top-hatsolid: ∆v f = 0; dashed: ∆v f = s o 0 12 3 1 2 4 8 16 32s / s ob KPPsolid: ∆v f = 0; dashed: ∆v f = s o Figure 1.Dependence of the travelling wave speed on resolution and simulationreference frame.Reaction rates:top-hat,left;KPP,right.Simulation reference frame:v u =−s 0,squares;v u =0,circles;v u =s 0,triangles.Solid linecorresponds to isochoric case (∆v f =0)and dashed line corresponds to variable-density case (∆v f =−s 0).0.00.20.40.60.81.01.20 1 2 34 5 6s / s o - ∆v f / s o Figure 2.The travelling wave speed as function of expansion parameter ∆v f .Reaction rate:KPP,circles;top-hat,squares.The dashed line shows theapproximation with quadratic function,s/s 0=1−k 1(−∆v f /s 0)+k 2(−∆v f /s 0)2,where k 1=0.2982and k 2=0.0156.The dotted line shows the linearapproximation,s/s 0=1−0.3(−∆v f /s 0).Simulations were performed in thereference frame of the unburned fluid,v u =0,at resolution b =32.The flame speed,s ,measured for the isochoric and variable-density cases,is shown in Fig.1.As resolution increases,the flame speed for the KPP reaction rate converges to s =s 0for both cases.This confirms the flame speed is independent of the expansion parameter ∆v f ,for the KPP rate.For the top-hat reaction rate,the flame speed converges to s =s 0in the isochoric case.However,the variable-density flame speed converges to s =0.72s 0,independent of the reference frame.Next we quantify the difference between the isochoric and variable-density flame speeds as a function of the expansion parameter.We have measured flame speeds for several different different values of ∆v f at high resolution.The results for both top-hat and KPP reaction rates are shown inFig.2.As expected,theflame speed for the KPP rate does not depend on∆v f.Forthe top-hat reaction rate and small jump∆v f,theflame speed can be approximatedby a linear function.The expressions,KPP:s=s0,top-hat:s≈s0+0.3∆v f,(14) summarize our analytical and numerical study of the isolated ARD equation forvelocity profiles which account for thermal expansion.Advection against theflame(v u=−s0)at low resolutions is challenging for the numerical scheme for both KPP and top-hat reaction rates,but especially forKPP.We observe that oscillations precede the front and begin to burn,increasing the effectiveflame speed.It can be shown that without the reaction term,discretizing theadvection term with central differences implies a limit on the cell Reynolds number|v|∆x/κ<1/2,the violation of which results in an oscillatory solution[15].The derivation in[15]is for the advection-diffusion equation,discretized with second-ordercentral differences,and the value(1/2)of the limit is specific to that discretization.Nevertheless,we noticed a strong correlation between resolution and velocity and the appearance of oscillations in theflame front,with corresponding errors in theflame speed.For the ARD equation,the above cell Reynolds number constraint for theadvection-diffusion equation can be expressed in terms of the number of grid pointsper interface.Note that we use fourth-order differences,so these are just estimates. We obtain b≥8|v|/s0for the KPP reaction rate,and b≥2|v|/s0for the top-hat reaction rate.The coefficient in front of|v|/s0depends on reaction rate because of our definition of theflame front resolution,b.Recall that to match theflame thicknesses for the different rates we specified(4),so that at the same resolution,the diffusion coefficient for the KPPflame is one-fourth that for the top-hatflame.One way to avoid the restriction on the cell Reynolds number is to use an upwinddiscretization of the scheme advection terms.In later sections we will show resultsobtained with such a scheme,PPM,and we will see that it delivers much better results for the under-resolved cases.5.Input parameters forflame capturing modelAnalyzing the isolated ARD equation in the previous section,we treated theflame speed as an output of the problem.We wanted to know how s depends on the model parameters:diffusion coefficientκ,reaction timescaleτand the velocity jump∆v f. To use the ARD equation as aflame-capturing model,we want to solve the inverse problem.We want to specify s as an input and compute the corresponding model parameters;if the model behaves correctly,then the observedflame speedˆs in our simulations will be the same as the inputflame speed s.The parameters for theflame capturing model can be calibrated using(14),which expresses the dependence of s on∆v f.Recall that in the full system,the velocity jump is due to thermal expansion and is directly related to the the density ratio. Substituting∆v f=−(α−1)s in(14)and solving for theflame speed,we obtain s=s0/a,whereKPP:a=1,top-hat:a=a(α)≈1+0.3(α−1).Thus,we express theflame speed in terms of density ratioα,a more accessible quantity than the velocity jump across the interface.Next,using the definitions(6)we obtain the diffusion coefficient and the reaction time,κ=asδ0,τ=δ0/(as).(15) The length scaleδ0is related to the isochoricflame thickness as in(4).The variable-densityflame thickness is usually larger than the isochoricflame thickness.(We could calibrate the parameters to match theflame thickness,as well as the speed,but have not done so.)This section concludes our modifications to the ARDflame capturing model.In comparison to the original ARD model,we have added a calibration factor to the mapping of s andδ0toκandτ.This factor accounts for the effect thermal expansion on theflame speed.We have empirically determined the dependence of s on the density change across theflame for the top-hat rate(Fig.(2),)and the corresponding calibration factor,valid for small density jumps.We have also analytically and numerically demonstrated that for the KPP reaction rate,the calibration is not needed because theflame speed is insensitive to thermal expansion.In the next section we will demonstrate that with our calibration,theflame-capturing model with the top-hat rate is effective.6.Verification of the modelThe results above were obtained with a“prototype”code,which solves the isolated ARD equation for a prescribed velocityfield.To verify the coupling with the compressibleflow equations,we implemented the ARDflame capturing model in the FLASH code[4,2],a multidimensional,multiphysics,block-structured AMR code primarily intended for astrophysical applications.The FLASH code solves the system(7)using timestep splitting.The piecewise parabolic method(PPM)advances the solution in time accounting for the convective terms[3],and directional splitting is used whenever multiple spatial dimensions are considered[14].Then a second step is taken for the diffusion and reaction terms;these operators are treated in an unsplit(in time)fashion and advanced using thefirst-order explicit Euler method.Second-order central differences are used for the diffusion term.The ARDflame capturing model was implemented and tested in1-,2-,and 3-D Cartesian,2-D cylindrical,and3-D spherical coordinates.The FLASH code is a structured AMR code,but theflame-capturing model was implemented assuming the ARDflame is always discretized at thefinest refinement level.In the tests we performed,derefining the grid outside of theflame region did not directly affect the performance of theflame-capturing model.Several equation of state(EOS)models are implemented in FLASH.Here we show the results obtained using the gamma-law EOS;we have performed similar tests with the Helmholtz EOS(commonly used for degenerate stellar interiors)and did notfind any unexpected differences.6.1.Planarflame:test of conceptThis section contains a set of one-dimensional tests of the ARDflame capturing model in Cartesian coordinates.The goal is to verify that theflame model still performs its function,i.e.propagates theflame at the desired speed,when coupled to the Euler121 2 481632s / s obtop-hat non-adjustedsolid: α = 1; dashed: α = 20 1212481632s / s obKPP non-adjustedsolid: α = 1; dashed: α = 2Figure 3.Dependence of the travelling wave speed on resolution for top-hat (left)and KPP (right)reaction rates and different advection velocities v u =−s 0(squares),v u =0(circles)and v u =s 0(triangles).Solid lines correspond to isochoric case (α=1)and dashed lines correspond to variable-density case (α=2).The results were obtained without adjustment for compressibility effects (a =1.0).0 1212481632s / s obtop-hat adjustedα = 2Figure 4.Travelling wave speed for top-hate reaction rate shown in Fig.3,computed with adjustment for compressibility effects (a =1.3).equations.The physical conditions are chosen to match the simulations discussed in Sec.(5),in which the ARD equation (1)was solved in isolation.Given the unburned state and the flame speed,we can control the density ratio αby choosing the heat release q .If q =0,then α=1,and the ARD equation is decoupled from the Euler equations in (7).In this case,the only difference between system (1)and system (7)is the difference in the numerical method,i.e.between central differences and PPM advection schemes.Choosing the heat release so that α=2is equivalent to setting ∆v f =−s 0in (12).Recall that when q =0,(12)is an approximation which relies on several physical。

