Search Results (90)

Soccer’s global popularity as the world’s favorite sport is driven by many factors, with high player salaries being one of the key reasons behind its appeal. These salaries not only reflect on-field performance, but also capture a broader evaluation of player value. Despite the increasing use of performance data in sports analytics, a critical gap remains in establishing fair compensation models that comprehensively account for both quantifiable and intangible contributions. To address these challenges, this study adopts machine learning (ML) techniques that model player salaries based on a combination of performance metrics and contextual features. This research focuses on reducing bias and improving transparency in salary decisions through a systematic, data-driven approach. Utilizing a dataset spanning the 2016–2022 seasons, we apply both traditional and automated ML frameworks to uncover the most influential factors in salary determination. The results indicate a nearly 17% improvement in R² and about a 30% reduction in MAE after incorporating the newly constructed features and methods, demonstrating a significant enhancement in model performance. Gradient Boosting demonstrates superior effectiveness, revealing a group of significantly underestimated and overestimated players, and showcasing the model’s proficiency in detecting valuation discrepancies. Full article

The penetration of renewable energy, especially solar and wind, is increasing globally to promote a sustainable environment. However, in the Middle East, this momentum is slower compared to other regions, primarily due to abundant local fossil fuel reserves and historically low energy prices. This trend is shifting, with several countries, including the Kingdom of Saudi Arabia (KSA), setting ambitious goals. Specifically, KSA’s Vision 2030 aims to generate 50% of its energy from renewable sources by 2030. Due to favorable conditions for solar and wind, various mega-projects have either been completed or are underway in KSA. This study analyzes the potential and reliability impact of these projects on the power system through a three-step process. In the first step, all major projects are identified, and data related to these projects, such as global horizontal irradiance, wind speed, temperature, and other relevant parameters, are collected. In the second step, these data are used to estimate the solar and wind potential at various sites, along with annual averages and seasonal averages for different extreme seasons, such as winter and summer. Finally, in the third step, a reliability assessment of power generation is conducted to evaluate the adequacy of renewable projects within the national power grid. This study addresses a gap in the literature by providing a region-specific reliability analysis using actual project data from KSA, which remains underexplored in existing research. Sequential Monte Carlo simulations are employed, and various reliability indices, including Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Supplied per Interruption (ENSINT), and Demand Not Supplied per Interruption (DNSINT) are analyzed. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability, with improvements observed across all reliability indices, demonstrating the viability of meeting Vision 2030 targets. Full article

Food delivery apps (FDAs) have emerged as transformative tools in the digital age, reshaping consumer behavior and urban mobility through their convenience and accessibility. This study explores the factors influencing the adoption of FDAs among university students in a secondary city in Thailand, framed within the broader context of smart mobility. This study employs an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, incorporating key constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and environmental concerns. Data were collected from 396 students at Mahasarakham University through a structured questionnaire and analyzed using structural equation modeling. The results reveal that effort expectancy, social influence, and environmental concerns significantly impact behavioral intention, while behavioral intention and facilitating conditions drive actual usage behavior. Environmental concerns emerged as a critical determinant, reflecting the growing alignment between consumer preferences and sustainability goals. The findings underscore the role of FDAs as key enablers of smart mobility, optimizing urban logistics, reducing transportation inefficiencies, and supporting sustainable city systems. By integrating environmental concerns into the UTAUT model, this study contributes to understanding technology adoption dynamics in secondary cities. Practical implications include promoting eco-friendly practices, enhancing digital infrastructure, and leveraging FDAs to foster sustainable and inclusive mobility ecosystems. Full article

Involving wetland protection policies in the simulation of the wetland biodiversity pattern has the potential to improve the accuracy of policy-making. In this research, by combining the Cellular Automata Markov Model (CA-Markov) for land use change simulation and a wetland Biodiversity Estimation Model Based on Hydrological Pattern and Connectivity (BEHPC), we put forward a comprehensive framework that integrates policy stage division, the identification of stage characteristics, and biodiversity prediction. This framework divided the wetland conservation policies implemented in the study area into three stages: promoting (1995−2005), strengthening (2005–2010), and stabilizing (2010–2020). CA-Markov verification confirmed the stages’ consistency with actual policy implementation, indicating its usability. Using the land use data of different policy stages as input for the CA-Markov model, we then predicted the wetland biodiversity pattern in 2030 under different scenarios. The results showed that the land use and wetland protection policies implemented during 2010–2020 were most beneficial for enhancing wetland biodiversity in the study area, with an expected increase of about 8% if continued. This study offers technical and scheme references for the future evaluation of wetland-related policies at the regional scale. It also provides guidance for optimizing the spatial structure and providing numerical goals for land use and wetland protection. Full article

