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Vehicular fog computing (VFC) is a technology that enhances vehicular applications by offloading the task of the resource-restricted vehicle to the resourceful fog node (vehicle or static fog node). The mobility of vehicles leads to various challenges in task offloading, i.e., low successful offloading ratio, high response time, etc. Apart from this, the available idle computation resource at the fog node is different at the time of advertisement of computation capability and at the time of task allocation. This change results in less efficient resource utilization and unequal load balance at the fog node. Hence to overcome this, we have proposed an approach based on future mobility and imminent computation awareness (FMICA). The future mobility prediction allows us to offload the task to the fog node, which will be near during task retrieval. Additionally, imminent computation awareness of the available resources (at the imminent time τ ) allows us to efficiently utilize the idle resources through offloading higher deadline tasks at the τ time. FMICA optimizes resources according to various criteria such as offloading delay, load variance, within-range ratio, and successful offloading ratio using a Strength Pareto Evolutionary Algorithm 2 (SPEA2). An order preference by similarity to the ideal solution (TOPSIS) and multiple criteria decision-making (MCDM) technique is used to select the best solution. Through the experimental analysis, we have found that the FMICA has an improvement of 37.64%, 25.57%, 9.96%, and 12.62% in metrics average response time, successful offloading ratio, resource utilization, and average load variance against available approach, respectively. [ABSTRACT FROM AUTHOR]

Pilot recruitment is critical as they pose a multifaceted challenge for civilian and military organizations due to the complex traits impacting their missions and performance. In this study, a novel set of criteria and sub‐criteria were determined to compare twelve candidate pilots. Numerically immeasurable, imprecise, and non‐linear continuous fuzzy linguistic traits (variables) were studied which make the work unique and challenging due to individual preferences and disagreements between decision‐makers (DMs). The outcomes of three distinct fuzzy multiple criteria decision‐making (MCDM) approaches; fuzzy TOPSIS, fuzzy VIKOR, and fuzzy PROMETHEE were evaluated with trapezoidal fuzzy numbers (TFNs) to sort the positions of candidate pilots. Moreover, a unique defuzzification ranking method was employed to adjust the results of fuzzy MCDM methods for the synthesis and evaluation of outcomes of the pilot selection problem. All these efforts make the paper original and outstanding. Our findings and analysis suggested that fuzzy TOPSIS and PROMETHEE methods' outcomes showed maximum close similarity for ranking positions. However, substantial distinctions were noted when comparing these outcomes with the fuzzy VIKOR approach. Yet, the mission of predicting and revealing the best candidates is related to several traits, their weights, and the methods selected. Therefore, since vague information and ambiguous preferences match fuzzy superiority, a comprehensive and unbiased evaluation was achieved, ensuring the integrity of the decision‐making process. The results can be employed to enhance the safety and efficiency of airline operations and ensure that the most qualified and competent pilots are selected for the job. [ABSTRACT FROM AUTHOR]

The aim of this work is to investigate dynamic division of highway control subareas based on improved Label Propagation Algorithm (LPA) for implementing different signal control strategies in the subareas to improve operation efficiency. A highway network is used as the studied object, where each road segment is defined as a node and the network is divided into three types of nodes: only mainline segment, an on-off-ramp and its connecting mainline, and an overpass. The system is abstracted as a topological set of nodes and their relationships. The concept of multiroad segment correlation degree with predictive function of congestion and dissipation ranges is proposed. The correlation degree model of multiroad segments based on Pearson Correlation Coefficient (PCC) and the importance degree model of road segment based on technique for order preference by similarity to ideal solution (TOPSIS) are established, respectively. Then a dynamic division method of control subareas for highway network is proposed based on improved LPA, where the label updating model based on the importance degree of road segment is established by semisupervised classification algorithm. Multiple scenarios were discussed using the highway network of Baoding City in Hebei Province of China as an example. The results show that the number of subareas is moderate, the internal state is uniform, and the calculation is simple and efficient. The control effect is remarkable especially when the vehicle distribution is uneven. Total delay time is reduced by 10.47%, 4.38%, 4.24%, 9.73%, and 2.33%, respectively, and total travel time is reduced by 12.20%, 7.15%, 7.02%, 11.85%, and 3.78%, respectively, compared with no control method, global control method, the static division method by clustering algorithm, the method by normal LPA algorithm, and the method by Newman algorithm. [ABSTRACT FROM AUTHOR]

Environmental issues are often neglected in previous studies on site layout evaluation problems. To address this research gap, a comprehensive evaluation model is developed to evaluate construction site layout planning in this paper. The proposed evaluation model includes four aspects: (1) land use; (2) safety; (3) logistics; and (4) sustainability evaluations. To make the results of the evaluation more accurate, the evaluation model includes the subjective factors that need to be scored by the experts and the objective factors that could be gained from the inspections of the projects and the related materials. Then, the entropy weight method-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is chosen to solve the proposed multicriteria evaluation model. In this paper, the construction site layout planning for four different typical and practical cases, which include two commercial mixed buildings, a residential building, and a sewage treatment plant, is studied. The evaluation results proved that the proposed model is suitable to evaluate common types of construction, such as residential and commercial buildings but not specific functional types, such as the sewage treatment plant. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs)continue to be the primary cause of mortality, accounting for approximately one-third of all fatalities globally. This spawned the proposal of models in several studies. Accordingly, this study contributed to diagnosing heart disease through suggesting soft diagnosis paradigm. Various techniques have been volunteered for serving the suggested paradigm toward achieving its objective. Additionally, this study provided set of contributions. For instance, Tree Soft Technique (TrST) is applied for the first time for forming attributes and sub attributes of patients into nodes and sub-nodes of Tree to obtain relations between it. Even, the study support stakeholders to making accurate decision in mysterious circumstances and in problems with incomplete information through Collaborating the utilized techniques of entropy and Technique for order of preference by similarity to ideal solution (TOPSIS) in this study with Single Value Neutrosophic Sets (SVNSs) forked from neutrosophic uncertainty theory. As well, the relationship between sub-attributes which consider antecedent for obtaining consequent of detecting and diagnosing through collaborating TrST with association rules. Accordingly, we applied four transactions (cases) for obtaining findings of the relations in transactions as listed in Table 13 and Table14. [ABSTRACT FROM AUTHOR]

Given the complexity of today's world, we might need to work with numbers requiring multi-attribute functions, such as those having positive, neutral, and negative membership and those having truth, indeterminacy, and falsity membership. Adding these numbers together to get a single real number is the most important factor. In situations like this, decision-makers have more difficult choices and are unable to apply the single attribute function of the soft set theory. To address this constraint, the hyper-soft set theory with multi-attribute functions is introduced. We combine the notion of neutrosophic hypersoft set with picture fuzzy hypersoft set to form a single theory of neutrosophic picture hypersoft set in this study. We introduce the notions of correlation coefficient and weighted correlation coefficient and demonstrate its fundamental properties for neutrosophic picture hypersoft set. Then, we introduce the notions of a weighted average operator and a weighted geometric operator for neutrosophic picture hypersoft set by using the various aggregation operators with a suitable example. Making decisions based on several factors and choosing the best option is multi-criteria decision-making or MCDM. When ranking and choosing options based on a distance metric, one essential and useful strategy is the Technique of order preference by similarity to an ideal solution (TOPSIS). We demonstrate the accuracy of the fuzzy TOPSIS methodology by extending it to neutrosophic fuzzy TOPSIS and using neutrosophic picture hypersoft set theory to describe the MCDM problem in this study. We provide a generalized neutrosophic TOPSIS approach to demonstrate correlation coefficients and the effectiveness of this approach with an appropriate example. Finally, we offer a comparison to prior studies to demonstrate the viability of the proposed approach. [ABSTRACT FROM AUTHOR]

