A Practical Decision Making on Design of Fixed Offshore Wind Turbine Support Structure Considering Socio-economic Impact

Wind energy is considered one of the most promising alternative energy sources against the conventional fossil fuels.However,the deployment of these structures in deep-water for better power production is considered as a complex task.This also has raised the issue regarding selection of appropriate support structures for various sea conditions by considering environmental impact and carbon footprint.This paper considers a jacket like support structure as a case study for an intermediate water depth(50m).The jacket is considered to be located in North of Dutch Sea,and 100-extreme wave is applied as load condition.Here,the presented methodology provides an insight towards environmental/social impact made by the optimized designs in comparison with reference design.

Brain areas activated by uncertain reward-based decision-making in healthy volunteers

Reward-based decision-making has been found to activate several brain areas, including the ven- trolateral prefronta~ lobe, orbitofrontal cortex, anterior cingulate cortex, ventral striatum, and mesolimbic dopaminergic system. In this study, we observed brain areas activated under three de- grees of uncertainty in a reward-based decision-making task （certain, risky, and ambiguous）. The tasks were presented using a brain function audiovisual stimulation system. We conducted brain scans of 15 healthy volunteers using a 3.0T magnetic resonance scanner. We used SPM8 to ana- lyze the location and intensity of activation during the reward-based decision-making task, with re- spect to the three conditions. We found that the orbitofrontal cortex was activated in the certain reward condition, while the prefrontal cortex, precentral gyrus, occipital visual cortex, inferior parietal lobe, cerebellar posterior lobe, middle temporal gyrus, inferior temporal gyrus, limbic lobe, and midbrain were activated during the ＇risk＇ condition. The prefrontal cortex, temporal pole, inferior temporal gyrus, occipital visual cortex, and cerebellar posterior lobe were activated during am- biguous decision-making. The ventrolateral prefrontal lobe, frontal pole of the prefrontal lobe, orbi- tofrontal cortex, precentral gyrus, inferior temporal gyrus, fusiform gyrus, supramarginal gyrus, infe- rior parietal Iobule, and cerebellar posterior lobe exhibited greater activation in the ＇risk＇ than in the ＇certain＇ condition （P 〈 0.05）. The frontal pole and dorsolateral region of the prefrontal lobe, as well as the cerebellar posterior lobe, showed significantly greater activation in the ＇ambiguous＇ condition compared to the ＇risk＇ condition （P 〈 0.05）. The prefrontal lobe, occipital lobe, parietal lobe, temporal lobe, limbic lobe, midbrain, and posterior lobe of the cerebellum were activated during deci- sion-making about uncertain rewards. Thus, we observed different levels and regions of activation for different types of reward processing during decision-making. Specifically, when the degree of reward uncertainty increased, the number of activated brain areas increased, including greater ac- tivation of brain areas associated with loss.

Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.

Neural mechanism of proposer's decision-making in the ultimatum and dictator games

Previous studies have demonstrated that reactions to unfair offers in the ultimatum game are correlated with negative emotion. However, little is known about the difference in neural activity between a proposer＇s decision-making in the ultimatum game compared with the dictator game. The present functional magnetic resonance imaging study revealed that proposing fair offers in the dictator game elicited greater activation in the right supramarginal gyrus, right medial frontal gyrus and left anterior cingulate cortex compared with proposing fair offers in the ultimatum game in 23 Chinese undergraduate and graduate students from Beijing Normal University in China. However, greater activation was found in the right superior temporal gyrus and left cingulate gyrus for the reverse contrast. ＂The results indicate that proposing fair offers in the dictator game is more strongly associated with cognitive control and conflicting information processing compared with proposing fair offers in the ultimatum game.

A Model-Based, Aspiration-Led Decision Support System NY-IEDSS

An AI-aided simulation system embedded in a model-based, aspiration-led decision support system NY-IEDSS is reported. The NY-IEDSS is designed for mid-term development strategic study of the Nanyang Region in Henan, China, and is getting beyond its prototype stage under the decision maker's (the end user) orientation. The integration of simulation model system, decision analysis and expert system for decision support in the system implementation was reviewed. The intent of the paper is to provide insight as to how system capability and acceptability can be enhanced by this integration. Moreover, emphasis is placed on problem orientation in applying the method.

