Search Results (3,649)

Optimizing plant density and nutrient availability is essential for sustaining high forage yields and promoting environmental health, especially in semi-arid regions with sandy soil. Nonetheless, the mechanisms by which stoichiometric features govern nutrient utilization and forage output are still unidentified. We executed a two-year field experiment, integrating six nitrogen rates (0 (N0), 104 (N1), 138 (N2), 173 (N3), 207 (N4), and 242 (N5) kg N ha⁻¹) and four planting densities (3 (D1), 3.5 (D2), 4 (D3), and 4.5 (D4) million plants ha⁻¹). The C, N, and P contents, along with the C:N:P stoichiometry of different oat organs (leaf, stem, and root) and soil, were determined. It was found that the growth of oats in this area was limited by soil N. The pasture biomass increased nonlinearly with increasing planting density and N rate, and the maximum thresholds for C, N, and P uptake were 389.43 g kg⁻¹, 11.19 g kg⁻¹, and 3.10 g kg⁻¹ at N3, respectively. The maximum thresholds for C, N, and P uptake were 356.45, 9.47, and 2.78 g kg⁻¹ at D3, respectively, with an optimal biomass of 9221.74 kg ha⁻¹; at a planting density of D3, the maximum thresholds for C, N, and P uptake were 329.39, 8.54, and 2.47 g kg⁻¹, with an optimal biomass of 6276.10 kg ha⁻¹. SEM showed that N rate and density increases significantly changed the ecological balance of the soil. The C:N and C:P ratios in oat leaves tend towards lower values, while the N:P ratio tends towards higher values; in contrast, the C:N and C:P ratios in oat stems tend towards higher values, and the N:P ratio tends towards lower values. The nutrient use strategy maintains the stoichiometric balance at the organ level, which in turn improves the accumulation of oat biomass. The best NUE was obtained at an N rate and density of N3D3 with a 144% biomass increase as compared to N0D2. This study provides new insights into nutrient allocation, usage strategies, and the stability of oats in actual sandy land production. Full article

Background: disordered eating (DE) and eating disorders (ED) can negatively impact athletes’ health, wellbeing, and athletic performance. Objective: this cross-sectional study aims to assess DE risk, body composition, and nutrition knowledge among elite Hungarian athletes. Methods: DE risk was assessed using DESA-6H and EAT-26 scales, nutrition knowledge through the Abridged Nutrition for Sport Knowledge Questionnaire (A-NSKQ), and body composition with the OMRON BF511 device. The data were analyzed using Kendall’s tau correlations, Mann–Whitney U tests, and ROC analysis. Results: a total of 71 athletes participated (39.4% males, mean age = 24.8 years, SD = 4.8 years and 60.6% females, mean age = 24.3 years, SD = 4.3 years). At-risk scores on the DESA-6H scale were recorded for nine athletes (12.7%), while 32.4% scored in the risk zone on the EAT-26, with female athletes in aesthetic, endurance and weight-dependent sports being most affected. Low BF was observed in four males and four females. Nutrition knowledge (49.1%) was below the acceptable threshold. DESA-6H significantly correlated with EAT-26 scores, BMI, sports nutrition knowledge, and A-NSKQ total scores. A statistically significant difference by gender was found in the EAT-26 total score (p = 0.019, d = 0.65). Risk groups significantly differed in A-NSKQ scores (p = 0.026, d = 0.511) and sport nutrition knowledge, specifically (p = 0.016, d = 0.491). Using EAT-26 to identify at-risk athletes and the DESA-6H recommended cut-off, the ROC analysis showed a sensitivity of 29.1% and a specificity of 95.7%. Conclusions: insufficient nutrition knowledge plays a role in being at-risk for DE and ED. These results underscore the need for early detection, early sport nutrition education across all elite athletes, with particular attention to female athletes in aesthetic, endurance and weight-dependent sports, and for monitoring these athletes to prevent DE. Further work is warranted to optimize screening tools such as EAT-26 and DESA-6H for elite athletes. Full article

