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.

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.

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.

Modeling of cognitive framework in time-stressed decision making

An approach for modeling a human cognitive framework in time-stressed decision making is presented. The recognitive and metacognitive processes that represent the cognitive framework are modeled by the colored Petri nets （CPNs）. A structural and behavioral analysis method is adopted to obtain the static and dynamic property used to verify the CPNs model of the cognitive framework. Finally, an example from the command and control radar recognition system is used to evaluate the feasibility and availability of the CPNs model adopted in practical systems.

Discrete decision model and multi-agent simulation of the Liang Zong two-chain hierarchical organization in a complex project

Different from the organization structure of complex projects in Western countries, the Liang Zong hierarchical organization structure of complex projects in China has two different chains, the chief-engineer chain and the general-director chain,to handle the trade-off between technical and management decisions. However, previous works on organization search have mainly focused on the single-chain hierarchical organization in which all decisions are regarded as homogeneous. The heterogeneity and the interdependency between technical decisions and management decisions have been neglected. A two-chain hierarchical organization structure mapped from a real complex project is constructed. Then, a discrete decision model for a Liang Zong two-chain hierarchical organization in an NK model framework is proposed. This model proves that this kind of organization structure can reduce the search space by a large amount and that the search process should reach a final stable state more quickly. For a more complicated decision mechanism, a multi-agent simulation based on the above NK model is used to explore the effect of the two-chain organization structure on the speed, stability, and performance of the search process. The results provide three insights into how, compared with the single-chain hierarchical organization, the two-chain organization can improve the search process: it can reduce the number of iterations efficiently; the search is more stable because the search space is a smoother hill-like fitness landscape; in general, the search performance can be improved.However, when the organization structure is very complicated, the performance of a two-chain organization is inferior to that of a single-chain organization. These findings about the efficiency of the unique Chinese-style organization structure can be used to guide organization design for complex projects.

The Efficiency Analysis and A Market Decision Model on Equipment Maintenance

This paper presents and analyses the internal and external efficiencies of equipment maintenance, and presents that the objective of maintenance is the maximum external efficiency. It defines generalized reliability degree of equipment and deduces the correspondent calculating method. It overcomes the defect that traditional calculating method of reliability degree has, which only considers the factor of time not function, therefore we establish a market decision model of equipment maintenance based on it. This model can determine the marginal efficiency of maintenance investment and critical value of generalized reliability degree when it reaches break-even point. After combining the equipment maintenance with economical benefit of enterprise and marketing situation of products, an optimal maintenance strategy will be got. It provides a new method for scientific and rational decisions of equipment maintenance.

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 SOLVABLE ONE-DIMENSIONAL ALLOCATION DECISION MODEL OF RENEWABLE RESOURCES

In the paper, the determinate atlecation decision model and the probabilistic allocation decision model of a kind of renewable resource are separatly studied by means of dynamic programming, and the optimal allocation policy is given under some special conditions.

Opinion: the use of natural hazard modeling for decision making under uncertainty

Decision making to mitigate the effects of natural hazards is a complex undertaking fraught with uncertainty. Models to describe risks associated with natural hazards have proliferated in recent years. Concurrently, there is a growing body of work focused on developing best practices for natural hazard modeling and to create structured evaluation criteria for complex environmental models. However, to our knowledge there has been less focus on the conditions where decision makers can confidently rely on results from these models. In this review we propose a preliminary set of conditions necessary for the appropriate application of modeled results to natural hazard decision making and provide relevant examples within US wildfire management programs.

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.

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.

Decision Aid Model for Private-owned Electric Vehicles Participating in Frequency Regulation Ancillary Service Market

To reduce the difficulty and enhance the enthusiasm of private-owned electric vehicles(EVs) in participating in frequency regulation ancillary service market(FRASM), a decision aid model(DAM) is proposed. This paper presents three options for EV participating in FRASM, i. e., the base mode(BM), unidirectional charging mode(UCM), and bidirectional charging/discharging mode(BCDM), based on a reasonable simplification of users' participating willingness. In BM, individual EVs will not be involved in FRASM, and DAM will assist users to set the optimal charging schemes based on travel plans under the time-of-use(TOU) price. UCM and BCDM are two modes in which EVs can take part in FRASM. DAM can assist EV users to create their quotation plan, which includes hourly upper and lower reserve capabilities and regulation market mileage prices. In UCM and BCDM, the difference is that only the charging rate can be adjusted in the UCM, and the EVs in BCDM can not only charge but also discharge if necessary. DAM can estimate the expected revenue of all three modes, and EV users can make the final decision based on their preferences. Simulation results indicate that all the three modes of DAM can reduce the cost, while BCDM can get the maximum expected revenue.

