Search Results (227)

This study presents the development and validation of a high-efficiency optical interface designed for ultra-high-concentration photovoltaic (UHCPV) systems, with a focus on enabling clean and sustainable solar energy conversion. A Fresnel lens serves as the primary optical concentrator in a novel system architecture that integrates advanced optical design with system-level thermal management. The proposed modeling framework combines detailed 3D ray tracing with coupled thermal simulations to accurately predict key performance metrics, including optical concentration ratios, thermal loads, and component temperature distributions. Validation against theoretical and experimental benchmarks demonstrates high predictive accuracies within 1% for optical efficiency and 2.18% for thermal performance. The results identify critical thermal thresholds for long-term operational stability, such as limiting mirror temperatures to below 52 °C and photovoltaic cell temperatures to below 130 °C. The model achieves up to 89.08% optical efficiency, with concentration ratios ranging from 240 to 600 suns and corresponding focal spot temperatures between 37.2 °C and 61.7 °C. Experimental benchmarking confirmed reliable performance, with the measured results closely matching the simulations. These findings highlight the originality of the coupled optical–thermal approach and its applicability to concentrated photovoltaic design and deployment. This integrated design and analysis approach supports the development of scalable, clean photovoltaic technologies and provides actionable insights for real-world deployment of UHCPV systems with minimal environmental impact. Full article

Background: Blood-based comprehensive genomic profiling (CGP), a form of liquid biopsy, is often used for biliary tract cancer (BTC) when tissue-based CGP (tissue CGP) is unavailable, despite lower detection rates. This study explored factors linked to detecting actionable genomic alterations to optimize its use. Methods: We retrospectively analyzed BTC cases in Japan’s C-CAT (June 2019–January 2025), restricting panel comparisons to FoundationOne^® CDx (F1; n = 5019) and FoundationOne^® Liquid CDx (F1L; n = 1550). Missing covariates were handled by multiple imputations (m = 20). Between-panel balance used 1:1 propensity-score matching (caliper 0.2). Outcomes were modeled with logistic regression. Targets included MSI-H, TMB-H, FGFR2/RET/NTRK fusions, BRAF V600E, KRAS G12C, IDH1 mutations, and ERBB2 amplification. An exploratory analysis stratified results by the number of prespecified enrichment factors (0–4). Liquid biopsy was performed using plasma-based comprehensive genomic profiling assays (FoundationOne^® Liquid). Results: Missingness was low; after matching (n = 1549 per group) covariates were well balanced (all|SMD|≤0.05). Detection of any actionable alteration was lower with F1L than F1 (16.8% vs. 24.8%; OR 0.61, 95% CI 0.49–0.75; p < 0.001). F1L also had lower TMB-H (OR 0.62, 0.43–0.90; p = 0.01) and ERBB2 amplification (OR 0.42, 0.31–0.57; p < 0.001), with no significant differences for MSI-H, IDH1, KRAS G12C, or BRAF V600E. Within F1L, non-perihilar location (OR 2.05), liver (1.90), lymph-node (1.41), and lung metastases (1.52) predicted detection of actionable genomic alterations. F1L detection increased from 5.8% (zero factors) to 32.8% (four factors), approximating tissue at three factors. Conclusions: The utility of liquid biopsy can be maximized by carefully selecting samples on the basis of conditions that increase the detection rate. Full article

Power converters play an important role in incorporating renewable energy sources into power systems. Among different converter designs, Buck and Boost converters are popular, as they use fewer components and deliver cost savings and high efficiency. However, Boost converters are known as non–minimum phase systems, imposing harder constraints for designing a robust converter. Developing an efficient controller for these topologies can be difficult since they exhibit nonlinearity and distortion in high frequency modes. The Lyapunov-based Adaptive Backstepping Control (ABSC) technology is used to regulate suitable outputs for these structures. This approach is an updated version of the technique that uses the stability Lyapunov function to produce increased stability and resistance to fluctuations in real-world circumstances. However, in real-time situations, disturbances with larger ranges such as supply voltage changes, parameter variations, and noise may have a negative impact on the operation of this strategy. To increase the controller’s flexibility under more difficult working settings, the most appropriate first gains must be established. To solve these concerns, the ABSC’s performance is optimized using the Reinforcement Learning (RL) adaptive technique. RL has several advantages, including lower susceptibility to error, more trustworthy findings obtained from data gathering from the environment, perfect model behavior within a certain context, and better frequency matching in real-time applications. Random exploration, on the other hand, can have disastrous effects and produce unexpected results in real-world situations. As a result, we choose the Deep Deterministic Policy Gradient (DDPG) approach, which uses a deterministic action function rather than a stochastic one. Its key advantages include effective handling of continuous action spaces, improved sample efficiency through off-policy learning, and faster convergence via its actor–critic architecture that balances value estimation and policy optimization. Furthermore, this technique uses the Grey Wolf Optimization (GWO) algorithm to improve the initial set of gains, resulting in more reliable outcomes and quicker dynamics. The GWO technique is notable for its disciplined and nature-inspired approach, which leads to faster decision-making and greater accuracy than other optimization methods. This method considers the system as a black box without its exact mathematical modeling, leading to lower complexity and computational burden. The effectiveness of this strategy is tested in both modeling and experimental scenarios utilizing the Hardware-In-Loop (HIL) framework, with considerable results and decreased error sensitivity. Full article