高二英语数学建模方法单选题20题1. In the process of mathematical modeling, "parameter" means _____.A. a fixed valueB. a variable valueC. a constant valueD. a random value答案：A。

解析：“parameter”常见释义为“参数”，通常指固定的值，选项 A 符合；选项B“variable value”意为“变量值”；选项C“constant value”指“常数值”；选项D“random value”是“随机值”，在数学建模中“parameter”通常指固定的值。

2. When building a mathematical model, "function" is often used to describe _____.A. a relationship between inputs and outputsB. a set of random numbersC. a single valueD. a group of constants答案：A。

解析：“function”在数学建模中常被用来描述输入和输出之间的关系，选项 A 正确；选项B“a set of random numbers”表示“一组随机数”；选项C“a single value”是“单个值”；选项D“a group of constants”指“一组常数”。

3. In the context of mathematical modeling, "optimization" refers to _____.A. finding the best solutionB. creating a new modelC. changing the parameters randomlyD. ignoring the constraints答案：A。

simulation modelling practiceSimulation modelling is a crucial tool in the field of science and engineering. It allows us to investigate complex systems and predict their behaviour in response to various inputs and conditions. This article will guide you through the process of simulation modelling, from its basicprinciples to practical applications.1. Introduction to Simulation ModellingSimulation modelling is the process of representing real-world systems using mathematical models. These models allow us to investigate systems that are too complex or expensiveto be fully studied using traditional methods. Simulation models are created using mathematical equations, functions, and algorithms that represent the interactions and relationships between the system's components.2. Building a Basic Simulation ModelTo begin, you will need to identify the key elements that make up your system and define their interactions. Next, you will need to create mathematical equations that represent these interactions. These equations should be as simple as possible while still capturing the essential aspects of the system's behaviour.Once you have your equations, you can use simulation software to create a model. Popular simulation softwareincludes MATLAB, Simulink, and Arena. These software packages allow you to input your equations and see how the system will respond to different inputs and conditions.3. Choosing a Simulation Software PackageWhen choosing a simulation software package, consider your specific needs and resources. Each package has its own strengths and limitations, so it's important to select one that best fits your project. Some packages are more suitable for simulating large-scale systems, while others may bebetter for quickly prototyping small-scale systems.4. Practical Applications of Simulation ModellingSimulation modelling is used in a wide range of fields, including engineering, finance, healthcare, and more. Here are some practical applications:* Engineering: Simulation modelling is commonly used in the automotive, aerospace, and manufacturing industries to design and test systems such as engines, vehicles, and manufacturing processes.* Finance: Simulation modelling is used by financial institutions to assess the impact of market conditions on investment portfolios and interest rates.* Healthcare: Simulation modelling is used to plan and manage healthcare resources, predict disease trends, and evaluate the effectiveness of treatment methods.* Education: Simulation modelling is an excellent toolfor teaching students about complex systems and how they interact with each other. It helps students develop critical thinking skills and problem-solving techniques.5. Case Studies and ExamplesTo illustrate the practical use of simulation modelling, we will take a look at two case studies: an aircraft engine simulation and a healthcare resource management simulation.Aircraft Engine Simulation: In this scenario, a simulation model is used to assess the performance ofdifferent engine designs under various flight conditions. The model helps engineers identify design flaws and improve efficiency.Healthcare Resource Management Simulation: This simulation model helps healthcare providers plan their resources based on anticipated patient demand. The model can also be used to evaluate different treatment methods and identify optimal resource allocation strategies.6. ConclusionSimulation modelling is a powerful tool that allows us to investigate complex systems and make informed decisions about how to best manage them. By following these steps, you can create your own simulation models and apply them to real-world problems. Remember, it's always important to keep anopen mind and be willing to adapt your approach based on the specific needs of your project.。

limits and liabilities的描述内容-回复Limits and liabilities are two essential factors that shape various aspects of our lives and society. Understanding their significance and implications is crucial for making informed decisions and ensuring accountability in different areas. In this article, we will delve into the concept of limits and liabilities, analyze their role in different contexts, and explore their implications.Firstly, let us define limits and liabilities. Limits refer to the restrictions or boundaries imposed on certain actions, behaviors, or circumstances. These boundaries can be set by individuals, institutions, or legal systems. On the other hand, liabilities pertain to the legal and ethical responsibilities or obligations that individuals or entities have towards others. In simpler terms, limits define what we can or cannot do, while liabilities dictate what we should or should not do.Limits and liabilities exist in various domains, ranging from personal to professional, and from social to legal. For example, in personal relationships, there are limits on what is acceptable behavior. These limits are often guided by cultural norms, moral values, and legal frameworks. Violating these limits may lead tostrained relationships or even legal consequences. Similarly, in professional settings, there are limits on one's authority, access to resources, and acceptable practices. Failure to adhere to these limits can result in disciplinary action or legal liabilities.In the realm of technology and internet usage, limits and liabilities play a significant role. With the advent of social media platforms, people have been given unprecedented freedom to express themselves and connect with others. However, there are also limits to this freedom. Platforms have community guidelines and terms of service that outline acceptable behavior and content. Violating these limits can result in temporary or permanent suspension from the platform. Additionally, liabilities arise when individuals engage in cyberbullying, spread misinformation, or infringe upon someone's privacy. Properly understanding and respecting these limits and liabilities is essential for a safe and constructive digital environment.Legal systems also rely on limits and liabilities to maintain order and protect individuals' rights. Laws define the limits within which individuals and entities must operate. They establish the boundaries for acceptable conduct and provide a framework forresolving conflicts. Moreover, laws establish liabilities for those who breach these limits, ensuring accountability and providing recourse to individuals who have been harmed. Legal liabilities can involve consequences such as fines, imprisonment, or restitution.Limits and liabilities also extend to environmental and sustainability issues. There are limits to the earth's resources and the capacity of ecosystems to absorb pollution and waste. Understanding and respecting these limits is crucial to ensure the long-term well-being of our planet. Additionally, there are liabilities for individuals, industries, and governments that contribute to environmental degradation. Violating environmental regulations can result in heavy fines, penalties, or legal action.In the realm of finance and economics, limits and liabilities are also significant. Financial institutions have limits on the amount of risk they can take and the obligations they can undertake. These limits are governed by regulatory bodies and are crucial for maintaining stability in the financial system. Additionally, individuals have liabilities towards lenders when they borrow money, towards investors when they participate in financial markets, and towards society when they engage in economic activities. Propermanagement of these liabilities is essential for sustainable economic development.In conclusion, limits and liabilities are fundamental concepts that shape our lives and society. They exist in personal relationships, professional settings, technology usage, legal systems, environmental concerns, and economic activities. Understanding these limits and liabilities is crucial for making responsible choices, maintaining order, and ensuring accountability. As individuals and as a society, it is vital to recognize and respect the limits and liabilities that govern our actions to promote a harmonious and just world.。

第29卷第1期海洋通报V ol. 29, No. 1 2010年02月MARINE SCIENCE BULLETIN Feb. 2010海洋生态动力学模型在海洋生态保护中的应用樊娟1，刘春光1，冯剑丰1，王君丽1，彭士涛1,2 （1南开大学环境科学与工程学院环境污染过程与基准教育部重点实验室，天津 300071；2. 交通部天津水运工程科学研究院，300456）摘要：介绍了海洋生态动力学模型的基本组成和分类。

从初级生产力模拟、生态系统过程模拟和生态影响评价模拟三方面阐述了生态动力学模型在海洋生态保护中的应用，最后总结了海洋生态动力学模型研究中亟待解决的问题。

关键词：海洋生态动力学模型；初级生产力模拟；生态系统过程模拟；生态影响评价模拟；海洋生态保护中图分类号：P735 文献标识码：A 文章编号： 1001-6932(2010)01-0078-07Application of marine ecological dynamic modelto marine ecological protectionFAN Juan1, LIU Chun-guang1, FENG Jian-feng1, WANG Jun-li1, PENG Shi-tao1,2（1．College of Environmental Science and Engineering, Nankai University Key Laboratory of Pollution Processes and Environmental Criteria, Ministry of Education, Tianjin 300071, China; 2. Tianjin Research Institute of Waterborne Transportation Engineering, Tianjin 300456, China）Abstract: Basic composition and classification of marine ecological dynamic model were demonstrated in this review.The model application on the marine ecological protection was elucidated in three sections: primary productionsimulation, ecosystem process simulation, and ecological impact assessment simulation. Emerging concern problemsin this field were discussed as well.Keywords: marine ecological dynamic model; primary production simulation; ecosystem process simulation;ecological impact assessment simulation; marine ecological protection海洋生态动力学模型自20世纪40年代产生以来，一直被认为是除了现场调查和模拟实验（包括实验室模拟和现场模拟）之外研究海洋生态系统的一种有效方法[1,2]。

