Search Results (28,232)

This work presents a frequency-domain and modal-domain model to analyze how the length of a three-phase power cable influences conducted emission (CE) voltages measured through a line impedance stabilization network (LISN). The measurement setup considered consists of an equipment under test (EUT) connected to the LISN via a power cable whose cross-section is defined in this study as quadrilateral, namely, four conductors arranged at the corners of a quadrilateral: typically the three phases and the protective earth or neutral conductor. The cable is modeled as a multiconductor transmission line (MTL). To evaluate the system performance both with and without the cable, the concept of voltage insertion ratio (IR) is introduced, defined as the reciprocal of the typical insertion loss. Closed-form expressions are derived for both common mode (CM) and differential mode (DM) emissions. The objective is twofold: to understand under which conditions the LISN measurements overestimate or underestimate the actual emissions at the EUT terminals, and to provide a predictive tool to assess the impact of electrically long cables on CE measurements. The model is validated through numerical simulations of quadrilateral cable configurations considering both a homogeneous and inhomogeneous cross-section, highlighting the need to account for cable length in system design and EMC test interpretation. Full article

The growing importance of social networks has led to increased research into trust estimation and interpretation among network entities. It is important to predict the trust score between users in order to minimize the risks in user interactions. This article enables the identification of the most reliable and least reliable entities in a network by expressing trust scores numerically. In this paper, the social network is modeled as a graph, and trust scores are calculated by taking the powers of the ratio matrix between entities and summing them. Taking the power of the proportion matrix based on the number of entities in the network requires a lot of arithmetic load. After taking the powers of the eigenvalues of the ratio matrix, these are multiplied by the eigenvector matrix to obtain the power of the ratio matrix. In this way, the arithmetic cost required for calculating trust between entities is reduced. This paper calculates the trust score between entities using linear algebra techniques to reduce the arithmetic load. Trust detection algorithms use shortest paths and similar methods to eliminate paths that are deemed unimportant, which makes the result questionable because of the loss of data. The novelty of this method is that it calculates the trust score without the need for explicit path numbering and without any data loss. Full article

This study employed Deep Abstract Networks (DANets), independently and in combination with the Whale Optimization Algorithm (WOA), to generate high-resolution susceptibility maps for drought and wildfire hazards in the Oroqen Autonomous Banner in Inner Mongolia. Presence samples included 309 wildfire points from MODIS active fire data and 200 drought points derived from a custom Standardized Drought Condition Index. DANets-WOA models showed clear performance improvements over their solitary counterparts. For drought susceptibility, RMSE was reduced from 0.28 to 0.21, MAE from 0.17 to 0.11, and AUC improved from 85.7% to 88.9%. Wildfire susceptibility mapping also improved, with RMSE decreasing from 0.39 to 0.36, MAE from 0.32 to 0.28, and AUC increasing from 78.9% to 85.1%. Loss function plots indicated improved convergence and reduced overfitting following optimization. A pairwise z-statistic analysis revealed significant differences (p < 0.05) in susceptibility classifications between the two modeling approaches. Notably, the overlap of drought and wildfire susceptibilities within the forest–steppe transitional zone reflects a climatically and ecologically tense corridor, where moisture stress, vegetation gradients, and human land-use converge to amplify multi-hazard risk beyond the sum of individual threats. The integration of DANets with the WOA demonstrates a robust and scalable framework for dual hazard modeling. Full article

