Aspect based sentiment analysis using multi-criteria decision-making and deep learning under COVID-19 pandemic in India

The COVID-19 pandemic has a significant impact on the global economy and health.While the pandemic continues to cause casualties in millions,many countries have gone under lockdown.During this period,people have to stay within walls and become more addicted towards social networks.They express their emotions and sympathy via these online platforms.Thus,popular social media(Twitter and Facebook)have become rich sources of information for Opinion Mining and Sentiment Analysis on COVID-19-related issues.We have used Aspect Based Sentiment Analysis to anticipate the polarity of public opinion underlying different aspects from Twitter during lockdown and stepwise unlock phases.The goal of this study is to find the feelings of Indians about the lockdown initiative taken by the Government of India to stop the spread of Coronavirus.India-specific COVID-19 tweets have been annotated,for analysing the sentiment of common public.To classify the Twitter data set a deep learning model has been proposed which has achieved accuracies of 82.35%for Lockdown and 83.33%for Unlock data set.The suggested method outperforms many of the contemporary approaches(long shortterm memory,Bi-directional long short-term memory,Gated Recurrent Unit etc.).This study highlights the public sentiment on lockdown and stepwise unlocks,imposed by the Indian Government on various aspects during the Corona outburst.

Multi-Criteria Group Decision-Making Method Based on an Improved Single-Valued Neutrosophic Hamacher Weighted Average Operator and Grey Relational Analysis

This paper proposes a multi-criteria decision-making (MCGDM) method based on the improved single-valued neutrosophic Hamacher weighted averaging (ISNHWA) operator and grey relational analysis (GRA) to overcome the limitations of present methods based on aggregation operators. First, the limitations of several existing single-valued neutrosophic weighted averaging aggregation operators (i.e. , the single-valued neutrosophic weighted averaging, single-valued neutrosophic weighted algebraic averaging, single-valued neutrosophic weighted Einstein averaging, single-valued neutrosophic Frank weighted averaging, and single-valued neutrosophic Hamacher weighted averaging operators), which can produce some indeterminate terms in the aggregation process, are discussed. Second, an ISNHWA operator was developed to overcome the limitations of existing operators. Third, the properties of the proposed operator, including idempotency, boundedness, monotonicity, and commutativity, were analyzed. Application examples confirmed that the ISNHWA operator and the proposed MCGDM method are rational and effective. The proposed improved ISNHWA operator and MCGDM method can overcome the indeterminate results in some special cases in existing single-valued neutrosophic weighted averaging aggregation operators and MCGDM methods.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.

Ranking environmental projects model based on multicriteria decision-making and the weight sensitivity analysis

With the fast growth of Chinese economic, more and more capital will be invested in environmental projects. How to select the environmental investment projects （alternatives） for obtaining the best environmental quality and economic benefits is an important problem for the decision makers. The purpose of this paper is to develop a decision-making model to rank a finite number of alternatives with several and sometimes conflicting criteria. A model for ranking the projects of municipal sewage treatment plants is proposed by using exports＇ information and the data of the real projects. And, the ranking result is given based on the PROMETHEE method. Furthermore, by means of the concept of the weight stability intervals （WSI）, the sensitivity of the ranking results to the size of criteria values and the change of weights value of criteria are discussed. The result shows that some criteria, such as “proportion of benefit to project cost”, will influence the ranking result of alternatives very strong while others not. The influence are not only from the value of criterion but also from the changing the weight of criterion. So, some criteria such as “proportion of benefit to project cost” are key critera for ranking the projects. Decision makers must be cautious to them.

Approach of technical decision-making by element flow analysis and Monte-Carlo simulation of municipal solid waste stream

This paper deals with the procedure and methodology which can be used to select the optimal treatment and disposal technology of municipal solid waste （MSW）, and to provide practical and effective technical support to policy-making, on the basis of study on solid waste management status and development trend in China and abroad. Focusing on various treatment and disposal technologies and processes of MSW, this study established a Monte-Carlo mathematical model of cost minimization for MSW handling subjected to environmental constraints. A new method of element stream （such as C, H, O, N, S） analysis in combination with economic stream analysis of MSW was developed. By following the streams of different treatment processes consisting of various techniques from generation, separation, transfer, transport, treatment, recycling and disposal of the wastes, the element constitution as well as its economic distribution in terms of possibility functions was identified. Every technique step was evaluated economically. The Mont-Carlo method was then conducted for model calibration. Sensitivity analysis was also carried out to identify the most sensitive factors. Model calibration indicated that landfill with power generation of landfill gas was economically the optimal technology at the present stage under the condition of more than 58% of C, H, O, N, S going to landfill. Whether or not to generate electricity was the most sensitive factor. If landfilling cost increases, MSW separation treatment was recommended by screening first followed with incinerating partially and composting partially with residue landfilling. The possibility of incineration model selection as the optimal technology was affected by the city scale. For big cities and metropolitans with large MSW generation, possibility for constructing large-scale incineration facilities increases, whereas, for middle and small cities, the effectiveness of incinerating waste decreases.

