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This study aims to explore the dynamics involved in the non-medical use of tramadol among manual labourers in Nigeria.

Using the phenomenological approach, we conducted in-depth interviews with forty (40) manual labourers (age: 19–36 years). These participants were selected through purposive and snowball sampling techniques from two cities in Anambra state, Nigeria, and the data was analysed with thematic analysis.

The results revealed that most of the participants were introduced to tramadol use by their friends. They used tramadol for pain relief, euphoric feeling, energy for work and sexual performance. Unfortunately, most of them may have gradually become dependent on this drug to function well in life without knowing the possible medical and psychological implications.

The sample choice may limit the generalization of the findings; however, the study indicates a need to improve working condition, access to healthcare and awareness of the negative effects of non-medical use of tramadol.

Non-medical use of tramadol may have started as an attempt to help self in performing the daily routine. This action can jeopardize an individual’s mental health and, at the extreme state, hinder performance of daily routine. Friends and the quality of information they provide play a significant role in the onset of this action. The strong point is that many people who are involved in the non-medical use of tramadol are unaware of the possible risks. Improving working conditions and access to healthcare for chronic pain could help reduce these risks.

Unsupervised domain adaptation object detection not only mitigates model terrible performance resulting from domain gap, but also has the ability to apply knowledge trained on a definite domain to a distinct domain. However, aligning the whole feature may confuse the object and background information, making it challenging to extract discriminative features. This paper aims to propose an improved approach which is called intrinsic feature extraction domain adaptation (IFEDA) to extract discriminative features effectively.

IFEDA consists of the intrinsic feature extraction (IFE) module and object consistency constraint (OCC). The IFE module, designed on the instance level, mainly solves the issue of the difficult extraction of discriminative object features. Specifically, the discriminative region of the objects can be paid more attention to. Meanwhile, the OCC is deployed to determine whether category prediction in the target domain brings into correspondence with it in the source domain.

Experimental results demonstrate the validity of our approach and achieve good outcomes on challenging data sets.

Limitations to this research are that only one target domain is applied, and it may change the ability of model generalization when the problem of insufficient data sets or unseen domain appeared.

This paper solves the issue of critical information defects by tackling the difficulty of extracting discriminative features. And the categories in both domains are compelled to be consistent for better object detection.

Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.

To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.

Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.

Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.

Single-shot multi-category clothing recognition and retrieval play a crucial role in online searching and offline settlement scenarios. Existing clothing recognition methods based on RGBD clothing images often suffer from high-dimensional feature representations, leading to compromised performance and efficiency.

To address this issue, this paper proposes a novel method called Manifold Embedded Discriminative Feature Selection (MEDFS) to select global and local features, thereby reducing the dimensionality of the feature representation and improving performance. Specifically, by combining three global features and three local features, a low-dimensional embedding is constructed to capture the correlations between features and categories. The MEDFS method designs an optimization framework utilizing manifold mapping and sparse regularization to achieve feature selection. The optimization objective is solved using an alternating iterative strategy, ensuring convergence.

Empirical studies conducted on a publicly available RGBD clothing image dataset demonstrate that the proposed MEDFS method achieves highly competitive clothing classification performance while maintaining efficiency in clothing recognition and retrieval.

This paper introduces a novel approach for multi-category clothing recognition and retrieval, incorporating the selection of global and local features. The proposed method holds potential for practical applications in real-world clothing scenarios.

The negative effects of job stress and burnout on construction professionals (CPs) at the construction site have been widely concern in the construction industry. The purpose of this study is committed to explore the impact of job stress on CPs on the construction site, especially in the context of the widespread use of social media to express their emotions.

This study developed a job-related stress-burnout-health conditions-turnover intention (S-B-HT) framework to explore the direct and lagged effects of job stress, we also examined the moderating effects of online emotions, operationalized in terms of emotional intensity and expression pattern, on the relationship between job stress with job burnout under two evolution paths (i.e. health conditions or turnover intention). This study collected 271 samples through a survey questionnaire for empirical testing, and introduced structural equation models to validate the proposed conceptual model.

The results show that job stress has a significant positive effect on job burnout, and job burnout maintains a positive relationship with health conditions (or turnover intention) under the interference mechanism. Simultaneously, the online emotions expressed in social media have a positive moderating effect in two stages of the evolution path.

