Search Results (1,774)

The rise in extreme weather events due to climate change challenges the balance of supply and demand for high-quality agricultural products. In Taiwan, greenhouse cultivation, a key agricultural method, faces increasing summer temperatures and higher operational costs. This study presents the innovative AI-powered greenhouse environmental control system (AI-GECS), which integrates customized gridded weather forecasts, microclimate forecasts, crop physiological indicators, and automated greenhouse operations. This system utilizes a Multi-Model Super Ensemble (MMSE) forecasting framework to generate accurate hourly gridded weather forecasts. Building upon these forecasts, combined with real-time in-greenhouse meteorological data, the AI-GECS employs a hybrid deep learning model, CLSTM-CNN-BP, to project the greenhouse’s microclimate on an hourly basis. This predictive capability allows for the assessment of crop physiological indicators within the anticipated microclimate, thereby enabling preemptive adjustments to cooling systems to mitigate adverse conditions. All processes run on a cloud-based platform, automating operations for enhanced environmental control. The AI-GECS was tested in an experimental greenhouse at the Taiwan Agricultural Research Institute, showing strong alignment with greenhouse management needs. This system offers a resource-efficient, labor-saving solution, fusing microclimate forecasts with crop models to support sustainable agriculture. This study represents critical advancements in greenhouse automation, addressing the agricultural challenges of climate variability. Full article

The application of dynamic data in biomechanics is crucial; traditional laboratory-level force measurement systems are precise, but they are costly and limited to fixed environments. To address these limitations, empirical evidence supports the widespread adoption of portable force-measuring platforms, with recommendations for their ongoing development and enhancement. Taiyuan University of Technology has collaborated with KunWei Sports Technology Co., Ltd. to develop a portable 3D force measurement system. To validate the reliability of this equipment, 15 male collegiate students were randomly selected to perform four distinct movements: walking, running, CMJ, and side-cutting. The Bertec system served as a reference device alongside the KunWei system to collect the kinetic characteristics of the test movements. The consistency and fitting quality between the two devices were evaluated through t-tests, ICC, and NRMSE. The research results indicated that there were no significant differences in peak force between the KunWei system and the Bertec system across all four movements (p > 0.05). The ICC values for force-time curves were all above 0.98, with NRMSE not exceeding 0.165. The KunWei system exhibited high consistency and reliability under various motion conditions compared to the Bertec system. This system maintains data accuracy, significantly broadens the application scope of force measurement systems, and reduces procurement and maintenance costs. It has been successfully applied in technical support for multiple water sports and winter projects with ideal results achieved. Full article

The job shop scheduling problem (JSSP) is a popular NP-hard problem in combinatorial optimization, due to its theoretical appeal and its importance in applications. In practical applications, the online version is much closer to the needs of smart manufacturing in Industry 4.0 and 5.0. Here, the online version of the job shop scheduling problem is solved by a heuristics that governs local queues at the machines. This enables a distributed implementation, i.e., a digital twin can be maintained by local processors which can result in high speed real time operation. The heuristics at the level of probabilistic rules for running the local queues is experimentally shown to provide the solutions of quality that is within acceptable approximation ratios to the best known solutions obtained by the best online algorithms. The probabilistic rule defines a model which is not unlike the spin glass models that are closely related to quantum computing. Major advances of the approach are the inherent parallelism and its robustness, promising natural and likely successful application to other variations of JSSP. Experimental results show that the heuristics, although designed for solving the online version, can provide near-optimal and often even optimal solutions for many benchmark instances of the offline version of JSSP. It is also demonstrated that the best solutions of the new heuristics clearly improve over the results obtained by heuristics based on standard dispatching rules. Of course, there is a trade-off between better computational time and the quality of the results in terms of makespan criteria. Full article

RaceRunning is a sport for disabled people and successful performance depends on reducing the amount of time spent travelling a specific distance. Performance analysis in RaceRunning athletes is based on traditional methods such as recording race time, distances travelled and frequency (sets and reps) that are not sufficient for monitoring training loads. The aims of this study were to monitor training loads in typical training sessions and evaluate technical adaptations in RaceRunning performance by acquiring sensor metrics. Five elite and competitive RaceRunning athletes (18.2 ± 2.3 yrs) at RR2 and RR3 levels were monitored for 8 weeks, performing in their usual training sessions while wearing unobtrusive motion sensors. The motion sensors were attached to the waist and lower leg in all training sessions, each lasting between 80 and 90 min. Performance metrics data collected from the motion sensors included player loads, race loads, work/rest ratio and impact shock directions, along with training factors (duration, frequency, distance, race time and rest time). Results showed that weekly training loads (player and race loads) followed acceptable threshold levels, according to assessment criteria (smallest worthwhile change, acute/chronic work ratio). The relationship between race velocity (performance index) and race load was non-linear and statistically significant, which led to different performance efficiency groups. Wearable motion sensor metrics revealed small to moderate technical adaptations following repeated sprint attempts in temporal running performance, variability and consistency. In conclusion, using a wearable-based system is an effective feedback tool to monitor training quality, revealing important insights into adaptations to training volumes in disabled athletes. Full article

