Search Results (16,477)

Evaluating resource and environmental carrying capacity (RECC) serves as a fundamental approach for assessing regional environmental baselines and is widely applied in territorial spatial planning. Focusing on Daye City—a characteristic resource-exhausted city in Hubei Province—this study developed a comprehensive RECC evaluation system. By integrating the obstacle degree model, hotspot analysis, and Geodetector, we investigated the spatial differentiation mechanisms of RECC and the resulting production–living–ecological (PLE) spatial conflicts, ultimately proposing targeted optimization pathways. The core findings are as follows: (1) The RECC of Daye City exhibits pronounced spatial polarization and a distinct north–south gradient. (2) The spatial stress of industrial/mining land emerges as the primary obstacle (36.47%). Together with geological hazard risk and soil erosion sensitivity, it forms a core constraint chain. The highly significant hotspots of these factors strongly overlap in the north-central mining districts. (3) Geodetector analysis reveals robust bivariate and nonlinear enhancement effects among these core obstacle factors. This indicates that the cascading vicious cycle of mining disturbance, ecological degradation, and declining carrying capacity fundamentally underlies the constrained RECC in mining regions. (4) PLE spatial conflicts across the study area are dominated by production–ecological conflicts (47.73%), presenting a spatial pattern that heavily couples with the polarized obstacle zones. Based on these findings, this study proposes differentiated regulation strategies centered on mitigating mining-induced stress and interrupting the cascading transmission of disaster risks. These strategies aim to restructure and optimize the territorial spatial pattern, providing robust quantitative decision support for the sustainable transformation of similar resource-exhausted cities. Full article

Low-field benchtop nuclear magnetic resonance (NMR) spectroscopy has emerged as an accessible analytical platform for rapid, routine, and application-oriented analysis. However, its broader analytical adoption remains constrained by intrinsic limitations, including reduced spectral resolution, severe signal overlap, and lower sensitivity compared with conventional high-field instruments. To address these limitations, artificial intelligence (AI), including machine learning and deep learning approaches, has increasingly been explored alongside conventional chemometric strategies to enhance information extraction from low-field spectral data. This review examines recent developments in AI-assisted benchtop NMR across three major application domains: classification and authentication, quantitative analysis, and spectral processing or automated interpretation. Current evidence suggests that classification and authentication currently represent the most mature application area, whereas quantitative analysis shows promising but often condition-dependent performance. In contrast, spectral reconstruction and automated interpretation remain comparatively early-stage and exploratory, despite their potential long-term relevance for addressing intrinsic information limitations. Key challenges, including limited dataset diversity, poor model transferability, validation pitfalls, limited interpretability, and the lack of benchmarking and standardized workflows, are critically discussed. Future progress will likely depend not only on advances in AI algorithms, but also on the development of robust, reproducible, and analytically meaningful workflows. Overall, AI-assisted benchtop NMR is evolving from proof-of-concept applications toward a more structured analytical framework for extracting chemically meaningful information from spectrally constrained low-field data. Full article

Early fault detection in microsatellite thermal control systems (TCS) faces fundamental challenges: high-dimensional redundant telemetry channels, overlapping multi-scale periodicities that obscure anomaly signatures, and severely limited daily data downlink (1–2 passes per day) that restricts the temporal window for diagnosis. Existing data-driven approaches either rely on supervised learning, requiring labeled fault data that are scarce in practice, or employ univariate analysis that fails to capture inter-sensor spatial correlations. To address these limitations, this paper introduces a hybrid framework integrating information-theoretic feature selection and spatiotemporal deep learning. The Generalized Maximum Information Coefficient (GMIC) quantifies nonlinear dependencies between temperature channels for key channel selection, reducing dimensionality by 82% while preserving diagnostic information. A dual-level Seasonal Trend Decomposition (STL) method disentangles orbital-periodic dynamics from diurnal cycles, effectively isolating distinct thermal characteristics at multiple timescales. Each decomposed component is modeled using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) networks to capture spatiotemporal dependencies for accurate temperature prediction. An adaptive threshold-based weighted error fusion mechanism enables early fault detection within a single day of telemetry data. Experimental validation on real satellite telemetry data demonstrates that the proposed framework achieves high-precision fault detection across multiple fault types using a minimal set of temperature channels, significantly outperforming existing benchmarks in both prediction accuracy and detection reliability. Full article

