Technique For Order Performance Research Articles

Colour Choice as a Strategic Instrument in Neuromarketing

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).

The Assessment of Usability and Service Quality in E-Commerce Using the Integration of Analytical Hierarchy Process (AHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS)

The Assessment of Usability and Service Quality in E-Commerce Using the Integration of Analytical Hierarchy Process (AHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS)

Optimizing microbial strain selection for pyrethroid biodegradation in contaminated environments through a TOPSIS-based decision-making system

Improved and contemporary agriculture relies heavily on pesticides, yet some can be quite persistent and have a stable chemical composition, posing a significant threat to the ecology. Removing harmful effects is upon their degradability. Biodegradation must be emphasized to lower pesticide degradation costs, especially in the soil. Here, a decision-making system was used to determine the best microbial strain for the biodegradation of the pyrethroid-contaminated soil. In this system, the criteria chosen as: pH (C1), Temp (C2), RPM (C3), Conc. (C4), Degradation (%) (C5) and Time required for degradation(hrs) (C6); and five alternatives were Bacillus (A1), Acinetobacter (A2), Escherichia (A3), Pseudomonas (A4), and Fusarium (A5). The best alternative was selected by applying the TOPSIS (technique for order performance by similarity to ideal solution) method, which evaluates based on their closeness to the ideal solution and how well they meet specific requirements. Among all the specified criteria, Acinetobacter (A2) was the best and optimal based on the relative closeness value ((Ri∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{i}^{*}$$\\end{document}) = 0.740 (A2) > 0.544 (A5) > 0.480 (A1) > 0.403 (A4) > 0.296 (A3)). However, the ranking of the other alternatives is also obtained in the order Fusarium (A5), Bacillus (A1), Pseudomonas (A4), Escherichia (A3). Hence this study suggests Acinetobacter is the best microbial strain for biodegradation of pyrethroids; while least preference should be given to Escherichia. Acinetobacter, versatile metabolic nature with various xenobiotic compounds' degradation ability, is gram-negative, aerobic, coccobacilli, nonmotile, and nonspore forming bacteria. Due to less study about Acinetobacter it is not in that much frame as the other microorganisms. Hence, considering the Acinetobacter strain for the biodegradation study will give more optimal results than the other microbial strains. Novelty of this study, the TOPSIS method is applied first time in selecting the best microbial strain for the biodegradation of pyrethroid-contaminated soil, considering this selection process as multi-criteria decision-making (MCDM) problem.

Research on multiscale OpenStreetMap in China: data quality assessment with EWM-TOPSIS and GDP modeling

ABSTRACT OpenStreetMap (OSM) is a voluntary platform designed to provide free and up-to-date geographic data. Since OSM is based on multisource geographic data provided by the public, the quality of the data has become a concern of researchers. In this study, a unified measure for evaluating OSM data quality was constructed, and the entropy weight method (EWM) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model were used to evaluate the quality of multiscale OSM data in China from 2014 to 2020. In addition to evaluating the data quality, the use of OSM data quality index factors in economic modeling at different spatial scales in China was explored by using a geographic information system (GIS) analysis method and a geographically and temporally weighted regression (GTWR) model. Four machine learning models, SVM, RF, XGBoost and CatBoost, were used to simulate the grid-scale GDP, and the effectiveness of these simulations was discussed. The results showed that (1) the weights of OSM data quality indicator factors vary across different spatial scales. (2) From 2014 to 2020, the quality of national-scale OSM data first increased, then decreased and then gradually stabilized. In addition, the quality of OSM data at the provincial and municipal scales is significantly different, and the distribution is affected by the population and geographical environment. (3) Over time, the spatial clustering characteristics of OSM data quality at different spatial scales in China has continuously strengthened. In addition, the quality of Chinese OSM data displays obvious local spatial autocorrelation characteristics, which are dominated by H-H clustering and L-L clustering. (4) The GTWR model performs well in predicting and revealing the spatiotemporal correlation characteristics between GDP and OSM data quality indicators at the provincial and municipal scales in China. The correlations increase with decreasing spatial scale (provincial to municipal). Moreover, the GDP modeling ability is better in economically underdeveloped Northwest China and economically developed East China. (5) Four machine learning models coupled with road network length completeness, relative linear density, road name attribute completeness, POI name attribute completeness, road network accuracy, road network update frequency, POI update frequency, topological consistency and directional similarity yielded the best grid-scale GDP simulation values. Notably, the CatBoost model provided the best accuracy, and the results further verified that it is feasible to use the proposed OSM data quality index system to predict the regional economic development level in China.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

