Search Results (5,955)

This paper utilizes unique high-frequency, daily electricity consumption data across economic sectors to examine the impact of monetary policy shocks on economic output, with a particular focus on the network spillover effects and sectoral heterogeneity introduced by inter-sector linkages. The study finds that quantity-based monetary policy (e.g., M2) generates significant positive and cascading spillover effects within the production network. However, the total effects of monetary policy shocks are broadly similar across upstream, midstream, and downstream sectors, exhibiting only minor differences. Notably, the proportion of network (indirect) effects increases systematically from upstream to downstream sectors and displays marked sectoral heterogeneity. In contrast, interest-rate-based monetary policy displays insufficient spatial spillover through production networks. These findings offer important insights for policymakers to optimize structural policy design and promote coordinated sectoral chain development, which can guide the pursuit of sustainable economic strategies that balance growth, resource utilization and sectoral interdependencies. Full article

Background: Medical resident burnout is a critical threat to healthcare workforce sustainability, with physical activity (PA) posited as a protective factor. This bibliometric analysis maps the global research landscape on this topic from 2005 to 2025. Methods: Systematic search of the Web of Science Core Collection identified 110 relevant English-language articles. Performance analysis and scientific mapping were conducted using R and VOSviewer. Results: The field saw an annual growth rate of 3.35%, with a peak of 16 publications in 2019. The United States was the dominant contributor, accounting for 68% of the total output. Analysis identified several major thematic areas, including stress and behavioral factors, occupational mental health, and institutional support mechanisms. The findings reveal a rapidly growing but geographically concentrated body of research, underscoring a significant gap in globally representative evidence. Conclusions: This analysis provides a foundational map for future research, underscoring the need for institutional wellness programs incorporating PA, international collaborative studies, and policy-level interventions. We conclude that integrating physical activity is not a luxury but a critical strategy for healthcare system sustainability. Full article

A novel methodology for hybrid energy management in aquaculture is introduced, aimed at enhancing self-sufficiency and optimizing grid-related cash flows. Wind and solar energy generation are modeled using calibrated turbine performance curves and PVGIS data, respectively, with a photovoltaic capacity of 120 kWp. The system also incorporates a 250 kW small hydroelectric plant and a wood drying kiln that utilizes surplus wind energy. This study conducts a comparative analysis between HY4RES, a research-oriented simulation model, and HOMER Pro, a commercially available optimization tool, across multiple hybrid energy scenarios at two aquaculture sites. For grid-connected configurations at the Primary site (base case, Scenarios 1, 2, and 6), both models demonstrate strong concordance in terms of energy balance and overall performance. In Scenario 1, a peak power demand exceeding 1000 kW is observed in both models, attributed to the biomass kiln load. Scenario 2 reveals a 3.1% improvement in self-sufficiency with the integration of photovoltaic generation, as reported by HY4RES. In the off-grid Scenario 3, HY4RES supplies an additional 96,634 kWh of annual load compared to HOMER Pro. However, HOMER Pro indicates a 3.6% higher electricity deficit, primarily due to battery energy storage system (BESS) losses. Scenario 4 yields comparable generation outputs, with HY4RES enabling 6% more wood-drying capacity through the inclusion of photovoltaic energy. Scenario 5, which features a large-scale BESS, highlights a 4.7% unmet demand in HY4RES, whereas HOMER Pro successfully meets the entire load. In Scenario 6, both models exhibit similar load profiles; however, HY4RES reports a self-sufficiency rate that is 1.3% lower than in Scenario 1. At the Secondary site, financial outcomes are closely aligned. For instance, in the base case, HY4RES projects a cash flow of 54,154 EUR, while HOMER Pro estimates 55,532 EUR. Scenario 1 presents nearly identical financial results, and Scenario 2 underscores HOMER Pro’s superior BESS modeling capabilities during periods of reduced hydroelectric output. In conclusion, HY4RES demonstrates robust performance across all scenarios. When provided with harmonized input parameters, its simulation results are consistent with those of HOMER Pro, thereby validating its reliability for hybrid energy management in aquaculture applications. Full article

