Search Results (41,931)

Post-harvest deterioration of fruit quality represents a significant challenge in the dried fruit industry, particularly affecting the preservation of nutritional compounds and sensory attributes during the drying process. This research examined the potential protective effects of exogenous melatonin supplementation on the preservation of selected quality metrics and antioxidant characteristics in sun-dried apricots, utilizing a comparative analysis across disparate melatonin concentrations (10, 100, and 1000 µM). Our research findings demonstrated that melatonin treatment, particularly at 100 µM concentration, significantly enhanced quality preservation in sun-dried apricots. Specifically, the treatment resulted in improved color retention (increased L*, a*, and b* values), reduced oxidative stress markers (MDA and H₂O₂), and optimized sugar composition (glucose: 18.99 g/100 g, fructose: 12.58 g/100 g, sucrose: 15.52 g/100 g). The melatonin treatment at 100 µM concentration proved particularly effective, revealing the most significant results. Specifically, this concentration resulted in the highest β-carotene levels, reaching 223.07 mg/kg. These findings suggest promising applications for commercial-scale implementation through either dipping or spraying methods. The non-toxic nature of melatonin and its demonstrated efficacy in preserving fruit quality parameters position it as a valuable post-harvest treatment option in the fruit supply chain. This research contributes significantly to advancing sustainable post-harvest preservation strategies, though further investigation into melatonin stability and standardization of application protocols remains necessary for optimal commercial implementation. Full article

Objective: In treating obesity, energy intake control is essential to avoid exceeding energy expenditure. However, excessive restriction of energy intake often leads to resting energy expenditure (REE) reduction, increasing hunger and making weight loss difficult. This study aimed to investigate whether providing nutritional guidance that considers energy expenditure based on the regular evaluation of REE and physical activity could effectively reduce body weight (BW) in patients with obesity. Methods: A single-arm, prospective interventional study was conducted on 20 patients with obesity (body mass index ≥ 25 kg/m²) at the Nagoya University Hospital for 24 weeks. REE and physical activity were regularly assessed, and the recommended energy intake was adjusted based on the values. The primary outcome was the change in BW, and the secondary outcomes included changes in REE and hunger ratings, which were assessed using a visual analog scale. Results: Eighteen participants completed the study, demonstrating a significant reduction in BW after 24 weeks (−5.34 ± 6.76%, p < 0.0001). No significant changes were observed in REE or hunger ratings. No adverse events were reported throughout the study period. Conclusions: Guidance on energy intake based on REE and physical activity was effective for reducing BW in patients with obesity without decreasing REE or increasing hunger. This approach may reduce the burden on patients with obesity while losing BW. Full article

The composition of oil sands tailings is a complex mixture of water, fine clay, sand, silt, and residual bitumen that remains after the extraction of bitumen. Effective tailings disposal management requires an understanding of the mechanisms controlling water movement, surface settlement rates and extents (hydraulic conductivity and compressibility), and strength variation with depth. This investigation examines the self-weight consolidation behavior of oil sands tailings, typically assessed by utilizing large strain consolidation (LSC) methods such as the multi-step large strain consolidation (MLSC) test and seepage-induced consolidation test (SICT). These methods, however, are time consuming and often take weeks or years to complete. As an alternative, centrifuge testing, including both geotechnical beam type and benchtop devices, was utilized to evaluate the consolidation behaviors of three untreated high water content oil sands tailing slurries: two high-plasticity fluid fine tailing (FFT) samples and one low plasticity FFT. The centrifuge-derived compressibility data closely matched the LSC testing compressibility data within the centrifuge stress range. However, the hydraulic conductivity obtained from centrifuge testing was up to an order of magnitude higher than the LSC test results. Comparing centrifuge and large strain modeling results indicates that centrifuge test data demonstrate average void ratios 10–33% lower than those predicted by simulations using LSC parameters, highlighting a notable deviation. To examine the scale effect on result accuracy, validation tests indicated that the benchtop centrifuge (BTC) yielded comparable results to the geotechnical beam centrifuge (GBC) for the same prototype, saving time, resources, and sample volumes in the assessment of tailings consolidation behavior. These tests concluded that the small radius of the benchtop centrifuge had a minimal impact on the results. 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

