Search Results (3,435)

This study introduces a quantitative risk assessment (QRA) model aimed at evaluating the risk of invasive listeriosis linked to the consumption of ready-to-eat (RTE) smoked and gravad fish. The QRA model, based on published data, simulates the production process from fish harvest through to consumer intake, specifically focusing on smoked brine-injected, smoked dry-salted, and gravad fish. In a reference scenario, model predictions reveal substantial probabilities of lot and pack contamination at the end of processing (38.7% and 8.14% for smoked brined fish, 34.4% and 6.49% for smoked dry-salted fish, and 52.2% and 11.1% for gravad fish), although the concentrations of L. monocytogenes are very low, with virtually no packs exceeding 10 CFU/g at the point of sale. The risk of listeriosis for an elderly consumer per serving is also quantified. The lot-level mean risk of listeriosis per serving in the elderly population was 9.751 × 10⁻⁸ for smoked brined fish, 9.634 × 10⁻⁸ for smoked dry-salted fish, and 2.086 × 10⁻⁷ for gravad fish. Risk reduction strategies were then analyzed, indicating that the application of protective cultures and maintaining lower cold storage temperatures significantly mitigate listeriosis risk compared to reducing incoming fish lot contamination. The model also addresses the effectiveness of control measures during processing, such as minimizing cross-contamination. The comprehensive QRA model has been made available as a fully documented qraLm R package. This facilitates its adaptation for risk assessment of other RTE seafood, making it a valuable tool for public health officials to evaluate and manage food safety risks more effectively. Full article

The glaciers in the High Mountain Asia (HMA) region are highly vulnerable to global warming, posing significant threats to downstream populations and infrastructure through glacier lake outburst floods (GLOFs). The monitoring and early warnings of these events are challenging due to sparse observations in these remote regions. To explore reproducing the evolution of GLOFs with sparse observations in situ, this study focuses on the outburst event and corresponding GLOFs in August 2018 caused by the Kyagar Glacier lake, a typical glacier lake of the HMA in the Karakoram, which is known for its frequent outburst events, using a combination of multi-satellite remote sensing data (Sentinel-1 and Sentinel-2) and the HEC-RAS hydrodynamic model. The water depth of the glacier lake and downstream was extracted from satellite data adapted by the Floodwater Depth Elevation Tool (FwDET) as a baseline to compare them with simulations. The elevation-water volume curve was obtained by extrapolation and was applied to calculate the water surface elevation (WSE). The inundation of the downstream of the lake outburst was obtained through flood modeling by incorporating a load elevation-water volume curve and the Digital Elevation Model (DEM) into the hydrodynamic model HEC-RAS. The results showed that the Kyagar glacial lake outburst was rapid and destructive, accompanied by strong currents at the end of each downstream storage ladder. A series of meteorological evaluation indicators showed that HEC-RAS reproduced the medium and low streamflow rates well. This study demonstrated the value of integrating remote sensing and hydrodynamic modeling into GLOF assessments in data-scarce regions, providing insights for disaster risk management and mitigation. Full article

Land use/cover change (LUCC) significantly alters the carbon storage capacity of ecosystems with a profound impact on global climate change. The influence of land use changes on carbon storage capacity and the projection of future carbon stock changes under different scenarios are essential for achieving carbon peak and neutrality goals. This study applied the PLUS-InVEST model to predict the land use pattern in China’s arid Xinjiang Region in 2020–2050. The model assessed the carbon stock under four scenarios. Analysis of the historical LUCC data showed that the carbon storage in Xinjiang in 2000–2020 in five-year intervals was 85.69 × 10⁸, 85.79 × 10⁸, 85.87 × 10⁸, 86.01 × 10⁸, and 86.71 × 10⁸ t. The rise in carbon sequestration capacity in the study area, attributable to the expansion of cropland, water, and unused land areas, brought a concomitant increment in the regional carbon storage by 1.03 × 10⁸ t. However, prediction results for 2030–2050 showed that carbon storage capacity under the four scenarios would decrease by 0.11 × 10⁸ and increase by 1.2 × 10⁸, 0.98 × 10⁸ t, and 1.28 × 10⁸ t, respectively. The findings indicate that different land transfer modes will significantly affect Xinjiang’s carbon storage quantity, distribution, and trend. This research informs the past, present, and future of carbon storage in arid ecosystems of Xinjiang. It offers a reference for Xinjiang’s development planning and informs the efforts to achieve the carbon peak and neutrality goals. Full article

