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.

Pattaya City is a well-known tourist destination in Thailand, famous for its beautiful beachfront, lively nightlife, and stunning natural scenery. Since 2019, the Eastern Special Development Zone Act, the so-called EEC (Eastern Economic Corridor), has positioned the city as a focal point for Meetings, Incentives, Conferences, and Exhibitions (MICE), boosting its tourism-driven economy. Infrastructure improvements in the region have accelerated urban development over the past decade. However, it is uncertain whether this growth primarily comes from development within existing areas or the expansion of urban boundaries and what direction future growth may take. To investigate this, research using the Cellular Automata-Markov model has been conducted to analyze land use changes and urban growth patterns in Pattaya, using land use data from the Department of Land for 2013 and 2017. The findings suggest an upcoming city expansion along the motorway, indicating that infrastructure improvements could drive rapid urbanization in coastal areas. This urban expansion emphasizes the need for urban management and strategic land use planning in coastal cities.

Using generative artificial intelligence systems in the classroom for law case analysis teaching can enhance the efficiency and accuracy of knowledge delivery. They can create interactive learning environments that are appropriate, immersive, integrated, and evocative, guiding students to conduct case analysis from interdisciplinary and cross-cultural perspectives. This teaching method not only increases students’ interest and participation in learning but also helps cultivate their interdisciplinary thinking and global vision. However, the application of generative artificial intelligence systems in legal education also faces some challenges and issues. If students excessively rely on these systems, their ability to think independently, make judgments, and innovate may be weakened, leading to over-trust in machines and reinforcement of value biases. To address these challenges and issues, legal education should focus more on cultivating students’ questioning skills, self-analysis abilities, critical thinking, basic legal literacy, digital skills, and humanistic spirit. This will enable students to respond to the challenges brought by generative artificial intelligence and ensure their comprehensive development in the new era.

Naturally occurring radionuclides can be categorized into two main groups: primordial and cosmogenic, based on their origin. Primordial radionuclides stem from the Earth’s crust, occurring either individually or as part of decay chains. Conversely, cosmogenic radionuclides originate from extraterrestrial sources such as space, the sun, and nuclear reactions involving cosmic radiation and the Earth’s atmosphere. Gamma-ray spectrometry is a widely employed method in Earth sciences for detecting naturally occurring radioactive materials (NORM). Its applications vary from environmental radiation monitoring to mining exploration, with a predominant focus on quantifying the content of uranium (U), thorium (Th), and potassium (K) in rocks and soils. These elements also serve as tracers in non-radioactive processes linked to NORM paragenesis. Furthermore, the heat generated by radioactive decay within rocks plays a pivotal role in deciphering the Earth’s thermal history and interpreting data concerning continental heat flux in geophysical investigations. This paper provides a concise overview of current analytical and measuring techniques, with an emphasis on state-of-the-art mass spectrometric procedures and decay measurements. Earth scientists constantly seek information on the chemical composition of rocks, sediments, minerals, and fluids to comprehend the vast array of geological and geochemical processes. The historical precedence of geochemists in pioneering novel analytical techniques, often preceding their commercial availability, underscores the significance of such advancements. Geochemical analysis has long relied on atomic spectrometric techniques, such as X-ray fluorescence spectrometry (XRFS), renowned for its precision in analyzing solid materials, particularly major and trace elements in geological samples. XRFS proves invaluable in determining the major constituents of silicate and other rock types. This review elucidates the historical development and methodology of these techniques while showcasing their common applications in various geoscience research endeavors. Ultimately, this review aims to furnish readers with a comprehensive understanding of the fundamental concepts and potential applications of XRF, HPGes, and related technologies in geosciences. Lastly, future research directions and challenges confronting these technologies are briefly discussed.

The study intends to identify the existing implementation bottlenecks that hamper the effectiveness of the Ethiopian forest policy and laws in regional states by focusing on the Oromia Regional State. It attempts to address the question, "What are the challenges for the effective implementation of the federal forest policy and law in Ethiopia in general and Oromia Regional State in particular?". The study followed a qualitative research approach, and the relevant data was collected through in-depth interviews from 11 leaders and experts of the policy, who were purposively selected. Furthermore, relevant documents such as the constitutions, forest policies and laws, and government documents were carefully reviewed. Based on this, the study found that there is the dichotomy between the provision of the constitution regarding the forest policy and lawmaking and the constitutional amendment on one hand and the push for genuine decentralization in the Ethiopian federal state on the other. To elaborate, the constitution is rigid for amendment, and it has given the power of forest policy and lawmaking to the federal government. On the other hand, the quest for genuine decentralization requires these powers to be devolved to the regional states. As the constitution is rigid, this may continue to be the major future challenge of the forest policy and lawmaking of the state. This demonstrates a conflict of interests between the two layers of governments, i.e., the federal and regional (Oromia Regional State) governments. Respecting and practicing the constitution may be the immediate solution to this pressing problem.

Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT's capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT's role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT's capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI's potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for "brainstormin" in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT's effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.