Probability and Stochastic ProcessesProbability and stochastic processes are important concepts in the field of mathematics and have applications in various areas such as engineering, finance, and computer science. In this response, I will discuss the significance of probability and stochastic processes from multiple perspectives, highlightingtheir practical applications, theoretical foundations, and potential limitations. From a practical perspective, probability and stochastic processes play a crucial role in decision-making under uncertainty. Whether it is predicting the weather, estimating the risk of a financial investment, or designing a reliable communication system, the ability to quantify and analyze uncertainty is essential. Probability theory provides a framework for modeling and analyzing random events, enabling us to make informed decisions based on the likelihood of different outcomes. Stochastic processes, on the other hand, allow us to model systems that evolve over time in a probabilistic manner, providing valuable insights into the behavior of complex systems. In the field of engineering, probability and stochastic processes are used extensively in reliability analysis and system design. By modeling the failure rates of components and the interactions between them, engineers can evaluate the reliability of a system and identify potential weaknesses. This information is crucial for designing robust systems that can withstand uncertainties and minimize the risk of failure. Stochastic processes, such as Markov chains and queuing theory, are also used to model and analyze various engineering systems, including communication networks, manufacturing processes, and transportation systems. From a financial perspective, probability and stochastic processes are essential tools for risk management and investment analysis. Financial markets are inherently uncertain, and understanding the probabilistic nature of asset prices and returns is crucial for making informed investment decisions. By modeling the behavior of financial variables using stochastic processes, such as geometric Brownian motion or jump-diffusion processes, analysts can estimate the probabilities of different market scenarios and assess the risk associated with different investment strategies. This information is invaluable for portfolio management, option pricing, and hedging strategies. From a theoretical perspective, probability theory and stochasticprocesses provide a rigorous mathematical foundation for understanding randomness and uncertainty. Probability theory, with its axioms and theorems, allows us to reason logically about uncertain events and make precise statements about their probabilities. Stochastic processes, as mathematical models for random phenomena, provide a framework for studying the long-term behavior of systems and analyzing their statistical properties. This theoretical understanding is not only important for practical applications but also for advancing our knowledge in various scientific disciplines, including physics, biology, and social sciences. However, it is important to acknowledge the limitations of probability and stochastic processes. Firstly, these concepts are based on assumptions and simplifications that may not always hold in real-world situations. For example, many stochastic models assume that the underlying processes are stationary and independent, which may not be true in practice. Secondly, probability and stochastic processes can only provide probabilistic predictions and estimates, rather than deterministic outcomes. This inherent uncertainty means that even with the best models and data, there will always be a degree of unpredictability. Lastly, the accuracy of probability and stochastic models heavily relies on the availability and quality of data. In situations where data is limited or unreliable, the predictions and estimates obtained from these models may be less accurate or even misleading. In conclusion, probability and stochastic processes are fundamental concepts with wide-ranging applications and theoretical significance. They provide a powerful framework for quantifying and analyzing uncertainty, enabling us to make informed decisions and understand the behavior of complex systems. From practical applications in engineering and finance to theoretical foundations in mathematics and science, probability and stochastic processes play a crucial role in our understanding of the world. However, it is important to recognize theirlimitations and the inherent uncertainties they entail. By embracing uncertainty and using probability and stochastic processes as tools for reasoning anddecision-making, we can navigate the complexities of the world with greater confidence and understanding.。