Empirical analysis of decision recommendation models for various processes from a pragmatic perspective

Decision recommendation models allow researchers and process designers to identify and implement high-efficiency processes in ambiguous situations. These models perform multipara metric analysis on the given process sets to recommend high-quality decisions that assist in improving process-based efficiency levels. A wide variety of models have been proposed by researchers for the implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for functionality-specific use. Therefore, they tend to validate multiple process models, which increase deployment time, cost and complexity levels. To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. Fuzzy logic, analytical hierarchical processing (AHP), the technique for order performance by similarity to ideal solution (TOPSIS), and their variants are highly useful for the recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models and functionality-specific models for their deployments. To further assist in the model selection process, this paper compares the reviewed models in terms of their computational complexity, recommendation efficiency, delay needed for recommendation, scalability and contextual accuracy. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This paper also evaluates a novel decision recommendation rank metric (DRRM), which combines these parameters, to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing the performance of their decision recommendations under real-time scenarios.

Evaluating and Selecting a Supplier in a Healthcare Supply Chain Using the Technique for Order Performance by Similarity to Ideal Solution Under Intuitionistic Fuzzy Environment

Evaluating and Selecting a Supplier in a Healthcare Supply Chain Using the Technique for Order Performance by Similarity to Ideal Solution Under Intuitionistic Fuzzy Environment

Land Use Change and Ecological Security Evaluation in Haikou City

With the continuous development of society and the acceleration of urbanization, profound changes have occurred in the utilization of land resources. Unreasonable land use and development pose serious threats to land ecological security, leading to a series of ecological security issues that constrain regional sustainable development. This study takes Haikou City as the research object and utilizes remote sensing (RS) and geographic information system (GIS) technologies to analyze the changes in land use in Haikou City from 2002 to 2022. Simultaneously, a land ecological security evaluation system is constructed based on the "Pressure-State-Response" (PSR) model. The weights of the indicators are determined by combining the Entropy weight method and the analytic hierarchy process (AHP). The TOPSIS (technique for order performance by similarity to ideal solution) method is then applied to evaluate the land ecological security index. The results indicate that during the period from 2002 to 2022, the cropland and impervious areas in Haikou City exhibited significant growth trends, while the areas of grassland, water, barren land, and forest decreased. Despite this, the comprehensive ecological security index of Haikou City showed an overall fluctuating upward trend, reaching its optimal state in 2022, with the land ecological security level improving from "Minor Warning" to "Relatively Safe". These research findings provide scientific evidence for adjusting land use policies and formulating relevant measures, and also serve as an important reference for promoting the sustainable utilization and ecological security protection of land resources in Haikou City.

Supply chain risk prioritization: a multi-criteria based Intuitionistic Fuzzy TOPSIS approach

Purpose Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.Design/methodology/approach The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.Findings The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.Research limitations/implications In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.Practical implications The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).Originality/value This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

AHP-TOPSIS: Multi-Criteria Decision Making Method With Hierarchical Weighting to Determine Priorities for Leak Work Drinking Water Pipe

This research aims to develop an effective multi-criteria decision making method by paying attention to the validity of the criteria weighting. In previous research, the TOPSIS method was applied to the problem of determining priorities for repairing drinking water pipe leaks by paying attention to four (4) criteria, namely the duration of the complaint, the size of the leak, the leak point, and the distance from the leak point to the location of the officer handling the leak.. The research results show that the TOPSIS method has a simple concept and is able to provide priority recommendations for the order of work based on consumer complaints. However, in this research the results of decision making were considered not optimal because the TOPSIS method for calculating criteria weights did not yet have a hierarchical mediator that guaranteed the validity of the criteria weightings. Therefore, this research tries to combine two methods (AHP-TOPSIS), namely Analytical Hierarchy Process (AHP) and Technique for Order Performance by Similarity to Idea Solution (TOPSIS). AHP is a Multi Criteria Decision Making (MADM) method that can be used to determine criteria weights hierarchically, with a smooth order of levels and considering the relationship between criteria. In this research, AHP was used to obtain criteria weights and TOPSIS to rank priorities for work on drinking water pipe leaks. The research results show that AHP-TOPSIS can produce a more effective and valid rating to help solve the problem of determining priorities for working on drinking water pipe leaks. This solution is the best decision where work priorities are selected based on criteria that are in accordance with service priorities.

