Research on virtual entity decision model for LVC tactical confrontation of army units

According to the requirements of the live-virtual-constructive(LVC)tactical confrontation(TC)on the virtual entity(VE)decision model of graded combat capability,diversified actions,real-time decision-making,and generalization for the enemy,the confrontation process is modeled as a zero-sum stochastic game(ZSG).By introducing the theory of dynamic relative power potential field,the problem of reward sparsity in the model can be solved.By reward shaping,the problem of credit assignment between agents can be solved.Based on the idea of meta-learning,an extensible multi-agent deep reinforcement learning(EMADRL)framework and solving method is proposed to improve the effectiveness and efficiency of model solving.Experiments show that the model meets the requirements well and the algorithm learning efficiency is high.

A decision model for decentralized autonomous organization platform selection: Three industry case studies

Context:Decentralized autonomous organizations are a new form of smart contract-based governance.Decentralized autonomous organization platforms,which support the creation of such organizations,are becoming increasingly popular,such as Aragon and Colony.Selecting the best fitting platform is challenging for organizations,as a significant number of decision criteria,such as popularity,developer availability,governance issues and consistent documentation of such platforms,should be considered.Additionally,decision-makers at the organizations are not experts in every domain,so they must continuously acquire volatile knowledge regarding such platforms.Objective:Supporting decision-makers in selecting the right decentralized autonomous organization platforms by designing an effective decision model is the main objective of this study.We aim to provide more insight into their selection process and reduce time and effort significantly by designing a decision model.Method:This study presents a decision model for the decentralized autonomous organization platform selection problem.The decision model captures knowledge regarding such platforms and concepts systematically.This model is based on an existing theoretical framework that assists software engineers with a set of multi-criteria decision-making problems in software production.Results:We conducted three industry case studies in the context of three decentralized autonomous organizations to evaluate the effectiveness and efficiency of the decision model in assisting decision-makers.The case study participants declared that the decision model provides significantly more insight into their selection process and reduces time and effort.Conclusion:We observe in the empirical evidence from the case studies that decision-makers can make more rational,efficient,and effective decisions with the decision model.Furthermore,the reusable form of the captured knowledge regarding decentralized autonomous organization platforms can be employed by other researchers in their future investigations.

Balanced scorecard-based analysis of customer expectations for cosmetology services:a hybrid decision modeling approach

The goal of this study is to analyze and characterize customer expectations in the cosmetics sector.Within this framework,first,the extant literature is reviewed,and 12 most prominent performance measurement criteria are identified.Then,these criteria are organized along the four different balanced scorecard dimensions.By employing an Interval Type-2 Fuzzy DEMATEL methodology,the weighted importance of these dimensions and criteria are identified.Additionally,with the Interval Type-2 Fuzzy TOPSIS approach,13 leading cosmetic service providers in Ukraine are ranked based on their relative scores.The findings of the study indicate that consumer is the most significant dimension while learning and growth seem to have the least importance.Similarly,it is also concluded that all consumerfocused criteria(i.e.diversification of services,feedback on the product and services,and customer loyalty)have the highest priorities in the complete criterion set.

A multi-paradigm decision modeling framework for combat system effectiveness measurement based on domain-specific modeling

Decision modeling is an essential part of the combat system effectiveness simulation (CoSES), which needs to cope with the cognitive quality, diversity, flexibility, and higher abstraction of decision making. In this paper, a multi-paradigm decision modeling framework is proposed to support decision modeling at three levels of abstraction based on domain-specific modeling (DSM). This framework designs a domain-specific modeling language (DSML) for decision modeling to raise the abstraction level of modeling, transforms the domain-specific models to formalism-based models to enable formal analysis and early verification and validation, and implements the semantics of the DSML based on a Python scripts framework which incorporates the decision model into the whole simulation system. The case study shows that the proposed approach incorporates domain expertise and facilitates domain modeler's participation in CoSES to formulate the problem using DSML in the problem domain, and enables formal analysis and automatic implementation of the decision model in the solution domain.

Irrigation decision model for tomato seedlings based on optimal photosynthetic rate

Soil moisture is a major environmental factor that influences tomato growth and development.Suitable soil moisture not only increases tomato production but also saves irrigation water.In this study,an irrigation decision model was developed,which called soil moisture regulation model,for optimizing growth of tomato seedlings while saving water.The data used for modeling were collected from a multi-gradient nested experiment,in which temperature,photosynthetic photon flux density(PPFD),carbon dioxide(CO2)concentration and soil moisture were variables and the corresponding photosynthetic rate was measured.Subsequently,a prediction model of tomato photosynthetic rate was constructed using support vector regression(SVR)algorithm.With photosynthetic rate prediction model as fitness function,genetic algorithm(GA)was used to find the optimal soil moisture under each combination of the above environmental factors.Finally,back propagation neural network(BPNN)algorithm was used to establish a decision model of tomato irrigation,which could provide the optimal soil moisture under current environment.For the soil moisture regulation model constructed here,the coefficient of determination was 0.9738,the mean square error of the test set was 1.51×10-5,the slope of the verified straight line was 0.9752,and the intercept was 0.00916.This model demonstrated high precision,which thereby provides a theoretical basis for accurate irrigation control in the greenhouse facility environment.

