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Evaluate the performance of progressive collapse of full-scale three-dimensional structure (3D) beam-slab substructures with and without the presence of reinforced concrete (RC) balconies using two concrete mixes [normal concrete (NC) and rubberized concrete (RuC)].

This study examines two concrete mixes to evaluate the progressive collapse performance of full-scale 3D beam-slab substructures with and without the presence of RC balconies using the finite element (FE) method.

The results showed that the vertical loads that affect the structures of the specimens after including the balconies in the modeling increased by an average of 29.3% compared with those of the specimens without balconies. The specimens with balconies exhibited higher resistance to progressive collapse in comparison with the specimens without balconies. Moreover, the RuC specimens performed very efficiently during the catenary stage, which significantly enhanced robustness to substantial deformation to delay or mitigate the progressive collapse risk.

All the experimental and numerical studies of the RC beam-slab substructures under progressive collapse scenarios are limited and do not consider the balcony’s presence in the building. Although balconies represent a common feature of multistory residential buildings, their presence in the building has more likely caused the failure of this building compared with a building without balconies. However, balconies are an external extension of RC slabs, which can provide extra resistance through tensile membrane action (TMA) or compressive membrane action (CMA). All those gaps have not been investigated yet.

Small farmers in India are collectivized and legalized as Farmer Producer Companies (FPCs) to progress in agri-food value chains as small agribusiness enterprises. FPCs are dependent on timely information for their sustainability and profitability. Mobile apps are a cost-effective form of information and communication technology. Hence, the purpose of this study is to explore the major determinants of mobile apps adoption by FPCs.

Quantitative and qualitative data are collected by administering a semi-structured questionnaire and conducting in-depth interviews with board members of 115 FPCs, with a total membership of 30,405 farmers operating in 14 districts of the state of Kerala, India. The logit model is used for quantitative analysis, while dialog mapping is used for qualitative analysis, based on an integrated technology acceptance model and technology organization environment framework.

Logistic regression results evidence that amongst FPC characteristics, while company size and age are significantly impacting apps adoption, there is no significant association between board size, education level, multiple commodities business or export intention of companies on apps adoption. Digital literacy and technical hands-on training for FPC board members are quintessential to facilitate mobile apps adoption.

The findings are pertinent to policymakers to earmark funds for technical handholding and digital upskilling of FPCs. The need for developing comprehensive, location-centric, farmer-friendly apps by agritech companies is evidenced.

To the best of the authors’ knowledge, this is a pioneering work in the domain of mobile apps adoption from a farmers’ agribusiness enterprise perspective in an emerging market economy using a mixed-methods approach.

To setup a theoretical model for grasping cutting pieces of garment better, which will help to design a special soft gripper and push forward the automated level of garment manufacturing.

This paper first analyzed the mechanics of the grasping process and concluded the main factors that affect the success of grasping. A theoretical model named grasping fabric model (GFM) was constructed to show the mechanical relationship between the soft gripper and the fabric pieces. Subsequently, two fabric samples were selected and tested for their friction properties and critical buckling force, and the test data were substituted into the theoretical model GFM to obtain the grasping parameters required for fabric grasping layer by layer.

It was found that (1) the critical buckling force of the fabric is mainly influenced by the bending stiffness and the deformation length of the fabric during grab. (2) The difference between the friction between the soft gripper and the fabric and the friction between the fabric, that is DF1-2, has an important influence on the accuracy of grasping layer-by-layer.

It showed that the grasping parameters provided by GFM enable the two samples to be more effectively separated layer by layer, which verifies that the GFM model is strong enough for the possible application in garment automated production.

The purpose of this study is to assess a novel method for creating tangible three-dimensional (3D) morphologies (scaled models) of neuronal reconstructions and to evaluate its cost-effectiveness, accessibility and applicability through a classroom survey. The study addresses the challenge of accurately representing intricate and diverse dendritic structures of neurons in scaled models for educational purposes.

The method involves converting neuronal reconstructions from the NeuromorphoVis repository into 3D-printable mold files. An operator prints these molds using a consumer-grade desktop 3D printer with water-soluble polyvinyl alcohol filament. The molds are then filled with casting materials like polyurethane or silicone rubber, before the mold is dissolved. We tested our method on various neuron morphologies, assessing the method’s effectiveness, labor, processing times and costs. Additionally, university biology students compared our 3D-printed neuron models with commercially produced counterparts through a survey, evaluating them based on their direct experience with both models.

An operator can produce a neuron morphology’s initial 3D replica in about an hour of labor, excluding a one- to three-day curing period, while subsequent copies require around 30 min each. Our method provides an affordable approach to crafting tangible 3D neuron representations, presenting a viable alternative to direct 3D printing with varied material options ensuring both flexibility and durability. The created models accurately replicate the fidelity and intricacy of original computer aided design (CAD) files, making them ideal for tactile use in neuroscience education.

The development of data processing and cost-effective casting method for this application is novel. Compared to a previous study, this method leverages lower-cost fused filament fabrication 3D printing to create accurate physical 3D representations of neurons. By using readily available materials and a consumer-grade 3D printer, the research addresses the high cost associated with alternative direct 3D printing techniques to produce such intricate and robust models. Furthermore, the paper demonstrates the practicality of these 3D neuron models for educational purposes, making a valuable contribution to the field of neuroscience education.

New sustainable approaches to fashion products are needed due to the demand for customization, better quality and cost reduction. Therefore, the decoration of fashion products using 3D printing technology can create a new direction for manufacturing science.

This study aims to optimize the 3D printing of soft TPU material on textiles. In the past decade, trials of using 3D printing in tailored fashion products have been done due to the 3D printing simplicity, low cost of materials and time reduction. Therefore, soft polymers can be multi-layer stepped-deposited smoothly with the fused filament fabrication process.

