Weight Determination Research Articles

Learning the weights using attribute order information for multi-criteria decision making tasks

In multi-criteria decision making, the importance of decision criteria (decision attributes) plays a crucial role. Ranking is a useful technique for expressing the importance of decision criteria in a decision-makers’ preference system. Since weights are commonly utilized for characterizing the importance of criteria, weight determination and assessment are important tasks in multi-criteria decision making and in voting systems as well. In this study, we concentrate on the connection between the preference order of decision criteria and the decision weights. Here, we present an easy-to-use procedure that can be used to produce a sequence of weights corresponding to a decision-makers’ preference order of decision criteria. The proposed method does not require pairwise comparisons, which is an advantageous property especially in cases where the number of criteria is large. This method is based on the application of a class of regular increasing monotone quantifiers, which we refer to as the class of weighting generator functions. We will show that the derivatives of these functions can be used for approximating the criteria weights. Also, we will demonstrate that using weighting generator functions, weights can be inverted in a consistent way. We will deduce the generators for arithmetic and geometric weight sequences, and we will present a one-parameter generator function known as the tau function in continuous-valued logic. We will show that using these weighting generator functions, the weight learning task can be turned into a simple, one-parameter optimization problem.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species’ potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen’s kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

Determinants of low birth weight and its effect on childhood health and nutritional outcomes in Bangladesh

BackgroundThe high incidence of low birth weight (LBW) is associated with an increased risk of infant mortality, adverse pregnancy outcomes for mothers, and a decline in overall health and well-being. The current study aimed to identify the various determinants of LBW and its effect on adverse health and nutritional outcomes of children aged 0–23 months in Bangladesh.MethodsBangladesh Demography and Health Survey (BDHS) 2017-18 data was used. A chi-square test and multivariable logistic regression analysis were used to find out the associations between independent variables and outcomes (e.g., LBW, child illness and undernutrition).ResultsThe overall prevalence of LBW among was 16.3%. Mother with no formal education (AOR = 2.64, 95% CI = 0.55–3.30, p = 0.01), female child (AOR = 1.31, 95% CI = 1.04–1.65, p = 0.023); and poorest economic status (AOR = 1.69, 95% CI = 1.13–2.51, p = 0.010), were identified significant determinants of LBW. Of home environment and hygiene factors, unimproved toilet facilities (AOR = 1.38, 95% CI = 1.03–1.84, p = 0.030) had a significant effect on LBW. In addition, children born with LBW were more likely to suffer fever (AOR = 1.26, 95% CI = 1.05–1.60, p = 0.050), stunting (AOR = 2.42, 95% CI = 1.86–3.15, p = < 0.001), wasting (AOR = 1.47, 95% CI = 1.02–2.25 p = 0.049), and underweight (AOR = 3.19, 95% CI = 2.40–4.23, p = < 0.001).ConclusionOne out of five children was LBW in Bangladesh. Maternal education, sex of child, wealth index, and toilet facilities had significant effects on LBW. In addition, LWB contributed to children’s poor health and nutritional outcomes. Enhancing maternal pregnancy, and child health outcomes necessitates policies addressing poverty, gender inequality, and social disparities. Key strategies include promoting regular prenatal care, early medical intervention, reproductive health education, and safe hygiene practices. To combat the negative impacts of LBW, a comprehensive strategy is vital, encompassing exclusive breastfeeding, nutritional support, growth monitoring, accessible healthcare, and caregiver education.

Determinants of low birth weight among newborns delivered in public hospitals of North Shewa Zone, Amhara region, Ethiopia: A case-control study (2023).

