Search Results (6,579)

Background: Craniofacial and skull base procedures place cranial nerves at risk of injury, with consequences for movement, sensation, hearing, swallowing, vision, and quality of life. Methods: This narrative review integrates 46 original publications with six targeted references added during revision, addressing injury mechanisms, prevention, postoperative assessment, reconstruction, and rehabilitation across neurosurgery, otolaryngology, and maxillofacial surgery. Results: Recurring mechanisms include traction, compression, ischemia, thermal injury, direct trauma, and transection. Neuromonitoring contributes to localization, functional surveillance, and prognosis when paired with reproducible baseline recordings, appropriate modality selection, technical troubleshooting, and a predefined surgical response to alerts. Preoperative and postoperative electrophysiological testing complements but does not replace intraoperative monitoring. Recognized transection favors immediate tension-free reconstruction when feasible; an anatomically continuous nerve with an unfavorable electrical response requires contextual interpretation rather than automatic sacrifice. Rehabilitation combines target-organ protection, selective motor or sensory retraining, symptom control, and patient-reported assessment. Conclusions: The practical distinction is between a potentially reversible functional disturbance and confirmed structural discontinuity. Management should link monitoring changes to corrective action, preserve viable nerves, and coordinate timely reconstruction with rehabilitation. Heterogeneous, predominantly observational evidence does not support universal alert thresholds, a single reconstruction deadline, or guaranteed functional recovery. Full article

International marine environmental law increasingly depends on scientific information, but the legal consequences of new monitoring technologies remain underexplored. This article asks when environmental DNA (eDNA) monitoring may affect what states knew or ought reasonably to have known about marine biodiversity risks and consequently the application of due-diligence obligations. Drawing on Articles 192, 194, and 204–206 of the United Nations Convention on the Law of the Sea (UNCLOS), relevant jurisprudence of the International Court of Justice (ICJ) and the International Tribunal for the Law of the Sea (ITLOS), and the agreement under the UNCLOS on the Conservation and Sustainable Use of Marine Biological Diversity of Areas beyond National Jurisdiction (BBNJ agreement), it uses “epistemic due diligence” as an analytical label for the informational dimension of existing duties of prevention, vigilance, monitoring, and assessment rather than as a new freestanding obligation. The article argues that scientific maturity, accessibility, and standardization can alter the factual baseline against which constructive knowledge and methodological adequacy are assessed. The analysis focuses primarily on water-derived (aqueous) eDNA, for which marine monitoring practices and standardization are most developed. Sediment-derived eDNA is considered a distinct matrix whose greater persistence and temporal integration require separate interpretive caution. eDNA monitoring results cannot constitute automatic legal knowledge because transport, degradation, contamination, sampling design, and reference-database limitations constrain inference. To discipline this assessment, the article proposes a five-factor eDNA knowledge test based on reasonable detectability, scientific maturity, accessibility and capacity, environmental gravity, and corroboration, organized in two stages: adequacy of the monitoring method and evidentiary evaluation/institutional response. The framework identifies the legally defensible space between environmental ignorance and what states ought reasonably to know. Full article

Solar energy is one of the most important sources of clean and sustainable electricity. However, dust that accumulates on solar panel surfaces over time can significantly reduce their energy output. This study aims to automatically classify solar panel images as clean or dirty using deep learning methods. A publicly available dataset of 1440 solar panel images collected from different regions of Bangladesh was used. Six pre-trained convolutional neural network (CNN) models were employed for feature extraction: DenseNet201, EfficientNetB0, InceptionResNetV2, MobileNet, ResNet50, and Xception. The hyperparameters of the classification layer were optimized using the Grey Wolf Optimization (GWO) algorithm. The models were first evaluated using an 80:20 train/test split, and subsequently validated using 5-fold cross-validation with results reported as mean ± standard deviation. Under 5-fold cross-validation, all six models achieved mean accuracy above 97%, with Xception and MobileNet reaching the highest mean accuracy at 99.93%. These findings demonstrate that transfer learning models combined with metaheuristic optimization can provide a promising solution for automatic dust detection on solar panels. Full article

