Search Results (2,925)

Background/Objectives: The Japanese Veterinary Antimicrobial Resistance Monitoring System (JVARM) conducts longitudinal monitoring of antimicrobial resistance (AMR) in indicator bacteria from food-producing animals. For Escherichia coli from healthy pigs, slaughterhouse-based sampling has been conducted for approximately a decade, yielding a substantial accumulation of MIC data. While JVARM reporting has traditionally focused on annual resistance proportions by drug, the availability of long-term data enables investigation of cross-drug relationships, including MIC similarity and co-resistance patterns. This study aimed to (i) identify the co-resistance structure among antimicrobial agents using MIC- and phenotype-based similarity measures and (ii) identify drug resistances most strongly associated with multidrug resistance (MDR). Methods: We analyzed broth microdilution MIC data obtained annually for E. coli isolates from healthy pigs in the JVARM program in Japan between 2012 and 2023. Antimicrobial resistance was classified from MIC results and annual resistance prevalence was calculated for each antimicrobial. For the co-resistance and MDR analyses, isolate-level data were pooled across the full study period. To identify co-resistance structure, we performed hierarchical clustering using (i) correlation-based similarity of MIC profiles and (ii) Jaccard similarity of binary resistance profiles (resistant/susceptible classification). Multidrug resistance (MDR; ≥3 antimicrobial classes) was further modeled using XGBoost with each drug resistance as a predictive feature, and feature contributions were evaluated using gain, permutation importance, and SHAP values. We also examined how SHAP-based attributions varied when the outcome definition was set to ≥1-, ≥2-, or ≥3-class resistance. Results: Within the study period, resistance remained highest for tetracycline and moderate for streptomycin, ampicillin, sulfamethoxazole–trimethoprim, and chloramphenicol, whereas resistance to other agents was low. MIC-based correlation analysis revealed coordinated variation among ampicillin, sulfamethoxazole–trimethoprim, streptomycin, chloramphenicol, and tetracycline. Separately, Jaccard similarity of binary resistance profiles identified two closely positioned co-resistance groupings (Ampicillin/Streptomycin/Tetracycline and chloramphenicol/sulfamethoxazole–trimethoprim). Ampicillin was identified as the medoid in both MIC-based and resistance-profile similarity spaces, with streptomycin also positioned near the center in both structures. In the XGBoost model for MDR (≥3 classes), ampicillin resistance was consistently the highest-contributing feature when evaluated by gain, permutation importance, and SHAP. When we examined how SHAP-based attributions varied across outcome definitions (≥1-, ≥2-, and ≥3-class resistance), feature importance largely followed resistance prevalence at ≥1–≥2 classes (tetracycline highest) but shifted at ≥3 classes to ampicillin as the top feature. Conclusions: Both MIC-based and phenotype-based analyses revealed co-resistance structures. Under the MDR definition used in this study, explainable machine-learning analyses showed that ampicillin resistance emerged as a leading resistance feature associated with MDR. Because these findings are associative rather than causal, further work will be needed to clarify mechanisms. These findings have important implications for antimicrobial resistance control in the Japanese pig sector, indicating that stewardship strategies may need to be tailored according to antimicrobial class and underlying co-resistance structure. Full article

UAV path planning in complex 3D terrain faces the dual challenges of computational efficiency and reliable obstacle avoidance. To address these issues, this paper proposes a Keypoint–Sparse Cache (KSC) strategy and a hierarchical KSC-PPO (Proximal Policy Optimization) framework for mountainous environments with both static terrain and dynamic obstacles. The KSC strategy reduces search complexity through orthogonal slice-based sparse keypoint extraction and path caching reuse, thereby improving the efficiency of global path planning. On this basis, PPO-based local obstacle avoidance is activated only when safety thresholds are exceeded, while the remaining path is replanned globally after threat clearance, which confines avoidance computation to a local scope while preserving global path quality. Experiments in static mountainous environments show that KSC requires substantially less computation time than RRT* and Informed RRT* while maintaining competitive path efficiency, and it also outperforms four bio-inspired optimization algorithms across terrains of increasing complexity. Hybrid navigation validation experiments further show that KSC-PPO achieves high mission success, low collision rates, and low avoidance overhead in dynamic mountainous environments. Experiments demonstrate that KSC-PPO decomposes exponential global search space into controllable linear subproblems, significantly enhancing efficiency while ensuring path quality, providing an effective solution for UAV navigation in complex terrain. Full article