Low birth weight (LBW), defined as a birth weight less than 2500 g, irrespective of gestational age, poses a significant health concern for newborns. Despite efforts, the incidence of LBW in sub-Saharan Africa has remained stagnant over the past decade, warranting attention from healthcare providers, policymakers, and researchers. This study aimed to identify factors associated with LBW among newborns delivered in public hospitals of North Shewa Zone, Amhara Region, Ethiopia, from May 2 to June 10, 2023. An unmatched case-control study was conducted from May 2 to June 10, 2023, involving 318 participants (106 cases and 212 controls). Data were collected using pretested interviewer-administered structured questionnaires, medical record reviews, and direct anthropometric measurements. Bivariate analyses were conducted, and variables with a p-value ≤ 0.25 were included in a multivariable logistic regression model to determine significant determinants of LBW. A significance level of p < 0.05 was used. A total of 309 newborns (103 cases and 206 controls) were included, yielding a response rate of 97.2%. Among the findings, females exhibited a higher risk of LBW (adjusted odds ratio [AOR]: 3.13, 95% CI: 1.34, 7.32, p = 0.008), as did mothers aged 20 or younger (AOR: 3.42, 95% CI: 1.35, 8.66, p = 0.009). Lack of formal education was associated with increased risk (AOR: 6.82, 95% CI: 2.94, 15.3, p < 0.001), as were unplanned pregnancies (AOR: 3.08, 95% CI: 1.38, 6.84, p = 0.006) and missed antenatal care visits (AOR: 2.74, 95% CI: 1.16, 6.49, p = 0.021). No significant associations were found with residency type or maternal age above 35. Mothers aged ≤ 20 years, with inadequate minimum dietary diversity, lack of antenatal care attendance, and unplanned pregnancies, faced heightened risks of LBW. Addressing these factors is vital for reducing LBW occurrences and improving newborn health outcomes in Ethiopia.

A Cloud- and Game Model-Based Approach to Project Evaluations of Sustainable Power Supply Investments

In light of electrical energy’s increasing role in economic systems worldwide, prioritizing investments in sustainable power supplies has become paramount. This study proposes a model based on cloud theory and game theory to evaluate sustainable power supply investment projects. It establishes a foundation for assessing the merits of power supply investments, which are crucial for continuous electricity provision and economic advancement. By integrating an enhanced analytic hierarchy process and the entropy method, the study develops a dual-weighted evaluative index system. This hybrid approach addresses ambiguities and enhances the weight determination accuracy, which, when applied to the Liaojiawan Transformer Substation, verifies the project’s high benefit level, corroborated by empirical data. This innovative methodology offers a strategic framework for future power supply investments.

The optimal metric for viral genome space

Understanding the structural similarity between genomes is pivotal in classification and phylogenetic analysis. As the number of known genomes rockets, alignment-free methods have gained considerable attention. Among these methods, the natural vector method stands out as it represents sequences as vectors using statistical moments, enabling effective clustering based on families in biological taxonomy. However, determining an optimal metric that combines different elements in natural vectors remains challenging due to the absence of a rigorous theoretical framework for weighting different k-mers and orders. In this study, we address this challenge by transforming the determination of optimal weights into an optimization problem and resolving it through gradient-based techniques. Our experimental results underscore the substantial improvement in classification accuracy achieved by employing these optimal weights, reaching an impressive 92.73% on the testing set, surpassing other alignment-free methods. On one hand, our method offers an outstanding metric for virus classification, and on the other hand, it provides valuable insights into feature integration within alignment-free methods.

Real-Time Determination of Molecular Weight: Use of MaDDOSY (Mass Determination Diffusion Ordered Spectroscopy) to Monitor the Progress of Polymerization Reactions.

Knowledge of molecular weight is an integral factor in polymer synthesis, and while many synthetic strategies have been developed to help control this, determination of the final molecular weight is often only measured at the end of the reaction. Herein, we provide a technique for the online determination of polymer molecular weight using a universal, solvent-independent diffusion ordered spectroscopy (DOSY) calibration and evidence its use in a variety of polymerization reactions.