Forest fires are among the most devastating natural disasters, causing significant environmental and economic damage. Effective early prediction mechanisms are critical for minimizing these impacts. In our previous work, we developed a smart and secure wireless sensor network (WSN) utilizing physical sensors to emulate forest fire dynamics and predict fire scenarios using machine learning. Building on this foundation, this study explores the integration of virtual sensors to enhance the prediction capabilities of the WSN. Virtual sensors were generated using polynomial regression models and incorporated into a supervector framework, effectively augmenting the data from physical sensors. The enhanced dataset was used to train a multi-layer perceptron neural network (MLP NN) to classify multiple fire scenarios, covering both early warning and advanced fire states. Our experimental results demonstrate that the addition of virtual sensors significantly improves the accuracy of fire scenario predictions, especially in complex situations like “Fire with Thundering” and “Fire with Thundering and Lightning”. The extended model’s ability to predict early warning scenarios such as lightning and smoke is particularly promising for proactive fire management strategies. This paper highlights the potential of combining physical and virtual sensors in WSNs to achieve superior prediction accuracy and scalability of the field without any extra cost. Such findings pave the way for deploying scalable (cost-effective), intelligent monitoring systems capable of addressing the growing challenges of forest fire prevention and management. We obtained significant results in specific scenarios based on the number of virtual sensors added, while in some scenarios, the results were less promising compared to using only physical sensors. However, the integration of virtual sensors enables coverage of much larger areas, making it a highly promising approach despite these variations. Future work includes further optimization of the virtual sensor generation process and expanding the system’s capability to handle large-scale forest environments. Moreover, utilizing virtual sensors will alleviate many challenges associated with the huge number of deployed physical sensors. Full article

Rolling bearings, as critical components of rotating machinery, directly affect the reliability and efficiency of the system. Due to extended operation under high load, harsh environmental conditions, and continuous use, bearings become more susceptible to failure, leading to a higher likelihood of malfunction. To prevent sudden failures, reduce downtime, and optimize maintenance strategies, early and accurate diagnosis of rolling bearing faults is essential. Although existing methods have achieved certain success in processing acoustic and vibration signals, they still face challenges such as insufficient feature fusion, inflexible weight allocation, lack of effective feature selection mechanisms, and low computational efficiency. To address these challenges, we propose a dynamic weighted multimodal fault diagnosis model based on the fusion of acoustic and vibration information. This model aims to enhance feature fusion, dynamically adapt to signal characteristics, optimize feature selection, and reduce computational complexity. The model incorporates an adaptive fusion method based on a multi-branch convolutional structure, enabling unified processing of both acoustic and vibration signals. At the same time, a cross-modal dynamic weighted fusion mechanism is employed, allowing the real-time adjustment of weight distribution based on signal characteristics. By utilizing an attention mechanism for dynamic feature selection and weighting, the robustness of classification is further improved. Additionally, when processing acoustic signals, a depthwise separable convolutional network is used, effectively reducing computational complexity. Experimental results demonstrate that our method significantly outperforms other algorithms in terms of convergence speed and final performance. Additionally, the accuracy curve during training showed minimal fluctuation, reflecting higher robustness. The model achieved over 99% diagnostic accuracy under all signal-to-noise ratio (SNR) conditions, showcasing exceptional robustness and noise resistance in both noisy and high-SNR environments. Furthermore, its superiority across different data scales, especially in small-sample learning and stability, highlights its strong generalization capability. Full article

