Adaptive sampling immune algorithm solving joint chance-constrained programming

This work investigates one immune optimization algorithm in uncertain environments, solving linear or nonlinear joint chance-constrained programming with a general distribution of the random vector. In this algorithm, an a priori lower bound estimate is developed to deal with one joint chance constraint, while the scheme of adaptive sampling is designed to make empirically better antibodies in the current population acquire larger sample sizes in terms of our sample-allocation rule. Relying upon several simplified immune metaphors in the immune system, we design two immune operators of dynamic proliferation and adaptive mutation. The first picks up those diverse antibodies to achieve proliferation according to a dynamical suppression radius index, which can ensure empirically potential antibodies more clones, and reduce noisy influence to the optimized quality, and the second is a module of genetic diversity, which exploits those valuable regions and finds those diverse and excellent antibodies. Theoretically, the proposed approach is demonstrated to be convergent. Experimentally, the statistical results show that the approach can obtain satisfactory performances including the optimized quality, noisy suppression and efficiency.

Statistical Analysis of Students’Learning Effectiveness in Online Courses Offered by British Partners in China-UK Joint Education Program

In the context of internationalization,China-UK Joint Education Programs are receiving increasing attention from universities.Based on the difficulties faced in China-UK Joint Education Program,this paper adopts a questionnaire survey method to study the learning effectiveness of students majoring in digital media technology in the China-UK Joint Education Program at Guangxi University of Finance and Economics,focusing on four aspects:learning materials,learning content,teacher conditions,and student learning outcomes.The research analysis in this paper not only provides strong support for the construction of China-UK Joint Education Program but also offers references for other China-UK Joint Education Programs.

Chance-Constrained Approaches for Multiobjective Stochastic Linear Programming Problems

Multiple objective stochastic linear programming is a relevant topic. As a matter of fact, many practical problems ranging from portfolio selection to water resource management may be cast into this framework. Severe limitations on objectivity are encountered in this field because of the simultaneous presence of randomness and conflicting goals. In such a turbulent environment, the mainstay of rational choice cannot hold and it is virtually impossible to provide a truly scientific foundation for an optimal decision. In this paper, we resort to the bounded rationality principle to introduce satisfying solution for multiobjective stochastic linear programming problems. These solutions that are based on the chance-constrained paradigm are characterized under the assumption of normality of involved random variables. Ways for singling out such solutions are also discussed and a numerical example provided for the sake of illustration.

Reconstruction of geological surfaces using chance-constrained programming

Geological surface modeling is typically based on seismic data, well data, and models of regional geology. However, structural interpretation of these data is error-prone, especially in the absence of structural morphology information, Existing geological surface models suffer from high levels of uncertainty, which exposes oil and gas exploration and development to additional risk. In this paper, we achieve a reconstruction of the uncertainties associated with a geological surface using chance-constrained programming based on multisource data. We also quantifi ed the uncertainty of the modeling data and added a disturbance term to the objective function. Finally, we verifi ed the applicability of the method using both synthetic and real fault data. We found that the reconstructed geological models met geological rules and reduced the reconstruction uncertainty.

Chance-constrained programming (CCP)abatement of SO_2 emission for acid deposition control in Liuzhou City

A deterministic linear programming model which optimizes the abatement of each SO2 emission source, is extended into a CCP form by introducing equations of probabilistic constrained through the incorporation of uncertainty in the source-receptor-specific transfer coefficients. Based on the calculation of SO2 and sulfate average residence time for Liuzhou City, a sulfur deposition model has been developed and the distribution of transfer coefficients have been found to be approximately log-normal. Sulfur removal minimization of the model shows that the abatement of emission sources in the city is more effective, while control cost optimization provides the lowest cost programmes for source abatement at each allowable deposition limit under varied environmental risk levels. Finally a practicable programme is recommended.

Joint Active and Passive Beamforming Design for Hybrid RIS-Aided Integrated Sensing and Communication

Integrated sensing and communication(ISAC) is considered an effective technique to solve spectrum congestion in the future. In this paper, we consider a hybrid reconfigurable intelligent surface(RIS)-assisted downlink ISAC system that simultaneously serves multiple single-antenna communication users and senses multiple targets. Hybrid RIS differs from fully passive RIS in that it is composed of both active and passive elements, with the active elements having the effect of amplifying the signal in addition to phase-shifting. We maximize the achievable sum rate of communication users by collaboratively improving the beamforming matrix at the dual function base station(DFBS) and the phase-shifting matrix of the hybrid RIS, subject to the transmit power constraint at the DFBS, the signal-to-interference-plus-noise-ratio(SINR) constraint of the radar echo signal and the RIS constraint are satisfied at the same time. The builtin RIS-assisted ISAC design problem model is significantly non-convex due to the fractional objective function of this optimization problem and the coupling of the optimization variables in the objective function and constraints. As a result, we provide an effective alternating optimization approach based on fractional programming(FP) with block coordinate descent(BCD)to solve the optimization variables. Results from simulations show that the hybrid RIS-assisted ISAC system outperforms the other benchmark solutions.

