Dynamic constraint and objective generation approach for real-time train rescheduling model under human-computer interaction

Real-time train rescheduling plays a vital role in railway transportation as it is crucial for maintaining punctuality and reliability in rail operations.In this paper,we propose a rescheduling model that incorporates constraints and objectives generated through human-computer interaction.This approach ensures that the model is aligned with practical requirements and daily operational tasks while facilitating iterative train rescheduling.The dispatcher’s empirical knowledge is integrated into the train rescheduling process using a human-computer interaction framework.We introduce six interfaces to dynamically construct constraints and objectives that capture human intentions.By summarizing rescheduling rules,we devise a rule-based conflict detection-resolution heuristic algorithm to effectively solve the formulated model.A series of numerical experiments are presented,demonstrating strong performance across the entire system.Furthermore,theflexibility of rescheduling is enhanced through secondary analysis-driven solutions derived from the outcomes of humancomputer interactions in the previous step.This proposed interaction method complements existing literature on rescheduling methods involving human-computer interactions.It serves as a tool to aid dispatchers in identifying more feasible solutions in accordance with their empirical rescheduling strategies.

An Interactive Fuzzy Satisficing Method for Multiobjective Stochastic Integer Programming with Simple Recourse

This paper considers multiobjective integer programming problems involving random variables in constraints. Using the concept of simple recourse, the formulated multiobjective stochastic simple recourse problems are transformed into deterministic ones. For solving transformed deterministic problems efficiently, we also introduce genetic algorithms with double strings for nonlinear integer programming problems. Taking into account vagueness of judgments of the decision maker, an interactive fuzzy satisficing method is presented. In the proposed interactive method, after determineing the fuzzy goals of the decision maker, a satisficing solution for the decision maker is derived efficiently by updating the reference membership levels of the decision maker. An illustrative numerical example is provided to demonstrate the feasibility and efficiency of the proposed method.

Interactive Fuzzy Programming for Random Fuzzy Two-Level Integer Programming Problems through Fractile Criteria with Possibility

This paper considers two-level integer programming problems involving random fuzzy variables with cooperative behavior of the decision makers. Considering the probabilities that the decision makers’ objective function values are smaller than or equal to target variables, fuzzy goals of the decision makers are introduced. Using the fractile criteria to optimize the target variables under the condition that the degrees of possibility with respect to the attained probabilities are greater than or equal to certain permissible levels, the original random fuzzy two-level integer programming problems are reduced to deterministic ones. Through the introduction of genetic algorithms with double strings for nonlinear integer programming problems, interactive fuzzy programming to derive a satisfactory solution for the decision maker at the upper level in consideration of the cooperative relation between decision makers is presented. An illustrative numerical example demonstrates the feasibility and efficiency of the proposed method.

A New Interactive Method to Solve Multiobjective Linear Programming Problems

Multiobjective Programming (MOP) has become famous among many researchers due to more practical and realistic applications. A lot of methods have been proposed especially during the past four decades. In this paper, we develop a new algorithm based on a new approach to solve MOP by starting from a utopian point, which is usually infeasible, and moving towards the feasible region via stepwise movements and a simple continuous interaction with decision maker. We consider the case where all objective functions and constraints are linear. The implementation of the pro-posed algorithm is demonstrated by two numerical examples.

Improving Students’Participation and Collaboration With Adjusting Cloud Education Platform During the Real-Time Interactive Class

The purpose of this study was to find a way to promote the collaboration and interaction of students and bring about the growth of learners through feedback while taking advantage of real-time interactive class via video conferencing tools.Although real-time interactive class with using video conferencing tools had great advantages,but there were also limitations of active interaction.To this end,real-time interactive tool and cloud-based educational platform were applied to create cases of learner participation classes and analyze the cases.The convergence of real-time interactive class tools and cloud tools has been able to draw students’participation and collaboration in non-face-to-face situations,and it can be seen that it is very helpful in creating learner-centered educational activities based on communication and interaction with students.Through this,the application of the cloud-based educational platform in real-time interactive class could lead students to participate and collaborate even in non-face-to-face situations.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.