Global container liner shipping networks (GCLSNs) underpin world trade, yet their organization is increasingly reshaped by geopolitical fragmentation. Existing studies often model GCLSNs as single-layer networks, overlooking how carriers’ geopolitical affiliations structure both connectivity and disruption risk. This study constructs a weighted carrier–geopolitical multiplex network in which layers are defined by carriers’ geopolitical affiliations and coupled through shared port calls. Structural analysis reveals pronounced asymmetry in layer size, cohesion, and inter-layer dependence, with overlap concentrated in a limited set of shared hubs. Using the Red Sea crisis as an empirical stress-test scenario, we develop a load–capacity propagation model, incorporating intra-layer load redistribution, rerouting to substitute shared hubs, and inter-layer resource squeeze at same-port layer copies. Results show that direct losses concentrate in corridor-exposed layers, while indirect losses propagate selectively through bridge hubs, especially Singapore, Shanghai, Shenzhen, and Port Klang. Sensitivity analysis indicates nonlinear amplification when low tolerance, strong inter-layer squeeze, and elevated rerouting pressure coincide. These findings show that multiplexity does not imply resilience by itself; cross-layer connectivity buffers disruption only when spare capacity is distributed but amplifies vulnerability when it converges on a narrow set of shared hubs. The paper contributes a carrier–geopolitical perspective to shipping network analysis and a dynamic framework for studying disruption propagation in complex logistics systems. Full article

Wildfires are increasingly complex emergencies driven by climate variability, the expansion of wildland–urban interfaces, and the interaction between fire events and hazardous environments. These factors pose significant challenges for emergency management, particularly in the presence of cascading effects and multi-hazard interactions. This review examines the potential contribution of Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) frameworks to wildfire emergency management, focusing on preparedness and operational response. A narrative analysis of interdisciplinary literature was conducted to identify conceptual and operational overlaps between fire science and CBRNE-based approaches, with particular attention to command structures, hazard assessment, and response coordination. The analysis indicates that wildfire management systems often remain fragmented, with variability in procedures, training, and the integration of monitoring technologies. Evidence from CBRNE operational models suggests that structured command systems, field-based analytical capabilities, and interoperable procedures support improved situational awareness and decision-making. The review highlights how selected CBRNE principles, including structured command systems, zoning strategies, hazard characterization, and interoperability mechanisms, may address persistent gaps in complex wildfire emergency management, providing a basis for improved coordination, operational effectiveness, and system resilience. Full article

Inflammatory bowel disease (IBD) poses a significant global health burden with rising incidence, particularly in Asia. This study employed an integrative network pharmacology approach combined with molecular docking to elucidate the therapeutic mechanism of ganoderic acid A (GAA) against IBD. Potential GAA targets were retrieved from pharmacogenomic databases, while IBD-related genes were curated from OMIM and GeneCards databases. Weighted gene co-expression network analysis of IBD transcriptomic datasets (GSE38713, GSE126124) identified disease-associated modules, with the yellow module exhibiting the strongest positive correlation. Functional enrichment analyses demonstrated significant involvement of overlapping targets in lipid metabolism, the inflammatory response, and the mitogen-activated protein kinase (MAPK) signaling cascade pathway. We identified 14 IBD-GAA-ferroptosis-related genes and 54 key module genes. Intersection analysis revealed 5 overlapping targets, including tumor necrosis factor-α(TNF-α), peroxisome proliferators-activated receptor γ (PPARγ), MAPK14, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic α (PIK3CA), and Caspase 3 (CASP3). Molecular docking confirmed high-affinity binding of GAA to these targets, with binding energies ranging from −7.3 to −10 kcal/mol. Crucially, experimental evaluation demonstrated the pivotal role of GAA in alleviating disease pathology. GAA treatment suppressed the significantly elevated levels of TNF-α and p-MAPK14 in the organoids using a cytokine/LPS-induced IBD model. These findings collectively suggest a potential involvement of GAA in pathways associated with ferroptosis regulation, although direct experimental evidence for ferroptosis markers remains to be established. The observed multi-target effects on immune regulation and cellular proliferation/differentiation provide a foundation for further mechanistic investigation. Full article

