Abstract:The purpose of this study is to evaluate and compare the effects of pull-ups, push-ups, and burpees on arm muscle power, strength, and endurance. A total of 40 male untrained students (aged 16-18 years) were randomly divided into four groups: G1 (burpee), G2 (push-up), G3 (pull-up), and G4 (control group). All subjects performed respected exercise for six weeks. The main outcome variables were arm muscle strength, strength, and endurance that were measured at pretest (week 0) and posttest (week 6). All statistical analysis were performed using SPSS 30 for Mac. The burpee group (G1) demonstrated significant improvements in muscular power (p=0.000) and strength (p=0.002). The push-up group showed significant gains in muscular strength (p=0.000) and endurance (p=0.002). The pull-up group demonstrated significant improvements in power, strength, and endurance (p<0.005). Meanwhile, G4 did not show any significant changes in all measured variables. These results underscore the importance of selecting exercises based on specific training objectives and show that bodyweight training, if designed appropriately, can result in meaningful improvements in various aspects of upper arm muscle fitness.
Abstract:Introduction: To assess the impact of delayed surgery on the prognosis of patients with operable stage non-small cell lung cancer (NSCLC). Methods: A retrospective review of 140 patients with clinical operable stage NSCLC (I, II, IIIA) who underwent curative surgery was conducted. Patients were divided into two groups: those who underwent surgery within 3 months of initial lung tumor detection (non-delayed group, n=82) and those with surgery beyond 3 months (delayed group, n=58). Patient characteristics, reasons for surgical delay, pathology, surgical outcomes, and long-term survival were analyzed. Results: The median time from lung tumor detection to surgery was 13.7 months (range, 3.1 months-12.1 years) in delayed group, and 0.9 months (range, 3 days-2.8 months) in non-delayed group (P＜0.001). The delayed group had a higher proportion of patients at clinical stages I and II, and a lower proportion at clinical stage IIIA as compared with non-delayed group (P=0.046). The incidence of lymphovascular invasion was significantly lower in delayed group (P=0.031). There was no significant difference between groups in terms of pathological upstaging, positive surgical margins, surgical morbidity and mortality, adjuvant treatment, disease-free survival, and overall survival. Conclusions: The prognosis for patients with delayed diagnosis or surgery for NSCLC may not necessarily be worse than for those who receive timely surgery. For these patients, in spite of previous delay, surgical resection remains an important treatment option if lung cancer is assessed as an operable stage disease preoperatively.
Abstract:This study presents a comparative analysis of two advanced analytical methods—the Elzaki Transform Homotopy Perturbation Method (ETHPM) and the Fractional Reduced Differential Transform Method (FRDTM)—for solving nonlinear fractional partial differential equations (FPDEs) arising in biological population dynamics. After establishing the mathematical foundations of fractional calculus, the Elzaki transform, and homotopy perturbation theory, we demonstrate the applicability of both methods to FPDEs modelling population growth and swarm behaviour. Our results reveal that ETHPM and FRDTM yield highly accurate approximate solutions, underscoring their efficacy as computational tools for complex biological systems. The study highlights the broader implications of these fractional-order models in ecology and population dynamics, bridging theoretical mathematics with practical applications in life sciences. Through systematic comparisons, we provide insights into the strengths and limitations of each method, offering valuable guidance for researchers working with nonlinear fractional systems in biological contexts.
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 purpose of this study is to develop a self-assessment scale designed to measure teachers’ competencies in biomimicry teaching. The sample consists of 543 teachers. The validity of the scale was established through an extensive literature review, comparisons with existing instruments, and expert evaluations. The reliability of the scale, as measured by Cronbach’s alpha coefficients, ranged from 0.74 to 0.95. Exploratory factor analysis revealed that the 27 items in the scale were grouped under six factors: attitude towards biomimicry teaching, awareness of biomimicry applications, attitude towards nature, awareness of biomimicry concepts, self-assessment of biomimicry pedagogy, and self-efficacy in biomimicry teaching. This six-factor structure accounted for 70.08% of the total variance. Confirmatory factor analysis indicated that factor loadings ranged from 0.41 to 0.98. The stability of the scale was assessed using the test-retest method. To ensure external validity, a correlation analysis was conducted between the scores of this scale and the Environmental Literacy Scale for Adults, revealing a strong positive relationship. Overall, the Self-Assessment Scale for Biomimicry Teaching has been shown to be a valid and reliable instrument for assessing teachers’ competencies in biomimicry instruction based on their self-perceptions.
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 aims to assess the impact of plyometric training on increasing leg muscle strength and explosive power. The research method applied is quasi-experimental research with pre-test and post-test design. The research subjects were divided into three groups, consisting of 11 students each. The first group (K1) received a combination of squat jump and skater hops exercise, K2 received a combination of a jump for height and ins and outs exercise, while K3 received conventional training. All subjects were then instructed to perform a six-week respected training program consisting of three sessions a week, making it a total of 18 sessions. The intensity was set at 60% RM and gradually increased to 80% RM. Pre-test and post-test on strength and power were obtained using Leg Dynamometer and Jump MD. Data were presented descriptively and bivariate analysis was done using SPSS 23 for Windows. Paired sample t-test showed that each group had a significant increase (p < 0.05) after six weeks of treatment, with the highest improvement in strength and power observed in K1 ( strength = 11.57%, power = 7.57%). Meanwhile, K3 had the smallest but significant increase in both strength and power. Based on the aforementioned explanation, it can be concluded that subjects assigned to PT had a greater increase compared to those in the control group. It indicated that PT induces adaptations in neural and musculoskeletal systems, which lead to producing greater muscle force, resulting in higher gain in measured variables.
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.