RenderKernel:High-level programming for real-time rendering systems

Real-time rendering applications leverage heterogeneous computing to optimize performance.However,software development across multiple devices presents challenges,including data layout inconsistencies,synchronization issues,resource management complexities,and architectural disparities.Additionally,the creation of such systems requires verbose and unsafe programming models.Recent developments in domain-specific and unified shading languages aim to mitigate these issues.Yet,current programming models primarily address data layout consistency,neglecting other persistent challenges.In this paper,we introduce RenderKernel,a programming model designed to simplify the development of real-time rendering systems.Recognizing the need for a high-level approach,RenderKernel addresses the specific challenges of real-time rendering,enabling development on heterogeneous systems as if they were homogeneous.This model allows for early detection and prevention of errors due to system heterogeneity at compile-time.Furthermore,RenderKernel enables the use of common programming patterns from homogeneous environments,freeing developers from the complexities of underlying heterogeneous systems.Developers can focus on coding unique application features,thereby enhancing productivity and reducing the cognitive load associated with real-time rendering system development.

Real-time Risk-averse Dispatch of an Integrated Electricity and Natural Gas System via Condi-tional Value-at-risk-based Lookup-table Ap-proximate Dynamic Programming

The real-time risk-averse dispatch problem of an integrated electricity and natural gas system(IEGS)is studied in this paper.It is formulated as a real-time conditional value-at-risk(CVaR)-based risk-averse dis-patch model in the Markov decision process framework.Because of its stochasticity,nonconvexity and nonlinearity,the model is difficult to analyze by traditional algorithms in an acceptable time.To address this non-deterministic polynomial-hard problem,a CVaR-based lookup-table approximate dynamic programming(CVaR-ADP)algo-rithm is proposed,and the risk-averse dispatch problem is decoupled into a series of tractable subproblems.The line pack is used as the state variable to describe the impact of one period’s decision on the future.This facilitates the reduction of load shedding and wind power curtailment.Through the proposed method,real-time decisions can be made according to the current information,while the value functions can be used to overview the whole opti-mization horizon to balance the current cost and future risk loss.Numerical simulations indicate that the pro-posed method can effectively measure and control the risk costs in extreme scenarios.Moreover,the decisions can be made within 10 s,which meets the requirement of the real-time dispatch of an IEGS.Index Terms—Integrated electricity and natural gas system,approximate dynamic programming,real-time dispatch,risk-averse,conditional value-at-risk.

Efficient Graph-based Genetic Programming Representation with Multiple Outputs

In this work, we explore and study the implication of having more than one output on a genetic programming （GP） graph-representation. This approach, called multiple interactive outputs in a single tree （MIOST）, is based on two ideas. First, we defined an approach, called interactivity within an individual （IWI）, which is based on a graph-GP representation. Second, we add to the individuals created with the IWI approach multiple outputs in their structures and as a result of this, we have MIOST. As a first step, we analyze the effects of IWI by using only mutations and analyze its implications （i.e., presence of neutrality）. Then, we continue testing the effectiveness of IWI by allowing mutations and the standard GP crossover in the evolutionary process. Finally, we tested the effectiveness of MIOST by using mutations and crossover and conducted extensive empirical results on different evolvable problems of different complexity taken from the literature. The results reported in this paper indicate that the proposed approach has a better overall performance in terms of consistency reaching feasible solutions.

Real-time forward modeling and inversion of logging-while-drilling electromagnetic measurements in horizontal wells

Based on the pseudo-analytical equation of electromagnetic log for layered formation,an optimal boundary match method is proposed to adaptively truncate the encountered formation structures.An efficient integral method is put forward to significantly accelerate the convergence of Sommerfeld integral.By asymptotically approximating and subtracting the first reflection/transmission waves from the scattered field,the new Sommerfeld integral method has addressed difficulties encountered by the traditional digital filtering method,such as low computational precision and limited operating range,and realized the acceleration of the computation speed of logging-while-drilling electromagnetic measurements(LWD EM).By making use of the priori information from the offset/pilot wells and interactively adjusting the formation model,the optimum initial guesses of the inversion model is determined in order to predict the nearby formation boundaries.The gradient optimization algorithm is developed and an interactive inversion system for the LWD EM data from the horizontal wells is established.The inverted results of field data demonstrated that the real-time interactive inversion method is capable of providing the accurate boundaries of layers around the wellbore from the LWD EM,and it will benefit the wellbore trajectory optimization and reservoir interpretation.

