This study investigates the core competencies essential for product designers to excel in cross-cultural global markets, with particular emphasis on implications for human resource development and organizational leadership. As design practices increasingly transcend cultural and geographical boundaries, designers are required to integrate advanced technical proficiency, creative problem-solving, technological adaptability, and cultural intelligence to create inclusive, socially responsible, and market-relevant products. Employing a mixed-methods approach—including focus groups and surveys with design professionals, industry executives, and academic leaders—the research identifies key competencies such as flexibility, intercultural communication, ethical integrity, and systems thinking. The findings underscore the necessity of balancing technical expertise with emotional intelligence and transformational leadership capabilities to effectively lead diverse, cross-functional teams. These competencies contribute significantly to fostering innovation, enhancing employee well-being and job satisfaction, and strengthening organizational resilience, thereby supporting sustainable human resource strategies. Furthermore, the study highlights the importance of continuous professional development and lifelong learning in cultivating culturally competent and ethically driven design talent. The insights offer strategic guidance for human resource professionals, organizational leaders, and educational institutions aiming to develop adaptive, inclusive, and future-ready design capabilities aligned with evolving global demands.

Despite many investigations concerning antecedents of organizational commitment in the workplace, very few studies so far have analyzed the direct or indirect impact of HR change leadership role on organizational commitment via HR attribution. Therefore, given the reciprocal principle of social exchange theory, attribution theory and signal theory, this study formulated hypotheses and a model to test the relationships between included variables by employing the mixed-method approach. In-depth interviews were initially conducted to develop questionnaires to collect quantitative data. Employing PLS-SEM to analyze the data collected from 1058 employees working in 24 sustainable enterprises in Vietnam, the findings show that the degree of adopting HR change leadership role was positive, directly affecting organizational commitment. Also, both well-being and performance HR attribution play partially mediated roles in the relationship. The findings suggest that the organizational commitment depends on not only how the degree of adopting HR change leadership role is executed, but also how employees perceive and interpret the underlying management intent of these practices. In a sustainable context, adopting HR change leadership role plays a critical role in shaping employees’ interpretations of sustainable HR practices and their subsequent attributions. Besides, employees’ belief on why are sustainable HRM practices implemented has an influence on the organizational commitment that in turn contributes to the overall sustainable performance.

This study examines the determinants of audit quality and their impact on detecting financial statement fraud at public accounting firms member of OAI Solusi Manajemen Nusantara in Indonesia. Using a quantitative approach, data was collected through a structured questionnaire distributed to auditors and staff. Key findings highlight the significant influence of auditor independence, professional proficiency, and supervision actions on conducting effective audits, thereby enhancing fraud detection capabilities. The research identifies challenges such as the focus on Indonesian firms and potentially limiting broader applicability. Recommendations include enhancing auditor training, adopting stringent audit procedures and technology, and ensuring adherence to auditing standards to improve audit quality and uphold financial reporting integrity. This study underscores the critical role of audit quality in preventing and detecting financial statement fraud, suggesting avenues for future research to explore additional influencing factors.

The Belt and Road Initiative (BRI) aims to enhance connectivity and collaboration among 60 countries and beyond in Asia, Africa and Europe. Information and communications technology (ICT) is an indispensable component of the initiative, critical in providing fundamental communication channels for global financial transactions, trade exchanges and transport and energy connectivity, and socio cultural collaboration and scientific exchanges between people, organizations and countries along the BRI corridors. Previously constrained by infrastructure deficits in ICT, the Asia-Pacific region is accelerating its efforts to provide reliable and affordable broadband networks throughout the region, to contribute to successful implementation of the Sustainable Development Goals (SDG).

Within the BRI corridors, this study which has been undertaken as part of the research programme of the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP) on promoting regional economic cooperation and integration, focuses on the China-Central Asia Corridor (China, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), giving attention to the sub-region’s specific challenges, namely limited international transit opportunities and an increase in bandwidth requirements that is expected to grow exponentially, as the fourth industrial evolution centered on automation and artificial intelligence gathers momentum. The sub-region is characterized as highly dependent on the ease and costs of connecting to neighboring countries for transit, as many countries in the sub-region are landlocked developing countries (LLDC). Because of the geographical features and other factors, the development potential of Central Asia and its integration into globalization, continues to be stymied by insufficient international bandwidth and high transit costs to access international links. Therefore, improved ICT connectivity in Central Asia through the BRI corridor could result in improved availability and affordability of broadband networks and services in the sub-region.

For the purpose of this study, a gap analysis is the methodology that underpins the proposed topology for the China-Central Asia Corridor. The analysis included examining the current state of the optic infrastructure, such as existing and planned fiber-optic networks, existing Internet Exchange Points (IXPs) and international gateways. The study also identifies the key factors that determine the desired future state of infrastructure deployment for the BRI initiative. A topology that consists of connecting Almaty (Kazakhstan) and Urumqi (China), as core nodes, is proposed based on a partial mesh topology. Over and above this core finding, the study concludes that digital infrastructure connectivity has a tendency of lagging behind the rapid opportunities evolving, and the study therefore advocates for sub-regional and regional approaches, including the BRI and Asia-Pacific Information Superhighway (AP-IS) in further expanding regional broadband networks. A key recommendation of the study is co-deployment of broadband infrastructure along passive infrastructure, as an additional cost effective means of achieving fast and affordable broadband connectivity for all.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.