This study used a 12-week plyometric and strength training program as an intervention to improve upper- and lower-extremity muscle strength for jumping and landing when climbing high walls. Sixty general non-athlete male college students were openly recruited and divided into an experimental group and a control group. The experimental group underwent a plyometric and strength training program twice a week for 12 weeks (24 sessions). The intervention was divided into three phases, each lasting four weeks, with the training intensity gradually increasing in each phase. A hand grip dynamometer was used to measure grip strength, and a PASCO double-track force plate was used to assess upper-extremity push-up force and lower-extremity take-off and landing strength. The results of the 12-week intervention showed that the experimental group experienced significant increases in grip strength (both hands), hand-ground reaction force, and upper-extremity hang time. Additionally, the time of upper-extremity action on the force plate decreased. Lower-extremity take-off strength improved, as reflected in increased ground reaction force, rate of force development, and passage time. Upon landing, ground reaction force decreased by 3.2%, and cushioning time shortened by 52.7%. This study concludes that plyometric and strength training have promising effects in enhancing upper- and lower-extremity strength, particularly in climbing and landing tasks.

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization.

To address the inherent limitations of qualitative hygiene inspections, this study establishes a structured MCDM framework to evaluate kitchen hygiene management in Taiwan’s catering industry by integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The model integrates expert-weighted criteria to facilitate a structured risk-oriented assessment and support sustainable hygiene management through prioritized resource allocation and more systematic hygiene management. The AHP results determined hygiene behavior, cooking and processing, and storage operation management as the most influential criteria, underscoring the critical role of direct food handling practices. The framework was empirically applied to five large-scale catering enterprises and international tourist hotels with multinational operational backgrounds. TOPSIS analysis revealed significant performance variability, with establishment D achieving the highest relative closeness coefficient (0.6125) and establishment E the lowest (0.2358). These findings indicate that operational control measures play a more critical role in food safety and sustainable hygiene governance than supporting infrastructure alone. The proposed model serves as a quantitative decision-support tool for both industry self-assessment and regulatory inspections, facilitating prioritized resource allocation, continuous hygiene improvement, improved food safety governance, and more consistent long-term hygiene management practices. Sensitivity analysis further demonstrated that the overall comparative ranking structure remained generally consistent under alternative normalization conditions, although minor variation was observed between the two highest-performing alternatives.

Tourism industry has been growing in recent years, thus becoming a highly competitive market. Government improves its tourism competitiveness to attract a large amount of foreign tourists, increase tourist revenue, and create local employment opportunities. S tudies of tourism competitiveness abroad began in the 1990s, with the construction of competitive models, and the development of indexes and relevant empirical researches that are getting more sophisticated. Tourism competitiveness abroad can be divided into two categories. One approach is that researchers develop a set of indexes for tourism owners to evaluate the country's relative strengths based on the 7-point Likert scale. The other approach is aimed at calculating the tourism competitiveness comprehensive index. Case studies of tourism competitiveness are often restrained by high survey costs. The report from World Economic Forum 20010-2012 considered the deferred effect and used the statistical data from World Tourism Organization of United Nations from 2010 to 2012. This research explored the important indexes that affect the amount of foreign tourists and revenue in twelve nations in East Asia. This study revealed the dominant indexes affecting tourist arrivals and tourist revenue in East Asia include national safety and security, tourism infrastructures and cultural resources. The results can be applied to tourism industry and developing tourism strategy.