The principal motive of this work is to evolve and initiate an extension from interval-valued fuzzy sets to type-2 interval-valued fuzzy sets (T2IVFS) related to weighted aggregation functions containing the Einstein operator. The chief reason for this extension is that the constancy of the terms can also be taken into data during the aggregation operation. The main goal of this article is to compose the aggregation operators and their characteristics such as the Type-2 interval-valued fuzzy Einstein weighted arithmetic aggregating operator (T2IVFEWA), Type-2 interval-valued fuzzy Einstein weighted geometric aggregating operator (T2IVFEWG), and the characteristics are expressed. At last, to intimate the effectiveness of the suggested approach and explicate the purpose of these operators, a hybrid multi-criteria decision-making problem (MCDM) to select the best risk factor for Tuberculosis (TB) is considered and the result is compared with the outcome of the existing operators and methods. Additionally, a sensitivity analysis was conducted to verify the robustness of the proposed decision-making process.

The migratory lifestyle of nomadic communities, combined with the lack of a suitable health-related organizational structure, has made it difficult to provide health care services that can improve their health status. To achieve the concept of justice in health and sustainable development, it is imperative to improve the health status of all citizens in Iran, which consists of the nomadic communities, and urban and rural populations. In this ecological study national health indexes in nomadic tribespeople was Identified and prioritized by expert panel and fuzzy Delphi method. In the first step, the national health indexes were extracted from the literature, and then indexes that can be measured, evaluated and representative of the nomadic communities were extracted and prioritized by using fuzzy Delphi and TOPSIS methods, Questionnaire options were analyzed according to 3 criteria of economic efficiency, measurability, and simplicity in the form of 13 components and their indicators. The analysis of the results of the fuzzy Delphi method shows that the mental health component has the lowest real score in the criteria of measurability, simplicity and economic efficiency. The child care component has the highest real score in terms of economic efficiency and the vaccination component has the highest real score based on the criteria of measurability and simplicity in nomadic communities. The results of the TOPSIS method show that the components of vaccination, maternal care and child care have the highest priority for attention and investigation of their indicators in this segment of the population. In general, by designing and implementing systems to record the information of priority indexes extracted from the present study, it is possible for responsible organizations to make effective decisions and plans for the improvement of the health status of nomadic communities.

The primary objective of this study is to develop a robust model that employs a fuzzy logic interface (FL) and particle swarm optimization (PSO) to forecast the optimal parameters of a pyramid solar still (PSS). The model considers a range of environmental variables and varying levels of silver nanoparticles (Ag) mixed with paraffin wax, serving as a phase change material (PCM). The study focuses on three key factors: solar intensity ranging from 350 to 950 W/m2, water depth varying between 4 and 8 cm, and silver (Ag) nanoparticle concentration ranging from 0.5 to 1.5% and corresponding output responses are productivity (P), glass temperature (Tg), and basin water temperature (Tw). The experimental design is based on Taguchi\'s L9 orthogonal array. A technique for ordering preference by similarity to the ideal solution (TOPSIS) is utilized to optimize the process parameters of PSS. Incorporating a fuzzy inference system (FIS) aims to minimize the uncertainty within the system, and the particle swarm optimization algorithm is employed to fine-tune the optimal settings. These methodologies are employed to forecast the optimal conditions required to enhance the productivity of the PSS.

Multi-objective optimization holds particular significance for medical applications, wherein enhancing sensitivity is crucial to avoid costly missed diagnoses, and maintaining high specificity is imperative to prevent unnecessary procedures. In particular, when optimizing machine learning architectures for clinical diagnostics, it becomes essential to balance target quality measures such as accuracy, sensitivity, and specificity. Therefore, we developed MOOF, a multi-objective optimization framework that employs NSGA-II and TOPSIS to simultaneously optimize the model parameters of three selected ML algorithms: random forest, support vector machine, and multilayer perceptron. Finally, we evaluated the performance of the optimized MOOF models compared to gold standard methods such as multi-score grid search and single objective optimizations. Our results show that MOOF generally outperforms other approaches by inherently providing optimal solutions, representing the trade-offs between the target objectives. In conclusion, the study supports the importance of multi-objective optimization in medical informatics, with MOOF as a powerful tool for precise ML models, potentially improving patient care and clinical decision support systems.

BACKGROUND: Thermal spray coatings have emerged as a pivotal technology in materials engineering, primarily for augmenting the characteristics related to wear and tribology of metallic substrates.
METHODS: This study aimes to delve into applying High-Velocity Oxygen Fuel (HVOF) thermalsprayed WC-Co nanocoatings on Titanium Grade-5 alloy (Ti64). The coating process, utilizing nano-sized WC-Co powder, undergoes systematic optimization of HVOF parameters, encompassing the flow rate of carrier gas, powder feed rate, and nozzle distance. Experimental assessments via Pin-on-Disc (PoD) tests encompass Loss of Wear (WL), Friction Coefficient (CoF), and Frictional Force (FF). Later, an exhaustive optimization of responses is conductede using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method and the golden jack optimization algorithm (GJOA).
RESULTS: Outcomes show a substantial increase in WL, CoF, and FF with a rise in the carrier gas and powder feed rate. However, with increasing spraying distance of powder, the WL, CoF, and FF tend to lower due to higher bonding, which leads to increased wear resistance. The ideal parametric settings achieved from TOPSIS and GJOA are 245 mm of spray distance, 30 gpm rate of powder feed, and 11 lpm of carrier gas flow rate. The powder feed rate contributes 88.99% to the control action, as seen from ANOVA.
CONCLUSIONS: The confirmation experiment presents that the WL, CoF, and FF output responses are 42.33, 27.97, and 9.38% less than the mean of experimental data. These results highlight the HVOF process in spraying WC-Co nanocoatings to fortify the durability and performance of Ti64 alloy that can be patented for diverse engineering applications.

