A Synergistic Multi-Attribute Decision-Making Method for Educational Institutions Evaluation Using Similarity Measures of Possibility Pythagorean Fuzzy Hypersoft Sets

Due to the numerous variables to take into account as well as the inherent ambiguity and uncertainty,evaluating educational institutions can be difficult.The concept of a possibility Pythagorean fuzzy hypersoft set(pPyFHSS)is more flexible in this regard than other theoretical fuzzy set-like models,even though some attempts have been made in the literature to address such uncertainties.This study investigates the elementary notions of pPyFHSS including its set-theoretic operations union,intersection,complement,OR-and AND-operations.Some results related to these operations are also modified for pPyFHSS.Additionally,the similarity measures between pPyFHSSs are formulated with the assistance of numerical examples and results.Lastly,an intelligent decision-assisted mechanism is developed with the proposal of a robust algorithm based on similarity measures for solving multi-attribute decision-making(MADM)problems.A case study that helps the decision-makers assess the best educational institution is discussed to validate the suggested system.The algorithmic results are compared with the most pertinent model to evaluate the adaptability of pPyFHSS,as it generalizes the classical possibility fuzzy set-like theoretical models.Similarly,while considering significant evaluating factors,the flexibility of pPyFHSS is observed through structural comparison.

Medical Diagnosis Based on Multi-Attribute Group Decision-Making Using Extension Fuzzy Sets,Aggregation Operators and Basic Uncertainty Information Granule

Accurate medical diagnosis,which involves identifying diseases based on patient symptoms,is often hindered by uncertainties in data interpretation and retrieval.Advanced fuzzy set theories have emerged as effective tools to address these challenges.In this paper,new mathematical approaches for handling uncertainty in medical diagnosis are introduced using q-rung orthopair fuzzy sets(q-ROFS)and interval-valued q-rung orthopair fuzzy sets(IVq-ROFS).Three aggregation operators are proposed in our methodologies:the q-ROF weighted averaging(q-ROFWA),the q-ROF weighted geometric(q-ROFWG),and the q-ROF weighted neutrality averaging(qROFWNA),which enhance decision-making under uncertainty.These operators are paired with ranking methods such as the similarity measure,score function,and inverse score function to improve the accuracy of disease identification.Additionally,the impact of varying q-rung values is explored through a sensitivity analysis,extending the analysis beyond the typical maximum value of 3.The Basic Uncertain Information(BUI)method is employed to simulate expert opinions,and aggregation operators are used to combine these opinions in a group decisionmaking context.Our results provide a comprehensive comparison of methodologies,highlighting their strengths and limitations in diagnosing diseases based on uncertain patient data.

Healthcare providers’perspectives on factors influencing their critical care decision-making during the COVID-19 pandemic:An international pilot survey

BACKGROUND Understanding a patient's clinical status and setting priorities for their care are two aspects of the constantly changing process of clinical decision-making.One analytical technique that can be helpful in uncertain situations is clinical judgment.Clinicians must deal with contradictory information,lack of time to make decisions,and long-term factors when emergencies occur.AIM To examine the ethical issues healthcare professionals faced during the coronavirus disease 2019(COVID-19)pandemic and the factors affecting clinical decision-making.METHODS This pilot study,which means it was a preliminary investigation to gather information and test the feasibility of a larger investigation was conducted over 6 months and we invited responses from clinicians worldwide who managed patients with COVID-19.The survey focused on topics related to their professional roles and personal relationships.We examined five core areas influencing critical care decision-making:Patients'personal factors,family-related factors,informed consent,communication and media,and hospital administrative policies on clinical decision-making.The collected data were analyzed using theχ2 test for categorical variables.RESULTS A total of 102 clinicians from 23 specialties and 17 countries responded to the survey.Age was a significant factor in treatment planning(n=88)and ventilator access(n=78).Sex had no bearing on how decisions were made.Most doctors reported maintaining patient confidentiality regarding privacy and informed consent.Approximately 50%of clinicians reported a moderate influence of clinical work,with many citing it as one of the most important factors affecting their health and relationships.Clinicians from developing countries had a significantly higher score for considering a patient's financial status when creating a treatment plan than their counterparts from developed countries.Regarding personal experiences,some respondents noted that treatment plans and preferences changed from wave to wave,and that there was a rapid turnover of studies and evidence.Hospital and government policies also played a role in critical decision-making.Rather than assessing the appropriateness of treatment,some doctors observed that hospital policies regarding medications were driven by patient demand.CONCLUSION Factors other than medical considerations frequently affect management choices.The disparity in treatment choices,became more apparent during the pandemic.We highlight the difficulties and contradictions between moral standards and the realities physicians encountered during this medical emergency.False information,large patient populations,and limited resources caused problems for clinicians.These factors impacted decision-making,which,in turn,affected patient care and healthcare staff well-being.