Characteristics of Liver Functions in Patients With COVID-19 and Construction of a Prognostic Evaluation Decision Model Based on Liver Functions

A number of studies have suggested that coronavirus disease 2019(COVID-19)can cause liver damage.However,clinical features and outcome of COVID-19 in patients with liver injury remain to be further investigated.In this study,the clinical data of 265 COVID-19 patients admitted to seven tertiary hospitals were collected.Based on a threshold for transaminase or total bilirubin levels at two times the normal upper limit,patients were divided into mild or moderate/severe liver injury groups.Among the 265 patients,183 patients showed liver injury within 48 hours of admission.Aspartate aminotransferase levels were predominantly elevated in the liver injury group,but albumin levels were reduced.Moreover,fibrinogen and D-dimer were significantly increased.Furthermore,68%of the patients with moderate/severe liver injury had one or more underlying diseases.Almost half of these patients developed acute respiratory distress syndrome(44%)and secondary infections(46%).These patients showed increased interleukin-6 and interleukin-10 levels and a decrease in PaO2 and the oxygenation index.In addition,levels of alanine aminotransferase,aspartate aminotransferase,and albumin were correlated with the oxygenation index,D-dimer and lymphocyte counts.Furthermore,a novel prognostic assessment model based on liver function was established,which accuracy reached 88%and was able to accurately assess the prognosis of COVID-19 patients.

Role-based Bayesian decision framework for autonomous unmanned systems

In the process of performing a task,autonomous unmanned systems face the problem of scene changing,which requires the ability of real-time decision-making under dynamically changing scenes.Therefore,taking the unmanned system coordinative region control operation as an example,this paper combines knowledge representation with probabilistic decisionmaking and proposes a role-based Bayesian decision model for autonomous unmanned systems that integrates scene cognition and individual preferences.Firstly,according to utility value decision theory,the role-based utility value decision model is proposed to realize task coordination according to the preference of the role that individual is assigned.Then,multi-entity Bayesian network is introduced for situation assessment,by which scenes and their uncertainty related to the operation are semantically described,so that the unmanned systems can conduct situation awareness in a set of scenes with uncertainty.Finally,the effectiveness of the proposed method is verified in a virtual task scenario.This research has important reference value for realizing scene cognition,improving cooperative decision-making ability under dynamic scenes,and achieving swarm level autonomy of unmanned systems.

Development and Application of Decision Support Model for the Performance Optimization of Office Buildings Based on Grasshopper

With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.

A Decision Support Model for Predicting Avoidable Re-Hospitalization of Breast Cancer Patients in Kenyatta National Hospital

This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.

Planning Sports Facilities Based on a Multicriteria Model Built in GIS Environment

The objective of this work is to evaluate the coverage of the sports facilities at Oeiras Municipality,near Lisbon,in Portugal,identifying the well-served areas and those with deficit coverage,according to the national norms for sports facilities programming and characterization,based on a self methodology,in a geographic information system(GIS)environment.For the deficit covered areas,a multicriteria analysis was developed,based on the established national criteria,which allow the identification and prioritization of interventioned areas for sports facilities.The results obtained by the application of this tool enable more informed and more detailed knowledge of the Oeiras Municipality sports supply,providing essential information for decision making in the planning of sports facilities.

Comparison of machine learning models for gully erosion susceptibility mapping

Gully erosion is a disruptive phenomenon which extensively affects the Iranian territory,especially in the Northern provinces.A number of studies have been recently undertaken to study this process and to predict it over space and ultimately,in a broader national effort,to limit its negative effects on local communities.We focused on the Bastam watershed where 9.3%of its surface is currently affected by gullying.Machine learning algorithms are currently under the magnifying glass across the geomorphological community for their high predictive ability.However,unlike the bivariate statistical models,their structure does not provide intuitive and quantifiable measures of environmental preconditioning factors.To cope with such weakness,we interpret preconditioning causes on the basis of a bivariate approach namely,Index of Entropy.And,we performed the susceptibility mapping procedure by testing three extensions of a decision tree model namely,Alternating Decision Tree(ADTree),Naive-Bayes tree(NBTree),and Logistic Model Tree(LMT).We dichotomized the gully information over space into gully presence/absence conditions,which we further explored in their calibration and validation stages.Being the presence/absence information and associated factors identical,the resulting differences are only due to the algorithmic structures of the three models we chose.Such differences are not significant in terms of performances;in fact,the three models produce outstanding predictive AUC measures(ADTree=0.922;NBTree=0.939;LMT=0.944).However,the associated mapping results depict very different patterns where only the LMT is associated with reasonable susceptibility patterns.This is a strong indication of what model combines best performance and mapping for any natural hazard-oriented application.