Interactive graphics mechanical optimum design

Virtually all conventional optimizations are Performed in a batch computer environment. No graphic information during the optimization process is provided. The research tactics and implementation procedure of interactivegraphics in mechanical optimum design are presented. An interactive Graphics Mechanical Optimum Design Program(IGMODP) for microcomputers is developed. The example of wheeled loader' s working device optimum design usingIGMODP is carried out.

RGxE: An R Program for Genotype x Environment Interaction Analysis

Genotype x environmental interaction (GxE) can lead to differences in performance of genotypes over environments. GxE analysis can be used to analyze the stability of genotypes and the value of test locations. We developed an Rlanguage program (RGxE) that computes univariate stability statistics, descriptive statistics, pooled ANOVA, genotype F ratio across location and environment, cluster analysis for location, and location correlation with average location performance. Univariate stability statistics calculated are regression slope (bi), deviation from regression (S2d), Shukla’s variance (σi2), S square Wricke’s ecovalence (Wi), and Kang’s yield stability (YSi). RGxE is free and intended for use by scientists studying performance of polygenic or quantitative traits over multiple environments. In the present paper we provide the RGxE program and its components along with an example input data and outputs. Additionally, the RGxE program along with associated files is also available on GitHub at https://github.com/mahendra1/RGxE, http://cucurbitbreeding.com/todd-wehner/publications/software-sas-r-project/? and http://cuke.hort.ncsu.edu/cucurbit/wehner/software.html.

A DSS for Human Resettlement: Supporting Interactive Optimal Decisions

Decisions regarding relocation of people due to environmental requirements can be very complex and may have serious socio-economic implications. We present the design of a Decision Support System to support such decision making processes involving many inputs, human preferences and multiple objectives.

URI Online Judge： A New Interactive Learning Approach

The URI online judge is a new online tool created with the main purpose of making programming practice more dynamic, interesting and stimulating for those who have just entered into the art of programming. The URI online judge allows problem corrections in real time, interactivity between users, besides it allows flexibility in the choice of the programming language and it makes some supporting materials available. During the short time in which the tool has being used we have observed that it is a very good tool for self-study. As users of programming portals, the authors noticed some details that would be important to be implemented in a new tool, such as the separation of problems by categories. Another fundamental detail is the fact that this tool is available in two languages （Portuguese and English）. This might facilitate the learning process for beginners, both locally and globally.

Towards Sensor-free Academic Emotion Prediction in Programming Environment

he transition from traditional learning to practice-oriented programming learning will bring learners discomfort.The discomfort quickly breeds negative emotions when encountering programming difficulties,which leads the learner to lose interest in programming or even give up.Emotion plays a crucial role in learning.Educational psychology research shows that positive emotion can promote learning performance,increase learning interest and cultivate creative thinking.Accurate recognition and interpretation of programming learners’emotions can give them feedback in time,and adjust teaching strategies accurately and individually,which is of considerable significance to improve effects of programming learning and education.The existing methods of sensor-free emotion prediction include emotion prediction based on keyboard dynamic,mouse interaction data and interaction logs,respectively.However,none of the three studies considered the temporal characteristics of emotion,resulting in low recognition accuracy.For the first time,this paper proposes an emotion prediction model based on time series and context information.Then,we establish a Bi-recurrent neural network,obtain the time sequence characteristics of data automatically,and explore the application of deep learning in the field of Academic Emotion prediction.The results show that the classification ability of this model is much better than that of the original LSTM(Long-Short Term Memory),GRU(Gate Recurrent Unit)and RNN(Re-current Neural Network),and this model has better generalization ability.

重大突发公共卫生事件中的方舱医院建设情景重建研究

方舱医院建设是应对各类重大突发公共卫生事件的一项重要举措。现有研究大多从医学或政府管理视角定性地探讨方舱医院发挥的重要作用,还鲜有从情景重建视角研究方舱医院床位扩容的最优时空分布。本文首先构建针对重大突发公共卫生事件演化的SEIHRD模型,继而将方舱医院床位扩容问题构建为有限应急资源分配组合优化模型并设计免疫优化算法进行求解。测试结果显示,本文所给出的方舱医院床位扩容时空分布优化方案,能够对重大突发公共卫生事件中的方舱医院建设情景进行有效重建。方舱医院床位数量及其时间节点设置对累计感染者人数具有显著影响,这表明在疫情暴发初期尽早设立方舱医院,能够有效提升收治率、减少总感染人数。同时也要注意,应急救援资金投入存在阈值效应,需要设计合理的方舱医院床位扩容方案以避免应急预算资源的过度浪费。