Hydraulic fracturing is a crucial technology for resource development, such as shale gas, and its optimization is necessary to enhance development efficiency. However, evaluating fracture shapes involves technical uncertainties. Japan Organization for Metals and Energy Security (JOGMEC) and Kyoto University have conducted laboratory-scale hydraulic fracturing experiments using coagulable fluorescent resin as the injection fluid (resin fracturing test) to visualize hydraulic fractures and investigate their relationship with acoustic emissions (AEs) generated during fracturing. Since lab-scale experiments can only examine the phenomena near the injection hole owing to size limitations, we designed an experiment to apply the visualization method to the outcrop scale. This paper presents the results from an in situ, outcrop-scale hydraulic fracturing experiment conducted at the Kamioka Mine, Gifu Prefecture, Japan, from 2022 to 2023, with goals similar to those of the laboratory experiments. A resin fracturing borehole (RF1) with a diameter of 76 mm was core-drilled to a depth of approximately 10 m for the resin fracturing tests. AEs were observed in five boreholes drilled around RF1 at the same depth. Resin fracturing tests were performed at two different depths, with breakdown confirmed at both. A core of a larger diameter (205 mm) was recovered by coaxial overcoring around RF1, and resin-filled fractures were observed under black light on the core surfaces. After the resin fracturing experiment, two analyses were performed using the acquired core and AE data to predict the fracture extension and the mechanism of AE occurrence. We compared the distribution of AE events and visualized fractures in the core. Additionally, we compared the stress direction estimated from failure mechanism analysis using AE data with the maximum stress direction estimated from hydraulic fracturing. Our analysis provided several insights into fracture extension. The distribution of AE hypocenters was consistent with the direction of the hydraulic fractures confirmed by coring after the resin fracturing test. The failure mechanisms are classified based on the polarity of the first P-wave motion of the AE waveform. However, the actual scale of oil fields is significantly larger than that considered in this study. Discussing visible fractures created by hydraulic fracturing is deemed meaningful. We expect that the results of this study will provide valuable information for the precise estimation of hydraulic fractures. Full article

In ultra-precision optical components polishing, the shape of the Tool Influence Function (TIF) is an important factor that affects the processing efficiency and processing accuracy of optical components. For a self-rotating small tool polishing device commonly used in computer-controlled optical surfacing (CCOS), its TIF deviates from the Gaussian shape, and the processing is prone to cause surface figure divergence. Inspired by the theory of eccentric compression, this paper proposes a method to optimize the shape of the TIF based on pressure distribution control. Based on the finite element method, a contact pressure distribution model is established. The influence of different positions of the pressure contact points on the contact pressure distribution is analyzed, and the position of the pressure application point that makes the TIF close to the Gaussian shape is determined. On this basis, a new type of small tool polishing device that can realize the above optimization method is designed. The optimized actual TIF is obtained, and an aspheric mirror processing experiment is completed. After three rounds of processing, the value of PV of the surface form error converged from 1861.180 nm to 64.875 nm, with a convergence rate of 96.5%. The value of RMS converged from 299.857 nm to 6.043 nm, with a convergence rate of 97.9%. The surface figure accuracy has reached the expected goal with the root mean square value less than 10 nm, which verifies the feasibility and effectiveness of this optimization method. Full article