Especially in the energy supply of multi-family houses, a wide variety of stakeholders are involved, from owners, to users, to energy service providers and society. They usually have different requirements and understandings of optimality, but ultimately have to make joint decisions and thus sensible compromises. In Germany in particular, there are a large number of multi-family houses and, at the same time, many government restrictions and subsidies in terms of energy supply. This makes it difficult to make clear recommendations for the choice of an energy supply concept that takes all stakeholder interests into account. We first identify the relevant stakeholders and define their objectives. In order to relate these with one another, we present a methodology based on energy system simulation and TOPSIS to make energy concepts objectively evaluable. A generic multi-family house with 40 residential units is examined, combining different energy technologies and insulation standards. There is no energy concept that satisfies all stakeholders equally and it is difficult to build coalitions between them. The best results are achieved by air-source heat pumps in combination with photovoltaic. [ABSTRACT FROM AUTHOR]

In an area like India, where a large number of people are dependent upon agriculture, soil erosion assumes the role of a silent killer. A comprehensive soil management plan requires systematic development and planning at the scale of the natural boundaries, i.e., the watersheds. These watersheds are now actively encouraged to become the planning unit for a wide range of natural resource management. Sub-basin prioritisation with respect to erosion susceptibility, therefore, has become increasingly popular in the last few decades as this helps in optimal distribution, especially in a country like India, where the availability of resources is scarce. Erosion is a complex problem wherein a large number of factors play an important role in influencing this process. Therefore, multi-criteria decision-making (MCDM) techniques are becoming more popular in this domain. However, there are a myriad of MCDM models in literature, and a proper and systematic comparison between such MCDM models may help in deciding on the best possible model in the given categories. This paper has tried to make such a comparison for morphometry-based sub-basin prioritisation in the rocky terrain of the Chaka River Basin, Eastern India. The MCDM models that have been investigated include technique for order of preference by similarity to ideal solution (TOPSIS), VlseKriterijumsk Optimizacija Kompromismo Resenje (VIKOR), multi-objective optimisation on the basis of ratio analysis (MOORA), compound factor (CF), weighted sum model (WSM), weighted product model (WPM) and weighted aggregated sum product assessment (WASPAS). These results were compared with respect to the RUSLE-based annual soil loss of the Chaka Basin with the help of the ROC curve. Results reveal that the MOORA MCDM method is the most reliable (AUC = 0.711), whereas the TOPSIS is the least authentic (AUC = 0.378). The middle domain of the Chaka Basin appears to be highly prioritised as compared to the lower domain. [ABSTRACT FROM AUTHOR]

The Yellow River Basin plays a crucial role as both an ecological barrier and an economic development area in China. However, achieving synergistic development between the environment and industry in the coastal provinces remains challenging. Conducting in-depth studies on the synergy between these two aspects can provide valuable guidance for the development of each province. This paper employs the Topsis method to assess the development level of the industrial and environmental systems in the Yellow River Basin. Additionally, it utilizes the Harken model to analyze the synergy between the environment and industry in the coastal provinces of the Yellow River Basin from 2011 to 2021. Furthermore, the overall distribution of the level of synergistic development between the industry and environment in the Yellow River Basin is analyzed using kernel density estimation. The empirical findings reveal the following: 1. The development level of both environmental and industrial systems in each province of the Yellow River Basin has exhibited a consistent increase over the study period. 2. The average synergy between environment and industry in the Yellow River Basin has shown a declining trend from 2011 to 2021. In 2021, the synergy level follows the pattern of "upstream

Simultaneous optimization of multiple responses in machining processes is a challenging task to process engineers. Conflicting type response and cumbersome internal mathematical steps of optimization method itself make the task more difficult. Some discrete multi-response optimization methods namely WSN, GRA, VIKOR, MOORA and TOPSIS methods involve comparatively simpler implementation procedures. For simultaneous optimization of average depth of cut and average kerf taper in abrasive waterjet machining process, the five methods are employed. Different normalization techniques, data conversion methods and combination methods for responses are used. Optimum results are validated through confirmation test. Optimum results for different weight factor combinations are also checked. Performance of multi-response optimization methods is compared with the aid of Spearman’s rank correlation coefficient. [ABSTRACT FROM AUTHOR]

In practical decision-making, linguistic term set is a useful tool to describe the uncertainty and fuzziness of data sources. However, in some decisions, when the data source is unreliable or the decision involves future factors, the evaluation given by the linguistic term set will have a certain degree of error. This paper proposes a binary risk linguistic set based on linguistic term set and R-set. The binary risk linguistic set considers the linguistic term set and the risk factors that may lead to errors in language evaluation. In order to facilitate the use of binary risk linguistic set, the risk conversion function and operational laws are introduced. Next, since group decision-making involves multiple experts, considering the social relations between experts, a method to estimate the missing values in the social network matrix is proposed by utilizing the trust intensity propagation operator and the relationship intensity propagation operator. Risk perception can reflect the subjective judgment of experts on the characteristics and severity of a particular risk, and different judgment results can reflect the attitude of experts to risk. Hereby, this study proposes a risk clustering method based on the risk perception of experts. Furthermore, we propose an adaptive weight updating method based on social network matrix. Then, a binary risk linguistic fuzzy behavioral TOPSIS method is proposed to deal with the multi-attribute large-scale group decision-making (MALSGDM) problem. Finally, a case study is used to demonstrate the feasibility of the presented method, and its effectiveness is validated through comparison with other MALSGDM methods. To demonstrate the effectiveness of the proposed method, this study also perform sensitivity and stability assessments of the decision-makers' weight and behavior characteristics. [ABSTRACT FROM AUTHOR]

Subjectivity in the determination of criteria weights in the TOPSIS method can have a significant impact on the final result and reliability of the decision taken. When weights are determined subjectively, the decision depends heavily on the preferences of the individual or group assigning the weights, which may not always be objective or consistent. The Objective Weighting-TOPSIS (OWH-TOPSIS) method is a modification of the TOPSIS method that incorporates an objective weighting approach to determine criteria weights objectively. This approach aims to improve accuracy and consistency in determining criteria weights by considering decision makers' preferences and variations in data. OWH-TOPSIS can provide evaluation results that better suit the preferences and needs of decision makers, while considering the unique characteristics of the data being evaluated. This approach can be a powerful solution in complex multi-criteria decision-making, by producing more accurate and relevant alternative rankings. Although the OWH-TOPSIS approach can be used to determine the weights, this process can require complete and accurate data and a deep understanding of the decision maker's preferences. The comparison results show that the correlation value of the entropy-TOPSIS method is 0.725, the correlation value of the OWH-TOPSIS method is 0.714, the correlation value of the CRITIC-TOPSIS method is 0.714, and the correlation value of the OWH-TOPSIS method is 1. [ABSTRACT FROM AUTHOR]

Effective management of flood risks requires the prioritization of appropriate flood control solutions. This study aims to prioritize structural flood control options using multi‐criteria decision‐making (MCDM) methods. Four MCDM methods, namely analytic hierarchy process, technique for order preference by similarity to ideal solution, multi‐criteria optimization and compromise solution, and Fuzzy‐VIKOR are employed to assess and rank the flood control options based on multiple criteria. Field surveys, interviews with local authorities and experts, and on‐site assessments of existing flood control structures constituted the primary data collection methods. The findings demonstrate the effectiveness of reservoir dams, retention basins, and levees as viable solutions. Conversely, flood control gates and the no‐project options were assigned lower priorities. The findings highlight the importance of considering multiple MCDM methods to account for variations in rankings. The study provides valuable insights into the decision‐making process for prioritizing flood control options in the study area. These findings can assist policymakers and stakeholders in effectively allocating resources and implementing appropriate structural flood control measures to mitigate flood risks. [ABSTRACT FROM AUTHOR]