A Proxy Signature Scheme as Secure as Decisional Diffie-Hellman Problem

Based on decisional Diffie-Hellman problem, we propose a simple proxy-protected signature scheme. In the random oracle model, we also carry out the strict security proof for the proposed scheme. The security of the proposed scheme is not loosely related to the discrete logarithm assumption but tightly related to the decisional Diffie-Hellman assumption in the random oracle model.

A Decision Support System for Spatial Analysis of Agricultural Production in Madagascar

In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.

上海市居家康复治疗项目实施现况及影响因素

目的了解上海市居家康复治疗项目实施现状,探究影响社区开展居家康复的因素,为进一步提升居家康复服务质量及利用率提出可行建议。方法2021年8—12月对上海市3个区的所有社区卫生服务中心(共36家)进行问卷调查,分别建立决策树模型、神经网络模型分析社区开展居家康复服务的影响因素。结果总计发放机构调查问卷36份,回收36份,回收率100%。无论是西医康复治疗项目还是中医康复治疗项目,社区开展率均高于居家开展率。康复床位数、强制性运动疗法、轮椅操作训练等是影响社区开展居家康复的因素。结论上海市居家康复服务开展率低于社区,建议通过增加机构康复床位数量、探索适宜的居家康复治疗项目、增加康复人员轮椅操作培训等方式提高居家康复服务的数量和质量。

Selection of Wind Turbine Systems for the Sultanate of Oman

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades,and the government has struggled to find a solution.In addition,Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040,including solar and wind turbines.Furthermore,the use of small-scale energy from wind devices has been on the rise in recent years.This upward trend is attributed to advancements in wind turbine technology,which have lowered the cost of energy from wind.To calculate the internal and external factors that affect the small-scale energy of wind technologies,the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal solution.As a result,in the decision model,four criteria,seventeen sub-criteria,and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of Oman.This research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of Oman.Further,six distinct miniature wind turbines were investigated for four different locations.The outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes,schools,college campuses,irrigation,greenhouses,communities,and small businesses.The government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.

标准模型下一种实用的和可证明安全的IBE方案

组合公钥方案是一种用于基于身份密码体制中生成用户加密密钥和私钥的知名方案.针对组合公钥方案存在合谋攻击的问题,通过仅扩展该方案的私钥生成过程,实现了扩展方案的抗合谋攻击性.在此基础上构建标准模型下基于Decisional Bilinear Diffie-Hell man假设可证明安全的一种新的基于身份加密方案.最后,为了说明所构新方案的实用性,分析了扩展组合公钥方案的用户加密密钥抗碰撞性;对比了新方案和同类的3个知名方案在安全性证明的归约程度方面、加解密的时间复杂度方面和密文的长度方面的性能,表明了新方案在以上3点上具有目前最优的指标.因此新方案是相对较实用的.

基于访问树的属性基签名算法

提出了一种基于访问树的属性基签名算法,签名算法采用访问树结构有效地解决了门限属性基签名方案中阈值对签名算法的限制。该算法无需限定属性个数,可以灵活地设定签名策略。算法安全性证明基于标准模型而不是随机预言机模型,在标准模型中将算法的安全性归约到判定BDH困难假设。

指定验证方的门限验证签名方案及安全性证明

Laih提出了指定验证方的签名方案设计问题,并给出一种解决方案.首先分析指出该方案存在严重安全缺陷,然后提出了签名方案SV-EDL,解决了如上密码学问题.同时,把可证明安全理论引入这类方案的分析设计,并在RO(randomoracle)模型中证明:SV-EDL的抗伪造安全性和计算Diffie-Hellman(computational Diffie-Hellman,简称CDH)问题紧密关联,亦即伪造SV-EDL签名几乎和解决CDH问题一样困难;除指定方以外,任何人验证签名的能力都与决策Diffie-Hellman(decisional Diffie-Hellman,简称DDH)问题密切相关.由于CDH问题和DDH问题的困难性与离散对数(discretelogarithm,简称DL)问题紧密相关已成为广泛共识,因此与当前同类方案比较,该签名方案提供了更好的安全性保证.此外,上述签名方案还以非常简明、直接的方式满足不可否认要求.最后提出并构造了验证服务器系统的门限验证协议,并在标准模型中给出了安全性证明.该方案不要求可信中心的存在.

标准模型下可证安全的基于身份门限签密方案

提出了一种有效的基于身份的门限签密方案.采用双线性对映射技术,基于Waters和Paterson提出的签名方案,设计了标准模型下可证安全的门限签密方案,并利用决策双线性Diffie-Hellman问题的困难性证明了方案的语义安全性,同时利用计算Diffie-Hellman问题的困难性证明了方案的不可伪造性.相对随机预言模型下的同类方案,本文方案更具有实际意义.

一种可证明安全的基于身份的高效环签密方案

为了克服传统公钥基础设施中的证书管理以及提高环签密方案的安全性,提出一种有效的基于身份的环签密方案。通过使用双线性对技术,设计一个在标准模型下基于身份的环签密方案,并分析方案的正确性进行。通过两个困难问题假设CDH问题和DBDH问题,证明方案满足不可伪造性和不可区分性,同时通过与几个现有方案进行比较,指出该方案具有较高的效率。