Forests play a critical role in the global carbon cycle, with carbon storage being an important carbon pool in the terrestrial ecosystem with tree crown size serving as a versatile ecological indicator influencing factors such as tree growth, wind resistance, shading, and carbon sequestration. They help with habitat function, herbicide application, temperature regulation, etc. Understanding the relationship between tree crown area and stock volume is crucial, as it provides a key metric for assessing the impact of land-use changes on ecological processes. Traditional ground-based stock volume estimation using DBH (Diameter at Breast Height) is labor-intensive and often impractical. However, high-resolution UAV (unmanned aerial vehicle) imagery has revolutionized remote sensing and computer-based tree analysis, making forest studies more efficient and interpretable. Previous studies have established correlations between DBH, stock volume and above-ground biomass, as well as between tree crown area and DBH. This research aims to explore the correlation between tree crown area and stock volume and automate stock volume and above-ground biomass estimation by developing an empirical model using UAV-RGB data, making forest assessments more convenient and time-efficient. The study site included a significant number of training and testing sites to ensure the performance level of the developed model. The findings underscore a significant association, demonstrating the potential of integrating drone technology with traditional forestry techniques for efficient stock volume estimation. The results highlight a strong exponential correlation between crown area and stem stock volume, with a coefficient of determination of 0.67 and mean squared error (MSE) of 0.0015. The developed model, when applied to estimate cumulative stock volume using drone imagery, demonstrated a strong correlation with an R² of 0.75. These results emphasize the effectiveness of combining drone technology with traditional forestry methods to achieve more precise and efficient stock volume estimation and, hence, automate the process. Full article

The market demand for baby leaf lettuce is constantly increasing, while safety has become one of the most important traits in determining consumer preference driven by human health hazards concerns. In this study, the performance of visible and near-infrared (vis/NIR) spectroscopy was tested in discriminating pesticide-free against pesticide-treated lettuce plants. Two commercial fungicides (mancozeb and fosetyl-al) and two insecticides (deltamethrin and imidacloprid) were applied as spray solutions at the recommended rates on baby leaf lettuce plants. Untreated-control plants were sprayed with water. Reflectance data in the wavelength range 400–2500 nm were captured on leaf samples until harvest on the 10th day upon pesticide application, as well as after 4 and 8 days during post-harvest storage at 5 °C. In addition, biochemical components in leaf tissue were also determined during storage, such as antioxidant enzymes’ activities (peroxidase [POD], catalase [CAT], and ascorbate peroxidase [APX]), along with malondialdehyde [MDA] and hydrogen peroxide [H₂O₂] content. Partial least square discriminant analysis (PLSDA) combined with feature-selection techniques was implemented, in order to classify baby lettuce tissue into pesticide-free or pesticide-treated ones. The genetic algorithm (GA) and the variable importance in projection (VIP) scores identified eleven distinct regions and nine specific wavelengths that exhibited the most significant effect in the detection models, with most of them in the near-infrared region of the electromagnetic spectrum. According to the results, the classification accuracy of discriminating pesticide-treated against non-treated lettuce leaves ranged from 94% to 99% in both pre-harvest and post-harvest periods. Although there were no significant differences in enzyme activities or H₂O₂, the MDA content in pesticide-treated tissue was greater than in untreated ones, implying that the chemical spray application probably induced a stress response in the plant that was disclosed with the reflected energy. In conclusion, vis/NIR spectroscopy appears as a promising, reliable, rapid, and non-destructive tool in distinguishing pesticide-free from pesticide-treated lettuce products. Full article