Systems biologyAn illustration of the systems approach to biologySystems biology (Systeomics) is anemerging approach applied tobiomedical and biological scientificresearch. Systems biology is abiology-based inter-disciplinary field ofstudy that focuses on complexinteractions within biological systems,using a holistic approach (holisminstead of the more traditionalreductionism) to biological andbiomedical research. Particularly fromyear 2000 onwards, the concept hasbeen used widely in the biosciences in avariety of contexts. One of the outreaching aims of systems biology isto model and discover emergent properties, properties of cells, tissues and organisms functioning as a system whose theoretical description is only possible using techniques which fall under the remit of systems biology. These typically involve metabolic networks or cell signaling networks. Systems biology makes heavy use of mathematical and computational models.OverviewSystems biology can be considered from a number of different aspects:•As a field of study, particularly, the study of the interactions between the components of biological systems , and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway).•As a paradigm, usually defined in antithesis to the so-called reductionist paradigm (biological organisation),although fully consistent with the scientific method. The distinction between the two paradigms is referred to in these quotations:"The reductionist approach has successfully identified most of the components and many of the interactions but, unfortunately, offers no convincing concepts or methods to understand how system properties emerge...the pluralism of causes and effects in biological networks is better addressed by observing, through quantitative measures, multiple components simultaneously and by rigorous data integration with mathematical models"(Sauer et al.)."Systems biology...is about putting together rather than taking apart, integration rather than reduction. It requires that we develop ways of thinking about integration that are as rigorous as our reductionist programmes, but different....It means changing our philosophy, in the full sense of the term" (Denis Noble).•As a series of operational protocols used for performing research, namely a cycle composed of theory, analytic or computational modelling to propose specific testable hypotheses about a biological system, experimentalvalidation, and then using the newly acquired quantitative description of cells or cell processes to refine the computational model or theory. Since the objective is a model of the interactions in a system, the experimental techniques that most suit systems biology are those that are system-wide and attempt to be as complete aspossible. Therefore, transcriptomics, metabolomics, proteomics and high-throughput techniques are used tocollect quantitative data for the construction and validation of models.•As the application of dynamical systems theory to molecular biology. Indeed, the focus on the dynamics of the studied systems is the main conceptual difference between systems biology andbioinformatics.Wikipedia:Citation needed•As a socioscientific phenomenon defined by the strategy of pursuing integration of complex data about the interactions in biological systems from diverse experimental sources using interdisciplinary tools and personnel. This variety of viewpoints is illustrative of the fact that systems biology refers to a cluster of peripherally overlapping concepts rather than a single well-delineated field. However the term has widespread currency and popularity as of 2007, with chairs and institutes of systems biology proliferating worldwide.HistorySystems biology finds its roots in:Wikipedia:Citation needed•the quantitative modeling of enzyme kinetics, a discipline that flourished between 1900 and 1970,•the mathematical modeling of population growth,•the simulations developed to study neurophysiology, and•control theory and cybernetics.One of the theorists who can be seen as one of the precursors of systems biology is Ludwig von Bertalanffy with his general systems theory. One of the first numerical simulations in cell biology was published in 1952 by the British neurophysiologists and Nobel prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley, who constructed a mathematical model that explained the action potential propagating along the axon of a neuronal cell. Their model described a cellular function emerging from the interaction between two different molecular components, a potassium and a sodium channel, and can therefore be seen as the beginning of computational systems biology. In 1960, Denis Noble developed the first computer model of the heart pacemaker.The formal study of systems biology, as a distinct discipline, was launched by systems theorist Mihajlo Mesarovic in 1966 with an international symposium at the Case Institute of Technology in Cleveland, Ohio entitled "Systems Theory and Biology".The 1960s and 1970s saw the development of several approaches to study complex molecular systems, such as the metabolic control analysis and the biochemical systems theory. The successes of molecular biology throughout the 1980s, coupled with a skepticism toward theoretical biology, that then promised more than it achieved, caused the quantitative modelling of biological processes to become a somewhat minor field.Wikipedia:Citation needed However the birth of functional genomics in the 1990s meant that large quantities of high quality data became available, while the computing power exploded, making more realistic models possible. In 1992, then 1994, serial articles [1][2][3][4][5] on systems medicine, systems genetics and systems biological engineering by BJ. Zeng were published in China, and was giving a lecture on biosystems theory and systems approach research at the Fist International Conference on Transgenic Animals, Beijing, 1996. In 1997, the group of Masaru Tomita published the first quantitative model of the metabolism of a whole (hypothetical) cell.Around the year 2000, after Institutes of Systems Biology were established in Institute for Systems BiologySeattle and Tokyo, systems biology emerged as a movement in its own right, spurred on by the completion of various genome projects, the large increase in data from the omics (e.g. genomics and proteomics) and the accompanying advances in high-throughput experiments and bioinformatics.In 2002, the National Science Foundation (NSF) put forward a grand challenge for systems biology in the 21st century to build a mathematical model of the whole cell.[6] In 2003, work at the Massachusetts Institute of Technology was began to CytoSolve, a method to model the whole cell by dynamically integrating multiple molecular pathway models.[7][8] Since then, various research institutes dedicated to systems biology have been developed. For example, the NIGMS of NIH established a project grant that is currently supporting over ten systemsbiology centers in the United States. As of summer 2006, due to a shortage of people in systems biology several doctoral training programs in systems biology have been established in many parts of the world. In that same year,the National Science Foundation (NSF) put forward a grand challenge for systems biology in the 21st century to build a mathematical model of the whole cell.Associated disciplinesOverview of signal transduction pathwaysAccording to the interpretation of SystemsBiology as the ability to obtain, integrate andanalyze complex data sets from multipleexperimental sources using interdisciplinarytools, some typical technology platforms are:•PhenomicsOrganismal variation in phenotype as itchanges during its life span.•GenomicsOrganismal deoxyribonucleic acid (DNA)sequence, including intra-organisamal cellspecific variation. (i.e. Telomere lengthvariation etc.).•Epigenomics / EpigeneticsOrganismal and corresponding cell specific transcriptomic regulating factors not empirically coded in the genomic sequence. (i.e. DNA methylation, Histone acetylation and deacetylation, etc.).•TranscriptomicsOrganismal, tissue or whole cell gene expression measurements by DNA microarrays or serial analysis of gene expression•InterferomicsOrganismal, tissue, or cell level transcript correcting factors (i.e. RNA interference)•Translatomics / ProteomicsOrganismal, tissue, or cell level measurements of proteins and peptides via two-dimensional gel electrophoresis, mass spectrometry or multi-dimensional protein identification techniques (advanced HPLC systems coupled with mass spectrometry). Sub disciplines include phosphoproteomics, glycoproteomics and other methods to detect chemically modified proteins.•MetabolomicsOrganismal, tissue, or cell level measurements of all small-molecules known as metabolites.•GlycomicsOrganismal, tissue, or cell level measurements of carbohydrates.•LipidomicsOrganismal, tissue, or cell level measurements of lipids.In addition to the identification and quantification of the above given molecules further techniques analyze the dynamics and interactions within a cell. This includes:Wikipedia:Citation needed•InteractomicsOrganismal, tissue, or cell level study of interactions between molecules. Currently the authoritative molecular discipline in this field of study is protein-protein interactions (PPI), although the working definition does not preclude inclusion of other molecular disciplines such as those defined here.