The investigation into the complementary roles of essential oils (EOs) and organic acids in enhancing rumen physiology is increasingly gaining recognition within the field of animal nutrition. Essential oils are known for their antimicrobial effects, which can specifically target certain microbial populations in the rumen, thereby impacting fermentation processes, methane output, and nutrient digestion. In addition, the integration of organic acids plays a crucial role in stabilizing rumen pH and steering the metabolic activities of bacterial populations toward propionate production, a process essential for energy metabolism in ruminants. The concurrent use of essential oils and organic acids may yield synergistic benefits that could further optimize ruminal fermentation efficiency, enhance feed conversion rates, and lower methane emissions. This systematic review used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The literature search was meticulously designed to encompass parameters related to ruminant species, feed additives, essential oils, organic acids, synergistic effects, and rumen physiology. The efficacy of both organic acids and essential oils is highly dependent on their concentration and the specific combinations utilized. When certain essential oils are used in conjunction with selected organic acids, they may mitigate any potential negative effects on fermentation, thereby fostering a more favorable environment for the proliferation of beneficial microbial communities. Understanding the relationship between essential oils and organic acids is essential for the formulation of diets that enhance rumen health while concurrently reducing environmental pressures through diminished methane emissions. Future research should prioritize long-term in vivo investigations to gain more comprehensive insights into the interactions among these dietary components and identify the optimal combinations for ruminant feeding strategies. Full article

This study tested whether classic and specific bioelectrical impedance vector analysis (BIVA) parameters could explain metabolic and mechanical performance in endurance-trained trail runners. Fifteen males ($\dot{\mathrm{V}}$O₂max 61.04 ± 6.91 mL·kg⁻¹·min⁻¹) completed a 60-min treadmill protocol at 70% $\dot{\mathrm{V}}$O₂max across randomized slopes (−7% to +7%), with continuous gas-exchange, heart-rate, and running-power recording; whole-body BIVA was obtained immediately pre- and post-exercise. Post-test, impedance and resistance increased (+2.73%, +2.84%), while reactance (Xc) and phase angle decreased (−2.36%, −4.91%); all were significant and mirrored by both classic and specific indices, consistent with acute fluid loss and altered cellular status. After Benjamini–Hochberg adjustment, baseline Xc/height correlated inversely with $\dot{\mathrm{V}}$CO₂peak and $\dot{\mathrm{V}}$CO₂mean, whereas exercise-induced changes in ΔXc/height and ΔXcspecific correlated positively with both metabolic variables and mean power. Stepwise regression retained ΔXc/h or ΔXcspecific as the only BIVA predictors for $\dot{\mathrm{V}}$CO₂peak, $\dot{\mathrm{V}}$CO₂mean, and mean power output, explaining ~31–36% and ~22–23% of the variance, respectively; classic and specific approaches performed similarly. No bioelectrical variable predicted $\dot{\mathrm{V}}$O₂max. These preliminary findings suggest that acute reactance shifts may provide a modest yet sensitive, non-invasive index of exercise-induced physiological responses, warranting confirmation in larger and more diverse cohorts. Full article

Inconel 625 is a nickel-based superalloy widely applied in aerospace and energy sectors due to its high strength and corrosion resistance. However, its poor machinability remains a significant challenge in precision manufacturing. This study investigates the influence of tool geometry and cutting parameters on surface roughness of Inconel 625 during turning operations under the minimum quantity lubrication (MQL) conditions. Experiments were carried out using three types of cutting inserts with distinct chip breaker geometries while systematically varying the cutting speed, feed rate, and depth of cut. The results were statistically analyzed using analysis of variance (ANOVA) to determine the significance of individual factors. The findings reveal that both the type of cutting insert and the process parameters have a considerable effect on surface roughness, which is the key output examined in this study. Cutting forces and chip type were examined to provide complementary insights and improve understanding of the observed relationships. Based on the results, an optimal set of cutting data was proposed to achieve a required surface roughness during the turning of Inconel 625 with MQL. Furthermore, a practical algorithm was developed to support the selection of cutting parameters in industrial applications. Analysis of the results showed that a cutting insert with a 0.4 mm corner radius achieved the required surface finish (Rz ≤ 0.4 µm). Furthermore, the analysis revealed a significant effect of the thermal properties of Inconel 625 on machining results and chip geometry. Full article

The optimal control of cooling water systems is of great significance for energy saving inchiller plants. Previously optimal control methods optimize the flow rate, temperature or temperature difference setpoints but cannot control pumps and cooling tower fans directly. This study proposes a direct optimal control method for pumps and fans based on derivativecontrol strategyby decoupling water flow rate optimization and airflow rate optimization, which can make the total power of chillers, pumps and fans approach a minimum. Simulations for different conditions were performed for the validation and performance analysis of the optimal control strategy. The optimization algorithms and implementation methods of direct optimal control were developed and validated by experiment. The simulation results indicate that total power approaches a minimum when the derivative of total power with respect to water/air flow rate approaches zero. The power-saving rate of the studied chiller plant is 13.2% at a plant part-load ratio of 20% compared to the constant-speed pump/fan mode. The experimental results show that the direct control method, taking power frequency as a controlled variable, can make variable frequency drives regulate their output frequencies to be equal to the optimized power frequencies of pumps and fansin a timely manner. Full article

