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The purpose of this paper is to focus on the design of a dual-branch balance saliency model based on fully convolutional network (FCN) for automatic fabric defect detection, and improve quality control in textile manufacturing.

This paper proposed a dual-branch balance saliency model based on discriminative feature for fabric defect detection. A saliency branch is firstly designed to address the problems of scale variation and contextual information integration, which is realized through the cooperation of a multi-scale discriminative feature extraction module (MDFEM) and a bidirectional stage-wise integration module (BSIM). These modules are respectively adopted to extract multi-scale discriminative context information and enrich the contextual information of features at each stage. In addition, another branch is proposed to balance the network, in which a bootstrap refinement module (BRM) is trained to guide the restoration of feature details.

To evaluate the performance of the proposed network, we conduct extensive experiments, and the experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) approaches on seven evaluation metrics. We also conduct adequate ablation analyses that provide a full understanding of the design principles of the proposed method.

The dual-branch balance saliency model was proposed and applied into the fabric defect detection. The qualitative and quantitative experimental results show the effectiveness of the detection method. Therefore, the proposed method can be used for accurate fabric defect detection and even surface defect detection of other industrial products.

The development of the combined voltage reference and temperature sensor is focused on the RFID applications. The passive RFID systems derive power in the tag solely from rectifying the incident RF power. The dc power supply may be coupled with the RF signal, voltage drop, and noise. The voltage reference here is to provide a stable voltage for well‐biasing the internal analog circuitry. For the temperature sensing RFID applications, the combined device also gives a highly linear temperature sensor for wide‐temperature range measurements. Seeks to discuss this subject.

For voltage reference design, a self‐PTAT current is generated for compensating the diode‐connected NMOS transistor to achieve temperature‐stable voltage reference. Moreover, a temperature sensor with high linearity is developed by amplifying the linear portion and restricting the nonlinear part of temperature information.

Owing to better‐compensation, the voltage reference provides a stable voltage of 718.7±2.9 mV, and the temperature sensor has linearity over 99.8 percent for a wide‐temperature operation from −50 to 150°C.

Owing to the small size, 0.38 × 0.24 mm², of the combined device, it can be embedded into a RFID tag without increasing the RFID size. The voltage reference can serve as a stable voltage for stabilizing the behavior of analog circuits of the tag, and the temperature sensor probes the environment temperature. Then the information will be delivered to the RFID reader by the tag.

One of the major shortcomings in the data process of the traditional wheel force transducers (WFTs) is the theoretical errors of initial value determination. A new method to identify the initial values of the WFT for the solution of this problem is proposed in this paper. The paper aims to discuss these issues.

With this method, the initial values can be obtained by equations which are established based on multiple stops on horizontal road.

The calibration and contrast tests on the MTS calibration platform illustrate the better performance with the new method. Moreover, the real vehicle test confirms the effectiveness in practice.

The test results show that the new method of initial calibration has an advanced performance compared to the traditional one. In addition, it is effective in the brake test with a real vehicle.

The purpose of this paper is to explore whether an in-service life teacher training program can improve boarding students’ health, behavior, and academic performance.

The authors conducted a cluster-randomized controlled trial to measure the effect of life teacher training on student health, behavior, and academic performance among 839 boarding students in ten central primary boarding schools in Shaanxi. And the authors also tried to identify why or why not life teacher training works. Both descriptive and multivariate analysis are used in this paper.

The authors find significant improvements in health and behavior. Specifically, compared to boarding students in control schools, 15 percent fewer students in treatment schools reported feeling cold while sleeping at night. The results also showed that student tardiness and misbehaviors after class declined significantly by 18 and 78 percent, respectively. However, the in-service life teacher training program had no measurable impact on boarding students’ BMI-for-age Z-score, number of misbehaviors in class, and academic performance. The analysis suggests that improved communication between life teachers and students might be one mechanism behind these results.

This is the first empirical work which explored how to improve the welfare of boarding students via their life teachers. Because of the sudden increase in boarding students in rural China, it is almost certain that school personnel lack experience in managing boarding students. As such, one promising approach to improving student outcomes might be in-service training for life teachers.

Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.

The importance of shopping streets has long been considered a critical aspect of urban tourism. However, limited exploration focuses on the supply side, especially from the tour operator’s (TO) perspective. This paper aims to investigate TOs’ perceptions and attitudes towards packaging Tuanku Abdul Rahman Street (TARS) as an urban shopping spot in Kuala Lumpur.

Study data is gathered through qualitative in-depth interviews among 25 TO managers in Kuala Lumpur, Malaysia. The coding process was done manually, followed by qualitative data analysis using ATLAS.ti version 8 software.

The results show that the TOs regarded TARS as a must-visit shopping spot for international tourists. They argue that TARS’s competitiveness as a shopping street depends on the supporting infrastructure and safe environment, which are currently neglected by the relevant authorities and jeopardise the sustainability of TARS as a must-visit shopping street in the future.

This study’s findings generate value-added information on the potential of shopping tourism and TARS as must-visit shopping streets in Malaysia. On the other hand, the TOs’ concern about the lack of supporting infrastructure and unsafe environment generates varying consequences and implications for the individual TOs, tourism policymakers and government-related authorities.