A Study of Insurance Company Underwriting Risk Based on EWM and TOPSIS Methods

This study proposes a risk assessment model called CPSF, which aims to assess insurance risk in areas where extreme weather is frequent. The model adopts a multidimensional and comprehensive assessment system, considering climate change, demographic and socio-economic, and financial conditions as the main factors, and constructs corresponding assessment indicators. The study establishes a scientific and reliable risk assessment system through the steps of data source, data pre-processing, indicator establishment, weight determination, and comprehensive assessment. By applying the model to two regions, namely, Southeast China and Southeast United States, where extreme weather often occurs, the insurance risk situation of these regions is analyzed and the future trend is predicted, which provides an important reference for insurance companies and policy making.

Development of GIS-Based Pangasius Aquaculture Areas Using Analytical Hierarchy Process (AHP) in Tulungagung Regency, East Java, Indonesia

Graphical Abstract Highlight Research The author mentioned four highlights from their research: 1. Pangasius aquaculture in Tulungagung Regency continues to increase 2. GIS can be used to determine the suitability of aquaculture land 3. AHP can be used to determine the weight of aquaculture land suitability parameters 4. The development of aquaculture areas must consider other land uses. Abstract Tulungagung Regency in East Java Province has an export demand of 300,000-400,000 tons per year. The high demand for exports spurred the people and government of Tulungagung Regency to develop pangasius aquaculture areas. This will have a negative impact if carried out without a land suitability assessment. The study aimed to analyze the parameters of pangasius aquaculture and map the potential areas for pangasius aquaculture using AHP and GIS-based analysis. This research was conducted from January to August 2022 in Tulungagung Regency, Indonesia. Several stages were carried out, including primary data collection (water quality, soil quality, and infrastructure), secondary data collection (literature, reports, fisheries statistics data, government statistical data, and journals/articles), application of land suitability assessment, and pangasius farming area suitability analysis. Land suitability analysis consists of measuring water quality parameters (DO, temperature, TDS, pH, ammonia, and nitrate), soil quality (texture, pH, and slope), and infrastructure (roads, markets, hatcheries, and fillet factories) using the Analytical Hierarchy Process method (AHP), which includes standardization, determination of weights, and combining the weights of land suitability maps, which are then mapped by ArcGIS Software. Most of the land conditions for the pangasius aquaculture areas in Tulungagung Regency are moderately suitable, suitable, and most suitable, with percentages of 30, 21, and 28%. The area that can be developed for pangasius aquaculture is 976885.71 ha, with the most suitable category being 2619.35 ha, the appropriate category being 2235.74 ha, and the most suitable category being 4913.77 ha.

A Combined Decision-Making Analysis Under Single-Valued Neutrosophic Set for Selecting Favorable Knowledge Management Strategy

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are pivotal methods in multi-criteria decision-making, addressing causal relationships among criteria and ranking alternatives, respectively. This paper introduces a novel approach, integrating DEMATEL and TOPSIS under the framework of single-valued neutrosophic set (SVNS) to handle indeterminacy in decision-making. Applied to a knowledge management strategy case, the method utilizes eight criteria and four alternatives derived from the literature. The process involves DEMATEL for expert weight determination and direct-relation matrix creation through linguistic variable influence. Subsequently, TOPSIS is employed to rank alternatives based on distance measures. The combined method identifies the ‘Human Resource Department’ as the most crucial in knowledge management strategy. This integrated approach facilitates organizations in pinpointing vital criteria and alternatives for effective knowledge management. Comparative analyses with existing methods are also presented.