Determining the optimal intermix of various input parameters for an end milling process is an important task to explore its fullest performance along with the achievement of the desired responses. Several multi-criteria decision-making techniques, like technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis and multiobjective optimisation on the basis of ratio analysis (MOORA), have been applied for solving parametric optimisation problems of milling processes while assigning equal importance to the considered responses. In this paper, the optimal combinations of cutting speed, feed rate and depth of cut of an end milling process during the machining of Al 1070 alloy are determined using the weighted sum model (WSM), weighted product model (WPM), weighted aggregated sum product assessment (WASPAS), MOORA, TOPSIS, evaluation based on distance from average solution (EDAS), additive ratio assessment (ARAS) and complex proportional assessment (COPRAS) methods with the allocation of unequal weights to the responses determined by standard deviation and entropy techniques. The derived solutions are also compared when the same importance is assigned to the responses. From the results, a parametric combination of 255 rpm cutting speed, 82 mm/min feed rate and 0.75 depth of cut is identified by WSM, WPM, WASPAS, COPRAS, TOPSIS and ARAS methods to achieve the optimal values of material removal rate (MRR) and average surface roughness (Ra) as 595.23 mm3/min and 13.79 µm, respectively. It is also noticed that both feed rate and depth of cut significantly influence MRR and Ra during the machining of Al 1070 alloy. [ABSTRACT FROM AUTHOR]

Nickel-based superalloy grinding, as well as selecting the appropriate operational parameters, is one of the major challenges in machining engineering. The conducted research consists of two sections. The first section presents a systematic experimentation, which has been carried out to qualitatively and quantitatively investigate nickel-based superalloy grinding. Vitrified CBN grinding wheels, along with a rotary diamond cup dresser, were utilised to conduct the experiments with the workpiece of Inconel 738. Specific grinding energy, G-ratio and surface roughness were indicated as three major machining outputs, while the inputs were the dressing conditioning and grinding parameters. In the following, the effects of different operational parameters were relatively ranked according to their weighted impact on the grinding performance using hybrid multi-attribute decision-making (MADM) methods. For this purpose, a combination of three techniques: Analytic Hierarchy Process (AHP), Entropy and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was utilised to select the most appropriate grinding conditions, while considering the conflicting effect of the different dressing and grinding parameters. The results indicate that specific energy can be an important criterion for evaluating the process efficiency. The changes in G-ratio and surface roughness have the same trends, as opposed to specific grinding energy mutation. The grinding parameters are summarised in equivalent chip thickness. Our findings show that dresser cross feed rate, along with equivalent chip thickness, has the greatest impact on the grinding process efficiency. [ABSTRACT FROM AUTHOR]

Titanium alloys are frequently utilised in applications that call for high strength at high temperatures as well as excellent mechanical resistance. This alloy has excellent mechanical properties that enable improved performance from titanium alloys. In the current research, Taguchi L25 orthogonal array of experiments were conducted first, and MCDM techniques and ANNOVA were utilised to forecast the influencing machining factors. From MCDM three methods (SAW, VIKOR & TOPSIS) prediction algorithms which rank one for experiment no. 2. Experiment results shows that experiment no. 02 gives optimum/best results in terms of Tool life and surface roughness. While the process parameter has values cutting speed = 80 rpm, Feed rate = 018 rev/mm, Depth of cut = 0.12 mm, and Rake angle = 14 degrees. From ANOVA outcome for surface finish it is noticed that the process parameter rake angle is having Rank 01 for all levels. Thus rake angle is major influencing parameter to achieve a better quality of surface roughness. While in case of flank wear it is crystal clear that the process parameter DOC is having Rank 01 for all levels. Thus DOC is most influencing parameter to achieve lesser flank wear and better quality of tool life. [ABSTRACT FROM AUTHOR]

Friction stir welding (FSW) is a process that can join many materials by causing minimal internal stress without the need for a direct electric current, contrary to traditional welding methods. The effects of SiC and Al2O3 reinforcing powders on the joining of AA6061-T6 and AA7075-T6 plates, which are difficult to join with conventional welding methods by FSW, are investigated in this study. The metallurgical properties of the combined samples are examined in terms of strength characteristics to investigate the effects of the reinforcement powder. In addition, elemental analysis is carried out for the mixing behavior of the powders. Finally, we used the TOPSIS method to select the most appropriate powder types to improve welding quality. Furthermore, a game theory application is presented to determine which powder type is suitable considering the joined aluminum plate's strength expectations. [ABSTRACT FROM AUTHOR]

This paper suggests an ordering policy for strategic items. Based on the base stock policy, we modeled a bi-objective, multi-product inventory control system with partial back-ordering. The model aims to minimize total costs and maximize the average service level, considering stochastic constraints such as storage space, maximum allowable shortage, and maximum allowable lost sales to match real-world conditions. We used three Multi-Objective Decision-Making methods, namely Individual optimization, Desirability function, and Goal attainment, to solve the problem. TOPSIS method showed the Goal attainment method's superiority over the others. The sensitivity analysis revealed that the mean of storage space had the most significant impact on objective functions. Also, increasing the back-order percentage can reduce the system's total costs. [ABSTRACT FROM AUTHOR]

Aiming to help petrochemical companies monitor and manage risks in real time, this study presents a comprehensive risk assessment method for hydrogenation units based on the decision making trial and evaluation laboratory (DEMATEL), the technique for order preference by similarity to ideal solution (TOPSIS) and cloud model (CM). Through the interaction analysis by DEMATEL, the weights of risk factors are assigned more reasonable, and introduced into TOPSIS and CM for the prioritization of risk factors and the risk assessment of hydrogenation units. A case study is conducted on a petrochemical enterprise in Liaoning Province, China. The results show that this method can accurately identify risk factors and evaluate risk level of hydrogenation units to guide the enterprise in risk management. • A DEMATEL-TOPSIS-CM risk assessment method is proposed for hydrogenation units. • The critical risk factors in diesel hydrogenation reaction are identified. • The impact of key hazards is appropriately amplified to aid risk management. • This method is the basis for real-time risk assessment of hydrogenation units. [ABSTRACT FROM AUTHOR]

Under the background of the marketization of data elements, enhancing regional scientific and technological innovation capabilities has become the main driving force for urban sustainable development. It contributes to the sustainable development of the region. Based on the data of the Yunnan statistical yearbook, we built a regional science and technology innovation index system from three dimensions of science and technology innovation input, output, and benefit. Firstly, the entropy method is used to obtain the weight of each index. Secondly, TOPSIS is used to evaluate the 36 index data of each prefecture. Finally, the scores and rankings of the scientific and technological innovation ability of each prefecture are obtained. The research can effectively find the advantages and disadvantages of scientific and technological innovation in the Yunnan Province, provide data support and decision support for regional economic development, and play an important role in promoting the level of technological innovation in modern industries. At the same time, research projects can help various regions allocate various technological innovation resources reasonably, enhance their original innovation capabilities, lead the development of strategic emerging industries and future industries, and accelerate the formation of new quality productivity. [ABSTRACT FROM AUTHOR]