Early Building Design:Informed decision-making by exploring multidimensional design space using sensitivity analysis

This paper describes a novel approach to explore a multidimensional design space and guide multi-actor decision making in the design of sustainable buildings.The aim is to provide proactive and holistic guidance of the design team.We propose to perform exhaustive Monte Carlo simulations in an iterative design approach that consists of tw o steps:1) preparation by the modeler,and 2) a multi-collaborator meeting.In the preparation phase,the simulation modeler performs Morris sensitivity analysis to fixate insignificant model inputs and to identify non-linearity and interaction effects.Next,a representation of the global design space is obtained from thousands of simulations using low-discrepancysequences(LPτ) for sampling.From these simulations,the modeler constructs fast metamodels and performs quantitative sensitivity analysis.During the meeting,the design team explores the global design space by filtering the thousands of simulations.Variable filter criteria are easily applied using an interactive parallel coordinate plot w hich provide immediate feedback on requirements and design choices.Sensitivity measures and metamodels show the combined effects of changing a single input and how to remedy unw anted output changes.The proposed methodology has been developed and tested through real building cases using a normative model to assess energy demand,thermal comfort,and daylight.

Decision Making Support System(DSS)——A Synthesized and Integrated Crystallization of Systems Engineering, Artificial Intelligence and Electronic Technologies

I firmly believe that of systems engineering is the requirement-driven force for the progress ofsoftware engineering, artificial intelligence and electronic technologies. The development ofsoftware engineering, artificial intelligence and electronic technologies is the technical supportfor the progress of systems engineering. INTEGRATION can be considered as "bridging" the ex-isting technologies and the People together into a coordinated SYSTEM.

DEVELOPING EXPERT DECISION SUPPORT SYSTEM WTH VP-EXPERT AND LOTUS 1-2-3 IN RE-BLENDING BY USE

An expert decision support system (EDSS) for multi-bins balance and contro1 of orequality in production ore bins of some large-scale open pit iron mine in China has been developed byexpert svitem tool software VP-EXPERT and integration software LOTUS 1-2-3 in this paper. Itis known by practicing that a medium-scale EDSS constructed on microcomputer is completcly, feaasible by means of VP-EXEPERT to construct knowledge base system (KBS), LOTUS 1-2-3 tomake decision support system (DSS) and link them with BAT.

Feed back Mechanism in Decision Support Systems

Feedback mechanism is a specific feature of control systems, however, the similarity has been found in decision support systems. Just because of this, the DSS can provide a flexible learning and thinking environment, and help decision maker to solve semistructured and unstructured problems actively and creatively. In this paper, the role and type of the feedback in decision making are discussed from different point of view. This is also true in DSS because it supports decision making. The feedback design, especially the feedback interface design, is described through a case of practice DSS. Based on these points, the feedback mechanism is an important feature of DSS, and it is one of the differences between DSS and MIS.

Computerized decision support in adult and pediatric critical care

Computerized decision support(CDS) is the most advanced form of clinical decision support available and has evolved with innovative technologies to provide meaningful assistance to medical professionals. Critical care clinicians are in unique environments where vast amounts of data are collected on individual patients, and where expedient and accurate decisions are paramount to the delivery of quality healthcare. Many CDS tools are in use today among adult and pediatric intensive care units as diagnostic aides, safety alerts, computerized protocols, and automated recommendations for management. Some CDS use have significantly decreased adverse events and improved costs when carefully implemented and properly operated. CDS tools integrated into electronic health records are also valuable to researchers providing rapid identification of eligible patients, streamlining data-gathering and analysis, and providing cohorts for study of rare and chronic diseases through data-warehousing. Although the need for human judgment in the daily care of critically ill patients has limited the study and realization ofmeaningful improvements in overall patient outcomes, CDS tools continue to evolve and integrate into the daily workflow of clinicians, and will likely provide advancements over time. Through novel technologies, CDS tools have vast potential for progression and will significantly impact the field of critical care and clinical research in the future.