Seafood quality preservation remains a critical focus in the food industry, particularly as the freeze–thaw process significantly impacts the freshness and safety of aquatic products. This study investigated quality changes in frozen mackerel subjected to two thawing methods, room temperature (RT) and running water (WT), and assessed the potential of hyperspectral imaging (HSI) for classifying these methods. After thawing, mackerel samples were stored at 5 °C for 21 days, with physicochemical, textural, and spectroscopic analyses tracking quality changes and supporting the development of a spectroscopic classification model. Compared with the WT method, the RT method delayed changes in key quality indicators, including pH, total volatile basic nitrogen (TVB-N), and total viable count (TVC), by 1–2 days, suggesting it may better preserve initial quality. Texture profile analysis showed similar trends, supporting the benefit of RT in maintaining quality. A major focus was on using HSI to assess quality and classify thawing methods. HSI achieved high classification accuracy (R_c² = 0.9547) in distinguishing thawing methods up to three days post-thaw, with 1100, 1200, and 1400 nm wavelengths identified as key spectral markers. The HIS’s ability to detect differences between thawing methods, even when conventional analyses showed minimal variation, highlights its potential as a powerful tool for quality assessment and process control in the seafood industry, enabling detection of subtle quality changes that traditional methods may miss. Full article

Wireline formation fluid sampling is extensively utilized to acquire downhole fluid samples. Due to mud filtrate invasion, enough time is required to pump out the formation fluid so that an acceptable level of contaminant is reached. Excessive cleanup time would increase costs and the risk of the testing tool becoming stuck within the drilling mud. The challenge lies in deciding what type of formation-tester probe should be used to ensure minimally contaminated measurements for a specific tool configuration and when the withdrawal sample is sufficiently purged of contaminants. A numerical simulator to simulate the virgin formation fluid sampling was developed, and the accuracy of the simulator was validated based on the spherical flow theory. Through running 2515 simulation cases, the effects of various operational and formation conditions on the breakthrough and pumpout times with three different probes (i.e., the standard probe, the elliptical probe, the elongated probe, and their corresponding 3D radial probes) were compared and analyzed quantitatively. We numerically investigated the key factors influencing the breakthrough and pumpout times and delved into the impact of the formation anisotropy. This study reveals the parameters that encompass the first-order effect on the breakthrough and pumpout times, enabling the determination of the probe-type selection and the early predictions of pumpout time. By leveraging these insights, sampling operations can be optimized to enhance sample quality, reduce operational time, and mitigate the risks associated with tool entrapment. Full article

In view of the increasing demand for secondary aluminum, which is intended to partially replace the very energy- and resource-intensive primary aluminum production, effective treatment methods can maintain the high quality level of light metal castings. The transition from a linear to a circular economy can result in an accumulation of oxides or carbides in aluminum. Therefore, melt purification is crucial, especially as foundries aim to increase the use of often dirty end-of-life scrap. Nonmetallic inclusions in the melt can impact its flowability and mechanical properties. As the purity of the melt increases, its flow length also tends to increase. Available assessment methods like reduced pressure test or K-mold are capable of ensuring high levels of purity. This study demonstrates the implication of inclusions originating from dirty scrap. An experimental test run deals with various scrap contents in an AlSi7Cu0.5Mg alloy and shows correlations between impurity and performance, expressed by flowability and mechanical properties. These performance indicators have been connected to inclusion and porosity rates. In conclusion, these findings emphasize the need for further extensive research on contaminants in the field of scrap melting and the development of methods for easy-to-handle assessment methods. Full article