Judo, as a weight-category combat sport, exposes young athletes to body mass pressures that may foster abnormal eating behaviours. Building on a companion study that documented the prevalence and sex-specific characteristics of abnormal eating behaviours in this cohort, this secondary analysis aimed to identify training-related predictors of eating behaviours in young Polish judo athletes, examine body image satisfaction as a mediator, and assess whether patterns observed in elite adult athletes apply to younger populations. The participants were 150 athletes (70 girls, 80 boys) aged 12–17. Eating behaviours were assessed using the Three-Factor Eating Questionnaire (TFEQ-13) and the Test of Eating Situation Style (TSJ); training characteristics, pre-competition weight control, and appearance satisfaction were examined through hierarchical regression, mediation analysis, latent profile analysis, and two-way ANOVA. Training-related factors—including tenure, session frequency, competitive level, and pre-competition weight control—showed no significant associations with eating behaviours. However, in a subsample of N = 136 athletes, relative weight grouping predicted dietary restraint (p = 0.015, η²p = 0.066), with athletes in the heaviest tertile reporting higher restriction; lower appearance satisfaction was associated with greater restraint (p = 0.031, β = −0.192), independently of sport-mandated weight control; females demonstrated higher emotional eating across instruments (p < 0.001). These findings suggest that body image and weight classification may be more strongly associated with eating behaviours than training demands, highlighting the need for body image interventions and the monitoring of athletes near weight category boundaries. Full article

Inter-basin water transfer projects (IBWTPs) play a pivotal role in alleviating the spatiotemporal imbalances of water resources. However, their operation is exposed to multiple, highly interdependent safety risks that can significantly undermine system stability and water supply reliability. Existing studies predominantly focus on isolated risk factors or rely heavily on subjective data, which limits their ability to capture the complex interrelationships among risks and reveal their underlying propagation mechanisms. To address these limitations, this study proposes a novel risk correlation analysis framework that integrates Interpretive Structural Modeling (ISM) with copula functions. ISM is first employed as a preprocessing tool to structure expert knowledge and develop an initial risk correlation framework. It is then used to hierarchically organize the complex interrelationships among risks. Subsequently, copula functions are utilized to model nonlinear dependencies and tail behaviors among risk variables. This enables a quantitative assessment of correlation strengths and facilitates the construction of a risk topological network. An empirical case study is conducted based on the Middle Route of the South-to-North Water Diversion Project. The results reveal 13 significant correlations among six second-level risk categories. Natural risks (e.g., floods and geological hazards) are identified as the primary driving factors. They exhibit a strong positive correlation (0.6155) with engineering risks and serve as the most critical nodes for proactive risk prevention and control. Engineering risks function as central intermediary hubs in the risk transmission process, whereas water quality and economic risks are characterized as terminal endpoints. Furthermore, three principal risk propagation pathways are identified: (1) natural risks → engineering risks → economic risks; (2) natural risks → operational scheduling risks → social risks; and (3) engineering risks → water quality risks → economic risks. The resulting risk topological network demonstrates significant small-world properties, indicating highly efficient risk transmission within the system. Ultimately, this study provides a robust quantitative approach for analyzing risk interactions in complex engineering systems and enriches the theoretical framework of engineering risk management. It also identifies critical nodes and key transmission pathways for risk prevention and control in IBWTPs, thereby offering significant practical implications for operational safety. Full article

In response to the influence of extreme disasters, damage to distribution lines and user outages, a parallel implementation strategy is proposed for emergency repair of disaster-damaged distribution networks and rapid restoration of power supply for users, considering the collaboration of “human–vehicle–road–pile” resources. This strategy constructs a hierarchical optimization framework, with the upper-level model aiming to minimize the repair time for disaster damage. It adopts a collaborative optimization approach between repair resources and transportation routes to quickly repair the connection between the distribution network and the main power network. In the lower-level model, a model predictive control mechanism is adopted to schedule electric vehicles (EVs) in Real-time as mobile energy storage systems, and vehicle-to-grid (V2G) service technology is used to provide an emergency power supply for key loads during the repair period, achieving parallel optimization of “repair–restoration”. Considering constraints such as emergency repair resources, time-varying transportation, electric vehicle scheduling and power management, charging pile capacity, power flow safety of the distribution network, and topology of the distribution network, second-order cone relaxation technology is adopted to improve solving efficiency. The simulation results show that compared with the traditional serial restoration strategy, the proposed strategy delivers a dual benefit: it significantly eliminates the power supply vacuum period without compromising the efficiency of emergency repair operations. Specifically, it increases weighted load restoration by 57.2% compared with traditional sequential methods and reduces the average outage time for key loads from 3.22 h to 0.5 h, effectively enhancing the resilience and restoration ability of the power supply guarantee of the distribution network. Full article