基于群决策-云理论的装备保障能力评估

针对装备保障效能评估的复杂性、随机性和模糊性问题,建立了基于群决策-云理论的装备保障能力评估模型。首先,基于装备保障任务的需求,构建了装备保障能力评估指标体系;再利用序关系-群决策方法计算各级指标权重,计算过程简单快捷、更能体现群体决策;最后,将云理论引入装备保障能力评估中,能够实现定性概念与定量数据间的合理转换,清晰准确地表达了层级指标的聚合过程,有效解决了评估过程中的模糊性和随机性。实例表明:该方法计算简单、逻辑关系清楚、计算结果与客观实际相符,可为装备保障效能评估提供一种科学、客观的方法,为保障资源配置提供科学依据。

基于EWM-MARCOS综合评价模型的巷道支护参数优化研究

为应对云南某地下矿山巷道支护返修频繁、成本高的严峻挑战,结合矿山现有支护条件,并全面考虑支护安全、成本和效率等多方面因素,对锚杆长度、钢筋网网度和喷射混凝土厚度的参数进行优化。通过构建的EWM-MARCOS综合评价模型优选出最佳支护参数方案:锚杆长度2.5 m,钢筋网网度150 mm×200 mm,喷射混凝土厚度200 mm。现场实践表明:采用优选的支护方案对巷道进行支护,巷道顶板下沉量降低了3.37 mm,两帮收敛变形降低了3.38 mm,一定程度提高了围岩的稳定性;从经济上来看,考虑到支护返修频率情况下,其支护成本比原方案降低了85.10元/m。因此,采用EWM-MARCOS综合评价模型对支护方案进行优选,可以一定程度优化该矿巷道支护返修频率高、支护成本大的问题。

An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making(DM)procedure, in which the continuous approximation of Pareto front and decision-making is performed interactively, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition,combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experimental results show that the generated approximate continuous Pareto front has good accuracy and completeness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less computation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

基于DGWO-SVM的气象环境下武器作战效能评估方法

实际环境中,各种气象环境要素对于武器装备的作战效能影响显著,对于武器装备的作战效能评估很困难。为提高在气象环境影响下的武器装备作战效能评估效率,提出一种基于强化首领决策灰狼优化-支持向量机(DGWO-SVM)算法的评估模型。建立一个支持向量机模型应用于作战效能评估中,结合强化首领决策能力的灰狼优化算法进行求解,对惩罚因子以及核函数参数进行优化选取,并将最终的评估结果与其他方法进行比对。实验结果表明,所提评估模型的准确率达到了98.39%,评估的误差结果更优于其他方法,具有更高的实用性和有效性。

中医药知识服务的理论探索和平台化实践

中医药知识服务以中医药学科知识体系为基础,通过搜集、整理、分析和重组中医药领域的各种知识资源,提供有效的中医药知识检索、知识挖掘、决策支持、问题解决方案,帮助用户解决中医药领域的实际问题,提高业务技能水平,促进中医药领域的知识传承、创新和发展。知识服务具有定制化、个性化、差异化、互动性、场景化特征,并体现出领域垂直整合的平台化发展趋势。本文结合知识服务领域研究成果和该系统的建设经验,阐述中医药知识服务的理论框架和平台化实践,探讨服务模式和未来趋势。

社区血液透析中心临床决策支持系统的全流程构建与应用

目的:设计一种临床辅助决策支持系统用于社区血液透析中心的管理,并评价其应用效果。方法:构建临床辅助决策支持系统,该系统具备实时数据采集、智能预警、远程指导等技术特点。采用系统可用性问卷(PSSUQ)评价该系统的可用性及满意度,并比较实施该系统前后两组患者血液透析敏感质量指标。结果:在系统的评价中,PSSUQ单项题目平均得分及99%置信区间均低于平均数3.5分的分界点,单项题目平均得分和4个维度得分均低于PSSUQ最佳基准;实施后患者血液透析敏感指标均有不同程度的改善。结论:临床辅助决策系统具有较高的可用性及满意度,能够显著提升患者透析质量,减少不良事件的发生,具有良好的应用前景。