A fully automated, buoy-based deployment sensor system is being developed to acquire high-quality water column data, and requires a controller to accurately position an array of sensors at various depths. The sensor system will be potentially deployed under rough ocean conditions. Depth is measured by a pressure sensor and adjusted through a rotating drum powered by a stepper motor. The proposed controller uses a model predictive control algorithm, a type of optimal control that predicts system response to optimize control actions used to track a desired variable-depth, setpoint profile. The profile is calculated to ensure smooth motion of the system, preventing motor malfunction. A simplified system model was created and used to simulate an open-loop test and system response. Constraints were applied to the control actions to match the practical limitations of the stepper motor. The simulated results show successful tracking of both a shallow and deep profile. At this stage of testing, the effects of ocean currents are considered by using a simple disturbance that provides the effect of ocean currents. A practical prototype that can implement the model predictive controller was tested on the physical buoy-based system with good control performance. Full article

Performance analysis in elite football still faces significant challenges: traditional descriptive statistics often fail to capture tactical adaptability, and African teams remain underrepresented in the scientific literature despite achieving historic breakthroughs. The FIFA World Cup Qatar 2022 marked a turning point, with Morocco becoming the first African nation to reach the semi-finals. This study systematically analyzed the tactical, physical, and structural performance of the Moroccan national team across seven matches using official FIFA post-match reports. A three-level methodological framework was adopted: (i) descriptive analysis of key performance indicators (KPIs); (ii) visual profiling through radar charts, heatmaps, and passing networks; and (iii) exploratory modelling using principal component analysis (PCA) and clustering. Results revealed consistent defensive organization, low ball possession (<40% in five matches), and effective counter-attacking transitions, with pressing peaks against Spain (288 actions) and France (299 actions). PCA explained 76% of the variance, identifying two principal axes (physical intensity vs. technical mastery; verticality vs. build-up play) and clustering distinguished three match types: low-block defensive games, transition-oriented games, and open matches. These findings highlight Morocco’s tactical adaptability and sustained physical commitment. The study demonstrates how AI-enhanced analytics and multidimensional data visualization can uncover latent performance patterns and support evidence-based decision-making. Practical implications include actionable insights for performance analysts and coaching staff, particularly as Morocco prepares for the 2025 Africa Cup of Nations and the FIFA World Cups in 2026 and 2030. This integrative approach can serve as a model for federations seeking data-driven performance optimization in elite football. Full article

The increasing contradiction between ecological conservation and community development is a common challenge faced in most protected areas worldwide. Since 2019, China has used a “sky window” policy to alleviate the dilemma of environmental protection and sustainable production activities in national parks. This policy’s impact on household income in national park communities has received little attention. In this study, we aimed to evaluate the impact of the sky window policy on household income in Wuyi Mountain National Park communities in China and explore its mechanism of action in order to provide policy recommendations for achieving the protection goal of the national park and enabling win–win development of the community. Based on a total of 951 samples, which were collected through face-to-face interviews with 518 households in two periods, we used the difference-in-differences (DID) model to obtain consistent results and conducted robustness tests on the model by employing propensity score matching (PSM). The results showed that the “sky window” policy had a significant negative impact on the income of households in national park communities, which was mainly caused by the relaxation of restrictive regulations on farmers’ planting and breeding activities within national parks. The findings indicate that government departments in China need to further improve the laws and regulations regarding national park construction, establish a dynamic evaluation mechanism to regularly review the effects of the “sky window” policy, and make timely adjustments based on changes in the ecological environment of national parks and the development needs of local communities. At the same time, to ensure a stable source of income for residents, it is also necessary to establish a platform for realizing the value of ecological products, strengthen support for livelihood transformation, and establish long-term benefit linkage mechanisms. This study contributes to the research on the effective management of national parks, community welfare improvement, and sustainable development in developing countries. Full article