Assessment of clean production level in phosphate mining enterprises: Based on the fusion group decision weight and limited interval cloud model

To develop a robust cleaner production level assessment model for phosphate mining enterprises, this study investigated the key factors influencing their cleaner production levels. The model incorporates integrated group decision weights and a limited interval cloud model. Based on the comprehensive life cycle of phosphate mining production, we constructed a detailed evaluation index system to assess the cleaner production level of these enterprises. This system defined the quantitative ranges for each level and revealed the interrelationships among the indicators. To ensure comprehensive weight determination, we proposed a method that integrates the expertise of multiple stakeholders. This method leveraged group decision-making principles to effectively resolve conflicts in expert judgments during the weight determination process. In addressing the characteristics of evaluation indicators for the cleaner production level of phosphate mining enterprises, we proposed employing a finite interval cloud model to accurately characterize the distribution of the indicators. Leveraging the finite interval cloud model, we obtained numerical characteristics for various indicators and incorporated the weights of the integrated group decision-making attributes. This approach enabled us to determine the cleaner production level of phosphate mining using a finite interval cloud generator. Validation using five phosphate mining enterprises in Hubei Province, China, confirmed the model's high accuracy. This model demonstrated its applicability in evaluating cleaner production levels for phosphate mining enterprises, offering a theoretical foundation for developing targeted environmental management policies.

Research Review of Green Building Rating System under the Background of Carbon Peak and Carbon Neutrality

In order to foster a more sustainable and eco-friendly trajectory for the construction industry, while concurrently mitigating environmental pollution and energy inefficiency, it is imperative to cultivate an environmentally conscious building and urban environment. Under the background of Carbon Peak and Carbon Neutrality, the green building rating system has become a research hotspot in the field of green building. This paper systematically summarizes the research progress of the GBRS in weight setting, indicator setting, and the evaluation process, and creatively proposes the following three directions for future research: (1) Weight determination methods based on machine learning or deep learning models, and reasonable weight allocation by mixing multiple evaluation methods. (2) Setting dynamic evaluation indicators, strengthening interdisciplinary research and regional consideration, and introducing a life cycle assessment to solve the problem of setting indicators in the existing evaluation system. (3) Combine building information modeling with GBRS to realize the automation and intelligence of evaluation and improve the comprehensiveness and accuracy of evaluation.

Investigations on Xenopus laevis body composition and feeding behavior in a laboratory setting

The African clawed frog, Xenopus laevis, has been used as a laboratory animal for decades in many research areas. However, there is a lack of knowledge about the nutritional physiology of this amphibian species and the feeding regimen is not standardized. The aim of the present study was to get more insights into the nutrient metabolism and feeding behavior of the frogs. In Trial 1, adult female X. laevis were fed either a Xenopus diet or a fish feed. After 4 weeks, they were euthanized, weighed, measured for morphometrics and dissected for organ weights and whole-body nutrient analysis. There were no significant differences between the diet groups regarding the allometric data and nutrient contents. The ovary was the major determinant of body weight. Body fat content increased with body weight as indicator of energy reserves. In Trial 2, 40 adult female frogs were monitored with a specifically developed digital tracking system to generate heat-maps of their activity before and up to 25 min after a meal. Three diets (floating, sinking, floating & sinking) were used. The main feed intake activity was fanning the feed into the mouth, peaking until 20 min after the meal. The different swimming characteristics of the diets thereby influenced the activity of the animals. Our dataset helps to adjust the feeding needs to the physical composition and also to meet the natural behavioral patterns of feed intake as a prerequisite of animal wellbeing and animal welfare in a laboratory setting.

Enhancing Weighted Averaging for CNN Model Ensemble in Plant Diseases Image Classification

Deep learning, especially convolutional neural networks (CNN), has gained traction in the field of image classification. In the specific case of plant disease classification, improving the accuracy and reliability of image classification is paramount. This paper delves into the ensemble prediction technique using a weighted soft-voting method. Instead of assigning a generalized weight to each CNN model, our approach emphasizes giving weights to each label's prediction within every individual model. We employed three esteemed CNN architectures for our experiments: DenseNet201, InceptionV3, and Xception focusing on classifying various diseases affecting grapes. By harnessing transfer learning coupled with end-to-end fine-tuning, we achieved a streamlined and efficient training process. Notably, the f1-score for each grape disease class was used as a parameter for weight determination and as a metric for the final evaluation. In our study, the newly proposed method was tested across various datasets and ensemble scenarios, demonstrating its effectiveness by not only outperforming the conventional soft-voting and prevalent weighted soft-voting methods, which achieved best scores of 95.68% and 95.81% respectively, but also by achieving a remarkable accuracy of 96.56%. This method's efficacy is heightened when ensemble models exhibit distinct characteristics; the more varied the model characteristics, the more enhanced the ensemble results.