This research aimed to investigate the potential of using alkali activation technology to valorize steel slag and bauxite residue for the production of high-performance pavement blocks. By utilizing these industrial by-products, the study seeks to reduce their environmental impact and support the development of sustainable construction materials. Lab-scale testing showed that bauxite pavers showed a decrease in mechanical strength with increasing replacement of ordinary Portland cement. Partial replacement up to 20% still exceeded 30 MPa in compressive strength. Steel slag-based pavers achieved the 30 MPa threshold required for the application with selected mix designs. Pilot-scale production-optimized formulations and standards testing, including freeze–thaw resistance, confirmed the technical viability of these products. Life cycle analysis indicated a 25–27% reduction in CO₂ emissions for slag-based tiles compared to traditional concrete tiles. Moreover, using industrial residue reduced mineral resource depletion. This study examined the properties of the resulting alkali-activated binders, their ecological benefits, and their performance compared to conventional materials. Through a comprehensive analysis of these applications, our research promotes the circular economy and the advancement of sustainable construction products. Full article

Background: The aim of this study was to investigate the accuracy in fibrosis staging of a novel shear wave elastography (SWE) device (S-Shearwave Imaging by Samsung) and a previously validated 2D-SWE by Supersonic Imagine (SSI) in patients with biopsy proven metabolic dysfunction-associated steatotic liver disease (MASLD). Methods: This prospective study included 75 consecutive patients with MASLD who underwent liver biopsy for suspected MASH. All patients underwent S-Shearwave Imaging by Samsung and 2D-SWE with SSI on the same day of liver biopsy. Fibrosis was histologically assessed using the METAVIR classification system. Agreement between the equipment was assessed with the Pearson coefficient. A receiver operator characteristic curve (ROC) analysis with the Youden index was used to establish thresholds for fibrosis staging. Results: A good correlation was found between S-Shearwave Imaging by Samsung and 2D-SWE with SSI (Pearson’s R = 0.68; p < 0.01). At multivariate regression analysis, S-Shearwave Imaging was associated with advanced fibrosis (≥F3) independently from age, diabetes and platelets (OR 2.94, CI 1.69–5.11, p < 0.01). The fibrosis diagnostic accuracy of both S-Shearwave Imaging and 2D-SWE was good to optimal with AUROCs of 0.81 and 0.70 for significant fibrosis (≥F2), 0.94 and 0.91 for severe fibrosis (≥F3), respectively. The accuracy of S-Shearwave is not significantly different from Fibroscan and Agile3+ (DeLong test p value 0.16 and 0.15, respectively) while is slightly better than 2D-SWE, FIB4 and NFS (DeLong test p value < 0.05). For S-Shearwave Imaging by Samsung, the best cut-off values for diagnosing fibrosis ≥F2, ≥F3 were, respectively, 7.9 kPa (Sens 74.4%, Spec 87.5%) and 8.1 kPa (Sens 95.6%, Spec 78.8%). For 2D-SWE by SSI, the best cut-off values for diagnosing fibrosis ≥F2, ≥F3 were, respectively, 7.2 kPa (Sens 55.8%, Spec 84.4%) and 7.6 kPa (Sens 82.6%, Spec 84.6%). Conclusion: S-Shearwave Imaging is a useful and reliable non-invasive technique for staging liver fibrosis in patients with MASLD. Its diagnostic accuracy is non-inferior to other shear wave elastography techniques (TE and 2D-SWE by SSI). Full article

In the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-finding, the traditional method will produce false alarm targets and miss some targets. To address this issue, this paper draws on the idea of a spatial likelihood map which calculates the likelihood of target presence at each grid point within the observation area by partitioning the observation area into grids and utilizing bearing data from each array, yielding the distribution of targets in the observation area. Then, a multi-target cyclic peak extraction algorithm based on a statistical dual-threshold is proposed, which eliminates false peaks by cyclic extraction of target positions, so as to reduce false targets. Simulation results demonstrate that the spatial likelihood mapping-based localization exhibits good performance. Furthermore, when the multi-target cyclic peak extraction algorithm based on statistical dual-thresholds is applied, it outperforms direct target extraction from the spatial likelihood map, showcasing enhanced multi-target localization capabilities. Moreover, compared to the position non-linear least squares multi-target localization method, the proposed method has lower optimal sub-pattern assignment distance and lower localization error under the condition of interference and missing detection. Full article