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC. Full article

Urban heat island conditions increase heat exposure and constrain safe outdoor activities. Urban forests can mitigate thermal loads; however, stand morphology can produce divergent microclimates. We aimed to quantify how stand type (open vs. closed), season (spring, summer, fall), and activity intensity (MET 1.0–6.0) jointly modulate thermal comfort and to identify season-specific optimal MET levels in an urban forest in Daejeon, Republic of Korea. We combined site-specific 3D canopy modeling with hourly Predicted Mean Vote (PMV) simulations driven by AMOS tower data (2023–2024). Comfort was defined as |PMV| ≤ 0.5. Analyses included seasonal means, Cliff’s delta, and generalized estimating equation logistic models to estimate the SITE × SEASON × MET interactions and predict comfort probabilities. Across most seasons and MET levels, C1 was more comfortable than C2. However, at MET 1.0 in summer, the pattern was reversed, which may reflect the canopy shading and associated decreases in mean radiant temperature (MRT) of C2. Comfort peaked at MET 2.0–3.0 and declined sharply at ≥4.5 MET. The three-way SITE × SEASON × MET interaction was significant (p < 0.001). The season-specific optimal MET values under our boundary conditions were 3.0 (spring), 2.0–2.5 (summer), and 3.0 (fall). These simulation-based PMV-centered findings represent model-informed tendencies. Nevertheless, they support actionable guidance: prioritize high-closure stands for low-intensity summer use, leverage open stands for low-to-moderate activities in spring and fall, and avoid high-intensity programs during warm periods. These results inform the programming and design of urban-forest healing and recreation by matching stand type and activity intensity to season to maximize comfortable hours. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

With the widespread adoption of intelligent electronic devices (IEDs) in smart substations, the real-time data transmission and interoperability features of the IEC 61850 communication standard play a crucial role in ensuring seamless automation system integration. This paper presents a hardware-in-the-loop (HIL) platform experiment analysis based on a simulated IED and man-in-the-middle (MITM) attack, leveraging built-in IEC 61850 protocol software to replicate an existing substation communication architecture in cyber physical systems. This study investigates the framework performance and protocol robustness of this approach. First, the physical network infrastructure of smart grids is analyzed in detail, followed by the development of an HIL testing platform tailored for discrete communication network scenarios. Next, virtual models of intelligent electrical equipment and MITM attacks are created, along with their corresponding communication layer architectures, enabling comprehensive simulation analysis. Finally, in the 24-h stability operation test and the test of three typical fault scenarios, the simulated IED can achieve 100% of the protocol consistency passing rate, which is completely consistent with the protection action decision of the physical IED, the end-to-end delay is less than 4 ms, and the measurement accuracy matches the accuracy level of the physical IED, which verifies that the proposed test platform can effectively guide the commissioning of smart substations. Full article

This study investigates the impact of injuries on National Basketball Association (NBA) player performance over 20 seasons, using large-scale performance data and a statistical evaluation. Injury events were matched with player–game performance metrics to assess how various injury types influence short-, medium-, and long-term performance outcomes, measured across 2-, 5-, and 10-game windows. Using paired sample t-tests and Cohen’s d, we quantified both the statistical significance and effect size of changes in key performance metrics before and after injury. The analysis applies paired t-tests and Cohen’s d to quantify the statistical and practical significance of performance deviations pre- and post-injury. Our results show that while most injury types are associated with measurable performance declines, especially in offensive and defensive ratings, certain categories, such as cardiovascular injuries, demonstrate counterintuitive improvements post-recovery. These patterns suggest that not all injuries have equivalent consequences and highlight the importance of individualized recovery protocols. This work contributes to the growing field of sports injury analytics by combining statistical modeling and sports analytics to deliver actionable insights for coaches, medical staff, and performance analysts in managing player rehabilitation and optimizing return-to-play decisions. Full article

This paper examines the integration of machine learning (ML) techniques in professional football, focusing on two key areas: (i) player and team performance, and (ii) match outcome prediction. Using a systematic methodology, this study reviews 172 papers from a five-year observation period (2019–2024) to identify relevant applications, focusing on the analysis of game actions (free kicks, passes, and penalties), individual and collective performance, and player position. A predominance of supervised learning, deep learning, and hybrid models (which integrate several ML techniques) is observed in the ML categories. Among the most widely used algorithms are decision trees, extreme gradient boosting, and artificial neural networks, which focus on optimizing sports performance and predicting outcomes. This paper discusses challenges such as the limited availability of public datasets due to access and cost restrictions, the restricted use of advanced visualization tools, and the poor integration of data acquisition devices, such as sensors. However, it also highlights the role of ML in addressing these challenges, thereby representing future research opportunities. Furthermore, this paper includes two illustrative case studies: (i) predicting the date Cristiano Ronaldo will reach 1000 goals, and (ii) an example of predicting penalty shoots; these examples demonstrate the practical potential of ML for performance monitoring and tactical decision-making in real-world football environments. Full article

