Search Results (6,651)

High-temperature proton-exchange membrane fuel cells (HT-PEMFCs) generate useful thermal energy that can be recovered for additional power production. This study investigates an integrated HT-PEMFC–organic Rankine cycle (ORC) system by combining response surface methodology (RSM) with thermodynamic energy analysis. A 17-run response-surface design was used to quantify the effects of pressure, temperature, and current density on polarization voltage. Power density was derived directly from the RSM-predicted voltage using P_d = iE to preserve physical consistency. The electrochemical model was benchmarked against published phosphoric-acid-doped polybenzimidazole HT-PEMFC polarization data under comparable conditions. The constrained optimization identified an operating condition of 400 kPa, 443 K, and approximately 1.198 A cm⁻², giving a predicted voltage of 0.5395 V and a power density of approximately 0.6462 W cm⁻². This represents a 12.9% increase in power density relative to the adopted reference condition. Separately, the reference thermodynamic case produced 13.08 kW of gross HT-PEMFC stack electrical power and 15.45 kW of thermal output assumed available to the ORC. The available legacy R409A reference case was evaluated at an evaporator pressure of 2 MPa, yielding approximately 1.24 kW of ORC net power and a net thermal efficiency of about 8.02%. The resulting combined modeled electrical output was approximately 14.32 kW before unmodeled balance-of-plant auxiliary power consumption, with the ORC contribution corresponding to about 9.5% of the gross HT-PEMFC stack output. The results demonstrate the complementary potential of physically consistent HT-PEMFC operating-condition optimization and waste-heat recovery, while the ORC results remain specific to the retained R409A reference dataset. Full article

Type 1 diabetes (T1D) is a chronic autoimmune disease in which autoreactive effector T cells destroy insulin-producing pancreatic β-cells, leading to impaired insulin production and long-term metabolic complications. This study develops a mathematical model of the interactions among pancreatic β-cells, autoreactive effector T cells, regulatory T cells (Tregs), and stem cell therapy, treating stem cell administration as an immunomodulatory and regenerative strategy that suppresses autoimmune activity and promotes β-cell recovery. The existence, uniqueness, positivity, and boundedness of solutions are established. For the auxiliary subsystem with $S_{E }=0$ and constant treatment input, the disease-free equilibrium and the threshold quantity $R_{0 }$ are derived, and local asymptotic stability of the DFE is established for $R_{0 }<1$. For the full model with $S_{E }>0$, the existence of a unique biologically feasible positive equilibrium is established, together with its local asymptotic stability under constant treatment input. An optimal control problem, formulated using Pontryagin’s Maximum Principle, is used to guide therapeutic dosing, and one-year numerical simulations compare two administration protocols: high-dose pulse injections and continuous infusion. Both reduce autoimmune activity and improve β-cell dynamics, with pulse administration producing stronger transient responses and continuous infusion producing smoother treatment-period dynamics, while both protocols approach similar long-term levels. A local sensitivity analysis identifies immune activation, β-cell destruction, and regulatory T-cell activity as the parameters most strongly shaping disease progression and treatment outcomes. By unifying stem cell therapy, stability analysis, sensitivity analysis, and optimal control, and directly comparing pulse and infusion protocols, this framework offers new insight for designing stem cell-based treatments for autoimmune diabetes. Full article