Both extreme-excess modeling and extreme-value analysis of precipitation events frequently utilize the Generalized Pareto (GP) distribution to model peaks above a selected threshold. However, selecting an appropriate threshold remains a complex and challenging task, which has discouraged many practitioners from employing Pareto or Pareto–Poisson distributions for extreme-value analysis. Recent analyses of threshold selection methods proposed in the technical literature, particularly when applied to rainfall records with high quantization levels, have shown that nonparametric methods are often unreliable. Additionally, methods relying on the asymptotic properties of the GP distribution tend to produce unrealistically high threshold estimates. In contrast, graphical methods and goodness-of-fit (GoF) metrics that account for the pre-asymptotic behavior of the GP distribution have demonstrated better performance. Despite these improvements, there remains no automatic and statistically robust methodology for threshold selection. This study develops an automatic, statistically sound procedure for optimal threshold selection, leveraging weighted mean square errors and internally studentized residuals. The proposed method outperforms existing approaches in terms of accuracy, as demonstrated through numerical experiments and its application to real-world data from the NOAA NCDC Daily Rainfall Database. Results indicate that the method not only improves threshold estimation precision but also enhances the reliability of extreme-value analysis for precipitation records, making it a valuable tool for hydrological applications. The findings emphasize the practical implications of the method for analyzing extreme rainfall events and its potential for broader climatological studies. Full article

Information and communication technology (ICT) components, especially actuators in automated irrigation systems, are essential for managing precise irrigation and optimal soil moisture, enhancing orchard growth and yield. However, actuator malfunctions can lead to inefficient irrigation, resulting in water imbalances that impact crop health and reduce productivity. The objective of this study was to develop a signal processing technique to detect potential malfunctions based on the power consumption level and operating status of actuators for an automated orchard irrigation system. A demonstration orchard with four apple trees was set up in a 3 m × 3 m soil test bench inside a greenhouse, divided into two sections to enable independent irrigation schedules and management. The irrigation system consisted of a single pump and two solenoid valves controlled by a Python-programmed microcontroller. The microcontroller managed the pump cycling ‘On’ and ‘Off’ states every 60 s and solenoid valves while storing and transmitting sensor data to a smartphone application for remote monitoring. Commercial current sensors measured actuator power consumption, enabling the identification of normal and abnormal operations by applying threshold values to distinguish activation and deactivation states. Analysis of power consumption, control commands, and operating states effectively detected actuator operations, confirming reliability in identifying pump and solenoid valve failures. For the second solenoid valve in channel 2, with 333 actual instances of normal operation and 60 actual instances of abnormal operation, the model accurately detected 316 normal and 58 abnormal instances. The proposed method achieved a mean average precision of 99.9% for detecting abnormal control operation of the pump and solenoid valve of channel 1 and a precision of 99.7% for the solenoid valve of channel 2. The proposed approach effectively detects actuator malfunctions, demonstrating the potential to enhance irrigation management and crop productivity. Future research will integrate advanced machine learning with signal processing to improve fault detection accuracy and evaluate the scalability and adaptability of the system for larger orchards and diverse agricultural applications. Full article

Rural landscapes are experiencing ecosystem degradation due to urbanization and rapid suburban expansion. Ecosystem services derived from natural resources and essential public services facilitated by social capital collectively address the growingly diverse social and ecological requirements of rural residents. Even so, ecosystem services and public services are often trade-offs, highlighting the necessity to enhance their coordinated development. However, it remains unclear how to use the identified thresholds to delineate functional zones. This will scientifically guide sound and efficient spatial planning and ecological management. This study takes the suburban countryside of Jiangning in Nanjing as the study area. It explores the inclusion of the threshold value of rural public services and ecosystem services in the strategic design of sustainable suburban development in China. First, we quantify and map six types of ecosystem services (ESs) and 13 types of rural public services (RPSs). Secondly, we use the piecewise linear regression method to identify the response and threshold of 13 types of RPSs to six kinds of ESs. Finally, the combination and classification of threshold values are used to divide functional areas, and space-specific management and planning suggestions are put forward. The results are as follows (1) With the increase in RPSs, all ESs respond with a downward trend. (2) In addition to the negative linear relationship between education and social welfare services and ESs, the response thresholds of other RPSs and ESs were identified. (3) According to multiple density threshold analysis of each RPS’s response to ESs, four functional areas were obtained. We emphasize the priority of spatial planning and management, that is, the priority management of “ESs enhancement area and RPSs optimization area”. (4) The threshold values of ESs and RPSs can be used as tools to delineate functional zones and guide the spatial planning and management of rural functional areas. In general, our research helps ensure the maximization of rural ecological benefits while also meeting the growing diversity of needs of rural residents and enabling efficient, phased, gradient, and precise spatial management of suburban rural ecosystems and public services to promote the sustainable development of suburban rural areas and realize rural revitalization. Full article

