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.

Frequency Analysis on the Data of Field Fertilizer Efficiency Test and Fertilization Decision-making

To solve the problems of abnormal larger, abnormal lower or even negative of target yield and fertilizing amount recommended by part of non-typical fertilizer effect equations using agricultural experiments and statistical analysis software,Yangzhou analyzer（2.2）, regression analysis of Excel, which objected to local actual production, the study adopted the principle and method of basic knowledge and the frequency of using probability theory, and carried out statistical analysis on the rape field fertilizer experiment data by frequency analysis method, the rape yield after optimizing fertilizing amount was 1 732.4 kg/hm^2, the ranges of N, P and K optimal combinations were： N=210.36-149.64 kg/hm^2,P2O5=81.89-58.11 kg/hm^2, K2O=81.89-58.11 kg/hm^2,which was consistent with local actual production. This study was based on frequency analysis, using weighted average method to determine the production combinations of different yield objectives, hereinto, the combinations with high yield, high frequency of occurrence（dependable crop） and fertilizer-saving were viewed as the optimizing production measures, and they had the merits of increasing fertilization decision-making information, reducing or avoiding the risk of small probability event. The results of this study can solve the problem of abnormal values fertilizing amount and target yield recommended by non-typical fertilizer effect function, which did not accord with local actual production, caused by Yangzhou analyzer（2.2）, regression analysis of Excel, and DPS statistical analysis software. For the fertilizer effect function equation established by regression analysis which did not reach significance level using variance analysis, whether the method can be adapted to for carrying out fertilization decision-making, recommending optimization combinations of N, P and K fertilizers and yield under optimized fertilizing amount should be further researched in future working practice.

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.

Multi-dimensional blind separation method for STBC systems

Intercepted signal blind separation is a research topic with high importance for both military and civilian communication systems. A blind separation method for space-time block code （STBC） systems is proposed by using the ordinary independent component analysis （ICA）. This method cannot work when specific complex modulations are employed since the assumption of mutual independence cannot be satisfied. The analysis shows that source signals, which are group-wise independent and use multi-dimensional ICA （MICA） instead of ordinary ICA, can be applied in this case. Utilizing the block-diagonal structure of the cumulant matrices, the JADE algorithm is generalized to the multidimensional case to separate the received data into mutually independent groups. Compared with ordinary ICA algorithms, the proposed method does not introduce additional ambiguities. Simulations show that the proposed method overcomes the drawback and achieves a better performance without utilizing coding information than channel estimation based algorithms.

Measurement and Decomposition of Multi-Dimensional Poverty in China

Based on China Family Panel Studies(CFPS) data and global MPI standard,this paper measures and analyzes multi-dimensional poverty in China. The study finds that the level of multi-dimensional poverty in China is not high and tends to decrease over time.Uneven regional development significantly affects multi-dimensional poverty. The poor are deprived in health, education and other aspects, but indicator contributions vary among specific groups of people. Overlap between economic poverty and multi-dimensional poverty has a trend of inter-temporal reduction. China's development-centered poverty reduction policy has achieved great results and significantly improved the development capabilities of the poor. Development-oriented approach is China's important experience in poverty reduction, and forebodes China's bright prospect of achieving its goal to complete building a moderately prosperous society by 2020.

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.

System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS

Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.

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.

Re-Establishing the Merits of Thermal Maturity and Petroleum Generation Multi-Dimensional Modeling with an Arrhenius Equation Using a Single Activation Energy

Thermal maturation and petroleum generation modeling of shales is essential for suc- cessful exploration and exploitation of conventional and unconventional oil and gas plays. For basin- wide unconventional resource plays such modeling, when well calibrated with direct maturity meas- urements from wells, can characterize and locate production sweet spots for oil, wet gas and dry gas. The transformation of kerogen to petroleum is associated with many chemical reactions, but models typically focus on first-order reactions with rates determined by the Arrhenius Equation. A miscon- ception has been perpetuated for many years that accurate thermal maturity modeling of vitrinite re- flectance using the Arrhenius Equation and a single activation energy, to derive a time-temperature index （~TTIARa）, as proposed by Wood （1988）, is flawed. This claim was initially made by Sweeney and Burnham （1990） in promoting their ＂EasyRo＂ method, and repeated by others. This paper dem- onstrates through detailed multi-dimensional burial and thermal modeling and direct comparison of the ~TTIARR and ＂EasyRo＂ methods that this is not the case. The ~TTIA^R method not only provides a very useful and sensitive maturity index, it can reproduce the calculated vitrinite reflectance values derived from models based on multiple activation energies （e.g., ＂EasyRo＂）. Through simple expres- sions the ~TTIAaa method can also provide oil and gas transformation factors that can be flexibly scaled and calibrated to match the oil, wet gas and dry gas generation windows. This is achieved in a more-computationally-efficient, flexible and transparent way by the ~TTIARR method than the ＂EasyRo＂ method. Analysis indicates that the ＂EasyRo＂ method, using twenty activation energies and a constant frequency factor, generates reaction rates and transformation factors that do not realisti- cally model observed kerogen behaviour and transformation factors over geologic time scales.