Background/Objectives: Nutritional status affects postoperative outcomes, but the effect of standardized preoperative nutritional preparation on morbidity in malnourished patients undergoing pancreatoduodenectomy (PD) remains unclear. This study evaluated preoperative nutritional parameters following the standardization of nutritional screening and intervention in patients undergoing PD. The influence of nutritional parameters on postoperative morbidity was also assessed. Methods: This prospective cohort study was conducted from 2019 to 2021 at the Department of Surgery, University Hospital, Olomouc. A total of 133 patients were categorized nutritionally as “high risk” (weight loss or reduced appetite with restricted intake) or “low risk” (no weight or appetite loss). High-risk patients received enteral supplementation of 600 kcal/day. A multivariate logistic regression model was used to evaluate the association between major postoperative complications and risk factors, including sex, age, ASA score, BMI, weight and appetite loss, malignancy, duct diameter, pancreatic texture, serum albumin, prealbumin, MUST, and NRS2002 scores. Results: Eighty patients (60.2%) were “high risk,” and 53 (39.8%) were “low risk.” Major morbidity and 90-day mortality occurred in 24 (18.0%) and 4 (3.0%) patients, respectively. No significant differences were observed between high- and low-risk groups in CD morbidity grade, 90-day mortality, POPF, PPH, DGE, or hospital stay. Major morbidity was associated with prealbumin < 0.2 g/L, duct diameter ≤ 3 mm, soft texture, and male sex, with respective odds ratios of 3.307, 3.288, 4.814, and 2.374. Conclusions: High-risk patients receiving preoperative nutrition had comparable rates of major complications and POPF as low-risk patients. Low serum prealbumin predicts major postoperative complications after PD. Full article

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset—a 2.4 percentage point improvement over the baseline YOLO11—while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions. Full article

Silver tarnish is a major issue in many heritage institutions. Applying lacquer is frequently used when preventive conservation approaches are limited. The service lifetime of the lacquer has a strong impact on resources and sustainability. Little systematic work has been published on this. This work explores three thresholds on lifetime—visual, reversibility, and loss of protection. It uses thermodynamic modelling to predict lacquer lifetime from aging at four temperatures. Samples on sterling silver with Frigilene lacquer were used and aging was assessed with a Bruker Alpha FTIR using external reflectance. The FTIR ratio of produced carbonyl peak to nitrate peaks was used to quantify the aging. The commonly used C-O-C peak was found to suffer from dispersion in a high proportion of samples, so could not be used in this study. The results were compared with measurements of lacquer on silver objects displayed in showcases and from store (with almost no light exposure). Spectra were obtained with the Bruker Alpha or an Inspect infra-red microscope. Autocatalytic effects through concentration of emitted nitrogen oxide gases have also been explored using diffusion tubes and gas ingress analysis. No significant concentration was observed. The thresholds were clearly established, and the model produced similar results to the natural aging studied. Full article

This paper establishes dividend-based representations for two influence assessments. First, we define a system of min-dividends derived from the minimal-influence evaluation via a unique linear decomposition using unanimity-type spanning models. Building on this, we further construct a pair of internal and external min-dividends satisfying Completeness and Balancedness conditions, through which we express the stable min-value as the net difference of internal gains and external losses. We then demonstrate that the minimal self-stable value can be represented as accumulated average min-dividends across all coalitions they have participated in. Furthermore, the proposed expression also is adopted to analyze the stability of the minimal self-stable value. These results extend the classical notion of dividends into a minimal-influence-based framework with potential applications in fair resource allocation and responsibility apportionment. Full article

Due to the effects of pigment deposition and microstructure, the color of eggshells may influence the quality traits and hatchability of eggs. These traits are critical for breeding efficiency and economic outcomes in poultry production. Herein, Beijing-You chicken eggs were used as a model to investigate the effects of eggshell color due to their color-related polymorphism. A total of 4422 eggs were analyzed for their hatchability, categorized by storage duration and eggshell color. Results revealed that white-shelled eggs exhibit significantly lower hatchability and higher early embryo mortality compared to other colors, particularly after long-term storage. Purple-shelled eggs demonstrated superior eggshell quality, including higher strength, thickness, and weight, as well as better internal egg quality indicators such as thick albumen height, Haugh units, and yolk color. Eggshell translucency showed a positive correlation with storage time and egg weight loss at all shell color types, with higher translucency levels associated with greater weight loss over time. This study examines associations between eggshell color, hatchability, translucency, and quality traits. Full article

