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Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

This study aims to investigate and analyze the impact of soft total quality management (TQM) practices on employees’ organizational commitment in Sudanese governmental petroleum organizations and to propose targeted strategies on how to enrich the soft TQM practices.

The study used questionnaires to gather data from employees at Sudanese governmental petroleum organizations that adopt quality programs. A stratified random sampling procedure was followed and generated a sample size of 253. Data were analyzed via regression.

The study examined the relationship between employee’s organizational commitment and five soft TQM practices, namely training and education, top management commitment, employee empowerment, involvement and teamwork. These practices collectively explain most of the variance in the employees’ organizational commitment in Sudanese governmental petroleum organizations with R² = 75%. All independent variables are positively and statistically significant at a 5% significance level. Training/education was inferred as the most influencing factor, and teamwork was the least important among the five practices.

The positive relationship between soft TQM practices and organizational commitment suggests several actionable strategies for organizations. By fostering a culture that emphasizes continuous learning, empowerment and involvement, organizations can improve not only the quality of their products and services but also overall productivity. High levels of organizational commitment reduce turnover rates, which in turn lowers recruitment and training costs associated with replacing employees. Encouraging teamwork and employee involvement fosters a collaborative environment where innovation can thrive. Finally, policymakers could use these findings to advocate for the adoption of TQM practices in public sector organizations.

The findings from this study underscore the importance of soft TQM practices – specifically training and education, top management commitment, employee empowerment, involvement and teamwork – in enhancing employees’ organizational commitment. This focus on human-centric practices provides an important contribution to existing literature, particularly in contexts that are underrepresented in TQM research, such as developing countries and governmental organizations. It demonstrates how abstract TQM principles translate into practical outcomes like increased employee commitment, which is a crucial factor for organizational success.

The aim of this work is to present the results of qualitative research on the diaries of women living in the “Recovered Territories (RT)” after World War II in Poland. In particular, the diaries of the first settlers of the “RT” have been subjected to analysis. These women, between 1945 and 1956, were creating, organizing, and domesticating the new and unfamiliar space of the “RT.” The fundamental questions guiding my considerations concern how women coped with domestic and material space in their new places of residence, including dealing with their foreignness, the abundance of some items and the lack of others, essential for daily life, and the relationships between space, objects, and people in the process of domestication.

This paper aims to delve into the nuanced relationship between corporate governance dynamics, human capital disclosure and their impact on the competitive positioning of Japanese listed companies. The study primarily examines how these factors influence employee engagement, a critical determinant of overall business competitiveness.

Panel data for Japanese listed companies for FY 2019 to FY 2021 were analysed using multiple regression analyses with two models.

The results indicate that the presence of independent and female board members has a positive impact on human capital disclosure. Surprisingly, employee engagement was found to be negatively related with human capital disclosure, signifying a potential trade-off between transparency and engagement.

Amidst the escalating emphasis on non-financial information and corporate social responsibility, this paper unveils a previously underexplored aspect of Japanese corporate competitiveness. Specifically, this study offers a fresh empirical perspective on the relationship between corporate governance, human capital disclosure and employee engagement in Japanese listed companies, a topic with limited academic research and no legal regulations in Japan. The findings have significant implications for companies seeking to enhance their human capital disclosure and employee engagement practices, especially in light of the growing focus on non-financial information and social responsibility.

This research aims to develop a robust deep-learning approach for classifying emotion in social media.

This study integrates three deep learning techniques: Bidirectional Gated Recurrent Units (BiGRU), convolutional neural networks (CNN) and an attention mechanism, resulting in the Bidirectional Gated Recurrent Units Convolution Attention (BiGRU-CNN-AT) model. The BiGRU captures potential semantic features, the CNN extracts local features and the attention mechanism identifies keywords critical for classification.

The BiGRU-CNN-AT model outperformed several state-of-the-art emotion classification algorithms. The model was compared against various baselines across multiple emotion datasets, with deep learning methods consistently surpassing traditional approaches. BiGRU and Bi-LSTM networks demonstrated superior performance, particularly when combined with attention mechanisms. Additionally, analysis of execution times indicated that the BiGRU model processed data more efficiently. They were configuring hyperparameters and integrating GloVe word embeddings, which significantly enhanced model performance, with the adam optimizer proving effective for optimization.

This paper contributes to the development of a novel framework, BiGRU-CNN-AT, which integrates bidirectional GRU, CNN and attention mechanisms for text-based emotion classification. By leveraging the strengths of each component, this framework significantly enhances accuracy in emotion classification tasks. Furthermore, the study offers comprehensive experimental analyses across multiple emotion datasets.

The purpose of this study is to propose and develop a zakat model for digital assets from the Sharīʿah perspective.

This research adopts a qualitative research method while studying the literature thoroughly, and it analyzes the data through an exploratory research approach to propose a zakat model for the digital assets.

This research aims to develop a zakat model for digital assets within the framework of Sharīʿah. Using a qualitative research method, the study thoroughly examines existing literature and uses an exploratory research approach to propose this zakat model. The findings suggest that digital assets hold the potential to be considered for zakat in the contemporary digital age. Previous studies indicate that both commodity-based and currency-based digital assets meet the criteria for zakat imposition. Given zakat’s significant impact on socioeconomic development, it is imperative to carefully manage these assets to maximize their potential benefits. However, variations in interpretations by different jurisdictions and Sharīʿah scholars regarding the understanding and classification of digital assets lead to ongoing scrutiny from legal and religious perspectives. This research aims to contribute to the discourse by proposing a zakat model for digital assets and identifying potential assets eligible for zakat.

This research seems to be the pioneer in providing a zakat model for digital assets, combining different segments of digital assets.

Early childhood teachers play a significant role in building children’s success in their first years of school. Therefore, a healthy early childhood workforce in a healthy working environment is an essential aspect of effective early childhood services. This paper aims to explore the extent to which psychological hardiness can be considered as a mediator variable between exposure to workplace bullying and job anxiety among early childhood teachers.

A homogeneous sample comprised of 200 early childhood teachers. For data collection, the researcher used the workplace bullying scale, the psychological hardiness scale and the job anxiety scale among early childhood teachers (prepared by the researcher).

The findings indicated that psychological hardiness mediates the relationship between exposure to workplace bullying and job anxiety among early childhood teachers.

The research result highlighted the necessity of providing counseling programs for early childhood teachers helping them eliminate work stress that affects their job performance. In addition, the kindergarten administration must concentrate on how to effectively communicate and cooperate with early childhood teachers in light of regulations, policies and laws to defeat the spread of workplace bullying. The results of this research contributed to the existing literature by examining the relationship between the research variables, particularly in the early childhood education context.