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To investigate whether the actual effects of eight drape characteristics of virtual fabrics can be manifested in the Style 3D software.

Image analysis was conducted using MATLAB software to obtain the drape characteristics of virtual fabrics. Pair the drape characteristics of the real and virtual fabrics for difference. The S-W method was used to conduct a normality test to obtain the correlation of paired samples. A paired sample t-test was performed to obtain the significance values.

The simulation restoration performance of the drape coefficient, number of undulations, maximum undulation angle, minimum undulation angle and undulation angle uniformity was good. However, there are differences in the simulation performance of the other three indicators: maximum undulation amplitude, minimum undulation amplitude and undulation amplitude uniformity compared to the drape characteristics of real fabrics.

Provides reference value for the improvement of Style3D software in virtual fabric simulation and finds the main influential parameters and their impact levels that contribute to the realistic representation of virtual fabrics in software. It provides a theoretical basis for course teaching in digital fashion.

This study systematically investigated the effect of the binary rare earth oxide of La₂O₃ and Sm₂O₃ on the properties of the Al₂O₃/TiO₂ (AT) coating, including phase transform, wear behavior, etc.

AT coatings mixed with different components of binary rare earth oxides of La₂O₃ and Sm₂O₃ are prepared by atmospheric plasma spraying. The adhesion strength, micro-hardness, phase transition and tribological behavior of coatings are systematically investigated.

The X-ray diffraction (XRD) analysis shows that phase transformation is obvious after spraying, and a-Al₂O₃ is almost translated into γ-Al₂O₃ when La₂O₃ and Sm₂O₃ are doped together. Meanwhile, solid solution generated between rare earth oxide and Al₂O₃/TiO₂ coatings results in disappearance of TiO2 and rare earth oxide phase. The photos under the scanning electron microscope (SEM) indicate that binary rare earth oxide could increase the melting degree of powder and decrease porosity of coatings.The increasing of Sm₂O₃ rarely affect micro-hardness and adhesion strength, and the coating with 4 per cent Sm₂O₃ and 1 per cent La₂O₃ exhibits the best wear resistance and lowest friction coefficient among all the samples.

AT coatings mixed with different components of binary rare earth oxide of La₂O₃ and Sm₂O₃ are prepared by atmospheric plasma spraying. Binary rare earth oxide could increase the melting degree of powder and decrease porosity of AT coatings.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.

Recent attention to the developments focusing on the educational services has been noteworthy, with the educational environment specifically the smart campus emerging both as a domain and as an opportunity. As a domain worthy of exploration, a number of research efforts are being conceptualized around smart campus initiatives. The existing bouquet of research publications on smart campus provides a testimony of the enthusiasm and also exposes the heterogeneous attempts the domain has witnessed to date. The available evidence is still inadequate to provide clarity on the thrust areas of research around smart campus.

Given the understanding, this study intends to decode the domain to get an early impression of the focus of the research concentration around smart campus. Thereby the study resorts to an automated text-mining approach using Python on contents shortlisted systematically, and published between the period 2010 and May 2022, from select databases.

Based on the analysis it was possible to identify eight themes (i.e. smart campus characteristics, smart campus stakeholders, smart campus frameworks, smart campus technologies, smart campus infrastructure, smart campus evaluation, smart learning environment and smart campus applications) characterizing research efforts within the smart campus literature.

The themes around the smart campus showcase the thrust areas receiving attention. These characterize extant research endeavours in the smart campus domain and can offer useful pointers to researchers going forward. This awareness can also be beneficial to institutional leadership and technology providers intending to implement smart campus initiatives, contributing to the development of the educational environment.

In order to discover a new adsorbent that can be used to purify dye wastewater in the textile and apparel industry, a novel type of graphene oxide/gluten composite material using an improved acid bath coagulation method was synthesized, which can remove methylene blue in an aqueous environment.

After experimentally compounding different ratios of graphene oxide and gluten, the graphene oxide/gluten composite material with 20% graphene oxide content and superlative adsorption effect was chosen. The synthesized material was characterized by different techniques such as FT-IR and SEM, indicating the microstructure of the material and the success of the composite. Various factors were considered, namely, the influence of temperature, dosage, pH and contact time. The isotherms, kinetics and thermodynamic parameters were successively discussed.

The qmax value of 214.29 mg/g of the material was higher compared to the general sorbent, thus, the graphene oxide/gluten composite material was a suitable sorbent for methylene blue removal. Overall, the graphene oxide/gluten composite material can be considered as an effectual and prospective adsorbent to remove methylene blue in the textile and apparel industrial effluent.

Graphene oxide is a potentially excellent sorbent. However, the high dispersibility of GO is detrimental to adsorption, it disperses rapidly in an aqueous solution making separation and recovery difficult. The high load capacity and recyclability of gluten as a colloid make it a suitable carrier for fixing GO. Studies on the combination of GO and GT into composite adsorption material and for the removal of dyes from dyeing wastewater have not been reported. The composite material research on GO and GT can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.