基于驾驶场景与决策规则的智能汽车换道决策

复杂交通环境中,换道决策直接影响智能汽车自主换道效果,然而在换道决策过程中依旧存在着预测正确率低以及决策安全的问题。因此,针对这一问题,提出了基于驾驶场景和决策规则的换道决策模型。考虑换道后的交通行驶状况对换道决策的影响,引入换道后的期望速度和换道前后与前车的距离作为新的特征变量,基于特征变量与换道决策的相关性建立了换道决策规则。建立了模拟真实驾驶环境的换道场景数据集,扩充了NGSIM换道场景数据集,并对其进行了有效性验证。针对换道决策的多参数和非线性问题,提出了基于贝叶斯优化核函数的支持向量机模型,在换道场景数据集上进行测试验证。结果表明:新引入的决策特征变量对换道行为有积极作用,换道场景数据集能够模拟真实的换道场景,可进一步应用到换道决策和轨迹规划的研究中,支持向量机模型对换道行为的预测正确率达95.40%,高于其他机器学习分类器,提高了换道行为的安全性。

煤层气产量预测分析决策支持系统在上下游一体化项目生产管理中的应用

通过海量生产数据开展煤层气产量预测是上下游一体化大型项目管理中面临的挑战之一,针对该难题,中国石化集团国际石油勘探开发有限公司海外技术团队研发了煤层气产量预测分析决策支持系统。该系统以SQL、Spotfire、OFM及Enersight等主要应用软件为底层平台,通过二次开发及整合,搭建了数据整合、数据分析、预测转化及成果应用4个主要模块,以煤层气产量预测复杂性及经常出现的问题为切入点,开展煤层气产量预测综合分析、类比分析及递减分析,实现对产量预测的高效管理。该系统具有方便快捷、固定流程、分工明确、团队协作的特点。通过应用实例,展示了该系统针对海量生产数据,自上而下、由面到点、聚焦关键领域,高效筛查出产量预测的关键问题,并提出可行的修正方案,有效提升了煤层气产量预测精准度,为项目投资决策提供有力支持和保障。

基于渥太华决策支持框架语前聋人工耳蜗植入病人家属决策体验的质性研究

目的:了解语前聋人工耳蜗植入术(cochlear implant,CI)病人院前就医过程的内心体验,明确病人家属就医决策障碍的影响因素,为其决策制定提供解决思路,帮助病人家属权衡治疗利弊,做出决策。方法:2022年9月—12月基于渥太华决策支持框架设计访谈提纲,运用现象学研究方法对18例CI病人家属进行半结构式深入访谈,应用Colaizz 7步法进行资料分析。结果:提炼出3个一级主题和8个二级主题,分别为决策需求(信息需求、利弊分析需求、经济需求)、决策支持(国家政策支持、社会群体支持、自我认知)及决策结局(决策过程质量一般,决策结果较满意)。结论:CI病人家属就医决策障碍亟待解决,以增加信息支持和提高认知为前提,以培养良好的应对方式为关键,以提高社会支持力度为保障,对重度或极重度感音神经性耳聋病人及家属开展健康教育,促使CI病人家属及时做出符合价值观和自身情况的就医决策,让更多耳聋病人能够重获新声,同时为临床工作提供指导。

“三农”领域高校智库建设现状及发展路径探析

“三农”领域高校智库在“三农”问题分析、决策拟定及政策工具弥补纠正等方面发挥着重要作用。为促进中国“三农”领域学术研究与智库研究有效对接,分析相关高校智库建设现状,总结其发展行为、运行机制、成果水平等方面及特点。研究发现,中国“三农”领域的高校智库建设主要依托一批专业化的研究团队和学术机构,在“三农”领域智库中数量占比最高且呈现快速增长趋势,并表现出强烈的地域性特征,在推动农业产业高质量发展方面发挥重要作用;但面临着服务乡村振兴的激励有限、协同合作能力有限、农业生产活动复杂性与农村调查公益性、成果实效水平不高和政策转化力度不够、资源及资金投入缺乏多元化与灵活性等困境。针对此,提出发挥涉农学科优势、推动成果评价制度改革、营造良好生态体系、畅通资政服务渠道、打造政产学研联盟共同体、构建“旋转门”机制、与新农村发展研究院协同建设等对策建议,以期对“三农”领域高校智库体系的建设与转型升级有所裨益,更好地满足新时期全面推进乡村振兴战略的智力需求。