Although digital hoarding behavior does not directly affect physical space, with the popularization of cloud storage services, its impact on energy consumption has become increasingly significant, posing a challenge to environmental sustainability. This study focuses on the factors influencing consumer digital hoarding behavior on e-commerce platforms, aiming to provide management decision-making references for e-commerce enterprises to deal with consumer digital hoarding phenomena and improve transaction effectiveness. Based on the Motivation–Opportunity–Ability (MOA) Theory and through the Adversarial Interpretive Structure Modeling Method (AISM), this study systematically identifies and analyzes the influencing factors. The findings reveal that emotional attachment, burnout, and fear of missing out are the main motivational factors directly affecting consumer digital hoarding behavior, with strong interconnections between these factors. Perceived usefulness and platform interaction design are significant opportunity factors, indirectly affecting digital hoarding behavior by improving user experience and satisfaction. E-commerce platform convenience, anticipated ownership, perceived economic value, emotional regulation ability, auxiliary shopping decision-making, perceived behavioral control, and information organization ability are the foundational and intermediate factors. The research results emphasize the importance of understanding consumer digital hoarding behavior in the context of sustainable development. This is not only conducive to optimizing the shopping cart function and data management strategy of e-commerce platforms and improving transaction conversion rates but also provides a reference for policymakers to formulate data management and privacy protection policies. Full article

Rainwater pumping stations located near urban centers or agricultural areas help prevent flooding by activating an appropriate number of pumps with varying capacities based on real-time rainwater inflow. However, relying solely on rule-based pump operations that monitor only basin water levels is often insufficient for effective control. In addition to maintaining a low maximum water level to prevent flooding, pump operation at rainwater stations also requires minimizing the number of pump on/off switches. Reducing pump switch frequency lowers the likelihood of mechanical failure and thus decreases maintenance costs. This paper proposes a real-time pump operation method for rainwater pumping stations using Deep Reinforcement Learning (DRL) to meet these operational requirements simultaneously, based only on currently observable information such as rainfall, inflow, storage volume, basin water level, and outflow. Simulated rainfall data with various return periods and durations were generated using the Huff method to train the model. The Storm Water Management Model (SWMM), configured to simulate the Gasan rainwater pumping station located in Geumcheon-gu, Seoul, South Korea, was used to conduct experiments. The performance of the proposed DRL model was then compared with that of the rule-based pump operation currently used at the station. Full article

Effective air circulation is crucial for plant growth, requiring adequate airflow and environmental stability. This study utilized Computational Fluid Dynamics (CFD) to analyze airflow patterns in a controlled testing chamber, focusing on how miniature fan placement affects airflow direction and temperature distribution. Ten case studies were conducted, with the CFD model validated against experimental data collected from six monitoring locations on the plant growth table. Model validation was performed using statistical analyses including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE). The validation results showed strong agreement between simulated and experimental data, with R² values of 0.92 for temperature and 0.89 for airflow velocity. Statistical analysis showed significant differences in both airflow and temperature models at the 0.05 level, with the CFD model validation yielding an RMSE of 2.02 and an average absolute error of 1.17. Among the tested configurations, case M1 achieved the highest air velocity (0.317 m/s) and lowest temperature (27.03 °C), compared to M2 (0.255 m/s, 27.17 °C) and M3 (0.164 m/s, 27.18 °C). The temperature variations between cases significantly impacted cold storage efficiency, with case M1’s superior airflow distribution providing more uniform cooling. These findings offer practical guidelines for optimizing ventilation system design in medicinal plant cultivation facilities, particularly in maintaining ideal storage conditions through strategic fan placement and airflow management. Full article