Objective: Fluctuations in sodium levels (SLs) may increase mortality, severity, and prolonged length of stay (LOS) in critically ill patients. We aim to study the effect of SL on various clinical outcomes in patients with severe traumatic brain injury (TBI). Methods: This is a single-center, retrospective study of patients with severe TBI from 1 January 2020 to 31 December 2023, inclusive. Patients were identified using Abbreviated Injury Severity (AIS) scores and International Classification of Diseases (ICD) injury descriptions. Result: Variations in hospital (H) admission SLs were statistically significant across four age ranges (pediatric, young adult, older adults, and elderly). Intensive care unit (ICU) admission, H discharge, and death also showed significance. A statistical difference was noted in ICU discharge levels while comparing blunt versus penetrating injury. We found statistically significant differences in SLs at H admission, ICU admission, and ICU discharge when compared to the Injury Severity Score (ISS) and the Glasgow Coma Scale (GCS) at admission. A linear regression analysis revealed a statistically significant positive correlation between ICU admission SLs and ISS. We discovered statistically significant differences when comparing ICU admission levels to H LOS, ventilator days, and mortality. Conclusions: SL upon ICU admission is correlated with ISS, GCS, and mortality rates. The elevated admission SL was linked to adverse hospital outcomes, including prolonged LOS at the H, ICU, and mechanical ventilation. Moreover, variability in serum SLs is independently associated with mortality throughout the hospital stay, irrespective of the absolute serum sodium concentration. Full article

This study proposed a vision-based underwater vertical docking guidance and positioning method to address docking control challenges for human-operated vehicles (HOVs) and unmanned underwater vehicles (UUVs) under complex underwater visual conditions. A cascaded detection and positioning strategy incorporating fused active and passive markers enabled real-time detection of the relative position and pose between the UUV and docking station (DS). A novel deep learning-based network model, YOLO-D, was developed to detect docking markers in real time. YOLO-D employed the Adaptive Kernel Convolution Module (AKConv) to dynamically adjust the sample shapes and sizes and optimize the target feature detection across various scales and regions. It integrated the Context Aggregation Network (CONTAINER) to enhance small-target detection and overall image accuracy, while the bidirectional feature pyramid network (BiFPN) facilitated effective cross-scale feature fusion, improving detection precision for multi-scale and fuzzy targets. In addition, an underwater docking positioning algorithm leveraging multiple markers was implemented. Tests on an underwater docking markers dataset demonstrated that YOLO-D achieved a detection accuracy of [email protected] to 94.5%, surpassing the baseline YOLOv11n with improvements of 1.5% in precision, 5% in recall, and 4.2% in [email protected]. Pool experiments verified the feasibility of the method, achieving a 90% success rate for single-attempt docking and recovery. The proposed approach offered an accurate and efficient solution for underwater docking guidance and target detection, which is of great significance for improving the safety of docking. Full article

Graph neural networks (GNNs) are widely used in recommendation systems to improve prediction performance, especially in scenarios with diverse behaviors and complex user interactions within supply chains. However, while existing models have achieved certain success in capturing the temporal and dynamic aspects of supply chain behaviors, challenges remain in effectively addressing the time-sensitive fluctuations of market demands and user preferences. Motivated by these challenges, we propose SC-TKGR, a supply chain recommendation framework based on temporal knowledge graphs. It employs enhanced time-sensitive graph embedding methods to model behavioral temporal characteristics, incorporates external factors to capture market dynamics, and utilizes contrastive learning to handle sparse information efficiently. Additionally, static feature knowledge graph embeddings are incorporated to complement temporal modeling by capturing complex retailer–product relationships. Experiments on real-world electrical equipment industry datasets demonstrate that SC-TKGR achieves superior performance in NDCG and Recall metrics, offering a robust approach for capturing trend-level demand shifts and market dynamics in supply chain recommendations, thereby aiding strategic planning at a monthly scale and operational adjustments. Full article