Voices that matter:The impact of patient-reported outcome measures on clinical decision-making

The critical role of patient-reported outcome measures(PROMs)in enhancing clinical decision-making and promoting patient-centered care has gained a profound significance in scientific research.PROMs encapsulate a patient's health status directly from their perspective,encompassing various domains such as symptom severity,functional status,and overall quality of life.By integrating PROMs into routine clinical practice and research,healthcare providers can achieve a more nuanced understanding of patient experiences and tailor treatments accordingly.The deployment of PROMs supports dynamic patient-provider interactions,fostering better patient engagement and adherence to tre-atment plans.Moreover,PROMs are pivotal in clinical settings for monitoring disease progression and treatment efficacy,particularly in chronic and mental health conditions.However,challenges in implementing PROMs include data collection and management,integration into existing health systems,and acceptance by patients and providers.Overcoming these barriers necessitates technological advancements,policy development,and continuous education to enhance the acceptability and effectiveness of PROMs.The paper concludes with recommendations for future research and policy-making aimed at optimizing the use and impact of PROMs across healthcare settings.

A STUDY ON DECISION-MAKING OF ENVIRONMENTAL IMPACTS FOR REGIONAL WATER RESOURCES DEVELOPMENT

On the basis of overall analysis, this Paper, taking Beijing-Tianjin-Tangshan region as an example, tries to find out main environmental problems caused by water resources development and to discuss the mitigaive measures of the environmental impacts. The Paper, by means of Analytical Hierarchy Process (AHP, builds a multiple-hierarchy decision-making model including godl, water resources, environmental impact factors and mitigative measures. This study is very significant for alleviating and solving the problems of water resources-environmental system in the region.

Strategic Renewable Energy Resource Selection Using a Fuzzy Decision-Making Method

Renewable energy is created by renewable natural resources such as geothermal heat,sunlight,tides,rain,and wind.Energy resources are vital for all countries in terms of their economies and politics.As a result,selecting the optimal option for any country is critical in terms of energy investments.Every country is nowadays planning to increase the share of renewable energy in their universal energy sources as a result of global warming.In the present work,the authors suggest fuzzy multi-characteristic decision-making approaches for renew-able energy source selection,and fuzzy set theory is a valuable methodology for dealing with uncertainty in the presence of incomplete or ambiguous data.This study employed a hybrid method for order of preference by resemblance to an ideal solution based on fuzzy analytical network process-technique,which agrees with professional assessment scores to be linguistic phrases,fuzzy numbers,or crisp numbers.The hybrid methodology is based on fuzzy set ideologies,which calculate alternatives in accordance with professional functional requirements using objective or subjective characteristics.The best-suited renewable energy alternative is discovered using the approach presented.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

‘Partly'globalized networks and driving mechanism in resource-based state-owned enterprises:A case study of J Group

In the context of economic globalization,while multinational enterprises from developed countries occupy a high-end position in the global value chain,enterprises from developing countries are often marginalized in the world market.In China,resource-based state-owned enterprises(SOEs)are tasked with the mission of safeguarding resource security,and their internationalization development ideas and strategic deployment are significantly and fundamentally different from those of other non-state-owned enterprises and large multinational corporations.This study provides ideas for the globalization policies of enterprises in developing countries.We consider J Group in western China as a case and discuss its productive investment and global production network development from 2010 to 2019.We found that J Group was‘Partly'globalized,and there are multiple core nodes with the characteristics of centralized and decentralized coexistence in the production network;in addition,the overall layout centre shifted to Southeast Asia and China;however,its global production was restricted by the enterprise's investment security considerations,support and restrictions of the home country,political security risk of the host country,and sanctions from the West.These findings provide insights for future research:under the wave of anti-globalization and'internal circulation as the main body',resource SOEs should consider the potential risk of investment,especially keeping the middle and downstream industrial chain in China as much as possible.