Decision-making approach by employing grey incidence analysis and group negotiation

For the problems of the consistency ranking of the group decision-making scheme,from the view of group negotiation and system coordination,the grey incidence analysis and Nash bargaining model are used to establish a consistency group decision-making method.First,the concepts of the consensus partial decision-making program and the consensus overall ideal decision-making program are defined,and then a multi-object optimization model is constructed based on the satisfaction maximization of group negotiation and deviation minimization of system coordination to determine the consensus partial decision-making program and the consensus overall ideal decision-making program.Moreover,the grey incidence analysis is exploited to measure the close degrees between them.Finally,a real case of the online product evaluation verifies the validity and rationality of the proposed model.

Application of Fuzzy Decision-Making Theory and Conflict-Resolution Method to Failure Mode and Effect Analysis of Industrial Valve

It is not objective to rate the decision-making factors in the traditional failure mode and effect analysis,so fuzzy semantic theory is used in this paper.Six fuzzy semantic scales and their corresponding semantics are summarized,and a defuzzification method is studied to obtain the fuzzy value table of the six fuzzy semantic scales.For the conflicts between experts in the traditional failure mode and effects analysis,a conflict-resolution algorithm is studied to obtain the failure risk order.Finally,a certain type of industrial valve is used as an example to prove the validity of the theory proposed in this paper.

Study on effectiveness evaluation of weapon systems based on grey relational analysis and TOPSIS

To evaluate the effectiveness of weapon systems, the advantages and disadvantages of grey relational analysis and TOPSIS for multiattribute decision-making is pointed out, and an effectiveness evaluation model of weapon systems by combining grey relational analysis and TOPSIS is proposed. The model aggregates the grey relational grade and the distance to a new integrated closeness and reflects not only the trend but also the situation of the alternative. The example illuminates that the model is effective for the effectiveness evaluation of weapon systems.

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.

Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

Leveraging Robust Artificial Intelligence for Mechatronic Product Development—A Literature Review

Mechatronic product development is a complex and multidisciplinary field that encompasses various domains, including, among others, mechanical engineering, electrical engineering, control theory and software engineering. The integration of artificial intelligence technologies is revolutionizing this domain, offering opportunities to enhance design processes, optimize performance, and leverage vast amounts of knowledge. However, human expertise remains essential in contextualizing information, considering trade-offs, and ensuring ethical and societal implications are taken into account. This paper therefore explores the existing literature regarding the application of artificial intelligence as a comprehensive database, decision support system, and modeling tool in mechatronic product development. It analyzes the benefits of artificial intelligence in enabling domain linking, replacing human expert knowledge, improving prediction quality, and enhancing intelligent control systems. For this purpose, a consideration of the V-cycle takes place, a standard in mechatronic product development. Along this, an initial assessment of the AI potential is shown and important categories of AI support are formed. This is followed by an examination of the literature with regard to these aspects. As a result, the integration of artificial intelligence in mechatronic product development opens new possibilities and transforms the way innovative mechatronic systems are conceived, designed, and deployed. However, the approaches are only taking place selectively, and a holistic view of the development processes and the potential for robust and context-sensitive artificial intelligence along them is still needed.

Participatory Forest Management and Gender Inclusiveness within the Community Forest Management Groups of Bhutan

Community forest management groups (CFMGs) in Bhutan exhibit participatory forest management practices that recognize the importance of community’s collective participation in the management of natural forest resources. This approach involves the community in the stewardship of designated forest areas and resources to ensure sustainable livelihoods and realization of forest conservation objectives. The increase of CFMGs in the country has been successful. However, research on the extent of gender-inclusive participation in CFMGs is either insufficient or missing vis-à-vis the allocation of decision-making power. Therefore, this study analyzes the factors influencing gender participation in CFMGs and their integration into decision-making processes. Primary data were collected from 12 study sites spanning 4 regions, complemented by secondary data from the Forest Department. Regression models were used to identify factors significantly influencing CFMG member participation in decision-making. The empirical results of this study reveal that gender is a significant factor influencing participation in CFMG decision-making. The study concludes that there is insufficient participation of women members in decision-making processes. Therefore, consideration of gender should be included in the development phase of the CFMG policy in addition to promoting awareness of inequity between gender and the promotion of leadership roles for women in CFMGs.