To solve the prominent problem of water supply and demand contradictions, enhance water resource security capabilities, and improve economic, social, and ecological benefits, this paper comprehensively analyzes the water resource situation in Wu’an City and proposes a method for calculating the rigid water demand and total water demand threshold for the whole city and a method for calculating the water supply capacity of multiple water sources. At the same time, taking economic, social, and ecological benefits as the objective function and water resource allocation rules, water supply balance, water supply capacity, total water consumption, water consumption per Chinese Yuan (CNY) 10,000 of Gross Domestic Product (GDP), water consumption per CNY 10,000 of industrial added value, and non-negative as constraints, the water resource optimization allocation model for Wu’an City was constructed, and the Non-dominated Sorting Genetic Algorithm III (NSGA-III) combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOSPIS) was used to solve it. The results show that the rigid water demand of Wu’an City is met, the Gini coefficient of water supply satisfaction and ecological water shortage in the flat water scenario are both 0, the overall difference in water supply satisfaction of each township is very small, and the ecological water demand is met. Under the current situation, Wu’an City basically achieves a regional supply and demand balance, which can increase water supply by 5.841 million m³ and increase the net economic benefit of water supply by CNY 136.5515 million. The optimized water resource allocation plan has higher economic, social, and ecological benefits. The research can provide technical support for water resource management in Wu’an City. Full article

Global soil salinization has become an increasingly severe challenge for agricultural production, particularly affecting the cultivation of economic crops in marginal lands. Industrial hemp (Cannabis sativa L.), despite its economic potential, exhibits a notable sensitivity to salt-alkaline stress, limiting its expansion in saline-alkali regions. This study investigated the regulatory effects of nitrogen (N), phosphorus (P), and potassium (K) fertilizers on hemp growth and nutrient homeostasis under alkaline salt stress. Using a “3414” orthogonal experimental design, we evaluated fourteen NPK combinations under 200 mM NaHCO₃ stress, a concentration determined through preliminary experiments to simulate moderate alkaline stress. Plant growth parameters, biomass partitioning, and mineral nutrient profiles were analyzed after treatment with three biological replicates. The N1P2K2 treatment (N 120 mg·L⁻¹, P 238 mg·L⁻¹, K 348 mg·L⁻¹) significantly enhanced plant performance, increasing shoot biomass by 45.3% and root biomass by 38.7% compared to the control. This optimal combination maintains the K⁺/Na⁺ ratio in leaves above 1.2 and regulated Ca²⁺/Mg²⁺ homeostasis, maintaining a ratio of 2.8–3.2, indicating improved salt tolerance. Notably, excessive fertilizer applications (>400 mg·L⁻¹ total nutrients) exacerbated salt injury, reducing biomass accumulation by 25–30% and disrupting ion homeostasis. Our findings reveal the critical thresholds for NPK application in hemp under alkaline stress and provide practical fertilization strategies for sustainable hemp cultivation in saline-alkali regions. Full article

Evaluating the state of health (SOH) of lithium-ion batteries (LIBs) is essential for their safe deployment and the advancement of electric vehicles (EVs). Existing machine learning methods face challenges in the automation and effectiveness of feature extraction, necessitating improved computational efficiency. To address this issue, we propose a collaborative approach integrating an enhanced whale optimization algorithm (EWOA) for feature selection and a lightweight support vector regression (SVR) model for SOH estimation. Key features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The EWOA selects features by initially assigning weights based on importance scores from a random forest model. Gaussian noise increases population diversity, while a dynamic threshold method optimizes the selection process, preventing local optima. The selected features construct the SVR model for SOH estimation. This method is validated using four aging datasets from the NASA database, conducting 50 prediction experiments per battery. The results indicate optimal average absolute error (MAE) and root mean square error (RMSE) within 0.41% and 0.71%, respectively, with average errors below 1% and 1.3%. This method enhances automation and accuracy in feature selection while ensuring efficient SOH estimation, providing valuable insights for practical LIB applications. Full article

This study presents a novel system for diagnosing and evaluating soccer performance using wearable inertial sensors integrated into players’ insoles. Designed to meet the needs of professional podiatrists and sports practitioners, the system focuses on three key soccer-related movements: passing, shooting, and changes of direction (CoDs). The system leverages low-power IMU sensors, Bluetooth Low Energy (BLE) communication, and a cloud-based architecture to enable real-time data analysis and performance feedback. Data were collected from nine professional players from the SD Huesca women’s team during controlled tests, and bespoke algorithms were developed to process kinematic data for precise event detection. Results indicate high accuracy rates for detecting ball-striking events and CoDs, with improvements in algorithm performance achieved through adaptive thresholds and ensemble neural network models. Compared to existing systems, this approach significantly reduces costs and enhances practicality by minimizing the number of sensors required while ensuring real-time evaluation capabilities. However, the study is limited by a small sample size, which restricts generalizability. Future research will aim to expand the dataset, include diverse sports, and integrate additional sensors for broader applications. This system offers a valuable tool for injury prevention, player rehabilitation, and performance optimization in professional soccer, bridging technical advancements with practical applications in sports science. Full article