•NeuroElectroDynamicsOrganismal, brain computing function as a dynamic system, underlying biophysical mechanisms and emerging computation by electrical interactions.•FluxomicsOrganismal, tissue, or cell level measurements of molecular dynamic changes over time.•BiomicsSystems analysis of the biome.•SemiomicsAnalysis of the system of sign relations of an organism or other biosystem.•Systems biology of cancer is an important application of systems biology approach, which can be distinguished by the specific object of study (tumorigenesis and treatment of cancer). It works with the specific data (patient samples, high-throughput data with particular attention to characterizing cancer genome in patient tumour samples) and tools (immortalized cancer cell lines, mouse models of tumorigenesis, xenograft models, Next Generation Sequencing methods, siRNA-based gene knocking down screenings, computational modeling of the consequences of somatic mutations and genome instability). The long-term objective of the systems biology of cancer is ability to better diagnose cancer, classify it and better predict the outcome of a suggested treatment, which is a basis for personalized cancer medicine and virtual cancer patient in more distant prospective.Significant efforts in Computational systems Biology of Cancer have been made in creating realistic multi-scale in silico models of various tumours.The investigations are frequently combined with large-scale perturbation methods, including gene-based (RNAi, mis-expression of wild type and mutant genes) and chemical approaches using small molecule libraries.Wikipedia:Citation needed Robots and automated sensors enable such large-scale experimentation and data acquisition. These technologies are still emerging and many face problems that the larger the quantity of data produced, the lower the quality.Wikipedia:Citation needed A wide variety of quantitative scientists (computational biologists, statisticians, mathematicians, computer scientists, engineers, and physicists) are working to improve the quality of these approaches and to create, refine, and retest the models to accurately reflect observations.The systems biology approach often involves the development of mechanistic models, such as the reconstruction of dynamic systems from the quantitative properties of their elementary building blocks. For instance, a cellular network can be modelled mathematically using methods coming from chemical kinetics and control theory. Due to the large number of parameters, variables and constraints in cellular networks, numerical and computational techniques are often used (e.g., Flux balance analysis).Bioinformatics and data analysisOther aspects of computer science, informatics, statistics are also used in systems biology. These include:•New forms of computational model, such as the use of process calculi to model biological processes (notable approaches include stochastic π-calculus, BioAmbients, Beta Binders, BioPEPA, and Brane calculus) and constraint-based modeling.•Integration of information from the literature, using techniques of information extraction and text mining.•Development of online databases and repositories for sharing data and models, approaches to database integration and software interoperability via loose coupling of software, websites and databases, or commercial suits.•Development of syntactically and semantically sound ways of representing biological models.Wikipedia:Citation needed•Network based approaches for analyzing high dimensional genomic data sets. For example, weighted correlation network analysis is often used for identifying clusters (referred to as modules), modeling the relationship between clusters, calculating fuzzy measures of cluster (module) membership, identifying intramodular hubs, and for studying cluster preservation in other data sets.References[1]Zeng (B.) J., On the holographic model of human body, 1st National Conference of Comparative Studies Traditional Chinese Medicine andWest Medicine, Medicine and Philosophy, April, 1992 ( "systems medicine and pharmacology" termed).[2]Zeng (B.) J., On the concept of system biological engineering, Communication on Transgenic Animals, No. 6, June, 1994.[3]Zeng (B.) J., Transgenic animal expression system – transgenic egg plan (goldegg plan), Communication on Transgenic Animal, Vol.1,No.11, 1994 (on the concept of system genetics and term coined).[4]Zeng (B.) J., From positive to synthetic medical science, Communication on Transgenic Animals, No.11, 1995 (on systems medicine).[5]Zeng(B.)J., The structure theory of self-organization systems, Communication on Transgenic Animals, No.8-10, 1996. Etc.[6]The American Association for the Advancement of Science (/content/314/5806/1696.full)[7]National Center for Biotechnology Information (/pmc/articles/PMC3032229/)[8]Massachusetts Institute of Technology (/)Further reading•Asfar S. Azmi, ed. (2012). Systems Biology in Cancer Research and Drug Discovery. ISBN 978-94-007-4819-4.•Kitano, Hiroaki (15 October 2001). Foundations of Systems Biology. MIT Press. ISBN 978-0-262-11266-6.•Werner, Eric (29 March 2007). "All systems go". Nature446 (7135): 493. Bibcode: 2007Natur.446..493W (http:/ //abs/2007Natur.446..493W). doi: 10.1038/446493a (/10.1038/446493a). provides a comparative review of three books:•Alon, Uri (7 July 2006). An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall. ISBN 978-1-58488-642-6.•Kaneko, Kunihiko (15 September 2006). Life: An Introduction to Complex Systems Biology. Springer-Verlag.ISBN 978-3-540-32666-3.•Palsson, Bernhard O (16 January 2006). Systems Biology: Properties of Reconstructed Networks. Cambridge University Press. ISBN 978-0-521-85903-5.•Werner Dubitzky, Olaf Wolkenhauer, Hiroki Yokota, Kwan-Hyun Cho, ed. (13 August 2013). Encyclopedia of Systems Biology. Springer-Verlag. ISBN 978-1-4419-9864-4.External links•Biological Systems in bio-physics-wiki (http://www.bio-physics.at/wiki/index.php?title=Biological_Systems)Article Sources and Contributors7Article Sources and ContributorsSystems biology Source: /w/index.php?oldid=614606431 Contributors: A1078558, APH, Aeusoes1, Agor153, Alan Liefting, AlirezaShaneh, Amaher,Amandadawnbesemer, Amirsnik, Andreas td, Arthena, Arthur Rubin, Asadrahman, Aszarsha, Aua, Avimaayan2012, Bad Cat, Bamess, BatteryIncluded, Bci2, Bender235, Benedict Pope,Benjamin Barenblat, Betacommand, Bio-ITWorld, Biochaos, Biophysik, Blueleezard, Bobrayner, Boku wa kage, CRGreathouse, CX, Calimo, Can't sleep, clown will eat me, Cantor,Captain-tucker, Ceolas, Charlenelieu, CharonZ, Ckatz, Claronow, Clayrat, ColinGillespie, Cquan, Crodriguel, Curb Chain, D6, DFRussia, DGRichard, Danhash, DanielNuyu, David Ludwig, Dirk Hans, Djhbrown, Dmb000006, Dr Oldekop, Drgarden, Droyarzun, Dubitzky, Duelist135, Edaddison, Edward, Electric sheep, Erick.Antezana, Erkan Yilmaz, Ethically Yours, Eveillar, FLeader, Favonian, Fences and windows, Fenice, Fletcher04, Foggy29, Fred Stupor Mundi, Fredrik, G13140, GabEuro, Garychurchill, Gauravsjbrana, Gcm, Gdrahnier, Ggonnell, Giftlite,Giovannistefano35, GlenBrydon, Gwolfe, H4n9t3nn, Halx, Heisner, Hequba, HexiToFor, Hsyoo, IPSOS, Insouciantfiend, JRSocInterface, JaGa, Jdegreef, Jethero, Jondel, Jongbhak, Jpbowen, JulioVeraGon, Jwdietrich2, Kane5187, Karthik.raman, Kcordina, Kieran Mace, KirbyRandolf, Kkmurray, Kku, Klenod, Klipkow, Lauranrg, Lenov, Letranova, Lexor, Lilia Alberghina,Linkman21, Listmeister, Lkathmann, Magioladitis, Mark viking, Massbiotech, Mbadri, Mdd, Meters, Michael Fourman, Michael Hardy, Miguel Andrade, MikeHucka, Mkotl, Mkuiper, Mmxx, Mobashirgenome, Mohawkjohn, Molelect, MrOllie, N2e, NBeale, NIH Media, Narayanese, Natelewis, NavarroJ, Nbaliga, Neilbeach, Nekokoe, Nemenman, Netsnipe, Nick Green, Nono64, O RLY?, Oddleik, Ohnoitsjamie, Ombudsman, Opertinicy, OrcaMorgan, Patho, PaulGarner, Pgr94, Pkahlem, PointOfPresence, Pvosta, Quarl, RDBrown, Rajah, Reggiebird, Rich Farmbrough,Rjwilmsi, Robnpov, Rvencio, Rwcitek, Satish.vammi, SeanAhern, Second Quantization, SeeGee, Senu, Seuss01, Sholto Maud, Skittleys, Slon02, Smythph, Srlasky, Steinsky, Stevetihi,Stewartadcock, Strife911, Sunur7, Svick, Synthetic Biologist, Syp, TVleck1971, Tagishsimon, Template namespace initialisation script, The Bald Russian, TheoThompson, Thomas81, Thorwald, Touchstone42, Triamus, U+003F, Unauthorised Immunophysicist, Urselius, Vangos, Versus22, Vonkje, WLU, Waltpohl, Wavelength, Whosasking, Xeaa, Yobol, Zargulon, Zinovyev, Zlite,Zoicon5, Zuck3434, Éffièdaligrh, ﯽﻌﺳ, 447 anonymous editsImage Sources, Licenses and ContributorsFile:Genomics GTL Pictorial Program.jpg Source: /w/index.php?title=File:Genomics_GTL_Pictorial_Program.jpg License: Public Domain Contributors: MddFile:Signal transduction pathways.svg Source: /w/index.php?title=File:Signal_transduction_pathways.svg License: GNU Free Documentation License Contributors: cybertoryLicenseCreative Commons Attribution-Share Alike 3.0///licenses/by-sa/3.0/。