Autonomous underwater vehicles (AUVs) play a crucial role in marine environments, such as in inspecting marine structures and monitoring the condition of subsea pipelines. After completing their mission, AUVs dock with recovery systems at designated locations. However, underwater docking carries a significant risk of failure due to unpredictable maritime conditions. Considering the limitations in communication during the mission, docking failure can lead to the loss of collected data and failure of the entire AUV mission. In this study, a hypothetical AUV docking scenario was defined based on expert knowledge and without actual operational data. A Markov chain-based probabilistic model was employed to quantitatively assess the risk of the system during the mission. Environmental factors were excluded from the evaluation, and the simulation results were classified into five categories: success, timeout, internal component failure, exceeding a predefined sequence repetition limit, and spending the electrical energy under the battery SOC threshold. By analyzing the failure points of each category, strategies to improve the scenario success rate were discussed. This study quantitatively identified the interactions between constraints and risk factors that should be considered when establishing AUV docking plans through a virtual scenario-based failure analysis, thereby providing an evaluation framework that can be utilized in actual design. Full article

The integration of artificial intelligence (AI) and machine learning (ML) technologies in energy storage systems has emerged as a transformative approach in addressing the complex challenges of modern energy infrastructure. This comprehensive review examines current state of the art AI applications in energy storage, from battery management systems to grid-scale storage optimization. We analyze various AI techniques, including supervised learning, deep learning, reinforcement learning, and neural networks, and their applications in state estimation, predictive maintenance, energy forecasting, and system optimization. The review synthesizes findings from the recent literature demonstrating quantitative improvements achieved through AI integration: distributed reinforcement learning frameworks reducing grid disruptions by 40% and operational costs by 12.2%, LSTM models achieving state of charge estimations with a mean absolute error of 0.10, multi-objective optimization reducing power losses by up to 22.8% and voltage fluctuations by up to 71%, and real options analysis showing 45–81% cost reductions compared to conventional planning approaches. Despite remarkable progress, challenges remain in terms of data quality, model interpretability, and industrial implementation. This paper provides insights into emerging technologies and future research directions that will shape the evolution of intelligent energy storage systems. Full article

The spatiotemporal uneven distribution of water resources conflicts sharply with human demands, with pumping stations facing efficiency decline due to aging infrastructure and complex hydraulic interactions. This study employs numerical simulation to investigate operational mechanisms in a parallel pump system at the Yanhuanding Yanghuang Cascade Pumping Station. Using ANSYS Fluent 2024 R1 and the SST k-ω turbulence model, we demonstrate that variable-speed control expands the adjustable flow range to 1.17–1.26 m³/s while maintaining system efficiency at 83–84% under head differences of 77.8–79.8 m. Critically, energy losses (δH) at the 90° outlet pipe junction escalate from 3.8% to 18.2% of total energy with increasing flow, while Q-criterion vortex analysis reveals a 63% vortex area reduction at lower speeds. Furthermore, a dual-mode energy dissipation mechanism was identified: at 0.90n₀ speed, turbulent kinetic energy surges by 115% with minimal dissipation change, indicating large-scale vortex dominance, whereas at 0.80n₀, turbulent dissipation rate increases drastically by 39%, signifying a shift to small-scale viscous dissipation. The novelty of this work lies in the first systematic quantification of junction energy losses and the revelation of turbulent energy transformation mechanisms in parallel pump systems. These findings provide a physics-based foundation for optimizing energy efficiency in high-lift cascade pumping stations. Full article