Maritime object detection is essential for port-based surveillance, ship tracking, and maritime security. However, practical implementation encounters two primary challenges: significant class imbalance (IMO identification numbers represent only 1.2% of labelled objects) and spatial inconsistency (predicted IMO numbers often appear beyond ship boundaries). Standard detectors and conventional class-balancing strategies fail to adequately address these difficulties, resulting in a persistent mismatch between research validation and practical performance. We present a hybrid autoencoder–YOLO framework with variable spatial regularisation. To the best of our understanding, this is the first methodology to simultaneously address class imbalance and spatial inconsistency in maritime IMO detection via reconstruction-guided learning and differentiable spatial regularisation. The model uses a YOLOv8 encoder shared by both a detection head (designed for ships and IMO numbers), and an auxiliary reconstruction decoder (a SkipDecoder with U-Net-style skip connections). A unique spatial limitation loss provides the physical restriction that each IMO number must be within a ship’s bounding box, using only anticipated boxes. Training occurs in two phases: autoencoder pretraining on the marine dataset, followed by phased joint optimisation with a curriculum schedule for the spatial weighting. In a dataset of 297 annotated images (utilising five-fold cross-validation with a 47-image preserved test set), our comprehensive model achieved 50.1% IMO AP50-95, 97.8% IMO precision, and 94.6% IMO F1-score on the test set, beating the baseline YOLOv8s by +7.8 percentage points, +6.5 percentage points, and +5.2 percentage points, respectively. Ablation studies indicate that reconstruction instruction improves IMO AP50-95 by +4.5 percentage points, while spatial regularisation adds +3.3 percentage points. Although ship detection results in a small compromise (ship AP50-95 decreases from 77.0% to 55.6%), this appears to be practically acceptable given the essential role of IMO numbers as unique ship IDs. Significantly, inference speed improves by 20% (8.0 ms per image on an NVIDIA A100 GPU) relative to YOLOv8s (10.0 ms). Comparisons with leading detectors (RetinaNet, Faster R-CNN, DETR, EfficientDet), all initialised with standard COCO-pretrained backbones and fine-tuned on our dataset, reveal that none achieve an IMO AP50-95 exceeding 42.3%, highlighting the task’s challenge and the accuracy of our design. The proposed framework shows potential to address the gap in practical implementation through reconstruction-based feature learning accompanied by a specified geometric baseline. It is precise, accurate, and fast, aligns with specific physics, and shows promise for real-time maritime surveillance applications, though we acknowledge the need for additional thorough verification across many operational environments. Full article

Acoustic-to-articulatory inversion (AAI) predicts articulatory movements from acoustic signals; however, current methods often overlook external, visible articulatory features and frequency domain information. Limited paired acoustic–articulatory data remain a practical constraint on robust AAI modeling and evaluation. This paper proposes a novel AAI framework integrating external visible articulatory features and a channel attention mechanism for feature fusion. We first construct a Mandarin EMA dataset for language learning, then explore the impact of visible features (e.g., lip movements) on predicting internal articulatory trajectories. We additionally investigate short-time Fourier magnitude representations computed from EMA-measured lip–jaw trajectories as auxiliary kinematic features. SENet- and ECA-based group-attention variants are evaluated as alternatives for reweighting input groups before concatenation. Experiments on SAIT-EMA and MOCHA show that visible articulatory features and frequency-domain information improve prediction accuracy, with channel attention fusion providing further gains in selected configurations. These findings demonstrate the value of attention-based multimodal data fusion for intelligent audio and articulatory information processing in pronunciation-learning contexts. Full article

Amid the ongoing decarbonization of the international shipping industry, methanol has emerged as a promising alternative fuel for marine auxiliary boilers owing to its environmental advantages and engineering feasibility. However, its low viscosity and surface tension make the atomization process highly sensitive to operating conditions, posing challenges to stable and efficient burner operation. Existing studies have predominantly focused on engine applications, whereas systematic investigations into the atomization characteristics and operating-parameter matching of primary-air swirl-cup nozzles for marine auxiliary boilers remain limited. To address this gap, the present study combines experimental measurements and numerical simulations to investigate the effects of fuel flow rate, atomizing-cup rotational speed, and primary-air damper opening on spray characteristics. Spray imaging was employed to characterize the spray cone angle and macroscopic morphology, while PIV and PDA were used to measure the outer-flow-field velocity and droplet-size characteristics, respectively. Numerical simulations of liquid-film formation and breakup were performed using a coupled VOF-DPM framework. The predicted spray angle and outer-flow-field velocity showed good agreement with the experimental measurements, with overall deviations within 3–12%. Increasing the atomizing-cup speed generally promoted droplet refinement, while adjustment of the primary-air supply further influenced the droplet-size distribution. Under high-speed operating conditions, the atomized droplet size was generally maintained below 100 μm, and the SMD in the investigated near-field region was approximately 60–80 μm. Based on the multi-load experimental results, primary-air parameter-matching relationships were established for fuel flow rates ranging from 100 to 500 kg/h, providing guidance for maintaining stable atomization performance over a wide operating-load range. This study provides a quantitative basis for the operating-parameter design and stable operation of primary-air swirl-cup nozzles in marine methanol-fired auxiliary boilers and offers useful guidance for their engineering application. Full article