An Adaptive Hybrid Optimization Strategy for Resource Allocation in Network Function Virtualization

With the rapid development of Network Function Virtualization(NFV),the problem of low resource utilizationin traditional data centers is gradually being addressed.However,existing research does not optimize both localand global allocation of resources in data centers.Hence,we propose an adaptive hybrid optimization strategy thatcombines dynamic programming and neural networks to improve resource utilization and service quality in datacenters.Our approach encompasses a service function chain simulation generator,a parallel architecture servicesystem,a dynamic programming strategy formaximizing the utilization of local server resources,a neural networkfor predicting the global utilization rate of resources and a global resource optimization strategy for bottleneck andredundant resources.With the implementation of our local and global resource allocation strategies,the systemperformance is significantly optimized through simulation.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

AMAD:Adaptive Mapping Approach for Datacenter Networks,an Energy-Friend Resource Allocation Framework via Repeated Leader Follower Game

Cloud Datacenter Network(CDN)providers usually have the option to scale their network structures to allow for far more resource capacities,though such scaling options may come with exponential costs that contradict their utility objectives.Yet,besides the cost of the physical assets and network resources,such scaling may also imposemore loads on the electricity power grids to feed the added nodes with the required energy to run and cool,which comes with extra costs too.Thus,those CDNproviders who utilize their resources better can certainly afford their services at lower price-units when compared to others who simply choose the scaling solutions.Resource utilization is a quite challenging process;indeed,clients of CDNs usually tend to exaggerate their true resource requirements when they lease their resources.Service providers are committed to their clients with Service Level Agreements(SLAs).Therefore,any amendment to the resource allocations needs to be approved by the clients first.In this work,we propose deploying a Stackelberg leadership framework to formulate a negotiation game between the cloud service providers and their client tenants.Through this,the providers seek to retrieve those leased unused resources from their clients.Cooperation is not expected from the clients,and they may ask high price units to return their extra resources to the provider’s premises.Hence,to motivate cooperation in such a non-cooperative game,as an extension to theVickery auctions,we developed an incentive-compatible pricingmodel for the returned resources.Moreover,we also proposed building a behavior belief function that shapes the way of negotiation and compensation for each client.Compared to other benchmark models,the assessment results showthat our proposed models provide for timely negotiation schemes,allowing for better resource utilization rates,higher utilities,and grid-friend CDNs.

Starlet:Network defense resource allocation with multi-armed bandits for cloud-edge crowd sensing in IoT

The cloud platform has limited defense resources to fully protect the edge servers used to process crowd sensing data in Internet of Things.To guarantee the network's overall security,we present a network defense resource allocation with multi-armed bandits to maximize the network's overall benefit.Firstly,we propose the method for dynamic setting of node defense resource thresholds to obtain the defender(attacker)benefit function of edge servers(nodes)and distribution.Secondly,we design a defense resource sharing mechanism for neighboring nodes to obtain the defense capability of nodes.Subsequently,we use the decomposability and Lipschitz conti-nuity of the defender's total expected utility to reduce the difference between the utility's discrete and continuous arms and analyze the difference theoretically.Finally,experimental results show that the method maximizes the defender's total expected utility and reduces the difference between the discrete and continuous arms of the utility.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Research on decision-making behavior of multi-agent alliance in cross-border electricity market environment: an evolutionary game