Achieving a decarbonized built environment by 2050 requires significant advancements in building renovation strategies, with a strong emphasis on energy efficiency and emissions reduction. This study examined the compliance of buildings renovated between 2015 and 2022 with national energy performance regulations. While many buildings have undergone improvements, a substantial portion still fail to meet the stricter, current requirements, particularly in terms of window and floor insulation, highlighting the need for further retrofit measures. Comparing static and dynamic simulation models reveals that static models frequently overestimate energy savings, leading to misaligned investment decisions. Dynamic simulations, by incorporating real-time climate interactions and transient thermal behaviors, provide a more accurate assessment of energy demand and efficiency improvements. A financial analysis indicates that static models often predict unrealistically short payback periods, potentially resulting in suboptimal renovation investments. To meet decarbonization goals, future strategies must integrate advanced simulation methodologies, strengthen regulatory oversight, and enhance financial incentives for comprehensive energy renovations. A data-driven approach is essential to ensure that building retrofits achieve meaningful energy savings and contribute to climate neutrality. Strengthening compliance frameworks and promoting standardized renovation practices will be key to bridging the gap between expected and actual performance, ensuring a sustainable and resilient built environment. Full article

Investigating the spatio-temporal trends in agricultural green production level and proposing pathways to improve it can offer valuable insights for promoting the green, low-carbon, and sustainable development of China’s agriculture, as well as contributing to the achievement of the United Nations’ Sustainable Development Goals by 2030. Therefore, in order to investigate the spatio-temporal variations in agricultural green production level and its driving factors, and explore pathways to improve it in the major grain-producing areas of China, a new multi-dimensional framework for estimating the agricultural green production level was proposed, and based on the OLS regression and scenario prediction, the agricultural green production levels from 2012 to 2030 were estimated. The findings indicate that from 2012 to 2021, the agricultural green production level in the major grain-producing areas experienced a consistent annual increase. The average annual value for the agricultural green production level was recorded at 0.443. At a spatial scale, the agricultural green production level exhibited a pronounced regional pattern, showing higher levels in the central and eastern areas, while lower levels were noted in the northeastern and western regions. The actual utilization of foreign capital and the per capita disposable income of farmers positively influenced the agricultural green production level. In contrast, factors such as the proportion of the secondary industry, the proportion of the tertiary industry, and the urbanization rate negatively affected this level. From 2022 to 2030, the agricultural green production level is expected to demonstrate a gradual growth trend under the baseline scenario, although the rate of growth is expected to decrease over time. Conversely, under the green and sustainable development scenario, a notably significant growth trend in agricultural green production level is projected. However, under the rapid economic development scenario, it is estimated that the agricultural green production level will initially increase slowly before peaking in 2026 and then experiencing a decline. With the aim of ensuring the ongoing enhancement of agricultural green production level objectives, the major grain-producing areas should proactively encourage inter-provincial collaboration in agricultural green production, vigorously attract foreign investment to facilitate the advancement of green production technologies, promote the harmonious integration of primary, secondary, and tertiary industries in rural regions, and improve farmers’ income. Full article

Accurate disclosure and proactive engagement in ESG practices are essential for achieving high-quality economic development, particularly as China addresses significant challenges during its reform journey. The Classified Reform of State-Owned Enterprises (CRSOE) is a strategic initiative by the Chinese government aimed at fostering this development. Our study leverages the implementation of the CRSOE as an exogenous shock, employing the difference-in-differences approach to assess the policy’s governance impact on ESG decoupling from the perspective of ownership heterogeneity. The policy was found to alleviate ESG decoupling, particularly pronounced among SOEs with special functions. The governance effect is achieved by reducing the aspiration–performance gap. Specifically, the policy effectively narrows the disparity between a company’s actual performance and the expected performance based on the industry average, thereby mitigating ESG decoupling. However, the policy’s impact can be weakened by factors such as political connections among executives and media attention. Furthermore, the CRSOE effectively addresses greenwashing practices within ESG decoupling, with a particularly strong effect on SOEs that fail to disclose ESG information in alignment with Global Reporting Initiative (GRI) standards. These findings highlight the importance of understanding the broader implications and underlying mechanisms of the policy. Therefore, building on the assessment of how the CRSOE policy impacts ESG decoupling, we also examine the mechanisms through which this policy operates and how its effectiveness varies under different conditions of heterogeneity. By extending the application of principal-agent theory and performance feedback theory, our research suggests that policymakers should prioritize market-driven reforms for fully competitive SOEs and promote a stronger emphasis on non-financial goals. Additionally, it is essential to mitigate the undue influence of political promotions on the management of all SOEs. Full article