Progress of International Meridian Circle Program

In 2024,the Chinese Meridian Project(CMP)completed its construction,deploying 282 instruments across 31 stations.This achievement not only provides a robust foundation but also serves as a reference template for the International Meridian Circle Program(IMCP).The IMCP aims to integrate and establish a comprehensive network of ground-based monitoring stations designed to track the propagation of space weather events from the Sun to Earth.Additionally,it monitors various disturbances generated within the Earth system that impact geospace.Over the past two years,significant progress has been made on the IMCP.In particular,the second phase of construction for the China-Brazil Joint Laboratory for Space Weather has been completed,and the North Pole and Southeast Asia networks are under active construction.The 2024 IMCP joint observation campaign was successfully conducted.To facilitate these developments,the scientific program committee of IMCP was established,following the success of 2023 IMCP workshop and the space weather school,which was co-hosted with the Asia-Pacific Space Cooperation Organization(APSCO)and sponsored by Chinese Academy of Sciences(CAS)and Scientific Committee on Solar-Terrestrial Physics(SCOSTEP).Preparations are now underway for the 2024 workshop in collaboration with the National Institute for Space Research(INPE)in Brazil.

Joint Beamforming Design for Dual-Functional Radar-Communication Systems Under Beampattern Gain Constraints

The joint beamforming design challenge for dual-functional radar-communication systems is addressed in this paper.The base station in these systems is tasked with simultaneously sending shared signals for both multi-user communication and target sensing.The primary objective is to maximize the sum rate of multi-user communication,while also ensuring sufficient beampattern gain at particular angles that are of interest for sensing,all within the constraints of the transmit power budget.To tackle this complex non-convex problem,an effective algorithm that iteratively optimizes the joint beamformers is developed.This algorithm leverages the techniques of fractional programming and semidefinite relaxation to achieve its goals.The numerical results confirm the effectiveness of the proposed algorithm.

Multi-gene genetic programming extension of AASHTO M-E for design oflow-volume concrete pavements

The American Association of State Highway and Transportation Officials Mechanistic-Empirical Pavement DesignGuide (AASHTO M-E) offers an opportunity to design more economical and sustainable high-volume rigid pavementscompared to conventional design guidelines. It is achieved through optimizing pavement structural andthickness design under specified climate and traffic conditions using advanced M-E principles, thereby minimizingeconomic costs and environmental impact. However, the implementation of AASHTO M-E design for low-volumeconcrete pavements using AASHTOWare Pavement ME Design (Pavement ME) software is often overly conservative.This is because Pavement ME specifies the minimum design thickness of concrete slab as 152.4 mm (6 in.). Thispaper introduces a novel extension of the AASHTO M-E framework for the design of low-volume joint plain concretepavements (JPCPs) without modification of Pavement ME. It utilizes multi-gene genetic programming (MGGP)-based computational models to obtain rapid solutions for JPCP damage accumulation and long-term performanceanalyses. The developed MGGP models simulate the fatigue damage and differential energy accumulations. Thispermits the prediction of transverse cracking and joint faulting for a wide range of design input parameters and axlespectrum. The developed MGGP-based models match Pavement ME-predicted cracking and faulting for rigidpavements with conventional concrete slab thicknesses and enable rational extrapolation of performance predictionfor thinner JPCPs. This paper demonstrates how the developed computational model enables sustainable lowvolumepavement design using optimized ME solutions for Pittsburgh, PA, conditions.

Statistical Method for Block in Replication Design and Its SAS Program

Large-scale genetic population used for genetic breeding researches covers a large area in the field experiment,and the effect of local control would be gradually weakened.The block in replication(BIR)design is suitable for large population,which is applied to the field experiment of genetic population.The statistical methods of analysis of variance(ANOVA)and heritability estimation in single and multiple environments were derived and implemented using the statistical analysis system(SAS)program for the analysis of BIR.As a work example,a comparison of statistical analysis between BIR design and the completely random block(CRB)design were conducted for the protein content from a panel containing 455 soybean germplasms.The results indicated the different estimates of average heritability in multiple environments.The research results provided technical support for the application of BIR design in genetics and breeding studies.