Impact Damage Testing Study of Shanxi-Beijing Natural Gas Pipeline Based on Decision Tree Rotary Tiller Operation

The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.

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.

Analysis of hospitalization costs related to fall injuries in elderly patients

BACKGROUND With the aging world population,the incidence of falls has intensified and fallrelated hospitalization costs are increasing.Falls are one type of event studied in the health economics of patient safety,and many developed countries have conducted such research on fall-related hospitalization costs.However,China,a developing country,still lacks large-scale studies in this area.AIM To investigate the factors related to the hospitalization costs of fall-related injuries in elderly inpatients and establish factor-based,cost-related groupings.METHODS A retrospective study was conducted.Patient information and cost data for elderly inpatients(age≥60 years,n=3362)who were hospitalized between 2016 and 2019 due to falls was collected from the medical record systems of two grade-A tertiary hospitals in China.Quantile regression(QR)analysis was used to identify the factors related to fall-related hospitalization costs.A decision tree model based on the chi-squared automatic interaction detector algorithm for hospitalization cost grouping was built by setting the factors in the regression results as separation nodes.RESULTS The total hospitalization cost of fall-related injuries in the included elderly patients was 180479203.03 RMB,and the reimbursement rate of medical benefit funds was 51.0%(92039709.52 RMB/180479203.03 RMB).The medical material costs were the highest component of the total hospitalization cost,followed(in order)by drug costs,test costs,treatment costs,integrated medical service costs and blood transfusion costs The QR results showed that patient age,gender,length of hospital stay,payment method,wound position,wound type,operation times and operation type significantly influenced the inpatient cost(P<0.05).The cost grouping model was established based on the QR results,and age,length of stay,operation type,wound position and wound type were the most important influencing factors in the model.Furthermore,the cost of each combination varied significantly.CONCLUSION Our grouping model of hospitalization costs clearly reflected the key factors affecting hospitalization costs and can be used to strengthen the reasonable control of these costs.

Investment in deepwater oil and gas exploration projects:a multi-factor analysis with a real options model

Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.

The Effect of Emotion on Consumer Behavior in the Fan Economy

Fans’customer behavior is highly susceptible to emotions.Emotions play an important role in the interaction of consumers and objects of interest in the fan economy.Based on the bounded rationality hypothesis of game participants,the game model is constructed by using the emotional function.On this basis,we used the dynamic system theory to dynamically replicate the equations of the general 2×2“Eagle Pigeon”evolutionary game,analyzed the behavior of both sides in the evolution,and got the equilibrium point between the two sides of the game to explain the mechanism of influence of emotions on customer behavior in fan economy.

Water resources optimization and eco-environmental protection in Qaidam Basin

In order to realize sustainable development of the arid area of Northwest China, rational water resources exploitation and optimization are primary prerequisites. Based on the essential principle of sustainable development, this paper puts forward a general idea on water resources optimization and eco-environmental protection in Qaidam Basin, and identifies the competitive multiple targets of water resources optimization. By some qualitative methods such as Input-output Model & AHP Model and some quantitative methods such as System Dynamics Model & Produce Function Model, some standard plans of water resources optimization come into being. According to the Multiple Targets Decision by the Closest Value Model, the best plan of water resources optimization, eco-environmental protection and sustainable development in Qaidam Basin is finally decided.

Artificial Logging or Natural Growth

Global climate change makes forestry carbon sequestration a hot issue. In order to improve the comprehensive benefits of forest management, this paper studies the carbon accounting problem, and uses the forest stock conversion factor method to create a carbon sequestration accounting model based on the reserve transformation method. Then, the HWP carbon sequestration accounting algorithm is obtained after the improvement of the reserve change method and the atmospheric flow method with the HWP half-life as a bridge. Based on the ecological and economic benefits, a multi-objective and multi-attribute decision-making model for forest management plan is constructed, and the optimal strategy of stand structure based on selective cutting is proposed. Finally, the entropy weight TOPSIS method is used to quantitatively analyze the comprehensive benefit value and provide suggestions for forestry departments. To verify the model, we chose the Greater Khingan Mountains forest region as the research site. Through successive iterations of CSAM, we calculate that the forest will absorb 534 million tons of live forest and forest products in 100 years. From the stand structure of the forest area, when the selected cutting intensity is 20% and the selected cutting cycle is 10.7 years, the comprehensive benefit value of the Greater Khingan Mountains is the highest.