Coal gangue backfill serves as a primary supporting structure for overlying strata in mined-out areas, and its internal damage evolution is directly associated with the safety and stability of mining operations. Acoustic emission (AE) technology provides an effective approach for investigating damage evolution by capturing transient strain energy release events within materials in real time. However, conventional AE analysis methods predominantly rely on statistical interpretations of individual parameters, making them insufficient for revealing the underlying physical mechanisms governing the influence of fracture characteristics on energy evolution pathways. To address the aforementioned limitations, this paper proposes a fracture mode-informed physics-enhanced machine learning framework for predicting acoustic emission energy evolution in coal gangue backfill. First, based on the AE monitoring data of coal gangue backfill under uniaxial compression (a total of 18,992 valid AE events from three parallel specimens with identical mix proportion), the RA–AF parameters combined with the K-means unsupervised clustering algorithm were employed to automatically identify tensile and shear fracture modes, thereby constructing physically meaningful fracture mode labels. Second, the fracture mode information was integrated with AE statistical features, including rise time, duration, amplitude, counts, peak frequency, and center frequency. After selecting the most informative features using the minimum-redundancy, maximum-relevance (mRMR) algorithm, a Bayesian optimization-based support vector regression (BO-SVR) model was developed for AE energy prediction. The results demonstrated that after incorporating the fracture mode-based physical labels, the proposed model achieved a coefficient of determination (R²) of 0.9027 on the testing dataset, with an RMSE of 0.1562 and an MAE of 0.1204. Ablation experiments further confirmed that the introduction of fracture mode labels improved the R² value by approximately 3.4% compared with the model without physical constraints. Fivefold cross-validation yielded an average R² of 0.9460 with a standard deviation of 0.0023 for the SVR model. Furthermore, per-specimen independent holdout validation yielded an average R² of 0.9044 with a standard deviation of 0.0512 across three independent specimens, confirming the excellent stability and repeatability of the proposed framework. The original single-specimen results (4083 events, R² = 0.9727) are provided as a baseline reference. Mechanistic analysis revealed that the average energy release associated with shear fractures was approximately 739 times that of tensile fractures, demonstrating that fracture mode information provides physically consistent mechanical constraints for the machine learning model. This study provides an effective new method for the stability evaluation and intelligent monitoring of coal gangue filling materials. Full article

Particleboard has been widely used in furniture manufacturing, architectural decoration, packaging and logistics applications, and transportation, owing to its strong raw material adaptability, relatively low cost, and favorable processing performance, making it one of the foundational materials in the wood-based panel industry. Its surface quality directly determines the added value of final products. However, in actual production environments, the complex background textures of particleboard surfaces and the low contrast between defects and the background pose substantial challenges to automatic surface defect recognition. To address these issues, this paper proposes SFNet, a particleboard surface defect recognition network integrating spatial-domain and frequency-domain feature enhancement. The network adopts EfficientNet-B0 as its backbone, introduces a Dynamic Matrixed Color Correction (DMCC)module after Block 4 to adaptively adjust feature channel weights via a dynamic temperature mechanism, thereby enhancing the feature saliency of defects in the spatial domain, and embeds a Wavelet Transform Convolution module (WTConv) after Block 6 to map spatial features into the frequency domain, leveraging the differences in frequency response between defects and the background to improve the model’s sensitivity to high-frequency defect features and multi-scale texture information. The dataset comprised 2405 high-confidence defective image patches, including 2005 patches for the main classification experiment and 400 independent patches for illumination-robustness testing. Experimental results show that SFNet was evaluated on a test set of 401-images encompassing five types of defects on particleboard surfaces, namely shavings, dust spots, oil spots, glue spots, and pollution. Across five random seeds, the model achieved an average accuracy of 97.86% ± 0.28%, with the highest single-run accuracy reaching 98.25%. It outperforms the baseline classification models used for comparison in terms of accuracy, precision, recall, and F1-score. Grad-CAM visualization results further demonstrate that SFNet can effectively suppress interference from complex background textures and accurately focus on defective regions. These results indicate that the proposed method exhibits strong robustness and recognition stability, providing an efficient and reliable solution for automated particleboard surface quality inspection in industrial scenarios. Full article