The aim of the paper is to identify factors determining the intention to use refill cartridges in the cosmetics industry. As part of the work, two research questions and twelve corresponding hypotheses were formulated. Achieving the assumed goal was possible thanks to literature research and empirical research based on survey results from a sample of 226 respondents from different countries. The analysis of the collected data was carried out using the structural equation modelling method (PLS-SEM), thanks to which the scales measuring individual constructs (factors) were validated and a model was generated, based on which the constructs that had the greatest impact on the intentions to use refill technology were determined. The study showed that the factors that have the strongest impact on intentions to use refills are the expected ease of use and expected economic usefulness. Expected environmental utility turned out to be a non-significant predictor. Factors influencing actual consumer behaviour were also examined, including usage intentions and promotion. The study also analysed the mediating role of usage intention, which was confirmed in relation to the relationship of the constructs, including expected economic utility, the expected ease of use, and social pressure relating to actual behaviours, but was not confirmed in the relationship of the construct expected environmental utility with actual behaviours. The moderating role of the dichotomous variable background, dividing the respondents into the group of Poles and foreigners, was also examined. The obtained results showed that the moderation effect was confirmed only for the relationship between expected economic utility and intentions to use refills. Full article

Soccer is evolving into a science rather than just a sport, driven by intense competition between professional teams. This transformation requires efforts beyond physical training, including strategic planning, data analysis, and advanced metrics. Coaches and teams increasingly use sophisticated methods and data-driven insights to enhance decision-making. Analyzing team performance is crucial to prepare players and coaches, enabling targeted training and strategic adjustments. Expected goals (xG) analysis plays a key role in assessing team and individual player performance, providing nuanced insights into on-field actions and opportunities. This approach allows coaches to optimize tactics and lineup choices beyond traditional scorelines. However, relying solely on xG might not provide a full picture of player performance, as a higher xG does not always translate into more goals due to the intricacies and variabilities of in-game situations. This paper seeks to refine performance assessments by incorporating predictions for both expected goals (xG) and actual goals (aG). Using this new model, we consider a wider variety of factors to provide a more comprehensive evaluation of players and teams. Another major focus of our study is to present a method for selecting and categorizing players based on their predicted xG and aG performance. Additionally, this paper discusses expected goals and actual goals for each individual game; consequently, we use expected goals per game (xGg) and actual goals per game (aGg) to reflect them. Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting xGg and aGg, outperforming other models in our comparative assessment. Ridge regression’s ability to handle overlapping and correlated variables makes it an ideal choice for our analysis. This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model’s performance by as much as 12%. These features offer a more detailed understanding of player performance in specific leagues and roles, improving the model’s accuracy from 83% to nearly 95%, as indicated by the R-squared metric. Furthermore, our research introduces a player selection methodology based on their predicted xG and aG, as determined by our proposed model. According to our model’s classification, we categorize top players into two groups: efficient scorers and consistent performers. These precise forecasts can guide strategic decisions, player selection, and training approaches, ultimately enhancing team performance and success. Full article

In recent years, with the rapidly aging population, alleviating the pressure on medical staff has become a critical issue. To improve the work efficiency of medical staff and reduce the risk of infection, we consider the multi-trip autonomous mobile robot (AMR) routing problem in a stochastic environment. Our goal is to minimize the total expected operating cost and maximize the total service quality for patients, ensuring that each route violates the vehicle capacity and the time window with only a minimal probability. The travel time of AMRs is stochastically affected by the surrounding environment; the demand for each ward is unknown until the AMR reaches the ward, and the service time is linearly related to the actual demand. We developed a population-based tabu search algorithm (PTS) that combines the genetic algorithm with the tabu search algorithm to solve this problem. Extensive numerical experiments were conducted on the modified Solomon instances to demonstrate the efficiency of the PTS algorithm and reveal the impacts of the confidence level on the optimal solution, providing insights for decision-makers to devise delivery schemes that balance operating costs with patient satisfaction. Full article