Models and Modals∗Huw Price17December20031IntroductionPragmatists recommend that in approaching a problematic concept,philoso-phers should begin by examining the role of the concept concerned in the prac-tical,cognitive and linguistic life of the creatures who use it.I’m interested in pragmatic accounts,in this sense,of the various modal notions we encounter in science—causation,probability,counterfactual conditionals,and so on.In this paper,I want to propose that these accounts should avail themselves of the vocabulary of theoretical models.Although my concern is thus with the application of models to the study of modals in general,I have a special interest in the case of causation.In previous work,I’ve defended an‘agency’or‘manipulability’approach to causation.This approach links our possession of causal concepts to the fact that we are agents. In the version that I prefer,it is a pragmatic account,in the above sense.(It is also a perspectival account,in a sense I’ll be trying to clarify further below.) Some writers(e.g.,Pearl2000,Woodward2001)agree about the centrality of notions of agency and manipulation to an understanding of causation,but take the resulting view in a more realist or objectivist spirit.From my point of view, then,there are two groups of opponents—those who need convincing about the centrality of agency and manipulation in an account of causation in thefirst place,and those who need convincing only about the pragmatic or perspectival character of the best such account.In both cases,however,a pressing task is to clarify the perspectival option.That’s what I’ll be attempting here,with the aid of theoretical models.Clarifying the perspectival option is a matter of locating it on philosophical maps—or,in some cases,redrawing the maps so as not to exclude it by default. Philosophers have a tendency to think of the question‘Is there any such thing as causation?’as on all fours with‘Are there any Tasmanian tigers?’or‘Are there magnetic monopoles?’This has the effect of simply excluding some views of causation,views according to which there are important differences between the question about causation and the questions raised by physics and natural history.As an example of someone who appears to be missing the relevant possibility, consider this famous remark from Bertrand Russell:∗Forthcoming in Donald Gillies,ed.,Laws and Models in Science,King’s College Publi-cations,2004.1All philosophers,of every school,imagine that causation is one of thefundamental axioms or postulates of science,yet,oddly enough,inadvanced sciences such as gravitational astronomy,the word‘cause’never occurs...The law of causality,I believe,like much that passesmuster among philosophers,is a relic of a bygone age,surviving,like the monarchy,only because it is erroneously supposed to do noharm.(Russell,1913)Russell seems to be arguing that physics has shown us that there is no such thing as causation—a discovery about what the world contains,apparently,on a par with those of natural history and physics itself.But I want to show that there’s an important option that Russell thus overlooks.Extending Russell’s own metaphor,I’ll call it causal republicanism.Consider the political case.When we reject the view that political authority is vested in our rulers by god,we have two choices.We can reject the notion of political authority altogether,or we can regard it,as republicans do,as vested in our rulers by us.The republican option exists in metaphysics,too,where it is an alternative to realism and eliminativism.In the case of causation,it is the view that although notions of causal power are useful,perhaps indispensable,in our dealings with the world,they are a category constructed by us,not provided by God.In comparing causation dismissively to the monarchy,Russell seems largely blind to this republican possibility—a failing he shares,I think,with many of his realist opponents.In my view,thinking of eliminativism as the sole alternative to causal realism is like thinking of anarchy as the sole alternative to the divine right of kings.Thus I agree with Russell in rejecting a certain kind of realism about causation,but disagree about the relevance to this conclusion of the issue of the eliminability of causal notions from physics.For a republican,causation may turn out to be both ineliminable and anthropocentric.In my view,the best versions of the agency approach give causation this republicanflavour.As I noted above,my interest in agency accounts of causation is part of a broader concern with pragmatic approaches to the various modalities employed in science.There are deep connections among the modal notions,I think,and hence much to be learnt by considering them as a group.More importantly for present purposes,the points I want to make are more easily made for probability than for causation,the relevant landscape being simpler and better-known.In particular,there’s a familiar debate about the relevance of physics to the ques-tion as to whether there are(non-trivial)chances or objective probabilities—in some ways,a probabilistic analogue of the issue raised by Russell.I think that that debate,too,often misses important parts of the landscape.But it’s easier to explain why than for causation directly,because the landscape is so familiar. Dialectically,then,it makes sense to begin with probability.Most of the paper will thus be concerned with probability.In the next section,I’ll distinguish three conceptions of what we are doing in modelling probabilities.There are two aspects of this approach toflag at this stage,the first the three-way distinction itself,and the second the fact that it is couched in terms of the functions of theoretical models.The relevance of the three-way distinction is that one of these three conceptions is easily overlooked,and yet crucial,in my view,both in deciding what’s right and what’s wrong about Russell’s claim,and in understanding the nature of modal perspectivalism.2As for the focus on models,I’ve said that I’m trying to call attention to, and clarify,a kind of pragmatic perspectivalism about modal notions.It would be possible to do this by talking about the functions of theoretical language. Expressed this way,however,the concerns of pragmatism and realism are apt to seem orthogonal.Pragmatists focus on language,realists on reality,and the two sides can easily seem to be talking past one another.Focussing on models makes it easier tofind common ground.As we’ll see,the kinds of things a modal pragmatist wants to sayfind natural expression as theses about the functions of models(for creatures in our situation).While on the realist side,issues about the use and role of theoretical models are already sufficiently in play to present the questions the pragmatist wants to raise as‘more of the same’.The result, hopefully,is a more accessible and more‘scientific’modal pragmatism—and a pragmatism with a less linguistic face,via models with a more human face.12Probability—models and meta-modelsLet’s begin,then,with a question about probabilistic models.What are we modelling when model probabilities?Or,more neutrally,what is the function of probabilitistic models—what do we use them for?I want to distinguish three different conceptions of the role of probabilistic models,especially in science.In each case,I want to be able to think about the relation of the models in question to the needs and characteristics of the users of those models.I want to be able to consider the relevance of variations in char-acteristics of the users,to the utility and possibility of their use of probabilistic models,under particular conceptions of what those models involve.In order to think about these issues in the abstract,idealised way typical in science,I want to be able to model them.So I’ll need what I’ll call meta-models—models of the users of probabilistic models.Here,then,are three possible views of the func-tions of probabilistic models,together with some remarks about the associated meta-models.2.1Objectivist modelsOn this view our aim in modelling probabilities is to model an aspect of the (modeller-independent)physical world.Probability is regarded as an aspect of the objective world,on a par with other features studied by physics.There are different views about what such objective probabilities are,of course.The options include propensities,hypothetical limiting frequencies of various kinds, and theoretical entities,not further specified,but picked out in virtue of their relevance to our decision-theoretic psychology—that aspect of the world,knowl-edge of which makes rational certain degrees of belief.If probabilistic models are understood in this objectivist way,what can we1One important aspect of this shift from language to models is that it helps to distance us from na¨ıvely representational conceptions of the role of theoretical language in science. Pragmatists are often foes of representationalism.While theoretical models are not in-evitably conceived in anti-representational terms,it is uncontroversial that they can have non-representational functions(see,e.g.,Morgan and Morris,1999).Language wears on its face a representational complexion.Pragmatists want to argue that this complexion is no more than skin-deep—not a reliable guide to underlying function and structure—but the case is much easier to make if we start further from the surface.3say about the users of such models?How should we model such creatures,in our meta-models?There are two crucial points.First,the modelled creatures need to be modelled as representers—creatures whose modelling aims to‘mirror’some aspect of the world they inhabit.Second,their use of models of probability has to be seen as attempting to represent in this way—the models themselves must have a representational function.(The second point serves to rule out the case of users who,while representers in other ways,use their probabilistic models for some non-representational purpose.)2.2Subjectivist modelsAn alternative view is that in modelling probabilities our aim is to model psy-chological states—credences—to some feature of which the probability calculus is applicable,at least under idealisation.At least in a loose sense,then,this view holds that when we model probabilities,we model something subjective—some feature of our own minds.What meta-models are appropriate in this case?As in the objectivist case, the modelled creatures need to be modelled as representers,whose modelling aims to mirror some aspect of the world they inhabit.In this case,however,the feature represented is part of their own psychology.Inter alia,then,they need to be creatures with the relevant psychology—creatures with credences.Note that this wasn’t necessary in the objectivist case.If probabilities are part of mind-independent reality,then in principle creatures without credences ought to be able to model them,even if the part of reality thereby modelled needs to be characterised in terms of its relevance for creatures who do have credences.A detailed subjectivist meta-model may be expected to tell us,among other things,how the relevant psychological states vary with perceived features of the creatures’external environment.(We’re assuming here that the function of probabilistic models isn’t just navel-gazing—somehow,that self-descriptive psychological modelling has some wider point.)Presumably such things as ob-served relative frequencies will be relevant at this point.But if subjectivism is to retain its main advantage over objectivism—that of avoiding the meta-physical and epistemological‘queerness’of modal facts,in favour of something commonplace,though psychological—these features had better be non-modal.One point to emphasise.In the sense in which I’m using the term,sub-jectivist models of probability are‘self-descriptive’.They are models of the credences of an agent—of the user of the model,in fact,or some idealisation of the user.It is important to note that not all so-called subjectivist accounts of probability need be subjectivist in this sense.Some may be closer to the perspectival view I’m about to describe.And some,perhaps,may simply fail to distinguish between the two options.2.3Perspectival modelsThe third possibility is that what we model when we model probabilities is neither some mind-independent aspect of reality,nor an aspect of our own psychology,but rather the world‘as it looks’from the standpoint of such a credence-based psychology—we model a‘projection’from such a psychology. Of course,more needs to be said to make this notion precise.I’ve just used two metaphors,one visual and one projective.These are neither obviously compati-4ble,nor the only ones in play in this ter I’ll introduce a third metaphor and say something about each,and their connections.For the moment,however,we have enough to note some important points about the meta-model associated with this perspectival conception offirst-order models.On the one hand,it has something significant in common with the subjectivist case,in that the users modelled in our meta-model need to be endowed with credences.On the other hand,there is something important that differs compared to both the previous cases,in that the users are not modelled as representers.The function of theirfirst-order models is not representational.One question we might appeal to meta-models to address is that of the utility of such non-representational models for the users concerned.To address such a question,we need to include enough‘environment’in the model,and enough detail concerning the users themselves,to explain how such modelling contributes to the well-being of such a creature in such an environment.2In principle this question can be asked in the representational cases,too,of course, though there we might expect it to have been addressed at a higher level of generality,rather than specifically with respect to probabilities.3Thus we have three conceptions of the nature of probabilistic models,and three associated meta-models of the relation between such models and their users. This allows us to ask the question,which meta-model bestfits us?Notice that this is an empirical question—a roughly formulated empirical question,to be sure,but in principle a matter to be investigated by science(indeed,by the human sciences,for it concerns an aspect of human linguistic behaviour and psychology).Suppose it turns out that the perspectival option offers the best answer to this question.Then it would be seem to be a mistake to regard‘Are there really probabilities?’as itself an empirical question,on a par with‘Are there Tasmanian tigers?’or‘Are there magnetic monopoles?’Why?Because to read the question about probabilities in this way is to presuppose a representationalist conception of the functions of probabilistic modelling—the conception rejected in perspectival meta-models.In other words,it is to presuppose that what we are trying to do with our models of probability is the same kind of thing,roughly speaking,as we are trying to do with our models of Tasmanian megafauna or the quantum world,viz.,to represent aspects of our external environment.4 In order to address the question as to which meta-model bestfits our own practice,however,we need a better sense of what the perspectival option in-volves.Our next task is therefore to clarify the notion of a perspectival model.2Strictly,the perspective admits a range of options at this point.One,for example,is that the modelling has no particular advantage.3Though it is relevant here that some standard answers seem inapplicable in some modal cases,given certain views of the nature of modal facts.There may be a puzzle about why modal beliefs are useful,for example,if they represent causally isolated possible worlds.4I think this issue is actually more subtle than I here make it sound,because the con-trast between representational and non-representational uses of models less clear-cut than this formulation assumes.However,I think the contrast between the functions of probabilis-tic modelling and other kinds of scientific modelling survives a more careful formulation.53What is a perspectival model?Let’s begin with three metaphors for perspectivalism,a visual metaphor,a pro-jectivist metaphor,and afictionalist metaphor.By combining elements of these three metaphors,I want to bring into focus what I take to be the core of an interesting perspectivalism.3.1The visual metaphorThe visual metaphor thinks of a perspective as like an aspect of reality viewed from a particular standpoint.Here standpoint usually means a spatial standpoint—what is perspectival is the view from a particular spatial location and orienta-tion.But in principle it might include aspects of the observer’s‘location’in a more general sense,such as aspects of her visual system.