Evaluation of business strategies based on the financial performance of the corporation and investors' behavior using D-CRITIC and fuzzy MULTI-MOORA techniques: A real case study

Revealing the complex relationship between corporation-chosen strategies and financial performance is crucial in navigating today's dynamic business landscape. This research delves into the profound impact of business strategies on both corporation performance and investors' behavior, seeking to identify the strategy that yields the highest returns and efficiency for the corporation and its beneficiaries (investors, loan-holders, and stock-holders). This study proposes a multi-criteria decision-making (MCDM) model that integrates the distance correlation-based criteria importance through inter-criteria correlation (D-CRITIC) weighting method with multi-objective optimization on the basis of ratio analysis plus full multiplicative form (MULTI-MOORA) technique to evaluate the business strategies of corporations (i.e., defenders, analyzers, reactors, and prospectors). Notably, fuzzy MULTI-MOORA methodology is employed to account for uncertainty, addressing vague, imprecise, and ambiguous input and knowledge. This study is implemented on a real case of the Tehran Stock Exchange on a sample of eighty companies from 2017 to 2021. The study's results underscore the medium analyzers strategy as the optimal alternative among various business strategies, assuming the maximization of benefits for all beneficiaries, and simultaneously specify a prevalent preference for analyzer strategies within Iran's economic landscape. To validate the proposed method, the findings are compared to the other MCDM techniques such as technique for order performance by similarity to ideal solution (TOPSIS), multi-objective optimization on the basis of simple ratio analysis (MOOSRA), simple additive weighting (SAW), viekriterijumsko kompromisno rangiranje (VIKOR), and Taxonomy. Additionally, applying principal component analysis (PCA) as a visualization approach demonstrates the satisfactory performance of the proposed framework. Beyond its scientific contributions, this research holds practical managerial applicability, providing actionable insights for informed decision-making across various fields.

Effects of new urbanization on China's carbon emissions: A quasi-natural experiment based on the improved PSM-DID model

Examining the influence mechanisms of new urbanization on urban carbon emissions is of great practical significance to guide cities' low-carbon and sustainable development and achieve China's “dual carbon” goal. To investigate the impact of the new urbanization policy on carbon emissions, this study conducted quasi-natural experiments on 19 urban agglomerations, including 115 cities in China, from 2011 to 2018. Based on the improved propensity score matching-difference-in-differences model, the study constructed an evaluation system based on the technique for order performance by similarity to an ideal solution or the TOPSIS-entropy method for analyzing the development status of new urbanization. Finally, the influence mechanisms and regional heterogeneity of multidimensional new urbanization development on carbon emissions were examined. The results were threefold. (1) The new urbanization policy significantly inhibits carbon emissions in the pilot cities. (2) The new urbanization policy affects urban carbon emissions through economic and population urbanization, which have an inverted U-shaped relationship with carbon emissions, and public service and environmental urbanization, which inhibit carbon emissions. (3) Regional heterogeneity exists among mechanisms. For relatively developed regions, the policy significantly affects carbon emission reduction. In rapidly developing regions, the policy implementation increases carbon emissions. The policy does not significantly impact carbon emissions in remote and underdeveloped regions. This study is a significant reference for formulating a carbon emissions reduction strategy according to local conditions.

On some new fuzzy entropy measure of Pythagorean fuzzy sets for decision-making based on an extended TOPSIS approach

Fuzzy entropy measures are valuable tools in decision-making when dealing with uncertain or imprecise information. There exist many entropy measures for Pythagorean Fuzzy Sets (PFS) in the literature that fail to deal with the problem of providing reasonable or consistent results to the decision-makers. To deal with the shortcomings of the existing measures, this paper proposes a robust fuzzy entropy measure for PFS to facilitate decision-making under uncertainty. The usefulness of the measure is illustrated through an illustration of decision-making in a supplier selection problem and compared with existing fuzzy entropy measures. The Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) approach is also explored to solve the decision-making problem. The results demonstrate that the proposed measure can effectively capture the degree of uncertainty in the decision-making process, leading to more accurate decision outcomes by providing a reliable and robust ranking of alternatives.

Sustainable agility of product development process based on a rough cloud technique: A case study on China's small and medium enterprises.

The importance of incorporating an agile approach into creating sustainable products has been widely discussed. This approach can enhance innovation integration, improve adaptability to changing development circumstances, and increase the efficiency and quality of the product development process. While many agile methods have originated in the software development context and have been formulated based on successful software projects, they often fail due to incorrect procedures and a lack of acceptance, preventing deep integration into the process. Additionally, decision-making for market evaluation is often hindered by unclear and subjective information. Therefore, this study introduces an extended TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) method for sustainable product development. This method leverages the benefits of cloud model theory to address randomness and uncertainty (intrapersonal uncertainty) and the advantages of rough set theory to flexibly handle market demand uncertainty without requiring extra information. The study proposes an integrated weighting method that considers both subjective and objective weights to determine comprehensive criteria weights. It also presents a new framework, named Sustainable Agility of Product Development (SAPD), which aims to evaluate criteria for assessing sustainable product development. To validate the effectiveness of this proposed method, a case study is conducted on small and medium enterprises in China. The obtained results show that the company needs to conduct product structure research and development to realize new product functions.