Even though there have been many attempts in the literature to 3D print multilayer polymer filaments directly onto textile fabrics by special-purpose 3D printers, only a few reports of decorative or personalized artefact 3D printing using open-platform filament material extrusion 3D printers. Printing speed, nozzle Z distance, textile fabric thickness and deposited strand height significantly affect 3D printing on textile fabric.

This study investigates the potential of 3D printing on textiles by changing the printing speed, nozzle hot end, Z distance and layer thickness. It presents two critical case studies of 3D printing soft thermoplastic polyurethane material on a cotton T-shirt and on a tulle textile to reveal the 3D printing on textile fabrics manufacturing challenges.

The detrimental effects of increased workloads and high-stakes accountability that impact on teachers’ job satisfaction and wellbeing have been well documented in the international literature (Holloway et al., 2017; Perryman et al., 2011). This chapter will explore the factors which influence staff mental health and wellbeing in schools. The authors unpack issues of burnout, job satisfaction and teacher attrition. The authors consider the concept of teacher resilience by situating resilience within a socio-ecological framework. Specifically, the authors draw on Greenfield’s (2015) model of teacher resilience. The authors draw on our own research to explore the relationship between staff wellbeing and student wellbeing (Glazzard & Rose, 2020). In addition, the authors consider the specific issues related to the wellbeing of school leaders.

This paper aims to design and manufacture an equine hand prosthesis using additive manufacturing, with an estimated useful life of one year. This approach offers a fast and affordable manufacturing alternative while ensuring the horse's safety, comfort and functionality.

The ground reaction force and the frequency of a horse’s walking were obtained from the literature. Mechanical tests were conducted on specimens with different manufacturing directions to determine the mechanical properties of the printed material. Finite element simulations, along with fatigue equations were used to design a geometry that respected the stress constraints. Subsequently, a prototype was manufactured in thermoplastic polyurethane using additive manufacturing technique.

With the aid of the proposed methodology, a new low-cost equine hand prosthesis is developed, and a prototype is manufactured. And in accordance with the design requirements, this prosthesis is intended to exhibit proper durability.

This work presents an alternative way for horses facing amputation, offering a solution where euthanasia can be avoided through the use of a prosthesis to replace a part of the amputated limb. This approach could not only extend the reproductive life of matrices with high commercial value but also preserve the lives of animals with sentimental value to the owner.

To the best of the authors' knowledge, this is the first study of an equine hand prosthesis model designed for and manufactured by additive manufacturing.

The purpose of this study is to examine the impact of sustainable supply chain management (SSCM) practices on both competitive advantage (CA) and organizational performance (OP) in the manufacturing sector in Ethiopia.

Data for the study were collected from a sample of 221 manufacturing companies operating in the four manufacturing groups/sectors in Ethiopia. In addition, data analysis was performed using the partial least squares method, which is a variance-based Structural Equation Modeling approach in the Smart-PLS software version (SmartPLS 4.0).

Based on the statistical analysis of the collected data, it demonstrates that SSCM has a significant and positive impact on both competitive advantage and organizational performance. Furthermore, statistical findings offer proof of the clear connection between competitive advantage and organizational performance. Moreover, competitive advantage indirectly mediates the relationship between SSCM and OP.

The primary limitation of this research is its reliance on a cross-sectional design. The generalizability of the findings obtained from the present study may be hindered. The variable under investigation in this research assessed organizational performance, a concept that is widely acknowledged to be extremely dynamic.

The study provides managers and researchers with valuable information on Sustainable Supply Chain Management strategies and how they influence competitive advantage and organizational performance in commercial and industrial environments.

This paper adds to the body of knowledge by providing new data and empirical insights into the relationship between SSCM practices and the performance of manufacturing companies in Ethiopia.

This study aims to improve the stability and obstacle surmounting ability of the traditional wall-climbing robot on the surface of the ship, a wheel-track composite magnetic adsorption wall-climbing robot is proposed in this paper.

The robot adopts a front and rear obstacle-crossing mechanism to achieve a smooth crossover. The robot is composed of two passive obstacle-crossing mechanisms and a frame, which is composed of two obstacle-crossing magnetic wheels and a set of tracks. The obstacle-crossing is realized by the telescopic expansion of the obstacle-crossing mechanism. Three static failure models are established to determine the minimum adsorption force for the robot to achieve stable motion. The Halbach array is used to construct the track magnetic circuit, and the influence of gap, contact area and magnet thickness on the adsorption force is analyzed by parameter simulation.

The prototype was designed and manufactured by the authors for static failure and obstacle crossing tests. The prototype test results show that the robot can cross the obstacle of 10 mm height under the condition of 20 kg load.

A new structure of wall-climbing robot is proposed and verified. According to the test results, the wall-climbing robot can stably climb over the obstacle of 10 mm height under the condition of 20 kg load, which provides a new idea for future robot design.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image. The authors used three feature extraction methods: handcrafted features, convolutional features and autoencoder features. Subsequently, these features were mapped to contact locations through a contact location regression network. Finally, the network performance was evaluated using spherical fittings of three different radii to further determine the optimal feature extraction method.

This paper aims to estimate contact location from sparse and high-dimensional soft tactile array sensor data using the tactile image.

This research indicates that data collected by probes can be used for contact localization. Introducing a batch normalization layer after the feature extraction stage significantly enhances the model’s generalization performance. Through qualitative and quantitative analyses, the authors conclude that convolutional methods can more accurately estimate contact locations.

The paper provides both qualitative and quantitative analyses of the performance of three contact localization methods across different datasets. To address the challenge of obtaining accurate contact locations in quantitative analysis, an indirect measurement metric is proposed.