Abstract:Industry 4.0, integrating smart manufacturing, digital manufacturing, smart logistics, and industrial automation, has emerged as a core paradigm for industry leaders and scholars advancing sustainable development. This study develops a theoretical model to decode the key drivers influencing enterprises’ intentions to invest in smart manufacturing. The model was empirically analyzed using data from an online survey of 188 R&D and project development professionals from manufacturing and high-tech enterprises in Taiwan. The findings unveil critical insights into sustainable investment strategies for Industry 4.0, offering practical guidance for policymakers, industry stakeholders, and researchers, while paving the way for the future evolution of smart manufacturing.
Abstract:The aim of the study was to examine the differences in feedback actions and analysis between experienced and inexperienced physical education (PE) teachers. Thirty-three experienced elementary PE teachers (average age: 45 ± 6.41 years; teaching experience: 13.39 ± 9.39 years) and thirty-three inexperienced teachers (average age: 29 ± 7.75 years; teaching experience: 2.78 ± 0.47 years) participated. Data were collected via online surveys using a non-random sampling method, with the questionnaire developed in Microsoft Forms and shared through social media platforms like WhatsApp. The results indicated that no significant differences in feedback usage rates between experienced and inexperienced PE teachers. For verbal feedback related to motion, experienced teachers had an average usage rate of 4.76 ± 0.56, while inexperienced teachers had a similar rate of 4.79 ± 0.48. Visual feedback usage rates were 4.36 ± 0.90 for experienced teachers and 4.55 ± 0.56 for inexperienced teachers, with a significant difference in variance but not in average rates. Combined verbal and visual feedback usage rates were 4.15 ± 0.83 for experienced teachers and 4.00 ± 0.79 for inexperienced teachers. In consequence, the experience level of PE teachers does not significantly affect their feedback usage rates related to motion. Additionally, experienced teachers were found to be more consistent in providing feedback after each action. Both groups recognized the importance of timely and constructive feedback in the learning process.
Abstract:Green energy has become a central focus for power generation around the world. Many companies now require green energy in their production processes, which has led many countries to invest in its development. The Taiwan Strait is considered one of the top locations for offshore wind power in the world. Since 2018, Taiwan has actively promoted the development of offshore wind power. Wind power generation is intermittent and affected by climate variability, which creates significant challenges for its stability and reliability. This study collected wind power generation data from the Changhua Coastal Wind Power Station between May 2021 and June 2022. Climate data, including wind speed and wind direction, were obtained from nearby weather stations and were used to generate monthly and seasonal wind rose diagrams. Statistical analysis indicated that wind power generation in summer represented just 9.81% of the annual total, while winter accounted for 46.00%. A 24-hour generation analysis showed that the highest hourly generation percentage in summer was only 17.82%, while in winter, it consistently remained above 50% during most periods. These findings reveal a notable seasonal and daily variation in wind power generation, mainly influenced by wind speed and direction changes. To minimize the effects of periods with low power generation and to meet the peak electricity demand during the summer, it is crucial to implement large-scale energy storage systems along with effective power dispatch and management strategies. This approach will help ensure grid stability and reliability.
Abstract:This study investigates the impact of industrial agglomeration patterns on green innovation efficiency within the Yangtze River Delta (YRD) urban agglomeration in China. Utilizing panel data from 41 cities spanning 2010 to 2020, the research differentiates between specialized and diversified agglomeration patterns. A super-efficiency SBM-DEA model is employed to evaluate urban green innovation efficiency, incorporating undesirable outputs into the analysis. The findings reveal that specialized agglomeration positively influences green innovation efficiency, particularly in less-developed cities, by lowering production costs and alleviating financing constraints for small and medium-sized enterprises (SMEs) within clusters. In contrast, diversified agglomeration does not significantly improve green innovation efficiency and may even hinder it in noncore and developed cities due to heightened competition and resource inefficiencies. Furthermore, environmental regulation negatively moderates the relationship between both agglomeration patterns and green innovation efficiency. The results emphasize the need to optimize regional industrial structures and promote specialized industrial clusters based on comparative advantages. They also underscore the importance of developing cross-regional coordination mechanisms to enhance the flow of innovation resources and strengthen collaboration among industry, academia, and research institutions. These insights offer valuable guidance for policymakers seeking to refine environmental regulations and industrial policies in urban agglomerations.
Abstract:To solve the network slicing placement problem, the methods based on CNN/RNN were inadequate in handling the randomness of fluctuating channel quality and bandwidth needs for each network slice. While the Monte Carlo Tree Search (MCTS) methodology effectively deals with the unpredictability of each slice’s channel quality and bandwidth request to optimize throughput, it remains time-consuming in finding an optimal solution. The cause is that MCTS relies on a uniform distribution to randomly sample one possible solution, which leads to subpar sampling efficiency. Our objective is to integrate a deep neural network (DNN) to assist MCTS. Specifically, the DNN first analyses the current allocation situation to predict probability distributions for achieving optimizing throughput. MCTS then leverages this DNN-produced probability distribution to pinpoint the best allocation scenario. Experimental results indicate that the performance of DNN-based MCTS with only 50 search iterations surpasses that of the original MCTS with 4,000 search iterations.