Standard carbon accounting methods and metrics undermine the potential of fast-growing biogenic materials to decarbonize buildings because they ignore the timing of carbon uptake. The consequence is that analyses can indicate that a building material is carbon-neutral when it is not climate-neutral. Here, we investigated the time-dependent effect of using fast-growing fibers in durable construction materials. This study estimated the material stock and flow and associated cradle-to-gate emissions for four residential framing systems in the US: concrete masonry units, light-frame dimensional timber, and two framing systems that incorporate fast-growing fibers (bamboo and Eucalyptus). The carbon flows for these four framing systems were scaled across four adoption scenarios, Business as Usual, Early-Fast, Late-Slow, and Highly Optimistic, ranging from no adoption to the full adoption of fast-growing materials in new construction within 10 years. Dynamic life cycle assessment modeling was used to project the radiative forcing and global temperature change potential. The results show that the adoption of fast-growing biogenic construction materials can significantly reduce the climate impact of new US residential buildings. However, this study also reveals that highly aggressive, immediate adoption is the only way to achieve net climate cooling from residential framing within this century, highlighting the urgent need to change the methods and metrics decision makers use to evaluate building materials. Full article

Large-scale high-end equipment robotic precision operations have large workspaces and numerous uncertainties. They have an unevenly distributed error effect on the position in space. The current conventional stereotyping method ignores the uncertainty in the robotic system; the probabilistic or fuzzy method is often due to the lack of statistical samples in the project, and it is difficult to accurately define the probabilistic or fuzzy model because the probabilistic distribution pattern or fuzzy affiliation cannot be known in advance. In this paper, we propose a non-probabilistic reliability-based robot workspace partitioning method that only needs to know the upper and lower bounds of the values of the uncertain parameters and is adapted to realize accurate calibration of robots in small-sample or information-poor scenarios. The method considers the differences in non-probabilistic reliability of robot end positions in different workspace ranges and uses KLFCM clustering combined with a genetic algorithm to perform a two-stage hierarchical partitioning optimization. The experimental results show that compared with the global compensation, the average values of the upper and lower limits of the x, y, and z direction error intervals of the partitioned compensation are reduced by 31.17%, 7.26%, 14.30%, 34.91%, 2.48%, and 35.82%, respectively, verifying that the method in this study can more accurately realize the partitioned categorization calibration and compensation of the robot, and effectively improve the reliability and spatial adaptability of parameter calibration and compensation of the robot in the full workspace domain. The reliability and spatial adaptability of the parameter calibration and compensation are effectively improved in the entire workspace domain of the robot. Full article

The acoustic product development process, crucial for effective noise control, emphasises efficient testing and validation of materials for sound absorption in the R&D phase. Balancing cost-effectiveness, speed, and sustainability, the focus is on minimising excess materials. While strides have been made in reducing sample sizes for estimating random-incident absorption, challenges persist, particularly in establishing validity thresholds for smaller samples with increasing thickness, susceptible to potential overestimation due to edge effects. This study delves into analysing the absorption coefficients of widely used acoustic absorber types—polyester, fibreglass, and open-cell foam—in a full-scale reverberation chamber at Ventac, Blessington, and Wicklow. Demonstrating significant absorption above 500 Hz, these porous absorbers exhibit diminished effectiveness at lower frequencies. The strategic combination of these absorbers with different facings enhances their theoretical broadband absorption characteristics in practical applications. Moreover, the study assesses the validity threshold for reduced sample sizes, employing statistical analysis against ISO 354:2003 standard control samples of the absorber types. Analysis of Variance (ANOVA) on material groups underscores the significant influence of frequency components and sample sizes on the absorption coefficient. The determined validity threshold for 12.8 sqm ISO 354 standard control size is 7.7 sqm for the 25 mm open-cell foam. Similarly, the validity threshold of the 12 sqm ISO 354 standard control size is 9.6 sqm for the 20 mm 800 gsm polyester, 7.2 sqm for the 25 mm fibreglass, and the vinyl black on 25 mm fibreglass. Full article