During drilling in stress-sensitive fractured formations, fracture aperture dynamically evolves with wellbore pressure fluctuations. The sealing layer often undergoes repeated cycles of sealing, destabilization, and re-sealing. Formulation selection based on a single metric or empirical selection cannot simultaneously satisfy multiple objectives, including pressure-bearing capacity, loss control, and dynamic adaptability. This study proposes an entropy-weighted TOPSIS and grey relational analysis method to optimize lost circulation formulations for stress-sensitive fractured formations. A hierarchical evaluation system is established with four criteria layers and eight indicator metrics. A baseline formulation framework is determined through static fracture sealing tests. Experimental data for different elastic-material systems are obtained using a self-developed DTDL dynamic fracture plugging apparatus. Indicator weights are objectively determined using the entropy weight method. A Grey–TOPSIS model is applied to compute grey relational closeness to the positive and negative ideal solutions, enabling formulation ranking and optimal scheme identification. A case study shows that the ternary elastic formulation with Rubber:Graphite:Net = 3:2:1 achieves the highest grey relational closeness and delivers the best overall sealing performance. The ranking remains unchanged when the distinguishing coefficient ρ varies from 0.1 to 0.9, confirming the robustness and feasibility of the proposed method. Compared with entropy-weighted TOPSIS and classical TOPSIS, the proposed method provides a more integrated treatment of the multi-metric data and better aligns the evaluation with the underlying sealing behavior in stress-sensitive fractures. Therefore, it leads to more reliable and comprehensive evaluation results for formulation selection. The results demonstrate that the proposed model provides reliable support and a methodological basis for formulation optimization in dynamic fracture loss control. Full article

Background/Objectives: The COVID-19 pandemic intensified concerns regarding the resilience and financing architecture of mental health services, yet it remains unclear whether crisis-induced adjustments fundamentally altered hospital case-mix complexity or merely exposed pre-existing structural configurations. This study examines the relationship between financing regimes and case-mix complexity in psychiatric hospitals in Romania, a Central and Eastern European health system characterized by mixed financing arrangements and pronounced interregional heterogeneity. Methods: Using administrative data comprising 752 hospital section–year observations (2019–2024), we identify structural financing–organization regimes through a two-step clustering procedure (hierarchical Ward method followed by K-means refinement) based on revenue composition, expenditure allocation, workforce structure, and operational pressure indicators. Results: Three distinct regimes emerge, reflecting persistent institutional configurations rather than temporary crisis-induced groupings. Chi-square tests confirm that regime membership is statistically independent of pandemic timing. A multivariate regression model controlling for financing composition and expenditure structure shows that structural variables (particularly the share of contract-based revenues and the allocation of expenditures) exert systematic and economically meaningful effects on the case-mix index (CMI). Pandemic and post-pandemic indicators do not retain robust explanatory power once structural determinants are accounted for. Regional robustness analyses further demonstrate that financing architecture consistently outweighs temporal shock effects in explaining territorial variation in clinical complexity. Conclusions: The findings suggest that psychiatric hospital case-mix dynamics are structurally embedded within differentiated financing regimes whose influence persists beyond crisis periods. By integrating regime identification with outcome modeling in a Central and Eastern European context, this study contributes to the international literature on health system resilience and highlights the primacy of institutional financing architecture over episodic shock effects in shaping hospital complexity. Full article

In the complex cosmological system of the *Wu ji, spirits and body are closely interwoven. There are seventy-two gods arranged into hierarchical ranks, consisting of six primary gods (zhushen 主神), twelve great gods (dashen 大神), twenty-four earthly gods (qi 祇), twenty-eight ordinary gods (shen 神), and the gods of the Southern Gate (Nanmen 南門) and the Northern Dipper (Beidou 北斗). All these gods have distinctive names and functions. It is only through comprehensive mastery of this system that the ruler of the human realm can accomplish his governance. The human body and the cosmos are closely interconnected, and the different parts of the human body are also controlled by seventy-two gods; therefore, it is only through knowledge and mastery of one’s body that one can master the cosmos. If humans do not pay respect to the gods, they send down misfortunes. This notion of the presence of gods in the human body probably influenced the later practice of visualization (cunsi 存思) in religious Daoism. Apart from the seventy-two gods, there are also five types of demons in the human body, which cause illness. Full article

