Insights from an academic endeavor into central nervous system drug discovery

Historically,"big pharma"did most central nervous system drug discovery R&D in-house.Yet,in modern times their"management reductionism"resulted in disappointing pipelines and pharma resided to(late)development,regulatory approval,and marketing(Thong,2015).This had significant consequences for financing and executing research,resulting in a larger role for funding by governments and patient-organizations and a shift of research to academia(Mazzucato,2013).

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.

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.

Parental Educational Expectations,Academic Pressure,and Adolescent Mental Health:An Empirical Study Based on CEPS Survey Data

Background:This study aimed to investigate the relationship between parental educational expectations and adolescent mental health problems,with academic pressure as a moderating variable.Methods:This study was based on the baseline data of the China Education Panel Survey,which was collected within one school year during 2013–2014.It included 19,958 samples from seventh and ninth graders,who ranged from 11 to 18 years old.After removing missing values and conducting relevant data processing,the effective sample size for analysis was 16344.The OLS(Ordinary Least Squares)multiple linear regression analysis was used to examine the relationship between parental educational expectations,academic pressure,and adolescents’mental health problems.In addition,we established an interaction term between parents’educational expectations and academic pressure to investigate the moderating effect of academic stress.Results:The study found that adolescents whose parents had high educational expectations reported less mental health problems.(β=−0.195;p<0.001).Additionally,adolescents who had high academic pressure reported more mental health problems.(β=0.649;p<0.001).Furthermore,the study found that academic pressure had a significant moderating effect on the relationship between parental educational expectations and adolescents’mental health problems(β=0.082;p<0.001).Conclusion:Parental educational expectations had a close relationship with adolescents’mental health problems,and academic pressure moderated this relationship.For those adolescents with high levels of academic pressure,the association between high parental educational expectations and mental health problems became stronger.On the contrary,for those adolescents with low levels of academic pressure,the association between high parental educational expectations and mental health problems became weaker.These findings shed new light on how parental educational expectations affected adolescent mental health problems and had significant implications for their healthy development.

Thanks to our academic editors and peer reviewers

The Editorial Office of Water Science and Engineering would like to express their sincere appreciation to the academic editors including Prof.Carlo Gualtieri from University of Napoli Federico II,Italy,Prof.Guo-qing Wang and Prof.Zhong-zhi Fu from Nanjing Hydraulic Research Institute,China,and Prof.Yan-hui Ao,Prof.Ching-sheng Huang,Prof.Guang-qiu Jin,Prof.Bin Xu,Prof.Sai-yu Yuan,Prof.Zeng Zhou,Prof.Bo Chen,and Prof Da-wei Guan from Hohai Uiversity,China,for their great effort and contribution to WSE in the year 2023.

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.

Transcultural Validation and Adaptation of the Academic Stress Questionnaire (CEA) in Students of Puerto Rico

Background: There are multiple questionnaires to measure academic stress in university students, which have been used in nursing students. In Puerto Rico, a questionnaire valid in content and reliability was required to measure the variable of academic stress in nursing students. Purpose: The aim of this study was to adapt transculturally and validate the Academic Stress Questionnaire (CEA) for its use in Puerto Rico. Materials and Methods: Used for the first phase of this study consisted in the evaluation of the validity of content and appearance, whereas the second phase was the actual administering of the questionnaire to 20 (twenty) nursing students, to pilot test its internal consistency using the Cronbach’s α test. Results: Validity of content and appearance allowed for the modification of the questionnaire into one, consisting of 42 items, thus eliminating 34 premises from the original 76 items the questionnaire was composed of. Furthermore, the appearance of the questionnaire was modified by placing the measuring scales in columns, adapting social, demographic, and academic data to the required Puerto Rican reality. The sections meant to measure the academic stress variables were left intact, except for the linguistics adaptation, which was accomplished by a team of experts in the Spanish language. With an α global of 0.80 and coefficients larger than 0.7 in the multi-item sub scales, which oscillated between 0.750 and 0.860, the questionnaire provides a high reliability. Conclusion: Although the values reported in this study are somewhat lower than previous research, they were comparable the Cronbach’s Alpha coefficients reported by Cabanach, in which the numbers reported are considered high (α > 0.70) which show acceptable confiability of the subscales included in the study and a high degree of consistency and thus can be relied upon in future research. In synthesis, the Academic Stress Questionnaire (CEA) modified and adapted, thoroughly fulfills the established criteria of confiability and validity to evaluate academic stress of Puertorrican nursing students.

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.