Identifying the Contributing Sources of Uncertainties in Urban Flood Vulnerability in South Korea Considering Multiple GCMs, SSPs, Weight Determination Methods, and MCDM Techniques

This study quantified uncertainties involved in assessing the future flood vulnerability in 33 urban areas with population exceeding designated thresholds in South Korea. The driver-pressure-state-impact-response (DPSIR) framework was utilized as the study procedure, integrating social, economic, and environmental factors. In addition, a total of 220 cases of combinations were examined, encompassing twenty general circulation models combined with shared socioeconomic pathway scenarios, five weight determination methods, and three multi-criteria decision-making (MCDM) techniques, as sources of inherent uncertainties in the process. The rankings of urban flood vulnerability (UFV) for the selected cities were comprehensively assessed considering all combinations, followed by an analysis of variance test to investigate contributing sources of uncertainties. As a result, Incheon and Busan were found to be vulnerable to flooding, while Yeongcheon and Andong appeared to be safe cities. Some cities exhibited wide ranges in their rankings, such as Daegu, Yangpyeon, and Jeongeup. The identified contributing sources were weighting (58%), MCDM (27%), and the combination of weighting and MCDM methods together (15%). This study revealed that weight determination methods and MCDM techniques are the primary sources of uncertainties in the assessment of future UFV instead of multiple GCMs and SSPs. This finding underscores the importance for decision-makers and stakeholders to carefully consider these uncertainties for sustainable flood risk management and prevention.

Fulvic acid transdermal patch: Its properties, optimization and release

There is a high demand for the design of fulvic acids (FA) transdermal patches being natural biologically active substances with a wide action. The difficulties in FA transdermal patches creating are due to the peculiarities of FA obtaining with high purity and solubility, the existence of many several polymorphic forms, as well as the problems of their insertion into dosage forms. In this work, we modified the FA isolation as water-soluble polymorph. This operation allowed us to reduce the impurities in the product, to optimize the formulation of the emulsion-based transdermal patches, to provide a quantitative assessment of FA release. Data of IR, 13C NMR, AAS, ICP-AES-spectra, direct and reverse titration of carboxyl groups and phenolic hydroxyls, potentiometry, molecular weight determination by cryoscopy (M.m=740 g/mol), fluorescent analysis, solubility (3.3 mL of water per gram) confirmed the receipt of the desired polymorph. Optimization of the transdermal patches formulations by Response Surface Methodology and release evaluation and kinetics mathematical modeling using a Franz cell showed the preferences of Pluronic Kolliphor p237 as a transcutant. The obtained water-soluble FA can be used as a potential component of transdermal patches with controlled release parameters.&#x0D; Keywords: fulvic acid, transdermal patches, pluronics, release

A maximum satisfaction consensus-based large-scale group decision-making in social network considering limited compromise behavior

With the enrichment of social platforms, large-scale group decision-making (LSGDM) in social networks has gradually taken shape. In practice, experts may show limited compromise behavior during consensus reaching process (CRP), which may lead to bias or even failure in decision-making results. To tackle this issue, considering limited compromise behavior, this paper proposes a maximum satisfaction consensus (MSC) model, and focusing on LSGDM with fuzzy preference relation in social networks. Firstly, an expert weight determination algorithm integrating the interactive weights and individual weights is constructed. The interactive weights are determined by social influence strength, while the individual weights are measured by the compromise degree of limited compromise behavior and the consistency level of preferences. Subsequently, a grey clustering method is developed to classify experts into subgroups, considering both opinions similarity and limited compromise behavior similarity. In CRP, considering the limited compromise behavior, an MSC model under the limited budget determined by the minimum cost consensus (MCC) model is proposed. Based on MSC and MCC models, satisfaction-cost ratio is defined to measure the efficiency of CRP. Finally, a case study of selecting of shared bicycle operators and further discussion are conducted to verify the feasibility and superiority of the study.