(The view through rose-coloured spectacles might thus be thought of as a particular perspective,for example.)This metaphor makes it is easy to see how models can be perspectival. We just think of a model as containing only what is visible from the viewpointin question.Moreover,because the viewpoint in question is straightforwardly observer-dependent,the metaphor gives us an easy model of dependence on a contingent feature of an agent’s circumstances—their spatial location,the colourof their spectacles,and so on.On the other hand,the visual metaphor suggests that a perspective is merely an observer-dependent selection from a set of things which are—in themselves, so to speak—observer-independent.This wouldn’t be true of all versions of this kind of perspectivalism—it is explicitly not true of Russell’s(1914)constructionof objects from perspectival sensibilia,for example.But where it is true it is unhelpful.The more interesting cases are ineliminably perspectival,in the sense that we can’t achieve a non-perspectival description simply by including more. 3.2The projectionist metaphorThe classic statement of the projectionist metaphor comes from Hume’s distinc-tion between the operations of reason and taste:Thus the distinct boundaries and offices of reason and of taste areeasily ascertained.The former conveys the knowledge of truth andfalsehood:The latter gives the sentiment of beauty and deformity,vice and virtue.The one discovers objects as they really stand innature,without addition and diminution:The other has a produc-tive faculty,and gilding and staining all natural objects with thecolours borrowed from internal sentiment,raises in a manner a newcreation.(Hume1998,163)This metaphor has the great advantage,in my view,of calling attention to distinctive aspects of our psychology,relevant to the perspective in question—that from which we project,in effect.In the probability case,we know what this feature is:credence,in its raw or idealised form.But unlike subjectivism proper, which regards probabilistic models as models of our credences,this approach regards them as models of the projections of these psychological states—of the ‘objectifications’of the credences,the‘new creations’with which our faculties gild reality.6Of course,this remains rather metaphorical,and what it means needs clarifi-cation.It might be phenomenological,for example,as it seems to be in Hume.It might be cashed out in more linguistic terms,as the construction of a practice of making claims and reasoning in ways which ultimately‘express’these credences. Or it might be cashed in the‘model model’itself,so that what projection really amounts to is the construction of models whose ontologies stand in the appro-priate relationship to the inner states concerned.More on these options later. (It seems to me likely that the latter two options go hand-in-hand.) For now,I want to call attention to what seem to me the twin advantages of the projectionist metaphor.Firstly,in calling attention to a distinctive aspect of our psychology from which we‘project’,it identifies a contingent feature of the speaker that grounds the perspective in question.The identification of such a feature is absolutely central,in my view,to any interesting perspectivalism of this kind.Indeed,it is what makes it perspectival,for it is variation with respect to this‘contingent ground’that constitutes variation of perspective.Note that we shouldn’t assume that the relevant ground will always be psy-chological.In other cases it might be spatial or temporal location,for example. Or it might be location on some non-spatiotemporal scale of variability.(Some crude examples:when we call things hot or cold,tall or short,we are compar-ing their temperature or height to our own.)Whatever it is,thefirst important virtue of the projectionist metaphor is that it emphasises that perspectival mod-elling is a game creatures are equipped to play,in virtue of the fact that they possess or occupy the contingent ground in question.The second virtue of the metaphor is its comparative clarity on the important issue of what distinguishes perspectivalism from subjectivism.Projection is coloured by‘internal sentiment’(or its analogues in other cases),but it is not a representation of those internal sentiments.5Taken together,these two characteristics comprise the core of the most inter-esting notion of perspective,in my view.On the one hand,perspectival models (or concepts,or judgements)depend on some contingent feature or ground,pos-session of which is a precondition of use of the model,concept or judgement in question.On the other hand,such models,concepts or judgements don’t repre-sent that feature or ground,explicitly or implicitly.(The contingent ground ofa perspectival judgement is always backgrounded,as we might say.)3.3Thefictionalist metaphorThis metaphor has the advantage of helping to emphasise that the perspectivity belongs to the background,not the foreground.From within the perspective in question,its objects don’t look perspectival.They simply look like objects. This is obviously the case infiction—it is(almost always)inappropriate,within afictional context,to portray its objects asfictions.Ourfictions don’t say,‘It was a dark and stormy andfictional night’,except occasionally for self-consciously convention-busting effect.The label‘fictional’is imposed from the 5However,more needs to be said at this point about the nature of the distinction—especially by someone who,like me,is inclined(see, e.g.,Price2004)to deny that there are any genuine representations,in anything more than a deflationary sense.The distinction requires careful attention to the relations between models and judgement,and to the differ-ing assertion and rejection conditions for the judgements associated with self-descriptive and projective models.7outside,when we comment on the status of such objects.6Of course,we don’t want to banish perspectivity for ever.We want to be able to see it as theorists,for otherwise perspectivalism would have nothing to say.It gets back in when we ask why a particularfiction should be useful for these creatures in those circumstances.Perhaps a disadvantage of thefictionalist metaphor,compared to the projectionist metaphor,is that it does nothing to direct our attention to this issue.It would be easy to think of allfictions as on a par,and to fail to notice the ways in which particularfictions may be adapted to—indeed,dependent on—particular needs and circumstances.A better stategy is therefore to combine this third metaphor with the second—to think of projection as production offictional ontology,riding on the back of the various psychological and other commonalities that constitute the contingent grounds of the perspectival models in question.We thus ask,why is it useful for creatures occupying those grounds to invent such models?(Why ontology?And why talk of truth?)To make this work,fictionalism needs to have theflexibility to connect particular aspects of thefictions concerned to the relevant aspects of the user’s circumstances.Uniquely among allfictions,for example,thefiction of chances needs to connect with credences in an appropriate way—a way that looks(from the inside,as it were)like Lewis’s Principal Principle(Lewis1980).A further strategic advantage of thefictionalist metaphor is the way in which it connects with familar views about the functions of theoretical models in sci-ence.It is already well-recognised that models that are in some sensefictions may nevertheless play an important role in science.One version of this view is instrumentalism,which rejects a representationalist conception of the role of theoretical models altogether.Another version,less radical,recognises an important role for models embodyingfictional idealisations in the context of a generally realist view of scientific theories.In a sense,my modal perspectivalist simply wants to give these ideas an extra degree of freedom—to suggest,for example,that the utility of instrumentalist models is a more complex matter than usually assumed,and may rely on particular contingent features of the users of those models,such as the fact that their psychology includes credences. My view thus compares to a kind of multifunctionalfictionalism.Models are tools,and the kinds of tools we need depends on the kind of creatures we are.Thefictionalist metaphor has one significant disadvantage,from my point of view.Roughly,it is more anti-realist than it needs to be(or than I want to be).I’ll return to this point at the end of the paper.Until then,I’m happy to ride in tandem withfictionalist views.Summing up,then,we have three guiding metaphors for perspectivalism: the metaphor of visual perception from a particular viewpoint,the projectivist metaphor,and thefictionalist metaphor.I’ve suggested that the second and the 6Note that we have a choice about how we put the observer into thefictionalist meta-model.We can put the observer in thefiction,modelling her as a representer of the objects which arefictional from our point of view but not from hers.Or we can model her as a user of(rather than a participant in)thefiction in question.If the meta-model is to represent ourselves,then clearly the latter approach is the right one—we don’t want to model ourselves as merelyfictional.But the former approach is also useful for some purposes.In particular, it gives us a way of thinking about what the perspective is like‘from the inside’—from the standpoint from which itsfictional character is not apparent.How it seems to us is just how it would seem to thosefictional creatures,who perceive real chances.In this case both the chances and the perceivers of chances arefictions,butfictions which tell us a lot about our own real phenomenology.8third are more useful than thefirst,and all the more so if they are combined, so that the source of the projection explains the genealogy and utility of the relevantfictions.4Perspectival models in science?A republican view of causation or probability would agree with Russell that there is a sense in which these things are not among the constituents of the world discovered by physics,yet disagree that they should be banished from science.Republicans contend that despite their human origins,these modal notions may play a deep role in science.But how could this be?Isn’t science supposed to reveal the world as it appears‘from nowhere’,rather than the world as it appears from some particular human perspective?Two initial responses to this concern.First,I want to note,but set aside for time being,the possibility of arguments that there can be no such thing as a non-perspectival description,a viewpoint genuinely‘from nowhere’.I set this aside not because I believe that no such argument is available.(On the contrary, as I’ll explain.)But our present interest is in the contrast between modal and non-modal perspectives,and a global argument would be blind to this contrast.Second,even if there were a view from nowhere,and it were the job of science to describe it,it might nevertheless be helpful to distinguish between ‘pure’science,which did just this,and‘applied’science,which was allowed to be perspectival in various ways,in the service of distinctively human interests. Given such a distinction,it would be a legitimate question whether chance, causation,and the like fall on one side of it or the other.To understand the question,we need to be able to bring into focus the perspectival option.In other words,we need to be at home with the idea of modelling reality as it appears from some distinctively human perspective.Again,the probability case is helpful.On the one hand,it is a case in which the philosophical landscape is sufficiently well-mapped for it to be relatively uncontroversial that non-objective probabilities have some place in science.On the other hand,it has enough connections to modality in general to serve as a gentle introduction to the possibility of a broader perspectivalism.5Perspectival probabilitiesThe analogue of Russell’s claim for the case of objective probability would be that physics has shown that that are no such things.It is widely believed that this claim is false,because quantum mechanics has shown that there are objective probabilities.In the background here,however,is the view that this analogue of Russell’s claim would have been true,if physics had turned out to be deterministic;and would still be true,if the right version of quantum theory(or its successor)turned out to be deterministic(as in Bohm’s theory,for example, and other no-collapse interpretations of quantum mechanics).Even now,then, we should concede that Russell might turn out be right about chances—or so goes the orthodoxy.However,it’s not hard to see that the orthodoxy can’t be the last word on whether probability has a serious role to play in science.It would be a scandal9。