Meaningful partitioning of 2D binary shapes remains a challenging problem in shape analysis because many existing methods rely mainly on local geometric rules or skeleton simplification, which often struggle to separate the main body of a shape from its protruding parts in a perceptually meaningful way. This limitation becomes more evident in shapes with thin limbs, branching structures, or irregular extensions, where preserving topology while achieving human-consistent decomposition is difficult. We present a fully automatic framework for the hierarchical partitioning of 2D binary shapes into semantically meaningful core bodies and protruding limbs (tails). The pipeline begins by generating candidate structural lines through multi-directional centroid tracking along horizontal, vertical, and diagonal (±45°) bands. Three direction-specific Sugeno fuzzy controllers first evaluate these lines based on normalized length, angular alignment, and minimum distance to the boundary. A second pair of fuzzy systems then classifies segments as either tails or core parts using thickness statistics derived from the distance transform. For ambiguous merged tail groups, iterative midpoint splitting is applied until stable labeling is achieved. High-curvature boundary corners are then detected via signed turning-angle analysis, and candidate cutting rays are assessed through exact region splitting, tail area measurement, and label purity analysis. An adaptive third-stage fuzzy controller ranks these candidates according to cut length, purity, and area. The highest-scoring non-overlapping cuts are executed iteratively, progressively peeling peripheral parts while preserving the overall topology and symmetry of the shape. The proposed framework is evaluated on a targeted subset of 32 categories from the 2D Shape Structure Dataset Results on this evaluated subset indicate that the method produces coherent and topologically consistent partitions, with competitive agreement with the available human-annotated references. This training-free framework provides an interpretable tool for 2D shape analysis, with potential applications in object recognition, computer animation, and symmetry studies. Full article

Optimizing the trade-off between temperature-tracking precision and energy efficiency remains a significant challenge in industrial refrigeration systems. To address this, this paper presents a novel hierarchical control architecture combining Model Predictive Control (MPC), Fuzzy Logic Controller (FLC), and an Extended State Observer (ESO). Specifically, the MPC manages the system’s physical constraints, while the FLC dynamically tunes the objective function weights online, ensuring an optimal balance between performance and energy savings. Furthermore, the ESO is employed to estimate and actively compensate for exogenous heat load disturbances and model uncertainties. Comparative results confirm that the proposed strategy not only reduces energy consumption by 10.93% but also achieves highly disturbance rejection when compared to conventional PI control. The practical feasibility of the proposed algorithm is rigorously validated via hardware-in-the-loop (HIL) simulations utilizing an STM32F767ZI microcontroller. The successful hardware-in-the-loop validation on an embedded microcontroller demonstrates the industrial viability of the proposed architecture, proving it to be a highly deployable and cost-effective solution for refrigeration system. Full article

This paper presents a novel Multi-Scale Temporal Uncertainty-aware Hierarchical Adaptive Ensemble (MSTU-HAE) algorithm for intelligent ship emission monitoring and prediction in maritime environmental compliance applications. The maritime shipping industry contributes approximately 3% of global CO₂ emissions and significant amounts of nitrogen oxides and sulfur oxides, necessitating advanced predictive monitoring systems. The proposed MSTU-HAE algorithm integrates three key innovations: multi-scale temporal feature extraction using causal convolutions at short-term (5 samples), medium-term (20 samples), and long-term (60 samples) windows; gas-specific attention mechanisms that automatically weight temporal scales based on individual emission gas characteristics; and three-level hierarchical uncertainty quantification encompassing individual model uncertainty, ensemble disagreement, and regulatory compliance risk assessment. Experimental validation was conducted using emission data collected from a fishing vessel over 3 operational days (1732 original samples), augmented to 17,320 samples via controlled replication with noise injection to support model training. Rigorous temporal data splitting with 70%/15%/15% train/validation/test partitioning ensures no data leakage. Comparative analysis against six baseline methods (XGBoost, LSBoost, AdaBoost, Ridge Regression, Random Forest, and K-Nearest Neighbors) demonstrates that MSTU-HAE achieves superior average performance, with R² = 0.9670 and NSE = 0.9670 across all emission gases. This research contributes a robust, interpretable, and scalable prediction framework that advances the state of the art in maritime environmental monitoring through novel algorithmic innovations in temporal feature learning and uncertainty quantification. Full article