A risk assessment approach for road collapse along tunnels based on an improved entropy weight method and K-means cluster algorithm

Ground collapse often occurs when strata are disturbed, particularly during tunnel construction, threatening the safety of people and underground facilities. Therefore, evaluating the risk of road collapse during and after tunnel construction is essential. Considering this, an improved entropy weight method was adopted to establish an evaluation system for assessing the collapse risk level of the Qianjin West Road, through which the Kunshan metro line S1 passes. The main implementation steps of the evaluation system include ten assessment indices, data visualization of indices, weight determination for indices, a cluster algorithm for the classification of results, and analysis of the evaluation results. In contrast to the traditional single method, and to improve the reliability of the risk assessment results, the method proposed in this paper considers two aspects. First, to eliminate subjective or objective biases generated by a single evaluation method, the entropy weight method (EM) and analytic hierarchy process (AHP) were integrated to formulate the improved entropy weight method. Second, to divide the evaluation results more reasonably, the k-means cluster algorithm using the Calinski Harabasz method was applied. The reliability and accuracy of the proposed improved entropy weight method are verified based on the case of Line S1. Furthermore, a method comparison and the concept of the coefficient of variation method were adopted to demonstrate the reliability of the risk assessment results. The improved entropy weight method can be used to evaluate ground collapse risk levels during and after tunnel construction.

Optimal Planning for Wind Turbines in Mega Seaports Considering Practical Application Constraints: A Case Study of Ningbo-Zhoushan Port

In the context of global carbon neutrality, ports face significant electricity demand for cargo handling and pressure to reduce carbon emissions. The abundant wind energy resources in port areas make wind power highly promising for port applications. The optimal selection of site and turbine types for wind power systems can effectively reduce emissions in ports, achieving sustainability and improving economic benefits. The practical implementation of wind energy systems considering practical constraints holds significant research significance. Taking Ningbo-Zhoushan Port as an example, this paper analyzes the wind energy resources in the port area and provides an overview of wind power system construction sites. Based on the actual conditions of the port area, this paper comprehensively reviews the site selection of wind turbines from the perspectives of wind resources, specific climates, and noise impacts. With the consideration of engineering preferences, this paper selects performance indicators based on the four mainstream turbine models and proposes a comprehensive weight determination method using the entropy weight method and analytic hierarchy process (AHP) to determine the weights of the indicators. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is then employed to score and compare four turbine plans, enabling the turbine selection process to consider both engineering preferences and objectivity, thereby enhancing the accuracy and reliability of wind turbine planning and achieving significant ecological and economic benefits through benefit analysis.

AUTOMATIC WEIGHT CRITERIA FOR SAW-BASED DECISION SUPPORT SYSTEM USING GRADIENT DESCENT

Modern organizations always utilize a Decision Support Systems (DSS) to have informed decision-making. The Simple Additive Weighting (SAW) method is a prevalent approach used in DSS for evaluating alternatives based on specific criteria. However, the subjectivity inherent in determining criteria weights in SAW poses a significant challenge. This research introduces a new approach, combining the SAW method with Gradient Descent to automatically determine criteria weights. By doing so, it takes advantage of the similarities between SAW and linear equations. Our research shows that this method produces more accurate and unbiased criteria weights, as confirmed by the Mean Square Error (MSE) analysis. In conclusion, incorporating Gradient Descent into the Decision Support System has the potential to greatly improve its effectiveness by automating the criteria weight determination process in various decision-making scenarios, leading to more accurate and less subjective decision support in organizations.