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This study investigates fluid flow and convective heat transfer within a smooth, two-dimensional T-shaped junction using a numerical approach. Simulations were conducted by varying the volumetric flow rate ratio r (0.25, 0.5, 0.75, and 1), the Reynolds number Re (500 to 2500), the Prandtl number Pr (1), and the cross-sectional width ratio w (0.5 to 2.5) of the outlet. The fluid dynamics were solved using the vorticity–stream function formulation with a compact upwind finite difference scheme and the Implicit-Explicit (IMEX) method, implemented in MATLAB. Flow behavior was analyzed through streamline and isotherm contours, while local and average Nusselt numbers were computed along the junction walls. The results show that lower r values lead to stronger vortex formation and asymmetry in the flow and temperature fields, while r  = 1 yields symmetric and stable patterns. Increasing Re enhances heat transfer and transitions the flow toward unsteady regimes. Similarly, wider outlet configurations (higher w ) promote recirculation and thermal mixing. This study provides valuable insights into how inlet flow, outlet shape, and fluid characteristics interact to influence heat transfer and flow behavior in a smooth T-shaped junction. It also provides insights that can help improve the design of heat exchangers, microfluidic systems, and industrial piping.

Quality control at every stage of manufacturing is a key aspect of the quality management system of any organization. Inspection at different stages of manufacturing is essential to achieve required quality of the product. This knowledge area has been studied extensively in the past with respect to inspection strategies, inspection location, and inspection intervals to minimize inspection cost. However, there is a lack of literature that examines the relationship between inspection performance and factors related to human labor and inspection time of different products. Here, offline inspection is investigated to achieve the process target values by determining the optimal number of inspectors for different products. Three skill levels for inspectors are selected on the basis of their inspection errors, inspection quantities, and inspection cost. The purpose of this study is to achieve the optimum results of objective functions that consist of inspection cost, outgoing quality, and inspection quantity by determining the optimal value of decision variables, i.e., the number of inspectors with respect to their skill. A multi-objective optimization model is developed using a stochastic approach to determine the optimal results of the objective functions and decision variables. Firstly, goal programming is employed to verify the optimization model by using numerical examples. Secondly, sensitivity analysis is considered to illustrate the effect of incoming quantity on inspection performance and optimal combination of decision variables.

Competence becomes competitive advantage for a business at all times. Making human resource more effective, competence-based hiring, development, and performance evaluation are popular phenomena discussed in the literature though not very common in practice. Despite their importance, the reason these are not commonly implemented may be the complexity of the subject and the absence of a generalized framework, which can be adopted with little or no modifications. There have been efforts made for competency framework development, but these are occupation-specific and usually limited in implementation. A need for an easily replicated general framework exists, which has followed a structured and scientific methodology utilizing professional expertise during development, which is simple to understand and is applicable to as many jobs as required. This article examines in detail the development approach of a generic competency framework using scientific tools and producing weighted ratings of competencies. The purpose is to establish confidence in potential users for a methodology that is applicable to the development of a similar framework for a diverse array of jobs.

Overconsumption and unplanned disposal of garments result in millions of tons of textile products going to landfills. Understanding the environmental benefits and impact of various recycling options is crucial for integrating recycling into the apparel waste stream. This study aims to assess the environmental impacts of products made from post-consumer textile waste fibers, highlighting the importance of closed-loop textile supply chains in developing countries. Using Open LCA software, the cradle-to-gate approach for life cycle assessment is used to calculate the environmental impacts of post-consumer textile waste, virgin cotton, virgin polyester fibers, and their blends in two different scenarios. The life cycle inventory data for functional units (1000 kg apparel) has been collected from the industrial units and the Ecoinvent v3.0 database. The results of 16 environmental impact categories are computed, showing that textile products made from virgin cotton fiber have 60% more global warming potential than those made from post-consumer textile waste fibers. Hence, the environmental impact of textile products can be controlled by recycling them. Consumption of post-consumer textile waste fiber is the key to reducing the new material needs in the textile supply chain. The closed-loop apparel supply chain can help developing countries generate maximum financial value with minimal environmental damage. In developing countries, value extraction from post-consumer textile waste recycling is essential to meet international consumer demands for cleaner production.