Effects of a school-based karate intervention on academic achievement,psychosocial functioning,and physical fitness:A multi-country cluster randomized controlled trial

Purpose:To examine the effects of a school-based karate intervention on academic achievement,psychosocial functioning,and physical fitness in children aged 7-8 years.Methods:Twenty schools in 5 different European countries(2 second-grade classrooms per school)participated in a cluster randomized controlled trial(Sport at School trial).Participants were assigned to either a control group,which continued with their habitual physical education lessons,or to an intervention group,which replaced these lessons with a 1-year karate intervention(Karate Mind and Movement program).A total of 721 children(344 girls and 377 boys,7.4±0.5 years old,mean±SD)completed the study,of which 333 and 388 were assigned to the control group and intervention group,respectively.Outcomes included academic performance(average grade),psychosocial functioning(Strengths and Difficulties Questionnaire for parents),and different markers of physical fitness(cardiorespiratory fitness,balance,and flexibility).Results:The intervention provided small but significant benefits compared to the control group for academic achievement(d=0.16;p=0.003),conduct problems(d=-0.28;p=0.003),cardiorespiratory fitness(d=0.36;p<0.001),and balance(d=0.24;p=0.015).There was a trend towards significant benefits for flexibility(d=0.24;p=0.056).No significant benefits were observed for other variables,including psychosocial difficulties,emotional symptoms,hyperactivity/inattention,peer problems,or prosocial behaviour(all p>0.05).Conclusion:A 1-year school-based karate intervention was effective in improving academic achievement,conduct problems,and physical fitness in primary school children.The results support the inclusion of karate during physical education lessons.

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.

How Autonomy Support Environment Influences Student Online Game Addiction:The Mediating Roles of Academic Motivation and Academic Perseverance

Background:Online game addiction has become a serious global public health problem among adolescents.However,its influencing factors and mediating mechanisms remain ambiguous.Methods:The present study adopted stratified random sampling to collect 6146 junior high school student samples in China's Mainland.We used regression analysis,and Bootstrap mediation test through SPSS 26.0 and AMOS 24.0 to reveal the tendency of students’online game addiction with different background characteristics and whether autonomy support from parents and teachers can effectively reduce online game addiction.Results:The results showed that parental(β=−0.112,p<0.001)and teacher(β=−0.225,p<0.001)autonomy support were conducive to reducing students’online game addiction.Academic perseverance(β=−0.080,95%CI=[−0.103,−0.058])and academic motivation(β=−0.073,95%CI=[−0.095,−0.052])partially mediated the relationship between autonomy support and online game addiction.Conclusion:This result provides a theoretical basis for educational interventions.It suggests that strengthening autonomy support could enhance students’academic perseverance and motivation and further reduce online game addiction.This study utilized a cross-sectional research design,making it difficult to determine the causal relationship between autonomy support environment and online game addiction.

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.

Ranking academic institutions based on the productivity,impact,and quality of institutional scholars

Purpose:The quantitative rankings of over 55,000 institutions and their institutional programs are based on the individual rankings of approximately 30 million scholars determined by their productivity,impact,and quality.Design/methodology/approach:The institutional ranking process developed here considers all institutions in all countries and regions,thereby including those that are established,as well as those that are emerging in scholarly prowess.Rankings of individual scholars worldwide are first generated using the recently introduced,fully indexed ScholarGPS database.The rankings of individual scholars are extended here to determine the lifetime and last-five-year Top 20 rankings of academic institutions over all Fields of scholarly endeavor,in 14 individual Fields,in 177 Disciplines,and in approximately 350,000 unique Specialties.Rankings associated with five specific Fields(Medicine,Engineering&Computer Science,Life Sciences,Physical Sciences&Mathematics,and Social Sciences),and in two Disciplines(Chemistry,and Electrical&Computer Engineering)are presented as examples,and changes in the rankings over time are discussed.Findings:For the Fields considered here,the Top 20 institutional rankings in Medicine have undergone the least change(lifetime versus last five years),while the rankings in Engineering&Computer Science have exhibited significant change.The evolution of institutional rankings over time is largely attributed to the recent emergence of Chinese academic institutions,although this emergence is shown to be highly Field-and Discipline-dependent.Practical implementations:Existing rankings of academic institutions have:(i)often been restricted to pre-selected institutions,clouding the potential discovery of scholarly activity in emerging institutions and countries;(ii)considered only broad areas of research,limiting the ability of university leadership to act on the assessments in a concrete manner,or in contrast;(iii)have considered only a narrow area of research for comparison,diminishing the broader applicability and impact of the assessment.In general,existing institutional rankings depend on which institutions are included in the ranking process,which areas of research are considered,the breadth(or granularity)of the research areas of interest,and the methodologies used to define and quantify research performance.In contrast,the methods presented here can provide important data over a broad range of granularity to allow responsible individuals to gauge the performance of any institution from the Overall(all Fields)level,to the level of the Specialty.The methods may also assist identification of the root causes of shifts in institution rankings,and how these shifts vary across hundreds of thousands of Fields,Disciplines,and Specialties of scholarly endeavor.Originality/value:This study provides the first ranking of all academic institutions worldwide over Fields,Disciplines,and Specialties based on a unique methodology that quantifies the productivity,impact,and quality of individual scholars.