Deploying emergency vehicles has become a key guarantee for power supply in post-disaster distribution networks on account of their flexibility, maneuverability, safety, and reliability. However, due to limitations in configuration, the continuous power supply capacity of existing electrical vehicles (EVs) is insufficient, making it difficult to meet the needs of energy transfer and flow regulation in post-disaster distribution networks. Therefore, in this study, we comprehensively considered the energy time-shift characteristics of EVs and the flexible control function of soft open points (SOPs), integrated their advantages, and designed an emergency vehicle with SOP (EV-SOP) and its management strategy for distribution network line faults. Firstly, we present the EV-SOP architecture and its mathematical model. Then, aiming to minimize the economic losses caused by power loss during line faults, an EV-SOP emergency management strategy based on data collection, scheduling judgment, and optimal modeling techniques is proposed. Finally, by taking the case study of an IEEE33-node distribution network with contact switches as an example, we validate the effectiveness and superiority of the EV-SOP and its emergency management strategy compared with traditional energy storage emergency vehicles. Full article

Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts. Full article

A rock mass is mainly subjected to a high internal pressure load in the lined rock cavern (LRC) for compressed air energy storage (CAES). However, under the action of long-term cyclic loading and unloading, the mechanical properties of a rock mass will deteriorate, affecting the long-term stability of the cavern. The fissures in the rock mass will expand and generate new cracks, causing varying degrees of damage to the rock mass. Most of the existing studies are based on the test data of complete rock samples and the fissures in the rock mass are ignored. In this paper, the strain equivalence principle is used to couple the initial damage variable caused by the fissures and the fatigue damage variable of a rock mass to obtain the damage variable of a rock mass under cyclic stress. Then, based on the ANSYS 17.0 platform, the ANSYS Parametric Design Language (APDL) is used to program the rock mass elastic modulus evolution equation, and a calculation program of the rock mass damage model is secondarily developed. The calculation program is verified by a cyclic loading and unloading model test. It is applied to the construction project of underground LRC for CAES in Northwest China. The calculation results show that the vertical radial displacement of the rock mass is 8.39 mm after the 100th cycle, which is a little larger than the 7.53 mm after the first cycle. The plastic zone of the rock mass is enlarged by 4.71 m², about 11.49% for 100 cycles compared to the first cycle. Our calculation results can guide the design and calculation of the LRC, which is beneficial to the promotion of the CAES technology. Full article

Small- and medium-sized reservoirs significantly alter natural flood processes, making it essential to understand their impact on runoff for effective water resource management. However, the lack of measured data for most small reservoirs poses challenges for accurately simulating their behavior. This study proposes a novel method that utilizes readily available satellite observation data, integrating hydraulic, hydrological, and mathematical formulas to derive outflow coefficients. Based on the Grid-XinAnJiang (GXAJ) model, the enhanced GXAJ-R model accounts for the storage and release effects of ungauged reservoirs and is applied to the Tunxi watershed. Results show that the original GXAJ model achieved a stable performance with an average NSE of 0.88 during calibration, while the NSE values of the GXAJ and GXAJ-R models during validation ranged from 0.78 to 0.97 and 0.85 to 0.99, respectively, with an average improvement of 0.03 in the GXAJ-R model. This enhanced model significantly improves peak flow simulation accuracy, reduces relative flood peak error by approximately 10%, and replicates the flood flow process with higher fidelity. Additionally, the area–volume model derived from classified small-scale data demonstrates high accuracy and reliability, with correlation coefficients above 0.8, making it applicable to other ungauged reservoirs. The OTSU-NDWI method, which improves the NDWI, effectively enhances the accuracy of water body extraction from remote sensing, achieving overall accuracy and kappa coefficient values exceeding 0.8 and 0.6, respectively. This study highlights the potential of integrating satellite data with hydrological models to enhance the understanding of reservoir behavior in data-scarce regions. It also suggests the possibility of broader applications in similarly ungauged basins, providing valuable tools for flood management and risk assessment. Full article