Traditional ready-to-wear garments can mostly not conform to different body shapes because of the adoption of the generic sizing system, which leads to the local strain of concentration and morphological misfit. Auxetic structures, which have a negative Poisson’s ratio, permit enhanced redistribution of stress and geometry and allow deformation. Two auxetic knitted structures were developed by using 100% polyester and 100% nylon yarns with a fabric density of 41 Wales and 40 courses per inch. Characterization of the initial fabrics involved checking the behavior of negative Poisson’s ratio (NPR) where the polyester line (P1) structure shows the highest auxeticity, with a NPR of approximately −0.4 and peak strain reductions of 80–90%, as well as air permeability, moisture management, bend test, compression, roughness, friction properties and stiffness tests to check the mechanical and comfort-related performances. The standardized tunic garment was modeled in CLO 3D on three female body shapes—hourglass, pear and rectangle—with a constant size of 34. The fit map showed a strain of 91.49% in auxetic and 509.75% in single-jersey fabric at the hip area of the pear body shape when measuring fabric and body interaction. The findings indicate lower peak strain levels, which ascertain that increased adaptability is possible and support its use in the development of adaptive ready-to-wear garments.

This study investigates the effects of waste for value addition in form of use of textile waste to comfortable and durable garments based on blending recycled cotton fibers extracted from spinning, weaving, and cutting waste with virgin cotton in different ratios of 70:30, 80:20, and 90:10 to produce yarns of 22/1 count, which are used to develop single jersey knitted T-Shirt, examining key properties such as mechanical and thermos-physiological properties. Grey fabric (unprocessed fabric) with a higher virgin cotton content and from spinning waste exhibited superior bursting strength, overall moisture management capacity, and thermal conductivity. In contrast, air permeability and water vapor permeability were highest in fabric made with weaving waste. After scouring and bleaching, the finished fabric (processed fabric) was compared with the grey fabrics. The results demonstrate that the finished fabric has slightly reduced bursting strength, water vapor permeability, and moisture management capacity while significantly enhancing air permeability and maintaining thermal conductivity. T-shirt properties were evaluated across various blend ratios and waste types over multiple washing cycles. Overall, the study demonstrates that recycled cotton fibers, particularly those from spinning waste, can be successfully produced into high-performance knitted t-shirts, offering a sustainable alternative to fully virgin cotton products without compromising performance significantly.

This study aims at the proposition of a novel strategy to classify the upper body silhouette of the male population into different shapes. Instead of using an existing measuring scheme based on linear evaluation of the body, we devise the use of angle-based anthropometric data to capture the exhibited curvature of the upper body. The objectives are attained by quantifying and employing the chest-waist (CW) angle and hip-waist (HW) angle in the instigation of a more appropriate classification of the upper body. A sample of 241 males aged 17–29 years was scanned through a 3D body scanner to gather the anthropometric data including chest girth, waist girth, and hip girth and their respective heights. In the next phase, the extracted information was used to calculate the angles. Based on the empirical realizations of the resulting indices, criteria defining the classifications of the body shape were determined. The operational environment is further enriched by considering varying levels of angular differences capable of offering more notable stratification of the upper male body shapes. It is realized that the angular difference of 10° between the CW and HW angles results in the more prominent classification of the upper male body. The proposed body classification technique classifies the sample of 241 males into three body shapes that are, straight (61%), hourglass (36.51%), and torch shape (2.48%). The classification scheme proposed in this study is a step toward achieving a high degree of customer satisfaction regarding clothing fit. It is a reference for coming research in body shape classification to achieve clothing fit in customized clothing.