Non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH) encompasses a spectrum of liver conditions and involves gut–liver axis crosstalk. We aimed to evaluate whether oral vancomycin modifies liver injury and the cecal microbiota in a methionine–choline-deficient (MCD) diet model of NASH. Male C57BL/6J mice (n = 28) were block-randomized to four groups (n = 7 each) for 10 weeks: standard diet (STD); MCD diet; STD + vancomycin (VANC); and MCD + VANC (2 mg/mouse ≈ 50 mg/kg, every 72 h). After 10 weeks, liver tissues were analyzed for histological changes, cytokine levels [interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor beta 1 (TGF-β1)], and immunohistochemical markers [ubiquitin and cytokeratin 18 (CK18)]. Cecal microbiota composition was evaluated with 16S ribosomal RNA (rRNA) sequencing. The MCD reproduced key NASH features (macrovesicular steatosis, lobular inflammation). Vancomycin shifted steatosis toward a microvesicular pattern and reduced hepatocyte injury: CK18 and ubiquitin immunoreactivity were decreased in MCD + VANC vs. MCD, and hepatic IL-8 and TGF-β1 levels were lower in MCD + VANC vs. STD. Taxonomically, STD mice had Lactobacillus-rich microbiota. The MCD diet alone reduced alpha diversity (α-diversity), modestly lowered Firmicutes and increased Desulfobacterota/Fusobacteriota. Vancomycin alone caused a much larger collapse in richness, depleting Gram-positive commensals and promoting blooms of Escherichia–Shigella, Klebsiella, Parabacteroides, and Akkermansia. In the MCD + VANC group, vancomycin profoundly remodeled the microbiota, eliminating key commensals (e.g., Lactobacillus) and enriching Desulfobacterota, Fusobacteriota, and Campylobacterota. Oral vancomycin in the MCD model of NASH improved liver injury markers and altered steatosis morphology, but concurrently reprogrammed the gut into a low-diversity, pathobiont-enriched ecosystem with near-loss of Lactobacillus. These findings highlight a therapeutic trade-off—hepatic benefit accompanied by microbiome cost—that should guide microbiota-targeted strategies for NAFLD/NASH. Full article

Small targets in drone imagery are often difficult to accurately locate and identify due to scale imbalance and limitations, such as pixel representation and dynamic environmental interference, and the balance between detection accuracy and resource consumption of the model also poses challenges. Therefore, we propose an interpretable computer vision framework based on YOLOv12m, called LCW-YOLO. First, we adopt multi-scale heterogeneous convolutional kernels to improve the lightweight channel-level and spatial attention combined context (LA2C2f) structure, enhancing spatial perception capabilities while reducing model computational load. Second, to enhance feature fusion capabilities, we propose the Convolutional Attention Integration Module (CAIM), enabling the fusion of original features across channels, spatial dimensions, and layers, thereby strengthening contextual attention. Finally, the model incorporates Wise-IoU (WIoU) v3, which dynamically allocates loss weights for detected objects. This allows the model to adjust its focus on samples of average quality during training based on object difficulty, thereby improving the model’s generalization capabilities. According to experimental results, LCW-YOLO eliminates 0.4 M parameters and improves mAP@0.5 by 3.3% on the VisDrone2019 dataset when compared to YOLOv12m. And the model improves mAP@0.5 by 1.9% on the UAVVaste dataset. In the task of identifying small objects with drones, LCW-YOLO, as an explainable AI (XAI) model, provides visual detection results and effectively balances accuracy, lightweight design, and generalization capabilities. Full article

This article examines the potential role of the Antonine Plague (165–180 CE) and climate change in the mid-2nd-century collapse of the Afro-Eurasian world-system. Following the model proposed by Gills and Frank, the world-system cycles between phases of integration (A) and disintegration (B). Integrative phases are marked by increasingly complex exchanges of goods, services, information, and populations, which enhance connectivity and intensify the circulation of matter and energy. Yet, this very complexity, while driving growth and expansion, also generates systemic vulnerabilities. The plague and climate change are examined here as critical shocks that triggered the shift from an A phase to a B phase, destabilizing interconnected regions such as the Roman Empire in the West and the Han Dynasty in China. The demographic losses and logistical strains of the pandemic eroded the integrative structures underpinning Afro-Eurasian connectivity, creating conditions for prolonged disintegration. These developments are further situated within the broader history of the Silk Roads, whose role in fostering transcontinental connections had reached a peak in the centuries preceding the crisis. The analysis underscores how pandemics like the Antonine Plague, together with episodes of abrupt climate change, can act as decisive agents in the disintegration phases of world-systems, reshaping the trajectories of complex societies and accelerating the collapse of established networks. Full article

China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture. Full article