With the deep integration of communication technology and Internet of Things technology, the edge network structure is becoming increasingly dense and heterogeneous. At the same time, in the edge network environment, characteristics such as wide-area differentiated services, decentralized deployment of computing and network resources, and highly dynamic network environment lead to the deployment of redundant or insufficient edge cache nodes, which restricts the efficiency of network service caching and resource allocation. In response to the above problems, research on the joint optimization of service caching and resources in the decentralized edge network scenario is carried out. Therefore, we have conducted research on the collaborative caching of training data among multiple edge nodes and optimized the number of collaborative caching nodes. Firstly, we use a multi-queue model to model the collaborative caching process. This model can be used to simulate the in-network cache replacement process on collaborative caching nodes. In this way, we can describe the data flow and storage changes during the caching process more clearly. Secondly, considering the limitation of storage space of edge nodes and the demand for training data within a training epoch, we propose a stochastic gradient descent algorithm to obtain the optimal number of caching nodes. This algorithm entirely takes into account the resource constraints in practical applications and provides an effective way to optimize the number of caching nodes. Finally, the simulation results clearly show that the optimized number of caching nodes can significantly improve the adequacy rate and hit rate of the training data, with the adequacy rate reaching 84% and the hit rate reaching 100%. Full article

Background/Objectives: As a form of visual input, bodily expressions can be maintained and manipulated in visual working memory (VWM) over a short period of time. While the prefrontal cortex (PFC) plays an indispensable role in top-down control, it remains largely unclear whether this region also modulates the VWM storage of bodily expressions during a delay period. Therefore, the two primary goals of this study were to examine whether the emotional bodies would elicit heightened brain activity among areas such as the PFC and extrastriate body area (EBA) and whether the emotional effects subsequently modulate the functional connectivity patterns for active maintenance during delay periods. Methods: During functional magnetic resonance imaging (fMRI) scanning, participants performed a delayed-response task in which they were instructed to view and maintain a body stimulus in working memory before emotion categorization (happiness, anger, and neutral). If processing happy and angry bodies consume increased cognitive demands, stronger PFC activation and its functional connectivity with perceptual areas would be observed. Results: Results based on univariate and multivariate analyses conducted on the data collected during stimulus presentation revealed an enhanced processing of the left PFC and left EBA. Importantly, subsequent functional connectivity analyses performed on delayed-period data using a psychophysiological interaction model indicated that functional connectivity between the PFC and EBA increases for happy and angry bodies compared to neutral bodies. Conclusions: The emotion-modulated coupling between the PFC and EBA during maintenance deepens our understanding of the functional organization underlying the VWM processing of bodily information. Full article

As China’s largest inland river basin and one of the world’s most arid regions, the Tarim River Basin is home to an extremely fragile ecological environment. Therefore, monitoring the water storage changes is critical for enhancing water resources management and improving hydrological policies to ensure sustainable development. This study reveals the spatiotemporal changes of water storage and its driving factors in the Tarim River Basin from 2002 to 2022, utilizing data from GRACE, GRACE-FO (GFO), GLDAS, the glacier model, and measured hydrological data. In addition, we validate GRACE/GFO data as a novel resource that can monitor the ecological water conveyance (EWC) benefits effectively in the lower reaches of the basin. The results reveal that (1) the northern Tarim River Basin has experienced a significant decline in terrestrial water storage (TWS), with an overall deficit that appears to have accelerated in recent years. From April 2002 to December 2009, the groundwater storage (GWS) anomaly accounted for 87.5% of the TWS anomaly, while from January 2010 to January 2020, the ice water storage (IWS) anomaly contributed 57.1% to the TWS anomaly. (2) The TWS changes in the Tarim River Basin are primarily attributed to the changes of GWS and IWS, and they have the highest correlation with precipitation and evapotranspiration, with grey relation analysis (GRA) coefficients of 0.74 and 0.68, respectively, while the human factors mainly affect GWS, with an average GRA coefficient of 0.64. (3) In assessing ecological water conveyance (EWC) benefits, the GRACE/GFO-derived TWS anomaly in the lower reaches of the Tarim River exhibits a good correspondence with the changes of EWC, NDVI, and groundwater levels. Full article