This study addresses the prevalent challenges of inefficiency and suboptimal quality in indoor 3D scene generation and rendering by proposing a parameter-tuning strategy for 3D Gaussian Splatting (3DGS). Through a systematic quantitative analysis of various performance indicators under differing resolution conditions, threshold settings for the average magnitude of spatial position gradients, and adjustments to the scaling learning rate, the optimal parameter configuration for the 3DGS model, specifically tailored for indoor modeling scenarios, is determined. Firstly, utilizing a self-collected dataset, a comprehensive comparison was conducted among COLLI-SION-MAPping (abbreviated as COLMAP (V3.7), an open-source software based on Structure from Motion and Multi-View Stereo (SFM-MVS)), Context Capture (V10.2) (abbreviated as CC, a software utilizing oblique photography algorithms), Neural Radiance Fields (NeRF), and the currently renowned 3DGS algorithm. The key dimensions of focus included the number of images, rendering time, and overall rendering effectiveness. Subsequently, based on this comparison, rigorous qualitative and quantitative evaluations are further conducted on the overall performance and detail processing capabilities of the 3DGS algorithm. Finally, to meet the specific requirements of indoor scene modeling and rendering, targeted parameter tuning is performed on the algorithm. The results demonstrate significant performance improvements in the optimized 3DGS algorithm: the PSNR metric increases by 4.3%, and the SSIM metric improves by 0.2%. The experimental results prove that the improved 3DGS algorithm exhibits superior expressive power and persuasiveness in indoor scene rendering. Full article

Background and Aims: Hepatocellular carcinoma (HCC) represents the second leading cause of cancer deaths worldwide. Six-month imaging along with alpha-fetoprotein (AFP) serum levels detection are the current gold standard to exclude HCC. Protein induced by vitamin K absence (PIVKA-II) has been proposed as a potential screening biomarker for HCC. This study was designed to evaluate the role of PIVKA-II as diagnostic HCC marker, and the correlation between PIVKA-II levels and HCC stage. Methods: PIVKA-II levels were assessed on serum samples of Italian patients. The study population included 80 patients with HCC, 111 with liver cirrhosis (LC), and 111 with chronic hepatitis C (CHC). Results: PIVKA-II serum levels progressively increase from patients with CHC to patients with HCC. In the HCC group, PIVKA-II values are higher in the more advanced stages of the disease, assessed by the Barcelona Clinic Liver Cancer (BCLC) staging system (BCLC-B vs. BCLC-A vs. BCLC-0). Youden’s index analysis identified a value >37 mAU/mL as the optimal threshold for the best combination of sensitivity and specificity (80% and 76%, respectively) and, at the best cut-off of 5.2 ng/mL, AFP yielded 53% specificity and 78% sensitivity. The combination of PIVKA-II and AFP reached positive and negative predictive values of 73.9% and 94.2%, respectively. Conclusions: PIVKA-II levels are increased in the HCC patients, compared to control groups. The increase is more evident in patients with advanced HCC. The diagnostic performance of PIVKA-II seems more sensitive than AFP while the combination of PIVKA-II and AFP resulted in the best diagnostic accuracy, reaching 73.9% positive predictive value and 94.2% negative predictive value, thus improving the diagnostic capability of the single marker. Full article

In this article, a new extension of the standard Laplace distribution is introduced for house price modeling. Certain important properties of the new distribution are deducted throughout this study. We used the new extension of the Laplace model to conduct a thorough economic risk assessment utilizing several metrics, including the value-at-risk (VaR), the peaks over a random threshold value-at-risk (PORT-VaR), the tail value-at-risk (TVaR), the mean of order-P (MO^P), and the peaks over a random threshold based on the mean of order-P (PORT-MO^P). These metrics capture different facets of the tail behavior, which is essential for comprehending the extreme median values in the Boston house price data. Notably, PORT-VaR improves the risk evaluations by incorporating randomness into the selection of the thresholds, whereas VaR and TVaR focus on measuring the potential losses at specific confidence levels, with TVaR offering insights into significant tail risks. The MO^P method aids in balancing the reliability goals while optimizing the performance in the face of uncertainty. Full article