This study explores how machining parameters affect Surface Roughness (SR), Tool Wear Rate (TWR), and Material Removal Rate (MRR) during Electrical Discharge Machining (EDM) of a hybrid aluminum metal matrix composite (AMMC). The composite includes 6 % Silicon carbide (SiC) and 6 % Boron carbide (B4C) in an Aluminum 7075 (Al7075) matrix. A combined optimization approach was used to balance these factors, evaluating Pulse ON time, Current, Voltage, and Pulse OFF time. Response Surface Methodology (RSM) optimized single responses, while multi-response optimization employed a hybrid method combining the Entropy Weight Method (EWM), Taguchi approach, TOPSIS, and GRA. Analysis of Variance (ANOVA) assessed parameter significance, revealing substantial impacts on SR, MRR, and EWR. Based on TOPSIS and GRA, optimized parameters achieved a desirable balance: high MRR (0.4172, 0.5240 mm³/min), minimal EWR (0.0068, 0.0103 mm³/min), and acceptable SR (10.3877, 9.1924 μm) based on EWM-weighted priorities. Confirmation experiments validated a 15 % improvement in the closeness coefficient, and a 16 % improvement in the Grey relational grade, which considers combined SR, MRR, and EWR performance. Scanning Electron Microscope (SEM) analysis of surfaces machined with optimal parameters showed minimal debris, cracks, and no recast layer, indicating high surface integrity. This research enhances EDM optimization for AMMC, achieving efficiency in machining, minimizing tool wear, and meeting surface quality requirements.

Understanding the nutritional diversity in Perilla (Perilla frutescens L.) is essential for selecting and developing superior varieties with enhanced nutritional profiles in the North Eastern Himalayan (NEH) region of India. In this study, we assessed the nutritional composition of 45 diverse perilla germplasm collected from five NEH states using standard protocols and advanced analytical techniques. Significant variability was observed in moisture (0.39-11.67%), ash (2.59-7.13%), oil (28.65-74.20%), protein (11.05-23.15%), total soluble sugars (0.34-3.67%), starch (0.01-0.55%), phenols (0.03-0.87%), ferric reducing antioxidant power (0.45-1.36%), palmitic acid (7.06-10.75%), stearic acid (1.96-2.29%), oleic acid (8.11-13.31%), linoleic acid (15.18-22.74%), and linolenic acid (55.47-67.07%). Similarly, significant variability in mineral content (ppm) was also observed for aluminium, calcium, cobalt, chromium, copper, iron, potassium, magnesium, manganese, molybdenum, sodium, nickel, phosphorus, and zinc. Multivariate analyses, including hierarchical clustering analysis (HCA) and principal component analysis (PCA), revealed the enriched nutritional diversity within the germplasm. Correlation analysis indicated significant positive and negative relationships between nutritional parameters, indicating potential biochemical and metabolic interactions present in the perilla seeds. TOPSIS-based ranking identified promising genotypes for functional foods, pharmaceuticals, and nutritional applications. This study provides a first in-depth report of the nutritional composition and diversity of perilla germplasm in the NEH region, thus aiding in the identification of superior varieties for food and nutritional diversification and security.

Construction and demolition waste (C&DW) represents a pressing concern within the European Union, underscoring the urgent need for effective waste management strategies. The selection of these solutions constitutes a complex task, entailing the identification of efficient C&DW management strategies that balance appropriate practices, regulatory compliance, resource conservation, economic feasibility, and environmental considerations. LCA is widely utilized to assess environmental impact, yet the economic aspect has not been adequately incorporated into the LCA process in the field of C&DW management. The life cycle costing (LCC) methodology has been tailored to assess economic performance in conjunction with LCA. The selection of an appropriate multi-criteria decision-making (MCDM) method is vital for the C&DW system. This study proposes a novel framework for C&DW management by integrating LCA and LCC outcomes into MCDM, using AHP for weight determination, and applying TOPSIS to identify the favorable alternative. Four waste management alternatives were examined in the Lombardy region of Italy, namely (i) landfill; (ii) recycling for concrete production and road construction, incineration with energy recovery; (iii) recycling for road construction; (iv) recycling for concrete production and road construction. We determine that, with the implementation of various scenarios, the most suitable scenario emerges to be recycled for concrete production and road construction, with a score of 0.711/1; recycling for road construction with final score 0.291/1, ranks second; recycling for concrete production and road construction, incineration with energy recovery scores 0.002/1, ranks third; and landfill (scores: 0/1) is the worst choice, signifying it has the highest environmental impacts and the least economic benefits. Lastly, recommendations were formulated to enhance the environmental performance of the system.

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.

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.