The literature has explored various methods for assessing minimum environmental flow. Implementing holistic approaches proves to be prohibitively expensive and impractical for many small and medium projects. Hence, desktop and cost-effective methods are commonly employed without an integrated decision-making system to justify the assessed values. This study introduces a systematic decision-making framework aimed at selecting the most suitable method for assessing the actual needs of river habitats. Employing a fuzzy technique known as the Order Preference Similarity to the Ideal Solution (FTOPSIS), the study considers factors such as physical, thermal, and dissolved oxygen habitat suitability, maximum habitat area, and water demand loss function to determine the most appropriate method among established ones, including the Tennant method, flow duration curve analysis method, wetted perimeter method, and physical habitat simulation method. The results prioritize physical habitat simulation, wetted perimeter by slope method, and flow indices of 70%, 75%, and 80% by flow duration curve analysis method as the optimal approaches for assessing minimum environmental flow. This proposed decision-making system offers a viable platform to explore the applicability of existing cost-effective methods for assessing minimum environmental flow. It also serves as an effective mechanism for reducing negotiations among stakeholders by comprehensively considering all relevant aspects in the environmental management of river ecosystem requirements. [ABSTRACT FROM AUTHOR]

The study investigates the weighting and hierarchization of renewable energy sources in specific geographical regions of Colombia using the TOPSIS and Diffuse TOPSIS metaheuristic models. 5 regions were analyzed, two of them with different scenarios: Caribbean 1 and 2, Pacific 1 and 2, Andean, Amazonian and Orinoquia. The results reveal significant differences in the evaluation of technologies between the two models. In the Caribbean 1, Diffuse TOPSIS gave a higher score to Solar Photovoltaics, while TOPSIS favored Hydropower. In the Caribbean 2, Solar Photovoltaic obtained similar scores in both models, but Wind was rated better by TOPSIS. In the Pacific Region 1, Biomass and large-scale Hydropower led according to both models. In the Pacific 2, Solar Photovoltaic was better evaluated by TOPSIS, while Wind was preferred by Diffuse TOPSIS. In the Andean Region, large-scale hydroelectric and Solar photovoltaic plants obtained high scores in both models. In the Amazon, Biomass led in both models, although with differences in scores. In Orinoquia, Solar Photovoltaic was rated higher by both models. The relevance of this research lies in its ability to address not only Colombia's immediate energy demands, but also in its ability to establish a solid and replicable methodological framework. The application of metaheuristic methods such as TOPSIS and TOPSIS with fuzzy logic is presented as a promising strategy to overcome the limitations of conventional approaches, considering the complexity and uncertainty inherent in the evaluation of renewable energy sources. By achieving a more precise weighting and hierarchization, this study will significantly contribute to strategic decision-making in the implementation of sustainable energy solutions in Colombia, serving as a valuable model for other countries with similar challenges. [ABSTRACT FROM AUTHOR]

Recently, there have been several advancements in the field of sustainable energy solutions, particularly in the selection of solar window film sheets. In this research, a multi-criteria decision-making approach was applied to compare three different types of window film sheets, Silver 35, TrueVue 15, and Sterling 40, to aid in selecting the most suitable window film based on the United Arab Emirates market. The primary aim of this work is to provide decision-makers with a structured approach to enhance their choices for selecting window film sheets. The methodology employed involves evaluating various criteria, including visible light transmittance, solar energy rejected, energy transmittance, energy absorptance, cost, glare reduction, visible light reflectance interior, and fade reduction. These criteria are assessed using the Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results demonstrate that Sterling 40 is the best choice followed by Silver 35. Based on the final TOPSIS results, the difference between the scores of these two window film sheets was not significant, while they were far from the score of TrueVue 15. [ABSTRACT FROM AUTHOR]

With a substantial fraction of renewable energy integrated into the electrical grid, the new power system urgently requires grid planning scheme displaying adaptability to different energy types and their volatility. Considering the indeterminacy of renewable energy generation output and the different attitudes of decision-makers towards its risk, this paper proposes an adaptability assessment methodology for power grid planning schemes considering multiple decision psychology. First, an evaluation indicator framework is established based on the adaptive requirements of the grid planning for novel power system, and the weights of indicators are calculated based on an improved AHP-CRITIC combination weighting method. Second, improved cumulative prospect theory (ICPT) is adopted to improve to the calculation method of the distance between the evaluation program and the positive and negative ideal programs in the GRA and TOPSIS, which effectively characterize the different decision-making psychologies, and a combination evaluation model is constructed based on a cooperative game (CG), namely, an adaptability evaluation model of grid planning schemes for novel power systems based on GRA-TOPSIS integrating CG and ICPT. Finally, the proposed model serves to evaluate grid planning schemes of three regions in China's 14th Five-Year Plan. The evaluation results show that the adaptability of the schemes varies under different decision-making psychologies, and under the risk-aggressive and loss-sensitive decision-making psychologies, grid planning scheme of Region 1 with the greatest accommodation capacity of renewable energy is preferable. [ABSTRACT FROM AUTHOR]

Froth flotation is a widespread and important method for mineral separation, significantly influencing the purity and quality of extracted minerals. Traditionally, workers need to control chemical dosages by observing the visual characteristics of flotation froth, but this requires considerable experience and operational skills. This paper designs a deep ensemble learning-based sensor for flotation froth image recognition to monitor actual flotation froth working conditions, so as to assist operators in facilitating chemical dosage adjustments and achieve the industrial goals of promoting concentrate grade and mineral recovery. In our approach, training and validation data on flotation froth images are partitioned in K-fold cross validation, and deep neural network (DNN) based learners are generated through pre-trained DNN models in image-enhanced training data, in order to improve their generalization and robustness. Then, a membership function utilizing the performance information of the DNN-based learners during the validation is proposed to improve the recognition accuracy of the DNN-based learners. Subsequently, a technique for order preference by similarity to an ideal solution (TOPSIS) based on the F1 score is proposed to select the most probable working condition of flotation froth images through a decision matrix composed of the DNN-based learners' predictions via a membership function, which is adopted to optimize the combination process of deep ensemble learning. The effectiveness and superiority of the designed sensor are verified in a real industrial gold–antimony froth flotation application. [ABSTRACT FROM AUTHOR]

It is crucial to understand and meet the multi-dimensional affective image needs of users for product form in a user demand-oriented product development model. Multi-objective evolutionary algorithms based on decomposition will be introduced into the field of kansei engineering to carry out research on product form optimisation design based on multi-objective evolutionary algorithms. A constrained multi-objective discrete optimisation model was established using the kansei engineering prediction model constructed through machine learning techniques as the objective function, and a reference vector guided evolutionary algorithm was used to solve it. The superiority of this method was verified by comparing it with other commonly used solving methods in this field. Combining entropy weight method and TOPSIS, select the optimisation design scheme that best meets the multi-dimensional affective needs of users from the obtained pareto set. Taking the train as an example, the proposed method was explained. The results indicate that the optimisation scheme obtained by this method can achieve the improvement and optimisation of product form in multiple affective dimensions. Meanwhile, a comparative study on the applicability of multi-objective evolutionary algorithms in the form optimisation problem of different affective dimensions is carried out to provide reference and suggestions for subsequent product design research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a new approach to deal with multi-criteria group decision-making (MCGDM) problems under type-2 single valued neutrosophic set (T2SVNS) environment. Firstly, we give the concepts of single valued neutrosophic set (SVNS) and T2SVNS, and then we define probabilistic operators with T2SVN information. Secondly, two type of binary logarithm similarity measures (BLSM) and weighted binary logarithm similarity measures (WBLSM) for T2SVNSs are defined. Hybrid binary logarithm similarity measure (HBLSM) and weighted hybrid binary logarithm similarity measure (WHBLSM) for T2SVNSs are also defined. In addition, a MCGDM model based on TOPSIS method is proposed under T2SVNS environment. Finally, a numerical example is given to illustrate the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to reveal the temporal and spatial evolution characteristics of land ecological security in the Yellow River Basin, to clarify its deductive law, and to help accelerate the construction and development of ecological civilization in the Yellow River Basin. [Methods] Based on the statistical data from 2012 to 2021, 58 cities in the Yellow River Basin were selected as the research unit. system of evaluation index was established, integrated entropy weight TOPSIS method and Moran index were used to explore the temporal and spatial evolution law, and grey prediction GM(1.1) model was used to explore future trend. [Results] (1) In terms of time, the whole level of land ecological security was not high in Yellow River Basin, but showed a trend of good development year by year, and experienced an improvement from sensitive level' to good level'. (2) From the spatial distribution point of view, the land ecological security presented the characteristics of large cohabitation and small aggregation' which always showed a step-shaped distribution pattern of 'the highest level in downstream was, the medium level in the middle reaches, and the lowest level in upper reaches'. (3) The land ecological security of the Yellow River Basin had obvious positive correlation in space and significant agglomeration characteristics, Locally, the high safety values (H-H) were clustered in Shandong and Henan, the low safety value are clustered in Qinghai, Gansu and Shaanxi of the middle and upper reaches. (4) From 2022 to 2025, the land ecological security level will maintain a steady rise, and the land ecological security level will remain at 'good level'. [Conclusion] The level of land ecological security in the Yellow River Basin continues to improve, but the overall level is not high, and land ecosystem governance and joint protection in the areas along the basin should be continually promoted. [ABSTRACT FROM AUTHOR]