In manufacturing industries, well-maintained machines ensure their maximum utilization with good product quality, efficient time, and minimum cost. The objective of this study is to investigate a socks manufacturing line to propose an advanced maintenance plan based on Overall Equipment Effectiveness (OEE) to improve the overall maintenance along with better machine performance and product quality. Firstly, a socks manufacturing unit was selected and its maintenance-related problems were identified. It was observed that, among the six big losses, three losses have the major contribution in reducing the world-class value of OEE. Then a framework was designed based on the OEE model with the amalgamation of problem-solving tools to overcome the identified problems and reduce the contribution of three big losses. After the successful implementation of the proposed model, a significant reduction in the value of three big losses was observed that ultimately improve the value of OEE factors for the socks knitting machines by 2.18%. The statistical analysis is also conducted using three types of statistical tests, the Normality test, Wilcoxon signed-rank test, and Paired samples t-test. Kolmogorov–Smirnov (KS) test by Statistical Analysis Software (SAS). This paper points out a new proposed model of advanced OEE, which can improve machine maintenance, productivity, and quality in a better way as compared to the conventional OEE model.

The apparel industry is changing dynamically and quickly to manufacturing sustainable fashion products and the development of sustainable design strategies that minimize material consumption at the source. This study addresses a critical research gap by quantitatively evaluating the impact of fusing traditional South Asian garment construction (the kameez) with varied, Western-inspired sleeve geometries on key manufacturing metrics. Thirty-three distinct women’s garment styles, comprising three kameez types (simple, princess-cut, open-front) each paired with eleven different sleeve designs, were developed in the apparel industry to study the effect of fabric efficiency, wastage, and cost-effectiveness. The virtual patterns and markers were drafted and accomplished through Garment Gerber Technology (GGT) software to analyze fabric consumption, fabric efficiency, and cost-effectiveness. The results revealed that paneled kameez styles, such as the princess-cut and open-front, are significantly more material-efficient, achieving average fabric efficiencies of up to 83.95%, compared to the monolithic simple kameez, which averaged only 75.68%. Among sleeve types, multi-constructions like the slit sleeve and cuff sleeve proved most efficient (achieving up to 86.91% efficiency), while voluminous, single-piece designs like the umbrella sleeve consumed the most fabric and were the least efficient. Open-front kameez slit sleeves (OFSL3), simple kameez slit sleeves (SSL3), and princess-cut kameez slit sleeves (PCSL3), were better and more sustainable selections since they were most efficient in fabric efficiency (i.e., 86.91%, 86.17%, and 86.09%). Furthermore, the simple kameez style has the highest fabric wastage (above 22%), while the princess kameez style has the least (below 19%). The simple kameez slit sleeves design (SSL3) has the minimum wastage, while the simple kameez umbrella sleeves design (SSL4) has the maximum wastage. From a cost perspective, the open-front kameez slit sleeve (OFSL1) was identified as the most economical design, whereas the simple kameez with an umbrella sleeve (SSL4) was the most expensive. Statistical analysis confirmed that the differences between kameez styles were significant (p < 0.05). Thus, adoption of specific, sustainable, deliberate design choices and incorporating paneling into the garment body and utilizing multi-piece sleeve constructions offer a quantifiable and strategic approach for manufacturers to reduce material waste, optimize fabric utilization, and improve production cost-effectiveness.

The textile industry of Pakistan plays a vital role in the national economy; however, it faces increasing pressure to adopt sustainable and circular practices. The circular economy (CE) approach minimizes waste and enhances resource efficiency. However, prior studies have focused on technical and legislative elements of CE adoption, while eco-capability remains understudied in developing economies. This study investigates the role of green skills development (GSD), employee training on CE (ETCE), and employee proactive behavior (EPB) in enhancing circular economy performance (CEP) in Pakistan’s textile sector, supported by the Resource-Based View theory. Using a quantitative, cross-sectional survey design, primary data were collected from 215 middle-to-top-level employees working in textile manufacturing organizations across Pakistan through a structured five-point Likert scale questionnaire. Measurement and structural models were examined using PLS-SEM to assess reliability, and validity, and model relationships. Results indicate that GSD (β = 0.431, t = 5.846) and ETCE (β = 0.357, t = 4.728) significantly enhance CEP, emphasizing the importance of technical skills and structural training. EPB, however, showed no significant effect (β = −0.008, t = 0.080), indicating that individual initiative alone is insufficient. The finding suggested that formal skill development and systemic CE training are critical for improving sustainable performance and advancing UN SDGs 8 and 12 in Pakistan’s textile industry.