美国高校在线学习需求持续增长的战略转变——CHLOE 9报告的要点与反思

由Quality Matters、Eduventures和EDUCAUSE联合发布的2024年《美国在线教育发展全景报告》(CHLOE 9)呈现了美国在线教育的发展现状和变化趋势。报告主题包括:在线战略与优先事项、在线课程预算与定价策略、外包在线课程管理、在线学习者支持与跟踪服务、人工智能应用与教学实践、在线课程定期且实质性互动等。报告显示,面对持续增长的在线学习需求,美国高校正逐步将在线教育融入院校长期发展战略。报告内容对我国高等院校在线教育发展具有重要借鉴意义。基于对报告的分析,本文提出以下建议:在线教育要全面融入高校长期战略,有效融合在线学习和线下学习;构建以学生为中心的在线课程管理,健全多元主体参与模式;推进人工智能应用于在线教学和行政管理,提升师生人工智能素养;满足学生个性化学习需求,提升在线学习交互性,确保在线学习质量;全方位加强在线教育学生和教师支持,体现人文关怀;平衡在线课程和线下课程费用,合理制定学费政策。

不同结对编程策略对小学生学习效果影响的比较研究

结对编程是一种以协作方式学习编程的教学策略,该研究将结对编程与单独编程相结合,构成混合结对编程策略,采用准实验研究法,从编程总评成绩、结对融洽度、结对互动话语等方面,探究混合结对编程和常规结对编程两种策略对小学生学习编程效果影响的差异。实验结果表明,混合结对编程策略能够增进结对伙伴之间的融洽度,促进学生之间的积极互动,提高小学生的编程能力。教师应正确认识结对编程与单独编程的价值,通过组合应用,提高编程教学实效。

熔融不锈钢与液态钠相互作用的瞬态换热特性研究

钠冷快堆严重事故下,堆芯熔融物会与液态金属钠发生相互作用,如果导致堆芯区域内出现钠沸腾的现象,可能会导致堆芯的再临界,从而导致堆芯的进一步熔化。在重定位过程中,堆芯熔融物与液态钠的换热会导致钠出现沸腾,从而影响熔融物射流碎裂与碎片形成。因此,采用严重事故分析程序ACENA对COSA实验平台上开展的熔融不锈钢与液态钠的相互作用的实验进行分析,验证程序对熔融不锈钢与液态钠相互作用过程的瞬态换热特性的分析能力。验证结果表明:程序会高估熔融不锈钢射流与钠池接触时的换热,且不考虑熔融不锈钢外侧凝固形成的硬壳对换热的影响会导致对换热速率的高估。

大语言模型驱动的交互式建筑设计新范式——基于Rhino7的概念验证

随着社会对建筑设计质量要求越来越高,建筑设计软件也变得越来越专业和复杂。现在的设计软件不仅学习成本高,而且交互模式复杂。大语言模型(LLM)的最新突破使计算机清晰地理解人类自然语言指令,并准确生成代码语言具有可行性,有望为人与软件的交互范式提供新思路。因此,本文提出了LLM驱动的交互式建筑设计新范式--将设计师通过多次键鼠操作与设计软件交互转变为LLM根据设计师自然语言指令生成并执行API调用脚本的方式;提出了技术路线并验证了其在建筑设计场景落地的可能性。该技术路线包括:①LLM根据用户指令从API库中搜索与任务相关的API;②LLM基于指令和候选API摘要信息编写程序脚本并运行;③LLM根据来自软件环境、用户等反馈改进优化所编写的程序脚本。通过Rhino7设计软件、GPT-4和CodeLlaMa完成多个设计任务,测试当前LLM是否具备执行该技术路线各关键环节的能力。测试结果不仅证明了LLM驱动的交互式设计范式在建筑设计场景已初具落地前景,也为技术落地提供经验和建议。该设计范式的落地可以降低软件的使用门槛和学习成本,提高设计师工作效率;有望在未来的建筑设计软件中发挥重要作用。