This study investigates the behavior of heavy oil reservoirs under varying oil saturation conditions through laboratory-scale in situ combustion (ISC) physical simulation experiments. The research focuses on the propagation stability of the combustion front and key parameters influencing the in situ combustion process, with a systematic examination of crude oil composition changes and upgrading mechanisms during ISC. By employing gas chromatography (GC) and infrared spectroscopy (IR) analyses, this study evaluates the transformation of saturated hydrocarbons, aromatic hydrocarbons, resins, and asphaltenes in the produced oil before and after ISC. The results demonstrate that oil saturation plays a critical role in determining combustion efficiency, cracking dynamics, and upgrading performance. At high oil saturation (65%), combustion reactions are vigorous, yielding enhanced cracking efficiency, a significant increase in light hydrocarbon content, and effective conversion of heavy components, achieving optimal upgrading outcomes. Conversely, at low oil saturation (30%), cracking efficiency is reduced, light hydrocarbons are lost, and heavy components, including asphaltenes and polycyclic aromatic hydrocarbons, accumulate, resulting in less effective upgrading. Nonetheless, ignition and stable combustion are achievable even at low oil saturation levels. This study offers strategies to optimize ISC operations tailored to different oil saturation conditions, providing valuable insights for improving combustion efficiency and enhancing the quality of produced oil in heavy oil reservoirs. Full article

Ecosystem services (ESs) assessment plays a significant role in managing ecological resources. Uncovering the complex interdependencies between ESs and their key drivers is an essential preliminary step toward the coordinated management of ESs. Currently, a major challenge lies in precisely evaluating trade-offs and synergies among ESs across different spatial and temporal scales, particularly in capturing their dynamic evolution and determinants. This study focuses on the Tuo River Basin in China, quantifying four key ESs, namely, habitat quality (HQ), nitrogen export (NE), soil conservation (SC), and water yield (WY), and assessing their interactions from 2000 to 2020 at both grid and county scales. Moreover, this study explored the social-ecological driving factors influencing these ESs. The results showed that (1) SC and WY in the region exhibited an increasing trend, HQ and NE declined, and ESs at the county scale showed a central collapse feature; (2) synergies between HQ–NE, HQ–WY, and SC–WY pairs generally increased, the relationships between NE–SC and NE–WY pairs showed slight fluctuations, and there was a decline in the synergies within the HQ–SC pair; and (3) the interplay of all drivers positively affected ESs, with land use/land cover being the most significant and GDP exerting a lower influence. ES assessment results exhibited distinctive characteristics at two scales. Based on these findings, management strategies that incorporate both scales and cross policy boundaries are proposed to effectively meet management objectives. These results can facilitate improved synergy between regional ecological protection and economic development. Full article

Anomalies frequently occur during the operation of spacecraft in orbit, and studying anomaly detection methods is crucial to ensure the normal operation of spacecraft. Due to the complexity of spacecraft structures, telemetry data possess characteristics such as high dimensionality, complexity, and large scale. Existing methods frequently ignore or fail to explicitly extract the correlation between variables, and due to the lack of prior knowledge, it is difficult to obtain the initial relationship of variables. To address these issues, this paper proposes a new method, namely spatio-temporal graph learning reconstruction (STGLR), for spacecraft anomaly detection. STGLR employs a dynamic graph learning module to infer the initial relationships among telemetry variables. It then constructs a spatio-temporal feature extraction module to capture complex spatio-temporal dependencies among variables, leveraging a graph sample and aggregation network to learn embedded features and incorporating an attention mechanism to adaptively select salient features. Finally, a reconstruction module is used to learn the latent representations of features, capturing the normal patterns in telemetry data and achieving anomaly detection. To validate the effectiveness of the proposed method, experiments were conducted on two public spacecraft datasets, and the results demonstrate that the performance of the STGLR method surpasses existing anomaly detection methods, with an average F1 score exceeding 0.97. Full article