High-average-power extreme ultraviolet (EUV) sources are essential for large-scale nanoscale chip manufacturing, yet commercially available laser-produced plasma sources face challenges in scaling to the kilowatt level. We propose a novel scheme that combines the high repetition rate of a diffraction-limited storage ring with a regenerative amplifier free-electron laser (RAFEL) employing a transverse gradient undulator (TGU). By introducing dispersion in the storage ring, electrons of different energies are directed into corresponding magnetic field strengths of the TGU, thereby satisfying the resonance condition under a large energy spread and increasing the FEL gain. Simulations show that at equilibrium, the average EUV power exceeds 1 kW, with an output pulse energy reaching ∼2.86 $\mathsf{\mu }$J, while the energy spread stabilizes at ∼0.45%. These results demonstrate the feasibility of ring-based RAFEL with TGU as a promising route toward kilowatt-level EUV sources. Full article

Nowadays, Flutter with the Dart programming language has become widely popular in mobile developments, allowing developers to build multi-platform applications using one codebase. An increasing number of companies are adopting these technologies to create scalable and maintainable mobile applications. Despite this increasing relevance, university curricula often lack structured resources for Flutter/Dart, limiting opportunities for students to learn it in academic environments. To address this gap, we previously developed the Flutter Programming Learning Assistance System (FPLAS), which supports self-learning through interactive problems focused on code comprehension through code-based exercises and visual interfaces. However, it was observed that many students completed the exercises without fully understanding even basic concepts, if they already had some knowledge of object-oriented programming (OOP). As a result, they may not be able to design and implement Flutter/Dart codes independently, highlighting a mismatch between the system’s outcomes and intended learning goals. In this paper, we propose a guided self-study approach of integrating documentation, code, visual output, and exercise in FPLAS. Two existing problem types, namely, Grammar Understanding Problems (GUP) and Element Fill-in-Blank Problems (EFP), are combined together with documentation, code, and output into a new format called Integrated Introductory Problems (INTs). For evaluations, we generated 16 INT instances and conducted two rounds of evaluations. The first round with 23 master students in Okayama University, Japan, showed high correct answer rates but low usability ratings. After revising the documentation and the system design, the second round with 25 fourth-year undergraduate students in the same university demonstrated high usability and consistent performances, which confirms the effectiveness of the proposal. Full article

Expectation Propagation (EP) has emerged as a promising detection algorithm for large-scale multiple-input multiple-output (MIMO) systems owing to its excellent performance and practical complexity. However, transmit antenna correlation significantly degrades the performance of EP detection, especially when the number of transmit and receive antennas is equal and high-order modulation is adopted. Based on the fact that the eigenvector matrix of the channel transmit correlation matrix approaches asymptotically to a discrete Fourier transform (DFT) matrix, a DFT precoder is proposed to effectively eliminate transmit antenna correlation. Simulation results demonstrate that for high-order, high-dimensional massive MIMO systems with strong transmit antenna correlation, employing the proposed DFT precoding can significantly accelerate the convergence of the EP algorithm and reduce the detection error rate. Full article

The harvesting of dragon fruit remains challenging due to uneven clamping forces, high fruit damage rates, and low redundancy in conventional end-effectors. To address these issues, we developed a novel embracing end-effector with a Variable-Span Arch (VSA) structure. The VSA design enables adaptive clamping force distribution and effective torsional fruit separation, significantly reducing static pressure damage. Theoretical modeling, mechanical testing, and field experiments were conducted to evaluate its performance. Results show that the proposed end-effector achieves a 95% harvesting success rate, with an average picking time of 15 s per fruit, and can output a maximum torque of 18 kgf·cm, which is sufficient for dragon fruit detachment. These findings demonstrate that the VSA-based embracing end-effector offers a low-damage, efficient, and robust solution for dragon fruit harvesting, providing practical guidance for robotic applications in tropical fruit production. Full article