An increasing number of studies have explored the effects of precooling on endurance exercise performance in the heat, yet the available results remain inconsistent. Therefore, this study aimed to investigate the effects of different precooling strategies on endurance exercise performance in the heat. A comprehensive search was conducted across PubMed, Web of Science, Cochrane, Scopus, and EBSCO database. The Cochrane risk assessment tool was employed to evaluate the methodological quality of the included studies. A meta-analysis was subsequently conducted to quantify the standardized mean difference (SMD) and 95% confidence interval for the effects of precooling on endurance exercise performance in the heat. Out of the initially identified 6982 search records, 15 studies were deemed eligible for meta-analysis. Our results showed that precooling significantly improved time trial (TT) performance (SMD, −0.37, p < 0.01, I² = 0%) and time to exhaustion (TTE) performance in the heat (SMD, 0.73, p < 0.01, I² = 50%). Further subgroup analyses revealed that external precooling is more effective in improving TT performance (SMD, −0.43, p = 0.004, I² = 0%) and TTE performance (SMD, 1.01, p < 0.001, I² = 48%), particularly in running-based performances (TT, SMD, −0.41, p = 0.02, I² = 0%; TTE, SMD, 0.85, p = 0.0001, I² = 31%). Precooling is an effective approach to improve endurance exercise performance in the heat. External precooling is more effective in improving endurance exercise performance, particularly in running-based performance. Full article

The ever-increasing capacity of numerical weather prediction (NWP) models requires accurate flow information at higher spatial and temporal resolutions. The atmospheric motion vectors (AMVs) extracted from the Advanced Geostationary Radiation Imager (AGRI) mounted on the Fengyun-4A (FY-4A) satellite can provide information about atmospheric flow fields on small scales. This study focused on the assimilation of FY-4A AMVs and its impact on forecasts in the regional NWP system of the China Meteorological Administration—Beijing (CAM-BJ). The statistical characterization of FY-4A AMVs was firstly analyzed, and an optimal observation error in each vertical level was obtained. Three groups of retrospective runs over a one-month period were conducted, and the impact of assimilating the AMVs with different strategies on the forecasts of the CMA-BJ system were compared and evaluated. The results suggested that the optimal observation errors reduced the standard deviation of the background departures for U and V wind, leading to an improvement in the standard deviation in the corresponding analysis departures of about 8.3% for U wind and 7.3% for V wind. Assimilating FY-4A AMV data with a quality indicator (QI) above 80 and the optimal observation errors reduced the error of upper wind forecast in the CMA-BJ system. A benefit was also obtained in the error of surface wind forecast after 6 h of the forecasts, although it was not significant. For rainfall forecast with different thresholds, the score skills increased slightly after 6 h of the forecasts. There was an overall improvement for the overprediction of 24 h accumulated precipitation forecast including the AMVs, even when conventional observations were relatively rich. The application of FY-4A AMVs with a QI > 80 and adjustment to observation errors has a positive impact on the upper wind forecast in the CMA-BJ system, improving the score skill of rainfall forecasting. Full article

Driven by the increasing care needs of residents in long-term care facilities, Ambient Assisted Living paradigms have become very popular, offering new solutions to alleviate this burden. This work proposes an efficient edge-cloud system for indoor activity monitoring in long-term care institutions. Action recognition from video streams is implemented via Deep Learning networks running at edge nodes. Edge Computing stands out for its power efficiency, reduction in data transmission bandwidth, and inherent protection of residents’ sensitive data. To implement Artificial Intelligence models on these resource-limited edge nodes, complex Deep Learning networks are first distilled. Knowledge distillation allows for more accurate and efficient neural networks, boosting recognition performance of the solution by up to 8% without impacting resource usage. Finally, the central server runs a Quality and Resource Management (QRM) tool that monitors hardware qualities and recognition performance. This QRM tool performs runtime resource load balancing among the local processing devices ensuring real-time operation and optimized energy consumption. Also, the QRM module conducts runtime reconfiguration switching the running neural network to optimize the use of resources at the node and to improve the overall recognition, especially for critical situations such as falls. As part of our contributions, we also release the manually curated Indoor Action Dataset. Full article

Background and Objectives: Type 2 diabetes (T2DM) affects millions across the globe, generating a veritable public health issue through quality-of-life-reducing chronic complications, among which urinary tract infections are the most common. A shift in the disease managing paradigm from a glucose-centered view to a concept of cardio-reno-metabolic health has uniquely placed SGLT2 inhibitors as viable medication for the complex management of T2DM and its comorbidities. Some concerns have been raised over the increased likelihood of urinary tract infections (UTIs) associated with SGLT2 inhibitor use. The current study aims to evaluate the risk of developing urinary tract infections if patients with type 2 diabetes take SGLT2 inhibitors and determine those factors which make these patients more prone to develop this undesired complication. Materials and Methods: A cross-sectional, noninterventional evaluation of 328 patients with type 2 diabetes consecutively admitted to the Diabetes Clinic of “Pius Brinzeu” County Emergency Hospital in Timisoara, between January and February of 2024, was performed by examining medical charts and running statistical analyses using MedCalc version 22.26.0.0. Results: There was no statistical difference between patients taking SGLT2 inhibitors and those taking other glucose lowering medications when examining the presence of UTIs. Those patients with a higher HbA1c or BMI showed an increased predisposition to contracting UTI. The female gender was also associated with an increased likelihood of UTI. A further evaluation of the sublot of patients taking SGLT2 inhibitors revealed that not only higher BMI or HbA1c could be a predictor for the likelihood of developing UTI, but also a longer duration of T2DM was a predisposing factor. Conclusions: The use of SGLT2 inhibitors did not increase the likelihood of developing a urinary tract infection in this patient population. Full article