遥感与地理信息系统方面的好杂志国内的期刊：1）遥感学报（98年前《环境遥感》杂志，国内比较好的遥感专业杂志，主编是原遥感所所长、现国家科技部部长徐冠华院士，遥感文章比较多，象国内比较牛的遥感理论研究的大牛复旦大学的金亚秋教授和北京师范大学的新当选的院士李小文教授经常有文章发表；基于遥感和GIS资源环境等应用的文章也比较好，主要是中科院地理所和遥感所的；还有就是图像处理的算法研究或新型的遥感方法如雷达干涉测量、高光谱方面的研究，主要由武汉大学测绘遥感信息工程国家重点实验室(L)和中科院遥感所的文章。

（2）测绘学报（侧重测量基础理论的研究，但经常有非常好的综述型的文章，上面的测绘学博士论文摘要是非常好，还有主编陈俊勇院士治学非常严谨，一般的假冒伪劣文章很难找到市场，该刊宁缺勿滥,2001年仍然是季刊,文章少,但很精。

不过该刊刊登的文章比较偏重大地测量（GPS），GIS的文章相比比较少）。

（3）武测学报（2001年改名《武汉大学学报》信息科学版）本杂志是原武汉测绘科技大学学报，主编是中国科学院和中国工程院双院士李德仁教授,很多具有创新性和理论性的测绘研究成果都在该刊发表，展示中国测绘学术研究的最高水平,引导测绘理论研究的方向。

我认为上面的博士论文摘要比较好，真正体现了我国3S技术的研究动向和学术水准。

本刊出版内容包括综述与瞻望、学术论文和研究报告、本领域重大科技新闻等,涉及测绘学研究的主要方面,尤其是摄影测量与遥感、大地测量与地球重力场、工程测量、地图制图、地球动力学、全球定位系统(GPS)、地理信息系统(GIS)、图形图像处理等。

该刊现同时有英文版，名为GEO-SPATIAL INFORMATION SCIENCE，是中文版的精华版，万方科技期刊网上可以下载全文。

（4）中国图象图形学报1996年创刊，由中国图象图形学会、中科院遥感所、中科院计算所共同主办，主编是科技部部长徐冠华院士。

2001年起《中国图象图形学报》分A、B版。

Unit OneTask 1⑩④⑧③⑥⑦②⑤①⑨Task 2① be consistent with他说，未来的改革必须符合自由贸易和开放投资的原则。

② specialize in启动成本较低，因为每个企业都可以只专门从事一个很窄的领域。

③ d erive from以上这些能力都源自一种叫机器学习的东西，它在许多现代人工智能应用中都处于核心地位。

④ A range of创业公司和成熟品牌推出的一系列穿戴式产品让人们欢欣鼓舞,跃跃欲试。

⑤ date back to置身硅谷的我们时常淹没在各种"新新"方式之中，我们常常忘记了，我们只是在重新发现一些可追溯至涉及商业根本的朴素教训。

Task 3T F F T FTask 4The most common viewThe principle task of engineering: To take into account the customers ‘ needs and to find the appropriate technical means to accommodate these needs.Commonly accepted claims:Technology tries to find appropriate means for given ends or desires;Technology is applied science;Technology is the aggregate of all technological artifacts;Technology is the total of all actions and institutions required to create artefacts or products and the total of all actions which make use of these artefacts or products.The author’s opinion: it is a viewpoint with flaws.Arguments: It must of course be taken for granted that the given simplified view of engineers with regard to technology has taken a turn within the last few decades. Observable changes: In many technical universities, the inter‐disciplinary courses arealready inherent parts of the curriculum.Task 5① 工程师对于自己的职业行为最常见的观点是：他们是通过应用科学结论来计划、开发、设计和推出技术产品的。

生态调度效果评估方案1. 引言生态调度是指在计算机科学领域中，为了提高计算资源的利用率和效能，通过动态调整任务分配和资源利用策略来实现系统性能的最优化。

在一个复杂的集群环境中，为了评估生态调度的效果，需要设计一种评估方案来定量地分析和比较不同调度算法的性能。

本文将介绍一个生态调度效果评估方案，该方案可以帮助系统管理员或开发人员评估并选择最优的生态调度算法。

2. 评估指标在评估生态调度的效果时，需要考虑以下几个指标：2.1 利用率利用率是指系统中计算资源的使用率，可以通过以下公式计算：$$ 利用率 = \\frac{实际使用资源}{总资源} $$利用率越高，说明系统的资源利用效率越高。

2.2 响应时间响应时间是指任务在系统中执行完成所需要的时间。

较低的响应时间意味着系统能够更快地响应用户请求。

2.3 吞吐量吞吐量是指在单位时间内系统能够处理的任务数量。

较高的吞吐量表示系统具有较强的处理能力。

2.4 能耗在考虑生态调度效果时，还需要考虑系统的能耗情况。

较低的能耗表示系统运行效率高，能够节省能源。

3. 实验设计为了评估生态调度的效果，可以采用以下步骤进行实验设计：3.1 数据收集首先，需要收集一组任务和资源使用的数据。

可以使用真实的任务数据或者通过模拟生成一组测试数据。

3.2 算法实现实现不同的生态调度算法，可以选择常用的算法，例如最少任务数、最小响应时间或者随机分配等。

3.3 仿真实验使用收集到的数据和实现的生态调度算法，进行一系列仿真实验。

在每个实验中，记录下关注的指标，如利用率、响应时间、吞吐量和能耗。

3.4 比较和分析对不同算法的实验结果进行比较和分析。

可以使用统计方法或可视化工具来展示实验结果，并进行定量和定性的比较。

4. 结果分析通过实验结果的比较和分析，可以得出不同生态调度算法在不同指标下的效果。

根据实验结果，选择最优的生态调度算法，以实现最佳的系统性能。

5. 结论本文提出了一个生态调度效果评估方案，该方案基于一系列实验，通过比较和分析不同生态调度算法在各项指标下的表现，帮助系统管理员或开发人员选择最优的生态调度算法。