Sustainability of Bridges:Risk Mitigation for Natural Hazards

Bridges serve as essential parts of transportation infrastructure,facilitating the movement of people and goods across rivers,valleys,and other obstacles.However,they are also susceptible to a wide range of natural hazards,including floods,earthquakes,and landslides,which can damage or even collapse these structures,leading to severe economic and human losses.A risk index has been developed to address this issue,which quantifies the likelihood and severity of natural hazards occurring in a specific location.The application of risk indices for natural hazards in bridge management involves a data collection process and mathematical modelling.The data collection process gathers information on bridges’location,condition,and vulnerability,while mathematical modelling uses the data to assess the risk of natural hazards.Overall,risk indices provide a quantitative measure of the vulnerability of bridges to natural hazards and help to prioritize maintenance and repair activities.Mitigation measures are then evaluated and implemented based on the risk assessment results.By using this tool,the UBMS research group has developed an algorithm for risk assessment which will be essential in the decision-making process,specifically focused on enhancing Fund Optimization,Deterioration Modelling,and Risk Analysis.These developments effectively fulfill the primary objectives associated with addressing and mitigating hazards.This development also helps bridge managers understand the potential threats posed by natural hazards and allocate resources more efficiently to ensure the safety and longevity of critical transportation infrastructure.

旱塬地膜撮种玉米高产高效栽培技术方案的优化决策分析

应用系统工程的原理和方法,通过建模模拟分析,研究确定了旱源地膜撮种玉米获得高产高效单一目标及综合目标的栽培技术方案。并根据多目标综合决策分析的方法,以玉米毛收益、籽粒产量、可信度、化肥投入量、成本为综合决策目标,优选出达到玉米籽粒高产的最佳栽培技术方案。追求高产时,应增加氮、磷化肥的投入,追求高效时,应降低氛、磷化肥的投入。

A new framework for evaluation of rock fragmentation in open pit mines

The main purpose of blasting in open pit mines is to produce the feed for crushing stage with the optimum dimensions from in situ rocks. The size distribution of muck pile indicates the efficiency of blasting pattern to reach the required optimum sizes. Nevertheless, there is no mature model to predict fragmentation distribution to date that can be used in various open pit mines. Therefore, a new framework to evaluate and predict fragmentation distribution is presented based on the image analysis approach. For this purpose, the data collected from Jajarm bauxite mine in Iran were used as the sources in this study. The image analysis process was performed by Split-Desktop software to find out fragmentation distribution, uniformity index and average size of the fragmented rocks. Then, two different approaches including the multivariate regression method and the decision-making trial and evaluation laboratory(DEMATEL) technique were incorporated to develop new models of the uniformity index and the average size to improve the Rosin-Rammler function. The performances of the proposed models were evaluated in four blasting operation sites. The results obtained indicate that the regression model possesses a better performance in prediction of the uniformity index and the average size and subsequently the fragmentation distribution in comparison with DEMATEL and conventional Rosin-Rammler models.

A new method for performance evaluation of decision-making units with application to service industry

The decision-making units(DMUs)in the modern service industries may produce desirable outputs and undesirable outputs.For the decision makers,some outputs may be more desired than others although all of them are desirable.Considering these characteristics,this work combines the data envelopment analysis(DEA)and the multiple attributes decision-making(MADM)method,to make a reasonable and comprehensive performance evaluation for DMUs.Specifically,three DEA-based models are modified to obtain more reasonable efficiency scores for DMUs.The MADM method is used to determine the weights of outputs based on the preference ratings within the outputs.The efficiency scores are then multiplied by the aggregated outputs quantities to obtain the comprehensive performance scores for evaluation.The effectiveness of the proposed models is demonstrated by extensive numerical experiments.

