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This paper aims to propose the artificial neural network (ANN) and regression models for the estimation of the thread consumption at multilayered seam assembly stitched with lock stitch 301.

In the present study, the generalized regression and neural network models are developed by considering the fabric types: woven, nonwoven and multilayer combination thereof, with basic sewing parameters: sewing thread linear density, stitch density, needle count and fabric assembly thickness. The network with feed-forward backpropagation is considered to build the ANN, and the training function trainlm of MATLAB software is used to adjust weight and basic values according to the optimization of Levenberg Marquardt. The performance of networks measured in terms of the mean squared error and the layer output is set according to the sigmoid transfer function.

The proposed ANN and regression model are able to predict the thread consumption with more accuracy for multilayered seam assembly. The predictability of thread consumption from available geometrical models, regression models and industrial empirical techniques are compared with proposed linear regression, quadratic regression and neural network models. The proposed quadratic regression model showed a good correlation with practical thread consumption value and more accuracy in prediction with an overall 4.3% error, as compared to other techniques for given multilayer substrates. Further, the developed ANN network showed good accuracy in the prediction of thread consumption.

The estimation of thread consumed while stitching is the prerequisite of the garment industry for inventory management especially with the introduction of the costly high-performance sewing thread. In practice, different types of fabrics are stitched at multilayer combinations at different locations of the stitched product. The ANN and regression models are developed for multilayered seam assembly of woven and nonwoven fabric blend composition for better prediction of thread consumption.

This study aims to investigate the factors influencing the reduction in strength of sewing threads after stitching (after-stitch strength reduction) in different fabric assemblies, including nonwoven fabrics. This research expands upon previous studies, which primarily focused on woven fabrics.

A full-factorial experimental design was employed, considering three factors: fabric assembly type (woven, nonwoven, composite), yarn type (spun polyester, core-spun polyester) and thread count (30 Tex, 60 Tex). Also, the stitching parameters stitch density (3 spc, 5 spc) and needle count (75 Nm, 100 Nm) were considered. The predictive regression model was also developed with a second-degree order. Tensile testing was conducted on unravelled threads before and after stitching to assess strength reduction.

Fabric assembly type significantly impacted after-stitch strength reduction, with the composite assembly exhibiting the highest reduction. Yarn type played a crucial role in the composite assembly; core-spun yarn experienced higher strength reduction compared to spun polyester yarn. Thread count consistently affected strength reduction across all fabric assemblies, with finer threads experiencing higher reductions. Stitch density generally had a positive correlation with strength reduction for woven and nonwoven assemblies, but no significant difference was observed in the composite assembly. Needle count did not significantly impact strength reduction for any fabric assembly.

The findings can guide the selection of appropriate sewing threads, yarn types and sewing parameters for different fabric assemblies, including nonwoven fabrics, to optimize seam performance and garment durability.

This study contributes to the understanding of how fabric structure, yarn characteristics and sewing parameters interact to influence sewing thread strength reduction in diverse fabric assemblies, including nonwoven fabrics, which were not previously considered in similar studies. This broader investigation provides valuable insights applicable to a wider range of sewing applications.

The purpose of this study is to develop a protective coating system on mild steel panel incorporating epoxidized natural rubber with acrylic polyol resin.

In this work, a novel attempt is made to develop binder coatings using epoxidized natural rubber-based material and an organic resin (acrylic resin) for corrosion protection on metal substrate. Seven different samples of multifunctional coatings are developed by varying the compositions of epoxidized natural rubber (ENR) and acrylic resin. The properties of the developed coatings have been characterized using analytical methods such as Fourier transform infrared spectroscopy (FTIR) and electrochemical impedance spectroscopy (EIS). EIS has been carried out for 30 days to evaluate the corrosion resistance after immersing into 3.5 wt.% of sodium chloride. Cross hatch cut tester (CHT) has been used to study the adhesive properties. UV–Visible Spectroscopy (UV–Vis) was also used to assess changes in the coating-film transparency of the natural rubber-based coating systems in this study.

The developed coatings have formed uniform layer on the substrate. CHT results show excellent adhesion of the coatings. Higher concentrations of ENR have higher transparency level, which reduces when the acrylic concentration increases. FTIR analysis confirms the crosslinking that occurred between the components of the coatings. Based on the impedance data from EIS, the incorporation of natural rubber can be an additive for the corrosion protection, which has the coating resistance values well above 108Ω even after 30 days of immersion.

The blending method provides a simple and practical solution to improve the strength and adhesion properties of acrylic polyol resin with epoxidized natural rubber. There is still improvement needed for long-term applications.

The work has been conducted in our laboratory. The combination of natural rubber-based materials and organic resins is a new approach in coating research.

This study aims to comprehensively analyse the implementation and effectiveness of corporate social responsibility (CSR) policies within the context of the Indian coal mining sector. Furthermore, it investigates the alignment between CSR initiatives and the unique challenges faced by the coal mining sector and examines the outcomes and impacts of these initiatives on the employees of the sector and their perspective on the situation.

This study adopts a comprehensive qualitative research method, including a review of the literature, case studies and stakeholder interviews. This study seeks to deconstruct the application of CSR policies.