In the open science environment, this article evaluates the discourse power of academic journals from the perspective of multiple integration. It is conducive to scientific research management and provides a reference for enriching and perfecting the evaluation theory and indicators system of academic journals. Based on the theory of evaluation science, discourse power theory and communication theory, first, this article discusses the basic issues of the discourse power evaluation for academic journals. It defines the academic discourse power and the discourse power of academic journals. It is proposed that the discourse power of academic journals is composed of discourse influence and discourse leading. Discourse influence is composed of discourse influence ability and discourse power, and discourse leading is composed of social media discourse, news and policy discourse, encyclopaedia discourse, peer-review discourse and video discourse leading. This article explores the formation process and operation mechanism of the discourse power for academic journals. Then, this article constructs the discourse power evaluation model of academic journals. Second, this article integrates multi-source heterogeneous data, then adopts correlation analysis, integrated factor analysis, entropy weight method, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and two-dimensional four-quadrant mapping method to conduct empirical research on the discourse power evaluation of Medicine, General and Internal journals from the perspectives of multi-dimensions, multi-factors, multi-indicators and multi-methods fusion. The results show that the research on the discourse power evaluation for academic journals based on the theory, method and application logic is practical, comprehensive and reliable. [ABSTRACT FROM AUTHOR]

With the advent of numerous global precipitation estimates (GPEs) in the recent decades, dependability of hydrologists has lessened on the station data as the GPEs can be readily availed and utilized. Since the skills of GPEs may differ from region-to-region, it is vital to analyse their ability in resolving the regional precipitation climatology using appropriate statistical methods. In this study, a total of five GPEs, viz., APHRODITE, PERSIANN-CDR, CHIRPS, CMORPH, and IMERG were evaluated for their abilities in resolving regional precipitation climatology of WHR with respect to gridded precipitation product of India Meteorological Department (IMD). Different performance indicators i.e., Probability of Detection (POD), False Alarm Ratio (FAR), Normalised Root Mean Square Deviation (NRMSD), Pearson Correlation Coefficient (CC) and Skill Score (SS) were used for evaluating the GPEs. Multicriterion Decision Making (MCDM)approaches i.e., Compromise Programming (CP), Cooperative Game Theory (CGT), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Weighted Average Technique (WAT), and Fuzzy TOPSIS were used for ranking the GPEs across different grids in WHR. Entropy based weight assignment to NRMSD, CC, and SS were performed while applying them in MCDM methods. Group Decision Making (GDM) approach utilizing spearman correlation coefficient and additive ranking rule was employed to obtain the final ranking of GPEs from multiple rankings assigned through different MCDM methods. Across 115 grids, APHRODITE exhibits superior performance compared to other GPEs in 89 grids. Conversely, CHIRPS and CMORPH emerge as the least favorable products among the five GPEs across more than 70 grids, being consistently ranked either 4th or 5th. Notably, IMERG was identified as the best-performing product in 14 grids and as the second-best product in 63 grids, positioning it as the second most suitable option after APHRODITE for monthly rainfall time series analysis. Similar results, as detailed in the paper, were also obtained for month-wise rainfall time series analysis. [ABSTRACT FROM AUTHOR]

Drilling precise micro-holes in glass material has brought a new challenge, primarily due to its application in microfluidic devices. It is required to reduce machining time and simultaneously achieve repeatability of the process. The machined hole should have minimum overcut to get desired hole diameter. Maximum hole circularity and minimum heat-affected zone are the essential hole characteristics to achieve controlled machining. The multi-criteria decision-making (MCDM) method is quite effective in selection of best possible combination of outputs from several alternative solutions. The experimental data from the previously published paper is used in the current study. As input parameters, the experiment data include voltage, tool feed rate, and machining time. The radial overcut (ROC), circularity of the machined hole and heat-affected zone (HAZ) were calculated as output responses. The experiments were conducted using copper and nickel-coated copper tools in previous study and response data were used in current work. The present study uses the methods which include hybrid grey relational analysis (GRA), technique for order performance by similarity to ideal solution (TOPSIS), and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods for the selection of the best combination of process parameters in drilling to obtain the optimal geometric characteristic of a hole, i.e. optimal values of ROC, circularity and HAZ, in glass using electrochemical discharge machining (ECDM). In this study, weight calculation for MCDM was proposed using entropy and analytic hierarchy process (AHP) methods and combined to get final weights using the fuzzy logic tool, which determines the importance of AHP and entropy weights using the expert opinion. The final weights were used to calculate the ranks from each MCDM method and determine optimal process parameter selection in ECDM hole drilling with copper and nickel-coated copper tools. The fuzzy logic method determined that the weight contribution from AHP is 0.586 in the present study. Genetic algorithm-based multi-objective optimization was conducted, and a non-dominated Pareto front was generated. The Spearman correlation method was used to determine the relationship between different MCDM methods. The current study will be helpful in selecting the best combination of process parameters in drilling holes during ECDM machining using MCDM methods. [ABSTRACT FROM AUTHOR]

This study investigates the application of the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method to enhance Iraq's airport infrastructure. The research aims to analyze the performance of major airports based on data collected from the Central Statistical Organization (CSO) of Iraq. A model is proposed to prioritize the airport development projects based on essential criteria, such as aircraft movement (landing and takeoff) and passenger movement (arrivals and departures). The airports are ranked according to the priority they have in infrastructure development, which is linked to their economic growth. In terms of priority, the first is Baghdad International Airport, Basra Airport follows, and Najaf Airport ranks third. Erbil and Sulaymaniyah airports rank lower due to infrastructure limitations and administrative challenges. The research underscores the importance of ongoing investments in airport infrastructure to accommodate the increasing number of passengers and bolster economic expansion. [ABSTRACT FROM AUTHOR]