Millimeter-wave radar enables non-contact monitoring of cardiac activity and therefore has the potential to reconstruct electrocardiogram signals without surface electrodes. However, existing radar-based electrocardiogram reconstruction methods still suffer from incomplete extraction of heartbeat-related information and insufficient modeling of electrocardiogram-related features, which limits reconstruction accuracy. To address these issues, this study proposes a millimeter-wave radar-based electrocardiogram reconstruction method that integrates a respiratory-harmonic-suppressed multi-channel signal-processing frontend with the proposed CA-WTBNet deep reconstruction network. First, based on maximal overlap discrete wavelet transform-based multi-resolution analysis, respiratory harmonics mixed into heartbeat-related components are suppressed by combining respiratory harmonic detection with a heart-rate frequency protection strategy, while cardiac-related information is preserved as much as possible. A multi-channel input representation is then constructed. Meanwhile, the proposed deep reconstruction network is developed to jointly model complementary channel-wise features, local waveform morphology, and temporal dependencies by integrating channel-attention mechanisms, convolutional residual modules, window-based Transformer blocks, and bidirectional long short-term memory. Experiments conducted on the public dataset show that our method achieves an average Pearson correlation coefficient of 0.9641, a mean normalized root mean square error of 0.0458, an average R-peak F1 score of 0.9956, and an average R-peak timing error of 3.13 ms on the test set. In comparison with related studies on the same public Resting dataset, the proposed method achieves the best overall performance among the compared methods, with a 0.53% improvement in Pearson correlation coefficient and a 10.20% reduction in normalized root mean square error over the best-performing compared method. Full article

The fractional Whitham–Broer–Kaup model governs nonlinear wave propagation in memory-dependent media, including porous structures, viscoelastic fluids, and irregular seabeds, yet the full dynamical spectrum from quasi-periodicity to deterministic chaos, the role of stochastic forcing, and reliable identification from noisy data remains insufficiently explored, particularly how the fractional order $\beta$ influences these regimes. This study addresses these gaps through a comprehensive, multi-method dynamical analysis of a representative nonlinear oscillator embodying key FWBK features. Three-dimensional attractor visualizations, return maps, and surrogate data tests demonstrate a transition from quasi-periodic toroidal attractors to fully developed chaos via torus breakdown, confirming that observed complexity originates from deterministic nonlinearity. Poincaré sections reveal multistability and KAM-type structures, where coexisting attractors depend on initial conditions, while increasing noise progressively disrupts coherent dynamics. The OGY control method effectively stabilizes unstable periodic orbits across chaotic regimes with minimal perturbation, and Lyapunov analysis indicates that stochastic forcing attenuates chaos while enhancing dissipation. The Fokker–Planck framework shows that noise reshapes probability landscapes, driving transitions from unimodal to bimodal distributions. Comparative analysis of SINDy, JMAP and VBA highlights trade-offs in interpretability, computational efficiency, and uncertainty quantification, while an integrated Bayesian–PCE–Sobol approach quantifies parametric uncertainty and reveals time-dependent sensitivity variations. Additionally, the overlapping of soliton solutions extracted via the enhanced modified Sardar sub-equation method reveals structural relationships among soliton families and their stability under interaction. Soliton branches that maintain high overlap under noise correspond to stable regimes, while those losing coherence indicate the onset of chaos. Furthermore, while the reduced dynamics in $\eta$-space are independent of $\beta$, the fractional order controls spatial compression and temporal scaling in physical coordinates, directly influencing observable wave localization. These results imply that fractional effects can modify chaos transitions, support controllability through OGY, and influence noise–instability interactions depending on $\beta$. This framework provides a robust, transferable methodology for analyzing and controlling nonlinear oscillatory systems under deterministic and stochastic conditions, with direct applications to FWBK-based models in coastal engineering, fiber optics, and quantum interference systems. Full article