The reconstruction of velocity and displacement from acceleration measurements is essential for seismic or microseismic data interpretation. Many existing approaches produce excellent results in both velocity and displacement reconstruction. However, these reconstruction approaches involve a large number of parameters, which must be tuned for different datasets. There is room for improvement in high-precision and immediate reconstruction, particularly for large and noisy datasets. Therefore, we propose a deep learning approach that is parameter-free during inference to automatically reconstruct velocity and displacement from seismic acceleration measurements via a cycle-consistent generative adversarial network (CycleGAN). To obtain paired training data, we use a semi-synthetic method to prepare the dataset. Based on the dataset, we use CycleGAN to train two mapping functions for reconstructing the velocity and displacement from acceleration measurements. We then compare this method with two traditional methods on the test set. For the reconstruction of seismic velocity measurements, the mean average peak error (Erp), average deviation error (Err), and root-mean-square error (Ers) on the test set for the method proposed in this paper are 0.1478, 1.2269, and 0.0097, respectively. Moreover, for the reconstruction of seismic displacement measurements, the mean Erp, Err, and Ers values on the test set for the method proposed in this paper are 0.2004, 0.9783, and 5.6 × 10⁻⁴, respectively. Additionally, under noisy input conditions, the records reconstructed by our approach exhibit the smallest degradation in signal-to-noise ratio (SNR) and preserve more records in the higher SNR range than those reconstructed by traditional methods. The results show that the proposed method achieves comparable accuracy in velocity reconstruction and better accuracy in displacement reconstruction than traditional methods. More importantly, it can automatically reconstruct records and remain robust to input noise without manual parameter tuning, demonstrating good prospects for practical application. Full article

Although video-observed therapy (VOT) boasts substantial potential advantages in tuberculosis (TB) treatment management, insufficient staffing of community healthcare workers leads to inefficient and inadequate video review. The aim of the study was to develop an artificial intelligence (AI)-powered program to assist community healthcare workers by reviewing medication-taking videos, and then validate the program’s performance and effectiveness. We developed a video automatic review program (VARP) using a development dataset of 8000 medication-taking videos, which was partitioned at the participant level into 5781 videos for model training and 2219 videos for internal testing. We evaluated its performance versus community health workers based on TB specialists’ annotations and compared clinical management metrics before and after VARP adoption to verify its effectiveness. VARP combined YOLOv8n-based medication detection with dual-stream RGB and pose-based medication action recognition; the RGB stream used a 3D ResNet-50 backbone, whereas the skeleton stream used a modified 3D ResNet backbone (F₁ = 0.90, 95% CI 0.89–0.91). In an independent post-deployment validation set of 7237 videos, VARP achieved significantly better accuracy (84.7%, 6133/7237 vs. 78.1%, 5653/7237) and specificity (72.7%, 1151/1584 vs. 0%, 0/1584) than community health workers (p < 0.001), with 75.3% (5449/7237) consistent judgments against TB specialists and vastly shorter video processing time (7.72 s vs. 23.12 h, p < 0.001). Compared with standard VOT management (n = 149), the VARP-supported group (n = 158) delivered faster daily monitoring completion (80.4%, 14,842/18,454 vs. 58.0%, 13,124/22,635 within 24 h, p < 0.001) and higher adverse event detection (67.7%, 107/158 vs. 35.6%, 53/149, p < 0.001). AI-powered review of TB medication videos optimizes key community VOT indicators such as review efficiency and assessment consistency, offering a practical, effective solution to address flaws in conventional manual VOT and strengthening routine TB monitoring. Full article