Constructing a cross-border power energy system with multiagent power energy as an alliance is important for studying cross-border power-trading markets.This study considers multiple neighboring countries in the form of alliances,introduces neighboring countries’exchange rates into the cross-border multi-agent power-trading market and proposes a method to study each agent’s dynamic decision-making behavior based on evolutionary game theory.To this end,this study uses three national agents as examples,constructs a tripartite evolutionary game model,and analyzes the evolution process of the decision-making behavior of each agent member state under the initial willingness value,cost of payment,and additional revenue of the alliance.This research helps realize cross-border energy operations so that the transaction agent can achieve greater trade profits and provides a theoretical basis for cooperation and stability between multiple agents.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Mapping and resource evaluation of deep high-temperature geothermal resources in the Jiyang Depression,China

In China,geothermal resource utilization has mainly focused on resources at shallow and medium depths.Yet,the exploration of deep,high-temperature geothermal resources holds significant importance for achieving the“dual carbon”goals and the transition of energy structure.The Jiyang Depression in the Bohai Bay Basin has vast potential for deep,high-temperature geothermal resources.By analyzing data from 2187 wells with temperature logs and 270 locations for temperature measurement in deep strata,we mapped the geothermal field of shallow to medium-deep layers in the Jiyang Depression using ArcGIS and predicted the temperatures of deep layers with a burial depth of 4000 m.Through stochastic modeling and numerical simulation,a reservoir attribute parameter database for favorable deep,high-temperature geothermal areas was developed,systematically characterizing the spatial distribution of geothermal resources within a play fairway of 139.5 km2 and estimating the exploitable deep geothermal resource potential by using the heat storage method and Monte Carlo data analysis.The study reveals that the Fan 54 well block in the Boxing-Jijia region is of prime significance to develop deep,high-temperature geothermal resources in the Jiyang Depression.Strata from the Cenozoic to the Upper Paleozoic are identified as effective cap layers for these deep geothermal resources.The Lower Paleozoic capable of effectively storing thermal energy and possessing an exploitable resource volume up to 127 million tons of standard coal,is identified as a target system for the development of deep high-temperature geothermal resources,providing significant insights for the efficient development of geothermal resources in the Jiyang Depression.

Research on Maneuver Decision-Making of Multi-Agent Adversarial Game in a Random Interference Environment

The strategy evolution process of game players is highly uncertain due to random emergent situations and other external disturbances.This paper investigates the issue of strategy interaction and behavioral decision-making among game players in simulated confrontation scenarios within a random interference environment.It considers the possible risks that random disturbances may pose to the autonomous decision-making of game players,as well as the impact of participants’manipulative behaviors on the state changes of the players.A nonlinear mathematical model is established to describe the strategy decision-making process of the participants in this scenario.Subsequently,the strategy selection interaction relationship,strategy evolution stability,and dynamic decision-making process of the game players are investigated and verified by simulation experiments.The results show that maneuver-related parameters and random environmental interference factors have different effects on the selection and evolutionary speed of the agent’s strategies.Especially in a highly uncertain environment,even small information asymmetry or miscalculation may have a significant impact on decision-making.This also confirms the feasibility and effectiveness of the method proposed in the paper,which can better explain the behavioral decision-making process of the agent in the interaction process.This study provides feasibility analysis ideas and theoretical references for improving multi-agent interactive decision-making and the interpretability of the game system model.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter(CSIT).A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CsI feedback stage.In fact,the downlink communication generally includes three stages,i.e.,pilot training,CsI feedback,and data transmission.These three stages are mutually related and jointly determine the overall system performance.Unfortunately,there exist few studies on the reduction of csIT acquisition overhead from the global point of view.In this paper,we integrate the Minimum Mean Square Error(MMSE)channel estimation,Random Vector Quantization(RVQ)based limited feedback and Maximal Ratio Combining(MRC)precoding into a unified framework for investigating the resource allocation problem.In particular,we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio(SNR)based on the deterministic equivalence theory.Then the three performance metrics(the spectral efficiency,energy efficiency,and total energy consumption)oriented problems are formulated analytically.With practical system requirements,these three metrics can be collaboratively optimized.Finally,we propose an optimization solver to derive the optimal partition of channel coherence time.Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.