The internal quality of watermelons has garnered extensive attention. Conventional destructive quality detection for watermelons causes fruit loss, while existing acoustic techniques often rely on a single evaluation index. To address these issues, this study proposes a non-destructive method for comprehensive watermelon quality detection using acoustic and vibration signals. Signals from two watermelon varieties were collected under impact excitation to extract six time-domain and frequency-domain features. Factor Analysis of Mixed Data (FAMD) was employed to integrate ripeness, Soluble Solids Content (SSC), firmness, and sensory scores into a Comprehensive Quality Index (CQI), categorizing samples into High-Quality, Medium-Quality, and Low-Quality groups. Following physically constrained data augmentation to mitigate small sample size and class imbalance, an Extremely Randomized Trees (Extra-Trees) model was constructed. Results demonstrate that the Extra-Trees model achieved an overall testing accuracy of 0.92, with recall rates of 0.93 and 1.00 for Low-Quality and High-Quality watermelons, respectively. Recognition for Medium-Quality samples was lower due to overlapping physical and acoustic characteristics. Ultimately, this system aligns with actual consumer demands, providing technical support for low-cost, portable, and non-destructive watermelon grading. Full article

Uterine leiomyosarcoma (LMS) is a rare and aggressive malignancy with diagnostic challenges, particularly in cases with overlapping histological features with atypical leiomyoma or smooth muscle tumors of uncertain malignant potential. We report a comparative immunohistochemical analysis of LMS, leiomyoma, and adjacent myometrium obtained from a 40-year-old woman with discordant pathological diagnoses. LMS tissue showed increased Ki67 and NTS/NTSR1 immunoreactivity together with a distinctive cytoplasmic localization of estrogen receptor beta (ERβ), in contrast to the predominantly nuclear localization observed in leiomyoma and normal myometrium. Importantly, focal areas within adjacent morphologically non-neoplastic myometrium exhibited an immunophenotype resembling LMS, including cytoplasmic ERβ localization and increased Ki67 and NTS/NTSR1 expression. These observations suggest a potential association between ERβ subcellular localization, NTS/NTSR1 signaling, and molecular alterations occurring during uterine smooth muscle tumorigenesis. However, given the single-case nature of this report, these findings should be considered exploratory and require validation in larger studies. The diagnostic message conveyed by these images may assist in the interpretation of diagnostically challenging cases and provide a basis for future investigation. Full article

Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics. Full article

Human papillomavirus type 53 (HPV53) is one of the most prevalent HPV genotypes in China, frequently detected in cervical intraepithelial neoplasia and cervical cancer, yet remains outside the coverage of all currently available prophylactic vaccines and is relatively understudied. This study performed a comprehensive analysis of HPV53 clinical infection profiles, genomic diversity, and T-cell epitopes to inform therapeutic vaccine development. Clinical analysis of 158 HPV53-positive patients showed that infections were most prevalent in women aged 40–59 years, with persistent infection identified in 13.3% participants and a subset of cases associated with cervical lesions. Genomic analysis of 134 HPV53 isolates identified four lineages (A-D, with lineage D further subdivided into four sublineages, and an overall nucleotide variability of 4.4%. E2 was the most variable protein while E7 was the most conserved. Immunoinformatic prediction identified 176 HLA class I-restricted T-cell epitopes across E6, E7, E1, and E2, from which 20 candidates were selected for experimental validation. Ten demonstrated strong HLA binding affinity in vitro, and murine immunization identified a E6 peptide VYNFAYTDL as an immunodominant epitope. Three validated epitopes exhibited sequence overlap with 12 to 13 of other 13 high-risk HPV genotypes, suggesting their potential as broadly cross-reactive targets. These findings clarify the genomic diversity and immunogenic epitope landscape of HPV53, providing a foundation for the rational design of therapeutic vaccines. Full article

Image-based measurement of chopped maize stalk length remains difficult because the fragments are often slender, curved, touching, or partly overlapped. Bounding-box dimensions are therefore not reliable for length estimation, and manual measurement is too slow for repeated quality assessment. In this study, we developed a YOLO11n-based visual framework for measuring chopped maize stalk length under fixed imaging conditions. The dataset contained 1127 images collected on a laboratory platform and covered stalk lengths of 10–150 mm, different moisture states, and isolated, touching, and overlapping arrangements. To obtain more stable regions of interest, the YOLO11n detector was modified with large separable kernel attention (LSKA), a lightweight cross-scale decoupled detection (LSCD) head, and Wise intersection over union version 3 (WIoU v3). The detected stalk regions were then processed by local segmentation, morphological refinement, skeleton extraction, longest-path calculation, and washer-based scale conversion. The modified detector reached 94.8% precision, 90.4% recall, 96.5% mAP@0.5, and 71.1% mAP@0.5:0.95, with a detector inference speed of 174 FPS. In the length-measurement test, the mean relative errors were 5.8%, 8.3%, and 10.4% for the <40 mm, 40–80 mm, and >80 mm groups, respectively. Across all evaluated fragments, the complete pipeline produced an MAE of 6.0 mm, an RMSE of 9.4 mm, and a mean relative error of 8.2%. The framework therefore provides a practical way to measure chopped maize stalk length under controlled imaging conditions, although long, curved, and cluttered fragments still caused most of the remaining errors. Full article