Affected by strong wind environments, the vibration of trains will significantly intensify, which will severely impact the running quality of trains. To address such challenges, an improved wind load model is proposed in this paper to simulate the shock of strong wind on trains. The proposed model employs the integral approach to calculate the equivalent wind load on trains and applies it to the body of trains during the dynamics simulation process. Eventually, the two-level running quality threshold curve for passenger and freight trains is acquired through the conditional probability density function and the regularized regression model. This achievement covers train speed restrictions for wind speeds ranging from 0~25 m/s, providing a scientific basis for railway departments to adjust train speeds based on real-time wind speeds. It is of utmost importance for ensuring the safe and efficient operation of trains under strong wind conditions. Full article

In the rapidly evolving digital healthcare market, ensuring both the activation of the market and the fulfillment of the product’s social role is essential. This study addresses the service quality of smart running applications by utilizing big data text mining techniques to bridge the gap between user experience and service quality in digital health applications. The research analyzed 264,330 app reviews through sentiment analysis and network analysis, focusing on key service dimensions such as system efficiency, functional fulfillment, system availability, and data privacy. The findings revealed that, while users highly value the functional benefits provided by these applications, there are significant concerns regarding system stability and data privacy. These insights underscore the importance of addressing technical and security issues to enhance user satisfaction and continuous application usage. This study demonstrates the potential of text mining methods in quantifying user experience, offering a robust framework for developing user-centered digital health services. The conclusions emphasize the need for continuous improvement in smart running applications to meet market demands and social expectations, contributing to the broader discourse on the integration of e-commerce and digital health. Full article

This study analyzes the effects of governance on the relationship between research and development expenditure and economic growth in South Africa using annual data from 1997 to 2022 using an autoregressive distributed lag (ARDL) model. The calculated F-tests for the two models in the ARDL bounds testing approach to cointegration revealed a long-run relationship between the series. In the model without a mediating factor, an insignificant impact of research and development (R&D) expenditure on economic growth is reported. However, when R&D interacted with governance, a positive and significant impact was observed. This implies that for R&D to have a positive impact on economic growth, there is a need for strong and quality governance to provide a conducive productive environment. Furthermore, given the ambiguous relationship between governance and economic growth, the Granger causality test results showed that governance granger-causes economic growth and not the other way round. The findings presented in this paper are expected to provide some useful insights for policymakers in South Africa and the African continent. The findings demonstrate the important role that governance plays in enhancing the developmental performance of critical macro-economic growth factors. The study potentially generates new dimensions (by including governance as a mediating factor) in the understanding of how the impact of R&D and other macroeconomic parameters on economic growth can be promoted. Full article

This study introduces a novel hybrid model combining Bayesian Stochastic Partial Differential Equations (SPDE) with deep learning, specifically Convolutional Neural Networks (CNN) and Deep Feedforward Neural Networks (DFFNN), to predict PM_2.5 concentrations. Traditional models often fail to account for non-linear relationships and complex spatial dependencies, critical in urban settings. By integrating SPDE’s spatial-temporal structure with neural networks’ capacity for non-linearity, our model significantly outperforms standalone methods. Accurately predicting air pollution supports sustainable public health strategies and targeted interventions, which are critical for mitigating the adverse health effects of PM_2.5, particularly in urban areas heavily impacted by climate change. The hybrid model was applied to the Pleasant Run Airshed in Indianapolis, Indiana, utilizing a comprehensive dataset that included PM_2.5 sensor data, meteorological variables, and land-use information. By combining SPDE’s ability to model spatial-temporal structures with the adaptive power of neural networks, the model achieved a high level of predictive accuracy, significantly outperforming standalone methods. Additionally, the model’s interpretability was enhanced through the use of SHAP (Shapley Additive Explanations) values, which provided insights into the contribution of each variable to the model’s predictions. This framework holds the potential for improving air quality monitoring and supports more targeted public health interventions and policy-making efforts. Full article