Longnan is the largest olive cultivation area in China. The unique microclimates in Longnan make it an ideal testing ground for climate-resilient cultivation strategies with broader applications across similar regions, yet predictive models linking weather to oil quality remain scarce. This study establishes a climate suitability evaluation model for olive cultivation in central Longnan based on meteorological data and olive quality data in the Fotanggou planting base. Four key climatic factors are identified: cumulative sunshine hours during the fruit coloring to ripening period, average temperature during the fruit coloring to harvesting period, number of cloudy and rainy days during the harvesting period, and relative humidity during the fruit setting to fruit enlargement period. Olive oil quality is graded into three levels (Excellent III, Good II, Fair I) based on acidity, linoleic acid, and peroxide value using K-means clustering. A climate suitability index is developed by integrating these factors, with weights determined via principal component analysis. The model is validated against an olive quality report from the Dabao planting base, showing an 80% match rate. From 1991 to 2023, 87.9% of years exhibit suitable or moderately suitable conditions, with 100% of years in the past decade (2014–2023) reaching “Good” or “Excellent” levels. This model provides a scientific basis for evaluating and predicting olive oil quality, supporting sustainable olive industry development in Longnan. This model provides policymakers and farmers with actionable insights to ensure the long-term sustainability of olive industry amid climate uncertainty. Full article

To enhance regional flood control capacity, this study focused on the DX River section in Zhejiang Province. Unmanned vessel bathymetry was employed to obtain precise river cross-section data. A hydrodynamic model was established to simulate flood propagation processes and conduct flood routing analyses. Flood scenarios under 5-year, 10-year, and 20-year return periods were simulated to assess water level variations and overflow risks. The results indicate that under a 5-year flood, 19.5% of the right bank fails to meet flood control standards. This risk intensifies significantly with increasing return periods. Building on these findings, a flood optimal operation model was developed. The resulting coordinated strategy, which lowers the peak water level by 1.2 m during a 20-year flood, is sufficient to prevent overflow at the critical section and enhances regional flood control capacity. This is followed by dynamic gate regulation to match the outflow to the inflow. Dynamic regulation of spillway gates should then be implemented to achieve outflow rates commensurate with the incoming flood magnitude. This study demonstrates a robust workflow from high-resolution data acquisition to actionable operational rules, providing a transferable framework for mitigating flood risks in complex, regulated river systems. Full article

In football, accurately pinpointing key events like passes is vital for analyzing player and team performance. Despite continuous technological advancements, existing tracking systems still face challenges in accurately synchronizing events and positional data accurately. This is a case study that proposes a new method to synchronize events and positional data collected during football matches. Three datasets were used to perform this study: a dataset created by applying a custom algorithm that synchronizes positional and event data, referred to as the optimized synchronization dataset (OSD); a simple temporal alignment between positional and event data, referred to as the raw synchronization dataset (RSD); and a manual notational data (MND) from the match video footage, considered the ground truth observations. The timestamp of the pass in both synchronized datasets was compared to the ground truth observations (MND). Spatial differences in OSD were also compared to the RSD data and to the original data from the provider. Root mean square error (RMSE) and mean absolute error (MAE) were utilized to assess the accuracy of both procedures. More accurate results were observed for optimized dataset, with RMSE values of RSD = 75.16 ms (milliseconds) and OSD = 72.7 ms, and MAE values RSD = 60.50 ms and OSD = 59.73 ms. Spatial accuracy also improved, with OSD showing reduced deviation from RSD compared to the original event data. The mean positional deviation was reduced from 1.59 ± 0.82 m in original event data to 0.41 ± 0.75 m in RSD. In conclusion, the model offers a more accurate method for synchronizing independent datasets for event and positional data. This is particularly beneficial for applications where precise timing and spatial location of actions are critical. In contrast to previous synchronization methods, this approach simplifies the process by using an automated technique based on patterns of ball velocity. This streamlines synchronization across datasets, reduces the need for manual intervention, and makes the method more practical for routine use in applied settings. Full article