In order to design a high-efficiency two-stage gearbox to reduce power loss and conserve energy, a MultiCriterion Decision-Making (MCDM) method is selected for solving the Multi-Objective Optimization Problem (MOOP) in this research. The study's objective is to determine the best primary design factors that will increase gearbox efficiency and decrease gearbox mass. To that end, the first stage's gear ratio and the first and second stages' Coefficients of Wheel Face Width (CWFW) were chosen as the three main design elements. Furthermore, two distinct goals were analyzed: the lowest gearbox mass and the highest gearbox efficiency. Additionally, the MOOP is carried out in two steps: phase 1 solves the Single-Objective Optimization Problem (SOOP) to close the gap between variable levels, and phase 2 solves the MOOP to determine the optimal primary design factors. Furthermore, the TOPSIS approach was selected to address the MOOP. For the first time, an MCDM technique is used to solve the MOOP of a two-stage helical gearbox considering the power losses during idle motion. When designing the gearbox, the optimal values for three crucial design parameters were ascertained according to the study's results. [ABSTRACT FROM AUTHOR]

This study investigates the application of a new mathematical routing model, integrated with the TOPSIS Linear Programming (TOPSIS-LP) approach, to optimize tourist routes in Nong Khai, Thailand, within a Multi-Criteria Decision-Making (MCDM) framework. The research demonstrates the efficacy of TOPSISLP by consistently ranking the same alternative as the optimal route, achieving the highest rankings across various Multi-Attribute Decision Making (MADM) methods, including MOORA, WASPAS, and ARAS. These methods displayed significant consistency in outcome evaluation, with Spearman Correlation Coefficients (SCC) of 0.952 for MOORA WASPAS, and ARAS, indicating the influence of diverse weighting and aggregation strategies in route optimization. Moreover, the study confirmed a perfect alignment (SCC of 1.00) between TOPSIS-LP and the traditional TOPSIS method, affirming that the enhancements to the LP components maintained the integrity of the original model. The findings provide invaluable insights for tourism planners aiming to improve tourist satisfaction and operational efficiency and contribute to the academic discourse by highlighting the practical utility of sophisticated mathematical models in real-world scenarios. This research not only advances the methodological practices in tourist route optimization, but also sets a benchmark for future research aimed at enhancing the effectiveness, robustness, and adaptability of MADM methods in the tourism sector. [ABSTRACT FROM AUTHOR]

As an important indicator for assessing regional water resources, the study of the spatiotemporal evolution and driving factors of water resources carrying capacity (WRCC) is essential for achieving sustainable water resource utilization. This study focuses on Yulin City, a typical arid region located on the Loess Plateau in northwestern China. By constructing an evaluation index system for regional WRCC and combining an improved fuzzy comprehensive evaluation model with the TOPSIS evaluation model, a comprehensive WRCC evaluation model is established. Additionally, Geodetector is used to explore the main driving factors behind the evolution of regional WRCC. This multidimensional analytical framework aims to deeply analyze the dynamic evolution trends of WRCC and the driving mechanisms of different factors in its spatiotemporal changes. The results indicate that (1) from 2011 to 2020, the overall WRCC of Yulin City showed a trend of positive improvement, with Shenmu, Yuyang, and Fugu areas performing the best, and by 2020, more than half of the counties had achieved Grade 3 or above; (2) the spatial variability of WRCC in Yulin City was more significant than its temporal changes; and (3) in terms of driving mechanisms, the northern six counties gradually shifted from traditional economic-driven factors to ecological and environmental drivers, whereas the southern six counties remained constrained by economic factors. Overall, water resource factors remain the primary driving force for the socio-economic development and environmental sustainability of the entire Yulin City. The study provides valuable information for water resource allocation and differentiated management in arid regions. [ABSTRACT FROM AUTHOR]

Foxtail millet (Setaria italica) is an important cereal crop with rich nutritional value. Distinctness, Uniformity, and Stability (DUS) are the prerequisites for the application of new variety rights for foxtail millet. In this study, we investigated 32 DUS test characteristics of 183 foxtail millet resources, studied their artificial selection trends, and identified the varieties that conform to breeding trends. The results indicated significant differences in terms of the means, ranges, and coefficients of variation for each characteristic. A correlation analysis was performed to determine the correlations between various DUS characteristics. A principal component analysis was conducted on 31 test characteristics to determine their primary characteristics. By plotting PC1 and PC2, all the germplasm resources could be clearly distinguished. The trends in foxtail millet breeding were identified through a differential analysis of the DUS test characteristics between the landrace and cultivated varieties. Based on these breeding trends, the optimal solution types for multiple evaluation indicators were determined; the weight allocation was calculated; and a specific TOPSIS algorithm was designed to establish a comprehensive multi-criteria decision-making model. Using this model, the breeding potential of foxtail millet germplasm resources were ranked. These findings provided important reference for foxtail millet breeding in the future. [ABSTRACT FROM AUTHOR]

The seasonal temporal characteristics of runoff are crucial in regional water resources scheduling and administration, flood warning and forecasting, and efficient utilization of water resources through large-scale hub engineering scheduling. Traditional flood season division models are based primarily on consistency assumptions and deterministic indicators and can provide only a single division scheme. However, data consistency is greatly influenced by climate change and human activities, and indicators are becoming more uncertain. As a result, the consistency assumption of traditional flood season division models is unsatisfactory, and deterministic indicators cannot fully reflect the uncertainty of division indicators. Ultimately, the traditional flood season division model cannot adapt to the new form. In response to the above issues, the Flood Season Division Model based on the New Information Priority Principle - Interval Indicators (FSDMBNIP-II) is proposed. Following interval number theory, the IV-FSDI is calculated for both the training and validation sets. The IV-FSDI can represent the fluctuation of flood season division indicators through the interval radius to reflect the uncertainty of flood season division indicators. Optimization models are developed based on IV-FSDI for the two sets. NSGA-II is employed for solving optimization models, resulting in two Pareto solution sets. Two final flood season division schemes are selected for the two Pareto solution sets using the TOPSIS method. According to the new information priority principle, based on the matrix comparison method, the training and validation set are weighted, with more weight given to newer data. This allows final model to differentiate and learn from the information in new and old data to handle inconsistent data. Based on the weighting results, the two final flood season division schemes are combined. The performance of FSDMBNIP-II was validated using the control basin of the Wudongde hydropower station as the research area and compared with the performances of a model without considering the new information priority principle (MWCNIPP), a model without considering the interval of indicators (MWCII), and a set pair analysis method (SPA). The comparative results showed that, in the predetermined scenarios, when compared to MWCNIPP, FSDMBNIP-II was the optimal model for 66.7% of scenarios. Additionally, when compared to the MWCII and to SPA, FSDMBNIP-II was the optimal model for 100% of the scenarios. The results indicate that compared to the comparative model, FSDMBNIP-II can more effectively adapt to flood season division in situations with inconsistent data and nonstationary indicator values. [ABSTRACT FROM AUTHOR]

Due to the COVID-19 pandemic, face masks have been indispensable in the daily lives of people to reduce the risk of infection and spread of the virus. Polypropylene fabric, which is main part of the disposable surgical face mask (SFM), is waste that can remain in the environment for years because of its non-biodegradable structure, so this waste needs to be carefully managed. In the present study, the Life Cycle Impact Assessment (LCA) integrated Multi-Criteria Decision-Making (MCDM) approach was applied to prioritize SFM waste treatment/disposal strategies; incineration, pyrolysis, direct landfilling, and steam sterilization-landfilling. The Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS) were used for ranking as MCDM techniques. Thirteen different criteria were determined, and the criteria related to environmental impact were obtained from the LCA output. The weights of the significance of all criteria were computed using the Entropy weighting method. The ranking results show that pyrolysis is found as a being more appropriate alternative (with values of 0.4397 and 0.8140 for AHP and TOPSIS, respectively). In addition, the second ranking alternative is incineration, followed by the direct landfilling alternative which is assessed as the third preferable treatment/disposal option. The steam sterilization-landfilling option is found to be the least appropriate solution. These results reveal that the pyrolysis option might be considered as well, as it changes this waste into value-added energy products, thereby helping achieve a sustainable method for environmental benefit and advantage. [ABSTRACT FROM AUTHOR]