Blockchain applications rely on consensus protocols to maintain security, integrity, and coordination in decentralized environments while balancing performance, scalability, and resource cost. This study presents a comparative evaluation of four Hyperledger Besu consensus algorithms, namely Ethash, Clique, QBFT, and IBFT 2.0, executed under identical hardware, network topology, and genesis configurations to quantify latency, throughput, and system overhead. A distributed burst workload is employed to simulate high-intensity transaction conditions. Within this framework, sender and receiver accounts are automatically generated and funded, and 5000 transactions are submitted in parallel. Locally recorded millisecond-precision submission timestamps are aligned with second-resolution on-chain block timestamps to compute inclusion latency percentiles and end-to-end burst throughput. System and blockchain metrics, including block interval, transaction pool backlog, and block creation time, are collected through Prometheus over defined intervals and synchronized with transaction traces for time-series analysis. Across ten repeated runs per protocol, Clique achieves the lowest latency of approximately two seconds and the highest throughput of approximately 190 transactions per second. QBFT and IBFT 2.0 demonstrate stable and periodic performance near 110 transactions per second with low variance. In contrast, Ethash exhibits highly variable, high-variance and strongly right-skewed latencies, minimal throughput, and substantially higher energy consumption and disk input/output peaks. CPU, memory, and network traffic profiles further expose distinct operational trade-offs relevant to deployment scenarios. Statistical analyses using the Kruskal–Wallis and Dunn’s post hoc tests confirm significant differences among the protocols with large effect sizes. The proposed framework provides a rigorous and systematically documented methodology for comparative consensus performance evaluation in permissioned blockchain environments. Full article

To address the challenges of complex programming, high physicaltesting costs, and lengthy development cycles in conventional diesel engine controller development, this paper describes the design and prototype implementation of an embedded simulation control system for diesel engines, intended for early-stage controller algorithm pre-validation. The system is built around an STM32F407VE microcontroller and follows the model-driven development (MDD) paradigm. First, in accordance with the real-time and accuracy requirements of the simulation control system, core software modules—including real-time task scheduling, signal acquisition and processing, Ethernet communication, and host–target interaction—are designed to construct an embedded software framework that integrates simulation computation, signal sampling, command execution, and data exchange. Second, a modular diesel engine simulation model is developed in the MATLAB R2022b/Simulink environment and the graphical model is transformed and ported into embedded real-time C code via automatic code generation tools. Finally, an embedded real-time simulation verification platform is built. Distinct from our previous work on parallel power units, this study focuses on a single-engine diesel power system. Test results demonstrate that the proposed single-engine simulation platform can run the diesel engine model in real time on the target hardware and achieve closed-loop speed tracking under starting and multi-step command scenarios. The platform provides a low-cost, preliminary verification aid for early-stage diesel controller algorithm logic debugging and pre-parameter tuning. It should be highlighted that this platform is not intended for high-fidelity physical reproduction of real diesel engines. Quantitative model accuracy against real-engine dynamometer data remains to be established in future bench calibration campaigns. Full article

Central America faces a defining energy-growth challenge: sustaining economic development while curbing carbon emissions. Existing decoupling and Environmental Kuznets Curve (EKC) studies for Latin America typically pool heterogeneous economies or rely on single-country cases. This study fills that gap with a country-year panel for six Central American economies spanning 1990–2023 for GDP per capita and CO₂ emissions and 2000–2021 for the clustering, EKC and Random Forest analyses that additionally require energy intensity and renewable-share data, combining GDP per capita, CO₂ emissions per capita, energy intensity and renewable electricity share from World Bank and Global Carbon Project data. We apply k-means clustering, PELT structural break detection, panel fixed-effects EKC estimation with Driscoll–Kraay standard errors, Dumitrescu–Hurlin Granger non-causality tests, Random Forest importance with rolling-origin cross-validation, and the Tapio decoupling index. Clustering separates higher-income, higher-renewable-share regimes (Costa Rica, Panama) from lower-income, higher-energy-intensity regimes (the other four countries). The panel EKC yields an insignificant turning point, and Granger tests find no robust bidirectional causality. Random Forest ranks renewable share and energy intensity above GDP per capita as CO₂ predictors, and rolling-origin cross-validation (R-squared = 0.856) falls below naive five-fold validation (0.938). Tapio elasticities classify four countries under weak decoupling and Guatemala and El Salvador under expansive coupling. These findings indicate that Central American decoupling is structural, associated with the electricity generation mix, rather than an automatic byproduct of growth. Full article