Estimation of CARA Preferences and Positive Mathematical Programming

The purpose of this paper is to combine the estimation of output price risk and positive mathematical programming (PMP). It reconciles the risk programming presented by Freund with a consistent estimate of the constant absolute risk aversion (CARA) coefficient. It extends the PMP approach to calibration of realized production outputs and observed input prices. The results of this specification include 1) uniqueness of the calibrating solution, 2) elimination of the tautological calibration constraints typical of the original PMP procedure, 3) equivalence between a phase I calibrating solution and a solution obtained by combining phase I and phase II of the traditional PMP procedure. In this extended PMP framework, the cost function specification involves output quantities and input prices—contrary to the myopic cost function of the traditional PMP approach. This extension allows for a phase III calibrating model that replaces the usual linear technology with relations corresponding to Shephard lemma (in the primal constraints) and the marginal cost function (in the dual constraints). An empirical example with a sample of farms producing four crops illustrates the novel procedure.

Transcontinental Translational Medicine Collaboration: A Successful SinoCuban Joint B-to-B Program

Translational medicine is a comprehensive discipline that aims to convert laboratory research results into products and technology for clinical application using modern molecular biological techniques, to improve our understanding of the human body and disease and to optimize laboratory design for clinical observation and analysis for basic research. Its ultimate goal is improving holistic medicine and helping patients solve their health problems. Translational medicine includes two processes： bench to bedside and bedside to bench, known as B-to-B processes. The first B-to-B （bench to bedside） refers to the application of results of the laboratory to clinical use as a medical product or a diagnosis and treatment technology. The second B-to-B （bedside to bench） describes the process by which clinical observation and analysis provides ideas and guidance for experiment design for basic medical research. The two processes complement each other and constitute the two-way cycle of translational medicine. Translational medicine can be applied to clinical disease detection in the form of new biomarkers and can accelerate drug discovery. In recent years, with the biotechnology, increasing rapid development of outcomes of research on molecular pathogenesis can be directly applied to clinical theraDv.

2023年度国家自然科学基金联合基金项目申请受理、评审与资助情况综述

联合基金作为国家自然科学基金的重要组成部分,在引导和整合社会资源投入基础研究方面发挥了重要作用。2023年国家自然科学基金委员会稳步推进新时期联合基金改革,会同各联合资助方圆满完成了联合基金项目申请受理、评审与资助工作。本文统计分析了2023年度国家自然科学基金联合基金项目申请、受理与资助数据,分别介绍了项目指南发布、项目申请及其特点、项目初审、评审与资助的情况,并对2024年度联合基金工作进行了展望。

在地国际化临床医学人才培养实施路径

党的十九届五中全会提出,要加快构建以国内大循环为主体、国内国际双循环相互促进的新发展格局。以国内大循环为主体双循环战略的提出,对新时代国际化复合型医学精英人才培养提出新要求。重庆医科大学以中外合作办学为契机,以培养具有国际视野的复合创新型临床医学精英人才为目标,以临床岗位胜任力为导向,以学生发展为中心,充分利用国际国内资源,通过培养体系、管理体制、教学资源、师资队伍、评价体系、多元文化等“六大融合”,构建以“一体两翼”课程体系、增强虚拟混合学习模式、数字化资源赋能为主要特征的在地国际化临床医学人才培养模式,开启“以我为主”的临床医学人才培养在地国际化高质量发展新格局,在国内自主培养具有家国情怀、国际视野的创新型未来卓越医学人才。

多类病人的结直肠癌联合筛查策略优化

针对早期结直肠癌病人,考虑了资源约束下的多类病人联合筛查策略优化问题.建立了该问题的马尔科夫决策模型(MDP)用于求解最优动态筛查策略,提出了基于混合整数规划(MIP)的近似模型求解更大规模的问题,分析了不同资源水平与人群特征下的最优联合筛查策略,并对比了不同情景下各种筛查策略的效果.实验结果表明,联合筛查策略明显优于单独筛查策略,能够帮助提升健康人群的预期质量调整生命年(QALYs)达到1.84%,并且提升幅度随资源的紧张程度、疾病的严重程度、男性和老年人群的比例上升而增大.

Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

To address uncertainty as well as transient stability constraints simultaneously in the preventive control of windfarm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilisticmulti-objective particle swarm optimization based on the point estimate method is employed to cope with thestochastic factors. The transient security region of the system is accurately ensured by the interior point methodin the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforcedin the last stage. Furthermore, the proposed strategy is a general framework that can combine other optimizationalgorithms. The proposed methodology is tested on the modified WSCC 9-bus system and the New England 39-bussystem. The results verify the feasibility of the method.