To improve prompt utilization and boundary perception in SAM-based interactive medical image segmentation, this paper proposes ASAM2-UNet, a U-shaped visual foundation model built upon SAM2-UNet. Unlike methods that treat prompts as auxiliary spatial inputs, ASAM2-UNet explicitly incorporates prompt-derived priors into feature reasoning through an Interactive Prompt-Guided Focal Attention module. Specifically, a user-provided spatial prompt is converted into an explicit attention prior that modulates focal self-attention, allowing the network to emphasize target-relevant regions while retaining efficient local–global contextual modeling. In addition, a Contextual Semantic Information Complement module integrates multi-scale decoder features with uncertainty-aware and structure-aware cues derived from the initial prediction to refine ambiguous lesion boundaries. By integrating cross-layer spatial attention, local–global spatial fusion, and semantic-aware feature aggregation, the proposed module enhances the discrimination of ambiguous edges and complex foreground–background regions. Extensive experiments on five public polyp segmentation datasets demonstrate the effectiveness of the proposed method. Full article

Repurposing aging offshore platforms into liquefied natural gas (LNG) regasification or CO₂ injection facilities has been gaining attention as a viable alternative in the energy transition. However, a single systematic criterion for determining which equipment of the existing power system should be reused, retrofitted, or replaced is lacking. This study proposes a standards-attributed reuse–retrofit–replacement evaluation framework that utilizes provisions from established international standards and classification society regulations for ships and individual electrical equipment. Power system items are evaluated for functional adequacy, integrity, and regulatory compliance, then classified based on predefined decision rules. Item-level assessments are aggregated into a platform-level assessment (suitable, conditionally suitable, or not suitable) based on criticality tiers such as safety critical, operationally critical, and nonessential items. Criteria directly specified in the cited standards are distinguished from standard-based quantitative interpretations and from author-defined screening thresholds and decision rules; the quantitative interpretations are not themselves numerical limits directly prescribed by the cited standards, and the author-defined elements are not mandatory requirements of those standards. The operation of the framework is illustrated on a synthetic reference platform as a proof of concept, showing how the procedure yields differentiated reuse–retrofit–replacement (R–R–R) outcomes under the two conversion scenarios. The same power system was assessed as “conditionally suitable” and “not suitable” for LNG regasification and CO₂ injection conversions, respectively. This variance stems from the different load requirements and risk characteristics, particularly the higher safety-critical auxiliary load associated with CO₂ injection. Accordingly, the framework is intended as a screening and decision-support procedure that provides traceable rationale at the power-system level after high-level repurposing decisions; validation on actual platforms remains necessary before practical implementation. Full article

Ship detection in Synthetic Aperture Radar (SAR) imagery is essential for maritime surveillance and situational awareness. Despite the advances of deep learning for SAR ship detection, small-target detection is still hindered by severe feature degradation from repeated down-sampling and insufficiently discriminative representations under weak scattering and complex background clutter. To alleviate this dilemma, a Spatial Prior-guided feature enhancement and Multi-branch Complementary learning framework is proposed, termed SPMC, for small ship detection in SAR images. Specifically, a Spatial Prior-Guided Feature Enhancement (SPFE) module is designed to derive spatial attention priors for multi-scale features from ground-truth annotations, thereby emphasizing target-related responses and strengthening small-ship representations. Second, a Multi-branch Complementary Classification (MCC) module is developed, which introduces multiple auxiliary classification heads to learn complementary discriminative information from different classification perspectives. Furthermore, a dual-weighted complementary regularization strategy is proposed to encourages different classifiers to focus on hard samples, thereby improving the discriminative capability for small ships. Extensive experiments on the HRSID and LS-SSDD benchmarks validate the effectiveness of the proposed framework. For extremely small ships with very limited image coverage, SPMC improves the baseline YOLOv11 detector by 3.25%/0.99% in AP₅₀/AP_0.5:0.95 on LS-SSDD and by 1.28%/1.21% on HRSID, demonstrating its effectiveness in challenging SAR small ship detection. Full article