Risks in the supply chain can damage many companies and organizations due to sustainability risk factors. This study evaluates the supply chain risk assessment and management and then selects the best supplier in a gas company in Egypt. A comprehensive methodology can use the experts' opinions who use the linguistic variables in the spherical fuzzy numbers (SFNs) to evaluate the criteria and suppliers in this study based on their views. Selecting the best supplier is a complex task due to various criteria related to supply chain risk assessment, such as supply risks, environmental risks, financial risks, regularity risks, political risk, ethical risks, and technology risks and their sub-criteria. This study suggested a new combined model with multi-criteria decision-making (MCDM) under a spherical fuzzy set (SFS) environment to overcome uncertainty and incomplete data in the assessment process. The MCDM methodology has two methods: the Entropy and COmbinative Distance-based Assessment (CODAS) methods. The SFS-Entropy is used to compute supply chain risk assessment and management criteria weights. The SFS-CODAS method is used to rank the supplier. The main results show that supply risks have the highest importance, followed by financial and environmental risks, and ethical risks have the lowest risk importance. The criteria weights were changed under sensitivity analysis to show the stability and validation of the results obtained from the suggested methodology. The comparative analysis is implemented with other MCDM methods named TOPSIS, VIKOR, MARCOS, COPRAS, WASPAS, and MULTIMOORA methods under the SFS environment. This study can help managers and organizations select the best supplier with the lowest sustainability risks. [ABSTRACT FROM AUTHOR]

Maintaining and improving ecological resilience is of great practical significance for the Yellow River Basin to reduce potential ecological risks and deliver sustainable development. Based on the essential characteristics of evolutionary resilience, this paper developed an ecological resilience index system of "resistance-recovery-reconstruction-renewal" and calculated the ecological resilience of 75 prefecture-level cities in the Yellow River Basin from 2007 to 2021 with the improved TOPSIS method. Then the spatiotemporal evolution characteristics of ecological resilience were analyzed using the gravity center-standard deviation ellipse, Dagum Gini coefficient decomposition, and spatial autocorrelation analysis. Furthermore, the dynamic spatial Durbin model (DSDM) was used to investigate the influencing factors of ecological resilience. The main results are as follows: (1) The ecological resilience of the Yellow River Basin showed an overall fluctuating upward trend, and the average annual growth rate in the downstream region was larger than in the upstream and midstream regions. (2) Cities with similar levels of ecological resilience were distributed in a "large settlement, small scattered" pattern. The center of gravity shifted to the southeast, and the spatial distribution exhibited a "northwest-southeast" pattern and a trend towards an "east–west" pattern. The primary source of spatial differences was the intensity of transvariation. (3) The ecological resilience in the Yellow River Basin showed significant spatial clustering, with the H–H clustering area shifting from the Hubao-Eyu urban agglomeration to the Shandong Peninsula urban agglomeration, and the L–L clustering area mainly distributed around the Central Plains city cluster. (4) The ecological resilience of the Yellow River Basin exhibited significant snowball, spillover, and siphon effects in time, space, and space-time dimensions, respectively. In the short and long term, population density and openness significantly positively affected the ecological resilience of local and surrounding cities. Urbanization had a long-term effect on ecological resilience without a short-term effect. GDP per capita and industrial structure only imparted a significant positive influence on local ecological resilience. The negative spatial spillover of the intensity of financial investment in technological innovation gradually turned into a positive effect. [ABSTRACT FROM AUTHOR]

Based on data from A-share listed companies from 2007 to 2021, this study constructs an enterprise resilience index using the entropy-weighted TOPSIS method. On this basis, the impact of digital transformation on enterprise resilience is examined through empowerment theory. The empirical results indicate that digital transformation significantly enhances enterprise resilience, a conclusion that remains valid after a series of robustness tests. Mechanism analysis reveals that digital transformation enhances enterprise resilience by reducing agency costs, increasing information transparency, and alleviating financing constraints. Heterogeneity analysis shows that micro-characteristics of enterprises, such as the stage of the life cycle and factor intensity, as well as macro-environmental factors like regional intellectual property rights protection and industry competition level, have differential impacts on the resilience-enhancing effects of digital transformation. Further analysis suggests that the level of corporate governance influences the positive impact of digital transformation on enterprise resilience. [ABSTRACT FROM AUTHOR]

Land suitability assessment, as an important process in modern agriculture, involves the evaluation of numerous aspects such as soil properties, climate, relief, hydrology and socio-economic aspects. The aim of this study was to evaluate the suitability of soils for wheat cultivation in the Gavshan region, Iran, as the country is facing the task of becoming self-sufficient in wheat. Various methods were used to evaluate the land, such as multi-criteria decision-making (MCDM), which is proving to be important for land use planning. MCDM and machine learning (ML) are useful for decision-making processes because they use complicated spatial data and methods that are widely available. Using a geomorphological map, seventy soil profiles were selected and described, and ten soil properties and wheat yields were determined. Three MCDM approaches, including the technique of preference ordering by similarity to the ideal solution (TOPSIS), gray relational analysis (GRA), and simple additive weighting (SAW), were used and evaluated. The criteria weights were extracted using Shannon's entropy method. Random forest (RF) model and auxiliary variables (remote sensing data, terrain data, and geomorphological maps) were used to represent the land suitability values. Spatial autocorrelation analysis as a statistical method was applied to analyze the spatial variability of the spatial data. Slope, CEC (cation exchange capacity), and OC (organic carbon) were the most important factors for wheat cultivation. The spatial autocorrelation between the key criteria (slope, CEC, and OC) and wheat yield confirmed these results. These results also showed a significant correlation between the land suitability values of TOPSIS, GRA, and SAW and wheat yield (0.74, 0.72, and 0.57, respectively). The spatial distribution of land suitability values showed that the areas classified as good according to TOPSIS and GRA were larger than those classified as moderate and weak according to the SAW approach. These results were also confirmed by the autocorrelation of the MCDM techniques with wheat yield. In addition, the RF model showed its effectiveness in processing complex spatial data and improved the accuracy of land suitability assessment. In this study, by integrating advanced MCDM techniques and ML, an applicable land evaluation approach for wheat cultivation was proposed, which can improve the accuracy of land suitability and be useful for considering sustainability principles in land management. [ABSTRACT FROM AUTHOR]

Social relationships have been and are the basis for achieving objectives of all kinds, whether altruistic or lucrative. Among the aspects that make up non-verbal communication are physical appearance in general, clothing, and, in particular, colour combinations. In this article, we analyse whether colour combinations can be established in individuals' clothing that maximise their chances of success for a specifically established social objective. To measure this objective, we use multivalent logics, which are characterised by their great flexibility and adaptability. Within the framework of fuzzy logic, we extract evaluations for various colours based on the judgements of experts, provided by recognised authors in the literature, and compare these with the results obtained in a survey conducted by the authors. For the purposes of contrast, we employ two instruments with accredited validity: Similarity by Direct Computation (SDC) and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS multicriteria method). [ABSTRACT FROM AUTHOR]