Background/Objectives: About 25–35% of patients with cervical cancer treated with definitive concurrent chemoradiotherapy (CCRT) relapse within five years, and non-imaging clinical factors stratify their risk only modestly. We evaluated whether a general-purpose multimodal large language model (MLLM), used without fine-tuning, could estimate recurrence risk in this setting. Methods: In this retrospective single-center study, 82 patients treated with definitive radiotherapy (79/82 with concurrent platinum-based chemotherapy) who had a complete pretreatment MRI report were analyzed. Gemini 3.1 Pro generated a structured report from pelvic MRI images and, separately, estimated recurrence/metastasis risk from clinical, laboratory, treatment and imaging data. Because treatment cycles and overall treatment time actually completed were included, this was a retrospective treatment-complete risk assessment rather than a strictly pretreatment prediction. Five prompting strategies differing only in their inputs—an ablation of input modalities—were each run three times; AI reports were graded against paired human reports on a 14-item rubric by an independent model (Claude Opus 4.6). Results: Forty patients (48.8%) relapsed. Discrimination rose from an AUC of 0.704 with a clinical baseline to 0.790 with the full multimodal input (95% CI 0.687–0.883; ΔAUC +0.085). This gain was significant on our primary two-sided bootstrap test after Holm correction for ten pairwise comparisons (p = 0.032; a DeLong sensitivity analysis supported some but not all the imaging-benefit comparisons). Adding MRI report text significantly improved the AUC over the clinical baseline, and no statistically significant difference was detected between the human-report and AI-report strategies; this was not an equivalence or non-inferiority test; and the further lymph-node increment was not statistically significant. The AI reports scored 68.8% against an LLM judge on the rubric, with apparent overcalling of parametrial invasion and an inability to assess lymph nodes within the supplied field of view; all strategies underestimated absolute risk (E/O 0.78–0.83). Conclusions: A non-fine-tuned MLLM can integrate multimodal data into a prognostic estimate, but its automatically generated MRI reports are frequently discordant with radiologist reports in specific ways and require expert review and external validation before clinical use. Full article

Micro-volume dispensing precision is critical for in vitro diagnostic (IVD) analyzers. We present a noise-aware Bayesian optimization framework that minimizes the within-run coefficient of variation (CV) of a 50 μL dispensing process by optimizing five pump-control variables. Each setting was tested in five independent batches; the group mean CV was used as the response, and the squared group standard error was supplied to an automatic relevance determination Gaussian process as observation-noise variance. From 33 tested combinations, the lowest measured mean CV was 0.266% (SD, 0.044%). An engineering-rounded setting then achieved 0.270% (SD, 0.050%), an 81.8% relative reduction versus engineer-selected settings (1.480%, SD, 0.083%). In a retrospective surrogate-based replay, the noise-aware strategy reached CV < 1.5% in 2.8 ± 1.2 iterations, compared with 4.1 ± 2.0, 6.5 ± 3.4, and 11.3 ± 5.1 for homoscedastic GP, standard GP, and random search. Deionized water, diluted human serum, and 5% bovine serum albumin all yielded mean CVs below 0.45%. The method thus identified a repeatable low-CV operating region with a limited physical-experiment budget; prospective algorithmic comparisons and multi-instrument validation remain necessary. Full article