The findings of this study remind the project manager need to timely find and solve the job burnout characteristics of CPs due to excessive job stress, especially to prevent the accidental consequences caused by job burnout.

On this basis, this study provides an important value of using social media to express emotions for the project team to alleviate the adverse of professionals under job stress.

This study aims to examine the effect of regional discrimination on tourists’ unethical behavior intention and the mediating role of tourist anger. Furthermore, the study examines whether the impact of regional discrimination on tourist anger is moderated by tourist self-efficacy.

Three scenario experiments were conducted to test the research hypotheses.

The results indicated an indirect effect of regional discrimination on tourists’ unethical behavior intention via tourist anger. In addition, the findings showed that tourist self-efficacy would weaken the relationship between regional discrimination and tourist anger.

This study reveals tourists’ emotional and behavioral reactions to regional discrimination. Further research can examine the influence of regional discrimination from other theoretical lens and field experiments are encouraged.

This study enriches current knowledge on regional discrimination by developing an integral framework to explore tourists’ reactions toward regional discrimination.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

Drawing on cognitive appraisal theory, this study aims to explore the indirect effect of work/family boundary violations on teleworkers’ counterproductive work behavior (CWB). In addition, it examines the moderating role of involuntary teleworking in this context.

Based on the diary study method, this study used 543 questionnaires collected from 122 teleworkers in China.

The findings indicate that working time demands mediate the relationship between work boundary violations and teleworker CWB, and threats to family roles mediate the relationship between family boundary violations and teleworker CWB. In addition, involuntary teleworking positively moderates the relationship between work boundary violations and working time demands, as well as the relationship between family boundary violations and threats to family roles.

The study makes several valuable contributions to the extant literature. First, it enhances research on the antecedents of teleworker CWB. Second, by introducing two mediating variables corresponding to two types of boundary violations, the paper provides new insights and a fresh understanding of the effects of boundary violations in teleworking situations. Third, by considering the moderating role of involuntary teleworking, this research adds to the current knowledge on the impact of discussing boundary violations solely on teleworking or traditional office days.

This study aims to integrate conservation of resources theory discus the antecedents and consequences of cyberloafing as well as the boundary effect between cyberloafing and mental health.

The authors collected 431 valid questionnaires in four months. Each questionnaire was divided into two parts that had to be distributed. The interval between the first distribution and the second distribution was 15 days.

The research study revealed that employees’ Zhong-Yong thinking and cyberloafing promote mental health, and cyberloafing has a mediating effect. Mindfulness weakens the positive impact of cyberloafing on mental health.

The research study’s results break the stereotype that cyberloafing is not good for organizations. When managers allow employees to engage in cyberloafing at work, this is conducive to employees’ mental health, which can ensure the company’s sustainable development.

Existing empirical studies emphasize the role of customer preference measurement accuracy (CPMA) on customized product quality while neglecting the role of manufacturing flexibility. However, the rise of web-based technology has highlighted consumer-manufacturer interaction in mass customization (MC). This paper explores the relationships between CPMA, manufacturing flexibility, customer participation and customized product quality, inspired by module decomposition and integration theory.

The study carried out a questionnaire survey of 241 directors who are specifically responsible for MC in the apparel industry in China and tested hypotheses with structural equation modeling (SEM).

CPMA has a direct positive influence on customized product quality and an indirect positive effect through manufacturing flexibility. Also, customer participation after placing an order (CPAPO) has a negative moderating effect on the linkage between CPMA and manufacturing flexibility.

A more dynamic relationship between CPMA and MF can be explored in the future, and the findings can be tested in other industries with more abundant data.

Manufacturing flexibility should be improved along with CPMA in MC. Also, disruptions caused by customer participation in the post-design stage should be noticed. In this context, better consumer service in the early design stage may be one possible solution.

First, the essential but unexplored mediating role of manufacturing flexibility is identified, adding knowledge to the consumer–manufacturer interaction literature in MC production. Second, module decomposition and integration are applied to unify consumers and manufacturers into one empirical model, enriched theoretical research on modular theory. Third, this study opens new discussions on the moderating role of customer participation.