This study develops a fiber Bragg grating (FBG)-based method for identifying sectional loads on a full-scale RX1E-A composite aircraft wing. Thirty-two load-sensitive FBGs were installed at four spanwise sections. Multi-case ground loading with different spanwise distributions and chordwise positions was used to generate coupled combinations of bending moment, shear force, and torque. Multichannel linear models were calibrated using 89 measured samples from Cases 1 to 9 and independently evaluated using interpolation-oriented Cases 10–11 and limited-extrapolation Cases 12–13. Electrical strain gauges were used only for auxiliary ground-response comparison. The calibration NRMSE ranges were 0.454–1.087% for bending moment, 1.213–3.351% for shear force, and 0.526–1.306% for torque. Across the independent cases, the maximum section-level NRMSEs were 1.949%, 7.251%, and 2.484%. The fixed models were subsequently applied to a nominally identical flight-test wing without airborne refitting. During a pull-up maneuver with a maximum normal load factor of 2.058, the reconstructed bending-moment and shear-force increments exhibited continuous responses, extrema close to the load-factor peak, and a physically reasonable decrease from the wing root toward the tip. Full article

Automating the identification of International Classification of Diseases (ICD) codes from electronic health records (EHRs) presents a critical challenge, particularly for rare diseases where existing computational methods severely underperform due to extreme long-tail label distributions. To address this, we propose a multi-modal deep learning framework known as MMAC-Net, designed to enhance the retrospective assignment of ICD-9 codes to admissions involving rare pathologies. The model integrates unstructured clinical narratives with structured auxiliary data, specifically pharmacological prescriptions and microbiology events, using a convolutional attention-based architecture. Through a late fusion mechanism, it synthesizes attention-weighted textual representations with dense embeddings of the structured data types. Validation on the MIMIC-III dataset shows consistent improvements over a matched text-only baseline evaluated under an identical protocol. On the full dataset of 8930 ICD codes, the framework achieved a Micro-AUC of 0.997 and Precision@8 of 0.875. On the subset of admissions carrying at least 1 of 568 rare codes, adding the two structured modalities to the text encoder raises Macro-F1 from 0.011 to 0.084 and Micro-F1 from 0.368 to 0.513 relative to the text-only baseline, corresponding to relative increases of 6.69 and 0.39, respectively, while Precision@8 rises from 0.092 to 0.159 and Micro-AUC from 0.966 to 0.985. While extreme class imbalance remains a formidable obstacle, these findings underscore that incorporating structured clinical context partially mitigates the limitations of purely natural language processing approaches. Practically, the framework is intended as a decision-support component that presents a ranked shortlist of candidate codes to a human coder or clinician; by recovering rare codes that text-only systems miss, it targets the under-coding of low-prevalence conditions that degrades registry completeness and downstream epidemiological estimates. Full article

Reliable visual perception is a prerequisite for autonomous cable duct inspection, particularly for recognizing pipe-joint dislocations and obstruction-related hazards. Images acquired inside cable ducts are often affected by restricted viewpoints, uneven illumination, wall-texture interference, partial occlusion, and substantial variations in target geometry and scale. These factors increase the likelihood of missed targets, false alarms, and inaccurate bounding boxes. This study develops DPR-YOLOv9 from the YOLOv9c detector, where DPR represents deformable-strip feature extraction, position-aware attention, and regression optimization. In the backbone, a Deformable Strip Convolution Network (DSCN) adjusts its sampling pattern to better describe elongated boundaries, displaced joints, and irregular obstacle contours. CoordAttention is introduced into the multi-scale fusion path to retain directional coordinate cues and emphasize spatially relevant features. In addition, Inner-IoU modifies the regression constraint through auxiliary boxes, providing more effective optimization for small or partially occluded targets. Across three independent runs, DPR-YOLOv9 achieved mean Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.944, 0.933, 0.940, and 0.751, respectively, while maintaining an inference speed of 67.85 FPS. The results indicate that the proposed detector improves both recognition reliability and localization quality for robotic cable duct inspection. Full article

In Global Navigation Satellite System (GNSS)-denied deep-sea environments, traditional scalar gravity matching navigation methods frequently suffer severe performance degradation in weak-feature, highly repetitive gravity anomaly regions. Inspired by the hippocampal spatial memory mechanism and natural graded foraging behavior of benthic marine organisms, this paper proposes a full-chain bionic framework named the Physics-Consistent Multi-Scale Adaptive Particle Filter for Gravity Matching Navigation (PC-MAPF-GM). This method endows the particle filter with four layers of biologically mimicked autonomous regulation capabilities: quantitative gravity field local suitability assessment, dynamically adjusted time-varying search scope, three-level multi-scale stepwise matching, and along-track trajectory motion physics consistency constraint. The verification of long-term shipborne lake experiments confirms that the proposed method reduces the final gravity matching positioning root mean square error (RMSE) to only 528.2 m, which is more than 41% lower than the classical terrain contour matching (TERCOM) benchmark and 31% lower than iterative closest contour point (ICCP). This biomimetic full-design-chain solution provides a robust new practical navigation paradigm for long-endurance fully autonomous underwater vehicles operating without any external auxiliary positioning information. Full article