基于半正定规划的交直流主动配电网三相有功无功联合优化

交直流主动配电网作为未来配电网的主要发展形态,实现其有功无功资源的协同优化控制具有重要的研究意义。然而配电网负荷单相接入和分布式发电三相功率不一致,将导致配电网三相不平衡现象越发严重。为了应对交直流主动配电网的三相不平衡问题,提出考虑相间耦合与三相不平衡度限制的三相有功无功联合优化方法。首先,建立考虑相间耦合的原始三相潮流模型并对其进行升维映射;其次,采用半正定松弛方法,推导了交直流主动配电网各子系统的完整三相潮流模型以及序分量形式下的三相电压不平衡度约束;再次,提出以总运行成本最小化为目标函数的交直流主动配电网三相有功无功联合优化模型;最后,采用改进IEEE 33节点、69节点、141节点和1056节点交直流配电网算例分析,验证了该文所提方法的有效性。

考虑道路受阻的森林火灾应急资源联合调度优化研究

针对森林火灾发生后可能存在的交通路网受阻情况,探讨一类新的森林火灾应急资源联合调度优化策略,构建了兼顾资源受限和道路受阻的森林火灾应急资源联合调度的混合整数线性规划模型。为减少森林火灾造成的资源损失,优化目标设定为最小化消防救援时间,并且根据问题特性设计两种编码方式的改进人工蜂群算法。最后,通过典型实例和随机仿真算例进行实验验证。实验结果表明:(1)对于实际案例和中小规模仿真案例,采用商业求解器CPLEX求解模型,能够在5 min内获得最优的消防救援方案;(2)对于大规模森林火灾,提出的改进人工蜂群算法求解性能优于商业求解器CPLEX,只需10 s就能够获得更高质量的消防救援方案,可为道路受阻下的森林火灾应急救援团队提供有效的消防救援方案。

基于故障观测器的机器人多关节手臂最优容错控制

针对机器人多关节手臂容易出现卡死、饱和及损伤等故障影响控制精度的问题,利用设计的故障观测器提出了一种最优容错控制律。首先根据机器人多关节手臂末端的空间位置与各关节转动角度的关系,建立了机器人多关节手臂的故障模型;然后定义了多关节手臂的转角和接触力的跟踪误差,并设计了故障观测器,通过引入径向基函数(Radial Basis Function,RBF)神经网络估计出模型参数;最后根据动态规划的思想,建立了综合位置/接触力跟踪误差和故障的机器人多关节手臂性能指标矩阵,并设计了包含Hamiltonian方程的容错控制律,利用RBF神经网络求解出最优的性能指标,从而得到最优的容错控制律。仿真结果表明,设计的最优容错控制律能够克服各类故障带来的影响,故障估计的最大误差仅为0.09 N·m,位置跟踪的最大误差仅为0.14 cm,接触力控制的最大误差仅为0.18 N,验证了设计方法的可行性。六自由度多关节手臂测试结果表明,在8个空间坐标上定位的最大误差仅为0.17 cm,接触力的最大误差仅为0.22 N,从而验证了设计的最优容错控制律具有优越的工程实用性。

程序化手法联合常规疗法对偏瘫后上肢痛患者疼痛程度、上肢关节活动度的影响

目的:探究程序化手法联合常规疗法在偏瘫后上肢痛患者治疗中的价值。方法:选取2019年1月—2023年1月河南省平煤神马医疗集团总医院收治的62例脑卒中偏瘫患者作为研究对象,采用电脑随机法将其分为研究组(n=31)和对照组(n=31)。对照组采用常规疗法,研究组在对照组基础上给予程序化手法。比较两组患者临床疗效、治疗前后视觉模拟量表(VAS)评分、Fugl-Meyer上肢功能量表(FMA)评分、肱动脉血流动力学[直径(D)、收缩期峰值血流速度(PSV)、阻力指数(RI)]、上肢关节活动度及不良反应情况。结果:研究组患者总有效率高于对照组,差异有统计学意义(χ^(2)=4.769,P<0.05)。治疗12周后,两组患者VAS评分较治疗前下降,且研究组低于对照组,两组患者FMA评分较治疗前升高,且研究组高于对照组,差异有统计学意义(t=5.478、2.587,P<0.05);两组患者D、PSV、RI均较治疗前改善,且研究组改善幅度大于对照组,差异有统计学意义(t=2.442、2.078、2.416,P<0.05);两组患者外旋范围、前屈范围均较治疗前增加,且研究组均高于对照组,差异有统计学意义(t=2.949、2.763,P<0.05)。治疗期间两组患者均未出现严重不良事件。结论:程序化手法疗效确切,可改善血流动力学指标,有效减轻疼痛,提高上肢运动功能,且具有较高安全性。