The analysis developed a deeper understanding of the complexities surrounding CSR policies in the Indian coal mining sector, offering insights into strategies for enhancing the effectiveness and relevance of these initiatives while fostering sustainable development.

This study reveals a rich tapestry of theoretical implications and how they connect to important organisational and societal paradigms. The results of this qualitative analysis can work as a foundation for creating scales to measure the level of efficiency of CSR policies implemented by different companies. Furthermore, this study goes beyond theoretical knowledge and gives companies, regulators and communities information they can use. By looking at how CSR policies work in the real world, a road map for responsible resource extraction and community growth can be made.

The findings are unique in exploring the CSR initiatives and the unique challenges faced by the coal mining sector. This study offers insight on the employees of the sector and their perspectives on the situation and delves into the multifaceted dimensions of CSR practices.

The Pacific Island nation of Fiji, spanning 100s of islands, has been characterised by both geographical and ethnic divisions between, mainly, Indigenous Fijians and Fijians of Indian descent. The latter took shape in quite blatant forms in the island nation's historical tendency towards ethnic politics but has also been enacted across its sporting traditions. Today, while ethnic politics still exists to a degree, encouraged by ethnopolitical entrepreneurs, the reality is more nuanced. Divisions remain not only along the popularised lines of ethnicity but also across hierarchical, class and gender boundaries that have received somewhat less scholarly attention. This nuance is visible in the performance and packaging of Fijian sport and through the meanings that local people attach to it. This chapter, therefore, draws upon the experience of ethnographic fieldwork within and across Fijian subcultures with a focus on rugby and soccer. Inclusive of participant observation and interviews with diverse Fijian sporting stakeholders from differing intersections of local sport and society, the key threads above will be untangled to reveal a more three-dimensional and collective impression of contemporary Fiji.

This paper aims to develop at sewing thread during the seam formation may lead to the compression of fabric under seam. In the present study, the model has been proposed to predict the seam compression and calculation of seam boldness, as well as thread consumption by considering seam compression.

The effect of sewing parameters on the fabric compression at the seam (Cf) for fabrics of varying bulk density was studied by the Taguchi method and also the multilinear regression equation is obtained to predict seam compression by considering these parameters. The framework has been set as per the single view metrology approach to measuring structural seam boldness (Bs). One of the basic geometrical models (Ghosh and Chavhan, 2014) for the prediction of thread consumption at lock stitch has been modified by considering fabric compression at the seam (Cf).

The multilinear regression model has been proposed which can predict the compression under seam using easily measurable fabric parameters and stitch density. The seam boldness is successfully calculated quantitatively using the proposed formula with a good correlation with the seam boldness rated subjectively. The thread consumption estimation from the proposed approach was found to be more accurate.

The compression under seam is found out using easily measurable parameters; fabric thickness, fabric weight and stitch density from the proposed model. The attempt has been made to calculate seam boldness quantitatively and the new approach to find out thread consumption by considering the seam compression has been proposed.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

Considering sustainability and resilience together is crucial in food supply chain (FSC) management, as it ensures a balanced approach that meets environmental, economic and social needs while maintaining the system's capacity to withstand disruptions. Towards this, a multi-objective optimisation model is proposed in this study to create an integrated sustainable and resilient FSC. The proposed model employs four objective functions – each representing a dimension of sustainability and one for resilience and utilises an augmented ϵ-constraint method for solving. The findings highlight the interplay between sustainability aspects and resilience, illustrating that overemphasis on any single dimension can adversely affect others. Further, the proposed model is applied to the case of Indian mango pulp supply chain and several inferences are derived. The proposed model would assist decision-makers in making a well-balanced choice based on sustainability and resilience considerations.

A wireless body area network (WBAN) plays a crucial role in the health-care domain. With the emergence of technologies like the internet of things, there is increased usage of WBAN for providing quality health services. With wearable devices and sensors associated with human body, patient’s vital signs are captured and sent to doctor. The WBAN has number of sensor nodes that are resource constrained. The communications among the nodes are very crucial as human health information is exchanged. The purpose of this paper aims to have Quality of Service (QoS) with energy aware and control overhead aware. Maximizing network lifetime is also essential for the improved quality in services. There are many existing studies on QoS communications in WBAN.

In this paper, with the aim of energy-efficient WBAN for QoS, a cross-layer routing protocol is designed and implemented. A cross-layer routing protocol that is ad hoc on-demand distance vector (AODV)-based, energy and control overhead-aware (AODV-ECOA) is designed and implemented for energy-efficient routing in WBAN. The cross-layer design that involves multiple layers of open systems interconnection reference model, which will improve energy efficiency and thus QoS.

Implementation is simulated using the network simulator tool, i.e. NS-2. The proposed cross-layer routing protocol AODV-ECOA shows least bandwidth requirement by control packets, leading to less control overhead, highest packet delivery ratio and energy efficiency. The experimental results revealed that AODV-ECOA shows better performance over existing protocols such as AODV and POLITIC.

An efficient control overhead reduction algorithm is proposed for reducing energy consumption further and improves performance of WBAN communications to realize desired QoS.