Transmission expansion planning (TEP) for renewable-rich power systems requires a computational workflow that links investment decisions with chronological operations and nodal economic signals. This study presents a shadow price-guided framework integrating hourly DC optimal power flow, multi-hour candidate screening, mixed-integer TEP, SCUC–SCED evaluation, and leave-one-line-out line value evaluation. Renewable curtailment is represented explicitly at renewable generators and is separated from involuntary load shedding. The SP-WCI candidate score aggregates 24 hourly flow–price difference products using congestion surplus weights. On a 101-bus reduced system, the automatically selected eight-line plan lowers average LMP from 597.33 to 259.98 CNY/MWh (56.5%), load shedding from 21,159.0 to 0.0 MWh (100.0%), and unused renewable/hydro availability from 117,882.9 to 50,398.7 MWh (57.2%). Comparison with weighted overload and rule-based screening uses the same candidate budget, while the random benchmark is summarized over ten independent seeds. Sensitivity tests show how the fixed plan responds to alternative load-shedding penalties. The complete screening–optimization–verification chain is additionally executed natively on the full-order 796-bus system, confirming that the framework scales to full network data while delineating the limits of the reduced surrogate. The results are interpreted as evidence from a stressed representative day case rather than as a general estimate of commercial project returns. Full article

Federated learning enables collaborative deep neural network training across distributed clients without sharing raw data, yet its performance degrades substantially when local data distributions are non-identically distributed (non-IID). Existing aggregation strategies either treat all clients uniformly or require expensive bi-level optimisation, failing to explicitly leverage the structural similarity among client data distributions. We propose DAPA (Distribution-Aware Personalised Aggregation), a personalised federated deep learning framework that adaptively tailors the global aggregation to each client based on inter-client distribution similarity. DAPA operates in three stages. In the sketch stage, each client computes a lightweight distribution sketch that summarises its local label and feature statistics through class-conditional moment vectors extracted from the deep network’s penultimate layer. In the affinity stage, the server constructs a pairwise client affinity matrix from these sketches using an approximate Wasserstein distance and derives personalised aggregation weight vectors via a softmax-temperature mechanism. In the clustering stage, a hierarchical two-level aggregation combines models within automatically discovered client clusters and then blends across clusters with adaptive mixing coefficients. To safeguard privacy, the distribution sketches are protected with a calibrated Gaussian mechanism that satisfies Rényi differential privacy. Theoretical analysis establishes a convergence bound showing that DAPA achieves a tighter error floor than uniform averaging under distribution heterogeneity. Extensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet under Dirichlet-controlled non-IID partitions demonstrate that DAPA outperforms nine state-of-the-art baselines, improving average test accuracy by 2.4 to 6.8 percentage points while maintaining competitive communication efficiency. Ablation studies confirm the contribution of each component, and privacy analysis verifies that the accuracy gain persists under strict differential privacy budgets. These findings advance the application of deep learning in privacy-sensitive distributed environments and offer a principled data-mining-based approach to characterising client heterogeneity in federated systems. Full article

Background: Close relationships may support goal-directed behavior not only through direct interpersonal contact but also through the activation of mental representations of supportive others. However, relatively little is known about whether imagining a supportive close person is associated with sustained behavioral engagement during a cognitive task. Objective: This study examined whether supportive-close-person imagery is associated with higher behavioral indicators of sustained task engagement compared with a non-social control condition and a socially neutral familiar-person imagery condition. Methods: Eighty adults participated in a within-subject repeated-measures study. Each participant completed The Maze Test in three counterbalanced conditions: observation of a neutral object, imagery of a socially neutral familiar person, and imagery of a supportive close person. The primary behavioral indicators were total task time and the number of completed mazes. Average time per maze was treated as an auxiliary indicator of task pace or efficiency. The DASS-42 was administered after each session as a symptom-stability screening measure. Results: Experimental condition had a significant effect on all behavioral outcomes. Participants showed the longest total task time and completed the highest number of mazes in the supportive-close-person condition, intermediate values in the socially neutral familiar-person condition, and the lowest values in the control condition. DASS-42 scores, session number, and condition order did not account for the observed pattern. A censoring-aware Cox model also confirmed the robustness of the condition effect on total task time despite the automatic 1500 s task limit. Conclusions: Imagining a supportive close person was associated with greater sustained engagement in a cognitive task. The findings are consistent with the possibility that close-relationship representations may support goal-directed behavior, although the psychological mechanisms underlying this effect require direct examination in future studies. Full article