Abstract:This study examines the impact of sports event quality on attendees' satisfaction, word-of-mouth (WOM), and return intentions through a chain mediation analysis. Adopting a multidimensional framework, we conceptualize sports event quality across four key dimensions: game quality, interaction quality, outcome quality, and physical environment quality. The study further investigates the mediating roles of satisfaction and WOM in linking event quality to attendees' return intentions, using survey data from 338 participants from running events in Ho Chi Minh City, Vietnam. The findings reveal that event quality significantly enhances satisfaction and WOM, both of which, in turn, positively influence return intentions. Notably, WOM has a more pronounced effect than satisfaction on attendees' likelihood of returning. Additionally, this study underscores the influence of cultural factors in Vietnam, where social proof and peer recommendations play a crucial role in shaping consumer behavior. These insights offer valuable implications for sports event organizers seeking to improve attendee experiences and foster long-term engagement, particularly in emerging markets.
Abstract:This paper explores the integration of carbon finance, particularly focusing on carbon emission rights pledge financing, as a pivotal instrument in attaining carbon peak targets. This integration is crucial in the context of growth stabilization and structural adjustment. The study identifies key challenges hindering the development of carbon emission rights pledge financing, such as process discrepancies, limited bank engagement, and suboptimal risk management. Addressing these issues, we propose a novel model featuring government intervention. This model employs an evolutionary game approach involving key stakeholders: power companies, carbon emission exchanges, and banks. The objective is to navigate towards a Pareto optimal outcome for the pledging of carbon emission rights. Numerical analyses reveal that appropriate government sanctions can effectively streamline the pledging process. Results show a higher compliance rate from carbon emission exchanges under stringent regulations and increased bank participation under direct government incentives. The study advocates for the establishment of a robust public disclosure mechanism for carbon emission pledge financing and urges the government to enact unified trading regulations. This proposed method not only tackles existing impediments in carbon finance but also significantly contributes to the global pursuit of sustainable development.
Abstract:The Georgian medical sector operates within a complex entrepreneurial framework where business structures, particularly holdings, play a dominant role. This paper examines the development and operational mechanisms of holdings in the medical field, analyzing their influence on market competition, pricing strategies, and state-business relations. The study highlights the monopolistic tendencies, cartel agreements, and elite lobbying practices that shape the sector. Additionally, it evaluates the state's regulatory inefficiencies and the economic and social consequences of medical business consolidation. Drawing comparisons with European regulatory practices, the paper advocates for a more structured and transparent approach to price regulation and market competition in Georgia’s healthcare industry.
Abstract:Introduction: Parkinson's disease (PD) affects tens of millions of people globally and is frequently associated with many comorbidities. Its economic burden on the patient, family, and society is heavy. Objectives: This study aimed to compare the outpatient medical costs of patients with PD to those of controls (non-PD group: people without Parkinson's disease) to understand potential future challenges in caring for PD patients. Methods: We identified 2,746 PD patients as the PD group and 5,492 persons, matched in age, gender, and significant comorbidities, as the non-PD group from the National Health Insurance (NHI) database in Taiwan. We calculated the costs and explored the differences in outpatient care costs, including medicines of different classes. Results: The total outpatient payment by NHI in 2013 for PD patients was 77,096 per person. In contrast, it was 40,259 for the non-PD group. For outpatient care in the PD group, the costs of (a) diagnostic fees, (b) medicines, (c) laboratories, images, small operations, and management, (d) unique medical materials, and (e) pharmacist service fees were (a) 12,093, (b) 33,412, (c) 30,254, (d) 1,729, and (e) 1,816, respectively. The non-PD group's costs were (a) 8,981, (b) 19,711, (c) 23,124, (d) 1,345, and (e) 1,181. Neurotic disorders and organic psychotic conditions are present at a much higher frequency in the PD group. Functional GI disorders were more common, too. Finally, the cost of tranquilizers is significantly higher in PD patients. Conclusion: Patients with Parkinson's Disease incur higher medical expenses compared to control groups. The increased reliance on tranquilizers suggests that PD patients face not only a significant economic burden but also substantial mental strain. Therefore, integrating psychotherapy with medical treatment may be essential for effectively supporting individuals with PD.
Abstract:The labor protection policies that policy makers implement to regulate the labor market are critical to labor productivity and unemployment rates. The theory generally holds that employment protect8on ra8ses the costs of layoffs for f8rms. Policy makers consider at the theory framework that it is possible to protect employment and reduce unemployment with Employment Protection Legislation (EPL). There 8s a large l8terature descr8b8ng the effects of str8ct EPL on employment and unemployment rates across countr8es. However, 8t 8s d8ff8cult to be prec8se about the est8mates of the effects of employment protect8on on youth unemployment and labour product8v8ty. The aim of this article is to investigate whether EPL is an important determinant of labor productivity and unemployment rates (unemployment and youth unemployment) in OECD countries during the period 2004-2019 using panel data analysis. The results show that EPL can reduce labor productivity by affecting employment decisions in the labor market.