Evidence has suggested that post-exercise heart rate recovery (PHRR) is a useful tool in evaluating cardiac autonomic function. Altered cardiac autonomic function is characterized by heightened sympathetic activation and the abnormal reactivation of the parasympathetic nervous system and is associated with delayed HRR. Although grape seed extract (GSE) supplementation has been shown to increase nitric oxide production and modify sympathetic output, there is limited evidence on its potential beneficial effects on PHRR. We investigated the effect of GSE supplementation on PHRR during sympathetic overactivation induced by muscle metaboreflex activation (MMA) in young individuals. Participants were randomly assigned, via a double-blind, cross-over design, to either receive GSE (300 mg, two capsules) or PL (300 mg, two capsules), with a washout period of at least 72 h. between trials. A submaximal exercise test was performed using a cycle ergometer combined with an isometric handgrip exercise using a handgrip dynamometer and blood flow occlusion by placing a cuff over the brachial artery of the dominant arm. PHRR was measured at 5 s. intervals throughout the experiment. The PHRR was evaluated between GSE and PL at every min. for 300 s. PHRR kinetics significantly improved following GSE supplementation (74.3 ± 7.5 s) compared with the PL condition (86.2 ± 10.4 s). Our results suggest that GSE is effective in improving HRR kinetics during heightened sympathetic activity induced by MMA in young individuals (p = 0.034; ES = 0.4). Thus, regular treatment with GSE may provide a nonpharmacological intervention to reduce sympathetic hyperactivity in conditions where excessive sympathetic activity is consistently present. Full article

Crosswind is a critical environmental factor affecting the dynamic interaction between the pantograph and catenary in high-speed trains, which can severely compromise the operational stability of the system. To address this challenge, this study develops an active pantograph control scheme for crosswind disturbances by employing a neural network-based PID controller. First, the target value is determined based on the train operating speed and inherent data of the pantograph-catenary system, and a PID controller is constructed. Subsequently, a neural network is integrated into the controller to train the system’s output contact force and PID parameters using its nonlinear approximation capability, thereby optimizing the parameters and achieving effective control of the system. The effectiveness of the controller is then validated by applying the proposed method to a high-speed train pantograph-catenary system under crosswind conditions, with its control performance thoroughly analyzed. The results indicate that the proposed control scheme demonstrates effective regulation of the pantograph-catenary system across various typical crosswind scenarios, achieving significant reduction or even complete elimination of pantograph-catenary’s contact loss rate while exhibiting strong robustness, thereby proving fully applicable for practical implementation in high-speed railway engineering applications. Full article

Parkinson’s disease (PD) is characterized by motor symptoms, with key diagnostic features, such as rigidity, traditionally assessed through subjective clinical scales. This study proposes a novel hybrid framework integrating model-driven biomechanical features (damping ratio, decay rate) and data-driven statistical features (maximum detail coefficient) from wearable sensor data during a modified pendulum test to quantify shoulder girdle rigidity objectively. Using weak supervision, these features were unified to generate robust labels from limited data, achieving a 10% improvement in PD/healthy control classification accuracy (0.71 vs. 0.64) over data-driven methods and matching model-driven performance (0.70). The damping ratio and decay rate, aligning with Wartenberg pendulum test metrics like relaxation index, revealed velocity-dependent aspects of rigidity, challenging its clinical characterization as velocity-independent. Outputs correlated strongly with UPDRS rigidity scores (r = 0.78, p < 0.001), validating their clinical utility as novel biomechanical biomarkers. This framework enhances interpretability and scalability, enabling remote, objective rigidity assessment for early diagnosis and telemedicine, advancing PD management through innovative sensor-based neurotechnology. Full article

Older adults often face significant challenges in understanding medical information due to cognitive aging and limited health literacy. Existing simplification models, while effective in general domains, cannot adapt content for elderly users, frequently overlooking narrative tone, readability constraints, and semantic fidelity. In this work, we propose GENSIM—a Generative Expert-Narrated Simplification Model tailored for age-adapted medical text simplification. GENSIM introduces a modular architecture that integrates a Dual-Stream Encoder, which fuses biomedical semantics with elder-friendly linguistic patterns; a Persona-Tuned Narrative Decoder, which controls tone, clarity, and empathy; and a Reinforcement Learning with Human Feedback (RLHF) framework guided by dual discriminators for factual alignment and age-specific readability. Trained on a triad of corpora—SimpleDC, PLABA, and a custom NIH-SeniorHealth corpus—GENSIM achieves state-of-the-art performance on SARI, FKGL, BERTScore, and BLEU across multiple test sets. Ablation studies confirm the individual and synergistic value of each component, while structured human evaluations demonstrate that GENSIM produces outputs rated significantly higher in faithfulness, simplicity, and demographic suitability. This work represents the first unified framework for elderly-centered medical text simplification and marks a paradigm shift toward inclusive, user-aligned generation for health communication. Full article