An efficient and impartial online algorithm for kidney assignment network

An online algorithm balancing the efficiency and equity principles is proposed for the kidney resource assignment when only the current patient and resource information is known to the assignment network. In the algorithm, the assignment is made according to the priority, which is calculated according to the efficiency principle and the equity principle. The efficiency principle is concerned with the post-transplantation immunity spending caused by the possible post-operation immunity rejection and patient’s mental depression due to the HLA mismatch. The equity principle is concerned with three other factors, namely the treatment spending incurred starting from the day of registering with the kidney assignment network, the post-operation immunity spending and the negative effects of waiting for kidney resources on the clinical efficiency. The competitive analysis conducted through computer simulation indicates that the efficiency competitive ratio is between 6.29 and 10.43 and the equity competitive ratio is between 1.31 and 5.21, demonstrating that the online algorithm is of great significance in application.

Customer Prioritization for Medical Supply Chain During COVID-19 Pandemic

During COVID-19,the escalated demand for various pharmaceutical products with the existing production capacity of pharmaceutical companies has stirred the need to prioritize its customers in order to fulfill their demand.This study considers a two-echelon pharmaceutical supply chain considering various pharma-distributors as its suppliers and hospitals,pharmacies,and retail stores as its customers.Previous studies have generally considered a balanced situation in terms of supply and demand whereas this study considers a special situation of COVID-19 pandemic where demand exceeds supply Various criteria have been identified from the literature that influences the selection of customers.A questionnaire has been developed to collect primary data from pharmaceutical suppliers pertaining to customerselection criteria.These criteria have been prioritized with respect to eigenvalues obtained from Principal Component Analysis and also validated with the experts’domain-related knowledge using Analytical Hierarchy Process.Profit potential appeared to be the most important criteria of customer selection followed by trust and service convenience brand loyalty,commitment,brand awareness,brand image,sustainable behavior,and risk.Subsequently,Multi Criteria Decision Analysis has been performed to prioritize the customerselection criteria and customers with respect to selection criteria.Three experts with seven and three and ten years of experience have participated in the study.Findings of the study suggest large hospitals,large pharmacies,and small retail stores are the highly preferred customers.Moreover,findings of prioritization of customer-selection criteria fromboth Principal Component Analysis and Analytical Hierarchy Process are consistent.Furthermore,this study considers the experience of three experts to calculate an aggregate score of priorities to reach an effective decision.Unlike traditional supply chain problems of supplier selection,this study considers a selection of customers and is useful for procurement and supply chain managers to prioritize customers while considering multiple selection criteria.

A group consensus model for evaluating real estate investment alternatives

Background:Individual decision-making largely influences the effectiveness of decisions and benefits of investments.Methods:In this article,a consensus model for group decision-making(GDM),based on the analytic hierarchy process(AHP),is developed to gather group ideas and analyze the real estate investment environment under multi-criteria problems.Twelve evaluation procedures of the developed model,which increase the convergence of the opinions of multiple experts,are proposed.Results:An empirical case about the real estate investment environment is applied to certify the feasibility of this developed model.Conclusions:the evaluation procedures have been fully observed with several rounds of discussions,and have manifested the experiences of experts.Besides,the evaluation results are in accordance with real-world situations,which demonstrates that our developed model is a feasible analysis tool for real estate investors to obtain better profits and lower risk.

Application of Integrated Multiple Criteria Data Envelopment Analysis to Humanitarian Logistics Network Design

This paper considers a humanitarian logistics network(HTLN)design problem,including the emergency relief facilities(ERFs)location-allocation decision for the efficient distribution of emergency supplies from the ERFs to the affected areas.A goal programming(GP)approach is applied to consider the multiple objectives simultaneously.Solving the GP model with a given weight assigned to each goal yields a single HTLN scheme,so there will be various schemes available by solving the GP with multiple values of the weights.For evaluating these schemes and identifying the most efficient one,we apply the data envelopment analysis(DEA)methods considering each scheme as a decision-making unit(DMU).Since the classical DEA(C-DEA)intrinsically aims to identify efficient DMUs and the efficient frontier,the use of C-DEA may not lead to a full ranking in many situations.There are several independent evaluation approaches to increasing discriminating power.Among them,this study integrates the multiple criteria DEA(MC-DEA)with the following three DEA methods,(i)stratification DEA(S^DEA),(ii)cross-efficiency DEA(CE-DEA),and(iii)super-efficiency DEA(SE-DEA),to make the most use of each method's strengths.Through a case study of designing the HTLN system for South Carolina,the procedure of implementing the integrated multiple criteria DEA(IMC-DEA)method is demonstrated.It is observed that the IMCDEA method performs well in terms of designing the HTLN system and would help the decision-makers consider more efficient options and make a final decision.