Biodiesel production using microreactor with integrated microheater through multi-objective optimization approach

Microreactor is one of the process intensification techniques which uses reaction dimensions below millimeter size and have greater yield within lesser reaction time and a high heat transfer rate due to their high surface to volume ratio. To develop energy efficient reactors, microheater is chosen as the heating source for microreactor which consume less power than the conventional heating elements. Using response surface methodology (RSM) and artificial neural network (ANN) the effects of the biodiesel process variables was analyzed: catalyst concentration (0.5–2.5 wt.%), reaction temperature (35–55 °C), flow rate (5–25 mL/min), oil to methanol molar ratio (1:6–1:18), and power supplied to the microheater (15–35 W). To observe more than set limit, Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) and VIKOR were used to evaluate the optimum conditions. Under the optimum conditions of 20 mL/min flow rate, 45 °C reaction temperature, 1.5 wt.% catalyst concentration, 1:9 oil to methanol molar ratio, and 20 W power supplied, the maximum biodiesel yield for microreactor with microheater is 91.1 %. Using ultrasonic mixing additionally for microreactor with microheater had maximum yield of 94.4 % under the optimum conditions.

Analyzing Chinese banking performance with a trigonometric envelopment analysis for ideal solutions model

Abstract Accepted by: Prof. Ali Emrouznejad Non-parametric data envelopment analysis (DEA) is susceptible to the curse of dimensionality, a challenge that can be mitigated through the use of the multi-criteria decision-making (MCDM) method. Conversely, DEA can overcome the limitations of the MCDM method by defining the weights of the decision-making unit to calculate the data envelop. This study addresses this issue by introducing a novel model, the Trigonometric Envelopment Analysis for Ideal Solutions (TEA-IS). TEA-IS combines DEA and the Technique for Order Performance by Similarity to Ideal Solution approaches. The proposed method is employed to assess the efficiency and performance of 367 Chinese banks over a 19-year period using various financial variables. The TEA-IS model leverages machine learning techniques to predict positive or negative outcomes for Chinese banks, taking into account various influencing factors. Our results indicate that TEA-IS scores demonstrate superior discriminatory power and reliability compared with non-parametric and MCDM methods. Furthermore, our findings reveal the presence of synergy amongst Chinese banks and illustrate a pattern of volatility in the Chinese banking industry’s performance. Notably, performance improved from 2000 to 2005, declined during the period from 2006 to 2013 and subsequently experienced a recovery until 2018. The majority of Chinese banks in the sample are categorized as medium performers with lower synergy levels. Additionally, the study underscores the positive impact of bank listing and age on bank performance, suggesting that regional banks outperform domestic ones.

Empirical Analysis of Decision Recommendation Models for Various Processes from A Pragmatic Perspective

Decision recommendation models allow researchers and process designers to identify & implement high-efficiency processes under ambiguous situations. These models perform multiparametric analysis on the given process sets in order to recommend high quality decisions that assist in improving process-based efficiency levels. A wide variety of models are proposed by researchers for implementation of such recommenders, and each of them varies in terms of their functional nuances, applicative advantages, internal operating characteristics, contextual limitations, and deployment-specific future scopes. Thus, it is difficult for researchers and process designers to identify optimal models for their functionality-specific use cases. Due to which, they tend to validate multiple process models, which increases deployment time, cost & complexity levels.To overcome this ambiguity, a detailed survey of different decision process recommendation models is discussed in this text. It was observed that Fuzzy Logic, Analytical Hierarchical Processing (AHP), Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), and their variants are highly useful for recommendation of efficient decisions. Based on this survey, readers will be able to identify recently proposed decision recommendation models, and identify functionality-specific models for their deployments. To further assist the model selection process, this text compares the reviewed models in terms of their computational complexity, efficiency of recommendation, delay needed for recommendation, scalability and contextual accuracy levels. Based on this comparison, readers will be able to identify performance-specific models for their deployments. This text also proposes evaluation of a novel Decision Recommendation Rank Metric (DRRM), which combines these parameters, in order to identify models that can optimally perform w.r.t. multiple process metrics. Referring to this parameter comparison, readers will be able to identify optimal recommendation models for enhancing performance of their decision recommendations under real-time scenarios.