Account-based state sharding improves blockchain parallelism by distributing account states and transaction execution across physical shards, but effective placement must preserve transaction locality without concentrating processing demand on a small number of shards. This paper presents LB-Louvain, a coarse-to-fine account-partitioning heuristic in which standard Louvain first extracts logical communities, followed by load-aware community-to-shard assignment and restricted boundary-account refinement. In controlled BlockEmulator experiments using 300,000 replayed Ethereum transactions and five repeated system runs per configuration, relative to CLPA, the complete LB-Louvain pipeline reduces the mean cross-shard transaction ratio by approximately 2.1%, increases active throughput by approximately 19.7%, and reduces average confirmation latency by approximately 9.4%. Component-wise ablation confirms complementary roles for load-aware assignment and boundary refinement. The evaluated behavior remains stable over $\beta \in [1,4]$, while partition-only profiling keeps the measured partition computation below 100 ms across the tested 50,000–300,000 transaction prefixes and 4–24 physical shards. An auxiliary migration experiment further shows that Fine-Grained activation reduces the observed migration-deferred set by approximately 39.8% relative to Full Locking while maintaining comparable throughput and confirmation latency. Broader archived experiments with Monoxide and CLPA are retained separately from the controlled revision results. Full article

The coupling of power and natural gas infrastructures introduces gas-side constraints and cyberattack risks into load frequency control (LFC). This paper proposes an auxiliary Kalman filter (AKF)-based tube-based MPC (TMPC) framework for gas–electricity coupling systems under gas pressure false data injection attacks (FDIAs). A pressure-dependent gas turbine (GT) power limit is incorporated into the frequency control constraint, and an auxiliary pressure model is identified from attack-free data as a virtual pressure sensor. Residuals, normalized innovation squared statistics (NIS), and cumulative sum (CUSUM) statistics are used for attack detection, while compromised pressure measurements are reconstructed using the AKF estimates. Simulations on a two-area power system coupled with an 11-node gas network show that under attack-free operations, the integral absolute error (IAE) values of TMPC, conventional MPC, and PI control are 0.7040, 1.9626, and 2.9834 Hz·s, respectively. Thus, TMPC reduces the accumulated frequency deviation by approximately 64% and 76%, compared with conventional MPC and PI control, respectively. Under FDIAs, the IAE decreases from 1.1645 to 0.7039 Hz·s after AKF-based pressure reconstruction, corresponding to an approximately 40% reduction. Meanwhile, the mean absolute error (MAE) of gas pressure reconstruction decreases from 0.1714 to 0.0157 bar, corresponding to an approximately 91% reduction in pressure reconstruction error. Compared with the denoising autoencoder (DAE) and graph signal recovery approaches, the proposed method achieves the highest detection rate of 98.39%, effectively limiting FDIA propagation to GT constraints and frequency regulation. Full article

Osteoporosis reduces bone integrity, significantly increasing the risk of fractures. This study aims to identify alternative screening methods for assessing osteoporosis in low-income settings, where access to dual-energy X-ray absorptiometry (DXA) is limited. A literature review was conducted, analyzing articles from Google Scholar, PubMed, and institutional libraries published between 2015 and 2025, to identify auxiliary methods for screening for osteoporosis when DXA is not available. Conventional radiographs, when analyzed through the assessment of the Cortical Thickness Index (CTI) and the Calcar-Canal Ratio (CCR), demonstrated high sensitivity and specificity as auxiliary screening tools. However, cutoff values vary significantly among different ethnic populations, indicating the absence of a universal diagnostic standard and underscoring the need for population-specific thresholds. Limited access to DXA worldwide delays early detection of osteoporosis and increases the risk of fragility fractures. Further studies are needed to standardize these metrics in Mexico, which would enable timely diagnosis and treatment in resource-limited settings, thereby improving patient outcomes and quality of life. Full article