Public concrete datasets often contain duplicate records, coupled variables, and cross-source distribution shifts, which may lead to overly optimistic model evaluation. Based on a deduplicated UCI high-performance concrete dataset (1005 samples), this study develops a leakage-controlled data-driven workflow with applicability-domain assessment. TabPFN, SHAP, and NSGA-II are used for compressive strength prediction, model-response attribution, and scenario-based candidate mix screening, respectively. Model evaluation follows a unified data split, inner training-set cross-validation, and an independent test-set protocol. In addition, 502 non-overlapping records from the Mendeley PCC dataset are used as an external benchmark to examine cross-source transferability and sensitivity to distribution shift. The results show that TabPFN achieves the highest R² and the lowest RMSE, MAE, and MAPE on the internal UCI test set, with values of 0.953, 3.744 MPa, 2.265 MPa, and 7.580%, respectively; however, its advantage over strong baselines such as CatBoost is limited. On the external Mendeley PCC dataset, TabPFN remains competitive, with R², RMSE, and MAE values of 0.490, 15.175 MPa, and 11.457 MPa, respectively, but its performance is close to that of random forest, XGBoost, and CatBoost. The 5NN applicability-domain stratification shows that external samples located within the 95% 5NN applicability domain achieve improved performance (R² = 0.634 and RMSE = 12.367 MPa), suggesting that external prediction errors are associated with the distance from the source-domain distribution. SHAP results indicate that cement, ground granulated blast-furnace slag, curing age, and water are the main attribution variables in the model output; their response directions should be interpreted as statistical attributions rather than material causal mechanisms. The Pareto candidate mixes generated by NSGA-II satisfy basic engineering constraints. Nevertheless, because the external benchmark reveals sensitivity to cross-source distribution shift, the resulting mix proportions should be treated as pre-experimental screening candidates rather than engineering-validated low-GWP concrete mix proportions. Full article

Background: Measles, mumps, rubella (MMR) and varicella vaccines are often co-administered to optimize coverage, yet safety concerns regarding febrile convulsions persist. In South Korea, MMR and varicella vaccines are administered as separate injections during a single visit (MMR + V). This study evaluated the real-world safety of concurrent MMR + V vaccination, focusing on the domestically implemented MAV/06 and Oka-derived strains. Methods: We conducted a multicenter self-controlled case series (SCCS) study of children aged 12–23 months who received MMR + V and hepatitis A vaccine (HAV) between 2015 and 2024. Using electronic health records, we identified predefined adverse events (AEs), including fever and healthcare visits. Adjusted relative risks (aRRs) were estimated using conditional Poisson regression. Results: Among 3035 children (52.3% male; median age, 12 months), 71.7% received the MAV/06 varicella strain. A distinct peak in AEs occurred 7–13 days after MMR + V administration, with fever showing the greatest increase (aRR, 4.27; 95% CI, 2.76–6.60). The risks of total sick visits (aRR, 2.15; 95% CI, 1.70–2.71) and acute care visits (aRR, 2.13; 95% CI, 1.46–3.10) were similarly confined to this interval and returned to baseline thereafter. Febrile convulsions were uncommon (aRR, 5.37; 95% CI, 1.20–24.01). No excess risks were observed during the HAV or overlap periods, and no synergistic effects of intensive multi-vaccine administration were detected. Conclusions: Concurrent administration of MMR and varicella vaccines in Korean infants—predominantly using the MAV/06 strain—was associated only with expected, transient increases in fever during days 7–13 postvaccination. No serious or sustained safety signals were identified, supporting the continued use of Korea’s separate-injection MMR + V strategy. Full article