Economic analysis and TOPSIS approach to optimize the CI engine characteristics using span 80 mixed carbon nanotubes emulsified Sapindus trifoliatus (soapnut) biodiesel by artificial neural network prediction model

The rise in fuel use within the vehicle sector leads to a corresponding escalation in energy demand. To achieve optimal operating conditions, it is necessary to reduce energy usage. For that, the primary objective of this study is to utilize an artificial neural network (ANN) and the optimization approach as a technique for order performance by similarity to the ideal solution (TOPSIS) to forecast the optimal features of a compression ignition (CI) engine. The experiment was performed with a four-stroke mono-cylinder compression ignition (CI) engine under different load situations. The study considers six operational parameters, including brake thermal efficiency (BTE), specific fuel consumption (SFC), as well as emission characteristics including carbon monoxide (CO), hydrocarbon (HC), Nitrogen oxides (NOx), and smoke. From the ANN results, the correlation coefficients for BTE, SFC NOx, smoke, HC, and CO were 0.9712, 0.9862, 0.964, 0.941, 0.998, and 0.978, respectively. The SNBD25 + 30 mg/L SP80 + 30 mg/L CNT blend exhibited the maximum closeness coefficient under various load conditions. The maximum closeness coefficient obtained from the TOPSIS optimisation approach under full load conditions is 0.982386. From the result of the ANN prediction technique, for TOPSIS optimised blend SNBD25 + 30 mg/L SP80 + 30 mg/L CNT a 13.84% increase in BTE, 11.21% decrease in SFC, 16.67% decrease in CO, 13.87% decrease in NOx, 19.23% decrease in HC, and 32.53% decrease in smoke. Based on the outcomes obtained from the ANN and TOPSIS methodologies, it can be concluded that the SNBD25 + 30 mg/L SP80 + 30 mg/L CNT blend exhibits superior performance in terms of reduced NOx emissions and enhanced efficiency, surpassing the other blends under consideration. The incorporation of carbon nanotubes (CNTs) into a system leads to an increase in the carbon-to‑oxygen ratio, resulting in a reduction in the generation of nitrogen oxides (NOx). The cost of a mixture consisting of SNBD25 + 30 mg/L SP80 + 30 mg/L blend is 20% lower than the cost of diesel on a per-litre basis.

A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping

Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

ECCANS: Enhanced CRITIC-based Context-Aware Network Selection algorithm for 5G HetNet

The ability of mobility management to provide seamless communication and better quality of service (QoS) to the multiservice multimode mobile node (MMMN), which can run multiple services simultaneously during roaming in ultra-dense heterogeneous networks with different radio access technologies, is a major challenge in implementing 5G and beyond. Ultra-dense heterogeneous networks (HetNet), restrictions on multiservice multimode mobile terminals, and user mobility all lead to a number of serious problems, such as the ping-pong effect, unnecessary handovers, and decreasing cell edge spectral efficiency. The dense deployment of small base stations also causes a high probability of frequent handovers in HetNet, which has led to a significant escalation in energy consumption resulting from unnecessary signaling, increased network overhead, and higher energy usage by network equipment. This increased energy requirement may lead to potentially increase the emissions of greenhouse gases to meet the energy production needs. Several handover decision algorithms have been proposed, but most of them deal with only running a single service at a time on a mobile node. In this paper, authors propose a new network selection model for MMMN which may run multiple services simultaneously. This model uses context-aware information about the user’s preferences, characteristics, and needs of the services, limitations of mobile terminal, and network conditions of an ultra-dense HetNet to choose the best network. The fundamental principle of this model is based on the comprehensive network criteria weight computed by the integration of Enhanced CRiteria Importance Through Inter-criteria Correlation (CRITIC), fuzzy analytic hierarchy process (FAHP), and group decision-making (GDM) techniques and the service priority of running services. For computing the objective weight of criteria, a novel improved CRITIC method has been proposed. At the same time, sigmoid utility functions and linear utility functions have been used to figure out the value of network criteria for running multiple services on the mobile node. Finally, the best network from HetNet for MMMN is selected using the technique for order performance by similarity to ideal solution (TOPSIS) and threshold by computing a comprehensive utility value network criteria decision matrix and comprehensive network criteria weight. The simulation results show that the proposed model increases the selection probabilities of an appropriate network according to the service requirements and network conditions. It also reduces unnecessary handovers and improves QoS while satisfying users’ preferences, service requirements, and terminal constraints, in solving the MMMN network selection problem.