As urbanization speeds up, the concept of healthy cities is receiving more focus. This article compares Chongzuo and Nanning in Guangxi with Beijing to assess the development gaps in cities in Guangxi. An indicator system for healthy cities was designed from six dimensions—healthy economy, healthy population, healthy healthcare, healthy environment, healthy facilities, and healthy transportation—and 26 secondary indicators, which were selected from 2005 to 2022, and an improved factor analysis was used to synthesize a healthy city index (HCI). The number of factors was determined by combining characteristic roots and the variance contribution rate, and the HCI was weighted using the entropy-weighted Topsis method. A comprehensive evaluation of the urban health status of these cities was conducted. The results showed that extracting six common factors had the greatest effect, with a cumulative variance contribution rate of 93.83%. Chongzuo city scored higher in the field of healthcare. The healthy environment score of Nanning was relatively high, which may be related to continuous increases in green measures. In terms of the healthy economy dimension, Beijing was far ahead. However, in recent years, the healthy economy level in Chongzuo has increased, and the GDP growth rate has ranked among the highest in Guangxi. In addition, the growth rate of healthy facilities in Nanning was relatively fast and has been greater than that in Chongzuo in recent years, which indicates that the Nanning Municipal Government believes urban construction and municipal supporting facilities are highly important. In terms of healthy transportation, Chongzuo and Nanning scored higher than Beijing. This may be because the transportation in these two cities is convenient and the traffic density is more balanced than that in Beijing, thereby reducing traffic congestion. Chongzuo had the highest score for a healthy population, and a steadily growing population provides the city with stable human resources, which helps promote urban economic and social development. Finally, relevant policy recommendations were put forwards to enhance the health level of the cities. [ABSTRACT FROM AUTHOR]

In recent years, despite a decline in international trade and disruptions in the supply chain caused by COVID-19, the main container terminals in Latin America and the Caribbean (LAC) have increased their container volumes. This growth has necessitated significant adaptations by seaports and their authorities to meet new demands. Consequently, there has been a focused analysis on the performance, efficiency, and competitiveness, particularly their most relevant logistical aspects. In this paper, a multi-objective hybrid approach was employed. The Principal Component Analysis (PCA) technique was combined with the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) to rank LAC container terminals and identify operational criteria affecting efficiency. The analysis considered all input variables (berth/quay length, quay draught, yard area, number of quay cranes (portainer), number of yard cranes (trastainer), reachstacker, multicranes, daily montainer movement capacity, number of station reefer container type, number of terminals, and distance to the Panama Canal) and output variable (port performance expressed in TEUs from 2014 to 2023). The results revealed noteworthy findings for several terminals, particularly Colón, Santos, or Cartagena, which stands out as the main container port in LAC not only in annual TEUs throughput, but also in resource utilization. [ABSTRACT FROM AUTHOR]

Cities are considered one of the most important elements in achieving the SDGs (Sustainable Development Goals) and are specifically addressed by SDG11, which identifies actions to ensure safe, resilient and sustainable urban living for residents. Sustainable energy is of key importance to the challenges facing the modern world, hence cities should be designed and built to use as little energy as possible, which translates into the implementation of SDG7 (Affordable and Clean Energy). The sustainable goals are interrelated, so it is crucial to study their interaction. This study formulated the following research objectives: to assess changes in the degree of SDG achievement in EU countries and to investigate interactions between SDGs, in particular between SDG11 and SDG7. Using the TOPSIS method for the years 2015 and 2021, it was found that there are more frequent synergies between the SDGs and SDG11. The top rankings in terms of SDG11 implementation were Sweden, Denmark, Finland and Austria, while the last positions were held by Romania, Bulgaria and Poland. In 2021, the relationship between the positions of countries in terms of SDG11 and SDG7 implementation rates, compared to 2015, increased significantly, indicating that action is being taken to implement green energy solutions. [ABSTRACT FROM AUTHOR]

This study introduces a spatial layout framework for the multi-objective optimization of low-impact development (LID) measures at an urban watershed scale, targeting the mitigation of urban flooding and water pollution exacerbated by urbanization. The framework, tailored for the Dahongmen area within Beijing's Liangshui River Watershed, integrates the storm water management model (SWMM) with the nondominated sorting genetic algorithm II (NSGA-II). It optimizes LID deployment by balancing annual costs, volume capture ratio of rainfall, runoff pollution control rate, and the reduction in heat island potential (HIPR). High-resolution comprehensive runoff and land use data calibrate the model, ensuring the realism of the optimization approach. The selection of optimal solutions from the Pareto front is guided by weights determined through both the entropy weight method and subjective weight method, employing the TOPSIS method. The research highlights the positive, nonlinear correlation between cost and environmental benefits, particularly in reducing heat island effects, offering vital decision-making insights. It also identifies a critical weight range in specific decision-making scenarios, providing a scientific basis for rational weight assignment in practical engineering. This study exemplifies the benefits of comprehensive multi-objective optimization, with expectations of markedly improving the efficacy of large-scale LID implementations. [ABSTRACT FROM AUTHOR]

In order to explore how to give full play to the comprehensive benefits of optimal allocation of water resources in water shortage areas and strengthen water resources management, a regional optimal allocation model of water resources with social, economic and ecological objectives was proposed. Considering the actual allocation of water resources, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) was applied to solve the allocation model and obtained a set of candidate schemes. Subsequently, by establishing a game theory-TOPSIS evaluation model, the scheme set was filtered to obtain the relatively optimal scheme. Taking Xinzhou-Yangquan water supply area as the research object, optimization allocation schemes for water resources with frequencies of 75%, 50% and 25% were analyzed. The results of the allocation cases indicate that the improved NSGA-III algorithm and the game theory-TOPSIS evaluation model have stronger practicality in the model-solving process, making the optimal scheme results more reasonable and demonstrating significant advantages compared to other methods. Finally, through analysis of different allocation schemes, it is revealed that the key to achieving sustainable development in Xinzhou-Yangquan water supply area lies in continuously improving the efficiency of water resources utilization and striving to achieve sustainable use of water resources. [ABSTRACT FROM AUTHOR]

• Proposed a Multi Criteria Decision Making based Reverse Vaccinology model for predicting Bacterial Protective Antigens. • The proposed model tested on extracted physicochemical features from bacterial protein sequence. • Applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour to handle the data imbalance problem. • Consider MCDM-based TOPSIS and CRITIC methods for order preference with soft and hard ranking model. Reverse vaccinology (RV) is a significant step in sensible vaccine design. In recent years, many machine learning (ML) methods have been used to improve RV prediction accuracy. However, there are still issues with prediction accuracy and programme accessibility in ML-based RV. This paper presents a supervised ML-based method to classify bacterial protective antigens (BPAgs) and identify the model(s) that consistently perform well for the training dataset. Six ML classifiers are used for testing with physiochemical features extracted from a comprehensive training dataset. Selecting the best performing model from different performance metrics (accuracy, precision, recall, F1-score, and AUC-ROC) has not been easy, because all the metrics has the same importance to predict BPAgs. To fix this issue, we propose a soft and hard ranking model based on multi-criteria decision-making (MCDM) approach for selecting the best performing ML method that classifies BPAgs. First, our proposed model uses homologous proteins (positive and negative samples) from Protegen and Uniprot databases. Second, we applied four strategies of Synthetic Minority Oversampling Technique and Edited Nearest Neighbour (SMOTE-ENN) to handle the data imbalance problem and train the model using ML methods. Third, we consider MCDM-based technique for order preference by similarity to the ideal solution (TOPSIS) method integrated with soft and hard ranking model. The entropy is used to obtain weighted evaluation criteria for ranking the models. Our experimental evaluations show that the proposed method with best performing models (Random Forest and Extreme Gradient Boosting) outperforms compared to existing open-source RV methods using benchmark datasets. [ABSTRACT FROM AUTHOR]