The assembly interface topography of precision machinery exhibits complex multi-scale geometric features, including roughness, waviness, and form error, which critically influence assembly accuracy and tolerance analysis. To address the lack of adaptivity in existing separation criteria, this paper proposes a multi-scale digital modeling approach oriented toward tolerance analysis of precision assembly interfaces, based on a fractal-wavelet framework. Firstly, multiple Weierstrass–Mandelbrot functions with independent fractal dimensions are superposed to construct a multi-fractal topography model with controllable multi-scale characteristics, grounded in the power spectral density energy additivity property. Subsequently, wavelet functions are employed to hierarchically decompose the topography height field information. The effects of the compact support length and vanishing moments of the wavelet functions on the decomposition performance are analyzed to establish a clear basis for their selection. Finally, an adaptive multi-scale separation criterion based on wavelet energy K-means clustering is then proposed, with the optimal number of scale classes determined by maximizing the silhouette coefficient, eliminating reliance on empirical thresholds. Case study results show that the fused waviness-and-form-error model retains 94.8% of the original energy while reducing convex peak count by over 90%, significantly simplifying the interface microstructure for downstream tolerance computation. The proposed method provides a high-fidelity, adaptive digital foundation for assembly accuracy prediction of precision interfaces. Full article

Urban drainage pipelines are crucial for flood control, drainage, and environmental quality. However, fog within pipelines degrades image quality, hindering the identification of damage features such as cracks and leaks. Existing dehazing algorithms struggle with the unique challenges presented by drainage pipelines, such as their cylindrical structure, non-uniform lighting, and multi-scale particulate interference, leading to inadequate feature extraction and weak cross-channel dependency modeling. To address these issues, we propose a novel drainage pipeline image dehazing network based on a pyramid attention mechanism. Specifically, our proposed method incorporates a custom-designed multi-scale spatial pyramid attention (MSPA) module, which combines hierarchical pyramid convolution and spatial pyramid recalibration modules. This enables the dynamic adjustment of multi-scale feature weights and the effective modeling of cross-channel long-range dependencies. Extensive experiments demonstrate that our network achieves superior dehazing performance across diverse underground environments, particularly in synthetic foggy dataset under real pipeline conditions, outperforming state-of-the-art dehazing algorithms. This proposed approach provides a reliable solution for high-precision visual inspection in complex pipeline scenarios. Full article

Background/objectives: Although informative, current insights into the inflammatory nature of colorectal cancer (CRC) have yet to have a meaningful impact on the prevention of, and development of novel therapies for, the treatment of this prevalent and challenging disease. Accordingly, the current study was focused on identifying putative, key, systemic, mostly pro-inflammatory biomarkers of metastatic CRC (mCRC) prognosis and outcome. Methods: Patients with mCRC (n = 38) and matched healthy controls (n = 30) were recruited to the study. A multiplex magnetic bead array system and an ELISA procedure were used to measure the plasma concentrations of selected cytokines (n = 25) and that of C-reactive protein (CRP) by immunonephelometry. Systemic inflammatory indices (n = 5) were derived from the hematological data. Results: Plasma levels of 17/25 of the cytokine biomarkers and CRP were found to be significantly elevated, while the neutrophil/lymphocyte ratio proved to be the most useful of the various inflammatory indices. Subgroup analysis of the data derived from the group of mCRC patients revealed that the intensity of the systemic inflammatory response was mostly unaffected by tumor location, age, gender, and treatment line. The exception was time to progression, with a shorter time (<120 days) being associated with increased levels of IL-6, IL-8 and TNF-α. Hierarchical cluster analysis of the data revealed a possible association with a small group of four cytokines, comprising IL-1β, IL-13, IL-6/CRP and TGF-β1. Conclusions: This study confirms a strong association of established mCRC with cytokine-driven systemic inflammation. Four of these cytokines, IL-1β/IL-13 IL-6/CRP, and TGF-β1, appear prominent and are possibly indicative of novel targetable therapeutic options. Full article

In the current context, classic software licensing and protection mechanisms based exclusively on host application checks can be circumvented by patching, emulation and replay attacks in user-controlled environments. This paper presents an adaptive hardware key implemented on the ESP32-S3 platform, which externalizes sensitive decisions and cryptographic operations from the host application to a dedicated device. The solution combines a device-anchored root of trust (secure boot and flash memory encryption), a PKI-verifiable identity (Public Key Infrastructure X.509 certificate and digital signatures as proof of ownership), hierarchical key derivation to avoid static secrets and the establishment of an authenticated encrypted session for all essential data exchanges. User access is conditioned by three-factor authentication (PIN—Personal Identification Number, TOTP—Time based One Time Password and USB physical presence) and a “code-in-dongle” mechanism, in which the important logic runs on the device and the application receives tokens with limited duration. Experimental validation demonstrates correct provisioning, secure session establishment, negative brute-force testing, as well as lifecycle support via signed OTA (Over-The-Air) with anti-rollback and encrypted backup/recovery. Build reports indicate a balanced flash distribution and available DIRAM (Data/Instruction RAM) margin, while IRAM (Instruction RAM) saturation (99.99%) reflects a normal architectural behavior of the ESP32-S3 unified memory model rather than a capacity constraint. Full article