This study offers new insight for urban tourism policymakers, managers and entrepreneurs to capture the attributes of a vibrant shopping street. There is exclusive potential for local tourism operators to take greater responsibility in shopping tourism planning processes and management operations through trustworthy planning partnerships among respective tourism stakeholders related to the shopping street domain.

Wireless sensor networks (WSN) have been widely adopted for various applications due to their properties of pervasive computing. It is necessary to prolong the WSN lifetime; it avails its benefit for a long time. WSN lifetime may vary according to the applications, and in most cases, it is considered as the time to the death of the first node in the module. Clustering has been one of the successful strategies for increasing the effectiveness of the network, as it selects the appropriate cluster head (CH) for communication. However, most clustering protocols are based on probabilistic schemes, which may create two CH for a single cluster group, leading to cause more energy consumption. Hence, it is necessary to build up a clustering strategy with the improved properties for the CH selection. The purpose of this paper is to provide better convergence for large simulation space and to use it for optimizing the communication path of WSN.

This paper plans to develop a new clustering protocol in WSN using fuzzy clustering and an improved meta-heuristic algorithm. The fuzzy clustering approach is adopted for performing the clustering of nodes with respective fuzzy centroid by using the input constraints such as signal-to-interference-plus-noise ratio (SINR), load and residual energy, between the CHs and nodes. After the cluster formation, the combined utility function is used to refine the CH selection. The CH is determined based on computing the combined utility function, in which the node attaining the maximum combined utility function is selected as the CH. After the clustering and CH formation, the optimal communication between the CH and the nodes is induced by a new meta-heuristic algorithm called Fitness updated Crow Search Algorithm (FU-CSA). This optimal communication is accomplished by concerning a multi-objective function with constraints with residual energy and the distance between the nodes. Finally, the simulation results show that the proposed technique enhances the network lifetime and energy efficiency when compared to the state-of-the-art techniques.

The proposed Fuzzy+FU-CSA algorithm has achieved low-cost function values of 48% to Fuzzy+Particle Swarm Optimization (PSO), 60% to Fuzzy+Grey Wolf Optimizer (GWO), 40% to Fuzzy+Whale Optimization Algorithm (WOA) and 25% to Fuzzy+CSA, respectively. Thus, the results prove that the proposed Fuzzy+FU-CSA has the optimal performance than the other algorithms, and thus provides a high network lifetime and energy.

For the efficient clustering and the CH selection, a combined utility function was developed by using the network parameters such as energy, load, SINR and distance. The fuzzy clustering uses the constraint inputs such as residual energy, load and SINR for clustering the nodes of WSN. This work had developed an FU-CSA algorithm for the selection of the optimal communication path for the WSN.

This study aims to examine how environmental, social and governance (ESG) initiatives and ISO 14001, which is an internationally agreed standard to set out the requirements for an environmental management system, affect firm performance in the context of the Industry 4.0 supply chain.

The authors develop a new chance-constrained network data envelopment analysis (DEA) in the presence of non-positive data to estimate innovation, operational and profitability performances for three main relation groups (suppliers, partners and customers) in Microsoft's supply chain.

Results of this study show the following: (1) the application of ISO 14001 will reduce profitability but increase overall performance (OP); (2) ESG implementation has a convex U-shaped influence on profitability and OP, which means that firms will benefit when ESG investment goes beyond a particular level; (3) the nonlinear U-shape is presented in the E and G components, but not in the S of the individual ESG initiatives, and (4) only specific subcomponents of S and G in the subcomponent of individual ESG initiatives are nonlinearly connected to OP. Research's results reveal that the customer group has a higher performance value than the other two groups, which suggests that this group will create competitive advantages for Microsoft.

Overall, the authors provide an insightful viewpoint into supply chain management by examining the ESG initiatives, ISO 14001 and performances of Microsoft's supply chain.

The current studies on remaining useful life (RUL) prediction mainly rely on convolutional neural networks (CNNs) and long short-term memories (LSTMs) and do not take full advantage of the attention mechanism, resulting in lack of prediction accuracy. To further improve the performance of the above models, this study aims to propose a novel end-to-end RUL prediction framework, called convolutional recurrent attention network (CRAN) to achieve high accuracy.

The proposed CRAN is a CNN-LSTM-based model that effectively combines the powerful feature extraction ability of CNN and sequential processing capability of LSTM. The channel attention mechanism, spatial attention mechanism and LSTM attention mechanism are incorporated in CRAN, assigning different attention coefficients to CNN and LSTM. First, features of the bearing vibration data are extracted from both time and frequency domain. Next, the training and testing set are constructed. Then, the CRAN is trained offline using the training set. Finally, online RUL estimation is performed by applying data from the testing set to the trained CRAN.

CNN-LSTM-based models have higher RUL prediction accuracy than CNN-based and LSTM-based models. Using a combination of max pooling and average pooling can reduce the loss of feature information, and in addition, the structure of the serial attention mechanism is superior to the parallel attention structure. Comparing the proposed CRAN with six different state-of-the-art methods, for the predicted results of two testing bearings, the proposed CRAN has an average reduction in the root mean square error of 57.07/80.25%, an average reduction in the mean absolute error of 62.27/85.87% and an average improvement in score of 12.65/6.57%.

This article provides a novel end-to-end rolling bearing RUL prediction framework, which can provide a reference for the formulation of bearing maintenance programs in the industry.