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.

The Impact of Sleep Patterns on Academic Performance and Social Integration among Medical Students

Introduction: The impact of sleep on student life is crucial, particularly for those in demanding fields such as medicine. This study examines the relationships between sleep patterns, academic performance, and social integration among medical students, who often face irregular sleep cycles and sleep deprivation due to rigorous academic demands. Aim: This study aims to assess how sleep issues affect academic achievement and social relationships among medical students. Method: Data were collected from 215 medical students through surveys and academic records. Quantitative data provided insights into sleep quality and academic performance, while qualitative interviews explored the effects of sleep on social interactions. Results: The findings revealed a significant correlation between sleep quality and academic performance, with students reporting better sleep hygiene achieving higher grades. Qualitative data indicated that poor sleep negatively impacts social interactions, leading to feelings of isolation and reduced social participation. Conclusions: The study highlights the importance of promoting good sleep practices in medical schools to enhance academic success and social well-being. Interventions aimed at improving sleep quality may help reduce burnout and improve overall well-being among medical students. Future research should focus on longitudinal studies to better understand the long-term effects of sleep on academic and social outcomes in this population.

Determination of the best materials for development and designing product using a multi-criteria decision-making

Purpose–Material selection,driven by wide and often conflicting objectives,is an important,sometimes difficult problem in material engineering.In this context,multi-criteria decision-making(MCDM)methodologies are effective.An approach of MCDM is needed to cater to criteria of material assortment simultaneously.More firms are now concerned about increasing their productivity using mathematical tools.To occupy a gap in the previous literature this research recommends an integrated MCDM and mathematical Bi-objective model for the selection of material.In addition,by using the Technique for Order Preference by Similarity to Ideal Solution(TOPSIS),the inherent ambiguities of decision-makers in paired evaluations are considered in this research.It goes on to construct a mathematical bi-objective model for determining the best item to purchase.Design/methodology/approach–The entropy perspective is implemented in this paper to evaluate the weight parameters,while the TOPSIS technique is used to determine the best and worst intermediate pipe materials for automotive exhaust system.The intermediate pipes are used to join the components of the exhaust systems.The materials usually used to manufacture intermediate pipe are SUS 436LM,SUS 430,SUS 304,SUS 436L,SUH 409 L,SUS 441 L and SUS 439L.These seven materials are evaluated based on tensile strength(TS),hardness(H),elongation(E),yield strength(YS)and cost(C).A hybrid methodology combining entropy-based criteria weighting,with the TOPSIS for alternative ranking,is pursued to identify the optimal design material for an engineered application in this paper.This study aims to help while filling the information gap in selecting the most suitable material for use in the exhaust intermediate pipes.After that,the authors searched for and considered eight materials and evaluated them on the following five criteria:(1)TS,(2)YS,(3)H,(4)E and(5)C.The first two criteria have been chosen because they can have a lot of influence on the behavior of the exhaust intermediate pipes,on their performance and on the cost.In this structure,the weights of the criteria are calculated objectively through the entropy method in order to have an unbiased assessment.This essentially measures the quantity of information each criterion contribution,indicating the relative importance of these criteria better.Subsequently,the materials were ranked using the TOPSIS method in terms of their relative performance by measuring each material from an ideal solution to determine the best alternative.The results show that SUS 309,SUS 432L and SUS 436 LM are the first three materials that the exhaust intermediate pipe optimal design should consider.Findings–The material matrix of the decision presented in Table 3 was normalized through Equation 5,as shown in Table 5,and the matrix was multiplied with weighting criteriaß_j.The obtained weighted normalized matrix V_ij is presented in Table 6.However,the ideal,worst and best value was ascertained by employing Equation 7.This study is based on the selection of material for the development of intermediate pipe using MCDM,and it involves four basic stages,i.e.method of translation criteria,screening process,method of ranking and search for methods.The selection was done through the TOPSIS method,and the criteria weight was obtained by the entropy method.The result showed that the top three materials are SUS 309,SUS 432L and SUS 436 LM,respectively.For the future work,it is suggested to select more alternatives and criteria.The comparison can also be done by using different MCDM techniques like and Choice Expressing Reality(ELECTRE),Decision-Making Trial and Evaluation Laboratory(DEMATEL)and Preference Ranking Organization Method for Enrichment Evaluation(PROMETHEE).Originality/value–The results provide important conclusions for material selection in this targeted application,verifying the employment of mutual entropy-TOPSIS methodology for a series of difficult engineering decisions in material engineering concepts that combine superior capacity with better performance as well as cost-efficiency in various engineering design.