This paper is the first part of a two-part series, which presents preliminary findings on a novel flexible curved wind turbine blade designed for passive control, comparing its aerodynamic performance and behavior against a conventional straight blade. Characterized by its ability to twist around its longitudinal axis under bending loads, the flexible curved blade is engineered to self-regulate in response to varying wind speeds, optimizing power output and enhancing operational safety. This design utilizes inherent elasticity and specific geometric configurations to develop torsional loads, resulting in continuous adjustment of the blade’s pitch angle via twist–bend deformation. The study focuses on a comparative analysis conducted in a wind tunnel, testing both a small-scale model of the conventional blade and the flexible curved blade of equivalent diameter. Results indicate that the flexible curved blade concept successfully moderates its rotational speed and power output at higher wind speeds and demonstrates the capability to start generating power at lower wind speeds and stabilize power effectively, aligning with sustainability goals by potentially reducing reliance on active control systems. Despite promising outcomes, passive control mechanisms did not activate at the designed wind speeds, revealing a misalignment between expected and actual performance and underscoring the need for further refinements in blade design and control settings. Additionally, the power coefficient (Cp) versus tip speed ratio (TSR) comparison showed that flexible curved blades operate within a lower TSR range and exhibit controlled capping of power under high wind conditions, marked by a distinctive ‘hook-like’ feature in C_p behavior. This study confirms the feasibility of designing and manufacturing passively controlled wind turbine blades tailored to specific performance criteria and underscores the potential of such technology. Future work, to be detailed in a subsequent paper, will explore further optimizations and the use of Glass Fiber-Reinforced Polymer (GFPR) composite materials to enhance blade flexibility and performance. Full article

Landscape has significant effects on hydrological processes in a watershed. In the Sudano-Sahelian area, watersheds are subjected to a quick change in landscape patterns due to the human footprint, and the exact role of the actual landscape features in the modification of the hydrological process remains elusive. This study tends to assess the effects of landscape on the genesis of the runoff in the Mayo Mizao watershed. To achieve this goal, 62 infiltration tests were performed at different points and depths (5 cm and 20 cm) using the double-ring method and the Porchet method. The results show that the combination of many factors (soil type, land use, and farming practices) can guide the hydraulic conductivity behavior of soils. For example, at 5 cm depths, clayey-evolved soils, such as vertisols and halomorphic soils, inhibit infiltration, as opposed to non-evolved mineral soils, such as lithosols and clayey-sandy soils. However, at 20 cm depths, gray soils with halomorphic tendencies followed by vertisols have a low sensitivity to infiltration, as opposed to soils derived from loose materials and halomorphic soils. For a given soil type, rainfed crops are the primary land use that runs against infiltration. However, the effect of tillage varies according to the soil type. Finally, given the extent of vertisols and halomorphic soils in the Far North region of Cameroon in general, and in the Mayo Mizao watershed in particular, and regarding the increase in cultivated areas, a probable reduction in the infiltration capacity of soils in this region is to be expected in the medium term. The results of this study can be used as a basis for land-use planning and sustainable watershed management in semi-arid tropical zones. Full article

Purpose: With an increase in the proportion of elderly patients, the global burden of spinal disease is on the rise. This is gradually expected to increase the number of surgical procedures all over the world in the near future. As we know, rehabilitation following spine surgery is critical for optimal recovery. However, the current literature lacks consensus regarding the appropriate post-operative rehabilitation protocol. The purpose of this review is to evaluate the optimal protocol for rehabilitation after lumbar spine surgery in adults. Materials and Methods: The goals of rehabilitation after lumbar spine surgery are to improve physical and psychosocial function and may include multiple modalities such as physical therapy, cognitive behavioral therapy, specialized instruments, and instructions to be followed during activities of daily living. In recent years, not only are a greater number of spine surgeries being performed, but various different techniques of lumbar spine surgery and spinal fusion have also emerged. (1) Our review summarizes post-operative rehabilitation under the following headings—1. Historical aspects, 2. Subjective functional outcomes, and (3) Actual rehabilitation measures, including balance. Results: Physical therapy programs need to be patient-specific and surgery-specific, such that they consider patient-reported outcome measures and take into consideration the technique of spinal fusion used and the muscle groups involved in these surgeries. By doing so, it is possible to assess the level of functional impairment and then specifically target the strengthening of those muscle groups affected by surgery whilst also improving impaired balance and allowing a return to daily activities. Conclusions: Rehabilitation is a multi-faceted journey to restore mobility, function, and quality of life. The current rehabilitation practice focuses on muscle strengthening, but the importance of spinal balance is less elaborated. We thus equally emphasize muscle strengthening and balance improvement post-lumbar spine surgery. Full article