Search Results (8,997)

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

With the rise in cultural tourism, visitors’ demand for historical and cultural experiences has grown, prompting historical architecture not only to focus on physical preservation but also to offer more intuitive, engaging, and interactive experiences. This study proposes a design method for real-space gamified interactive experiences through mobile applications in historical environments. Qinghui Garden, one of the Four Famous Lingnan Gardens, is used as a case study. A total of 54 visitors participated in an on-site field experiment, with data collected through pre- and post-experience questionnaires, behavioral tracking, and supplementary semi-structured interviews. Through a comparative experiment with three groups of visitors—free exploration, traditional guided tours, and real-space gamified interactive experiences—it was found that the gamified interactive method demonstrated superior clarity and reliability in its technology. Visitor cognitive performance and subjective satisfaction increased by approximately 62.5%, and the gamified interaction effectively guided the spatial flow and interaction behaviors with specific spaces. These findings provide new insights into the design of real-space gamified interactive experiences in historical spaces, contributing significantly to the preservation and cognition of cultural heritage. Full article

Ensuring the structural reliability of power transmission networks is a fundamental prerequisite for the stable operation of modern energy systems. To address the challenges posed by complex weather interference and the small scale of insulator defects during power line inspections, this paper proposes LID-YOLO, a lightweight insulator defect detection network. First, to mitigate image feature degradation caused by weather interference, we design the C3k2-CDGC module. By leveraging the input-adaptive characteristics of dynamic convolution and the spatial preservation properties of coordinate attention, this module enhances feature extraction capabilities and robustness in complex weather scenarios. Second, to address the detection challenges arising from the significant scale disparity between insulators and defects, we propose Detect-LSEAM, a detection head featuring an asymmetric decoupled architecture. This design facilitates multi-scale feature fusion while minimizing computational redundancy. Subsequently, we develop the NWD-MPDIoU hybrid loss function to balance the weights between distribution metrics and geometric constraints dynamically. This effectively mitigates gradient instability arising from boundary ambiguity and the minute size of insulator defects. Finally, we construct a synthetic multi-weather condition insulator defect dataset for training and validation. Compared to the baseline, LID-YOLO improves precision, recall, and mAP@0.5 by 1.7%, 3.6%, and 4.2%, respectively. With only 2.76 M parameters and 6.2 G FLOPs, it effectively maintains the lightweight advantage of the baseline, achieving an optimal balance between detection accuracy and computational efficiency for insulator inspections under complex weather conditions. This lightweight and robust framework provides a reliable algorithmic foundation for automated grid monitoring, supporting the continuous and resilient operation of modern energy systems. Full article

Remote sensing images acquired by UAVs under nighttime or low-illumination conditions suffer from insufficient illumination, leading to degraded image quality, detail loss, and noise, which restrict their application in public security and disaster emergency scenarios. Although existing machine learning-based enhancement methods can recover part of the missing information, they often cause color distortion and texture inconsistency. This study proposes an improved low-illumination image enhancement method based on a Weakly Paired Image Dataset (WPID), combining the Hierarchical Deep Convolutional Generative Adversarial Network (HDCGAN) with a low-rank image fusion strategy to enhance the quality of low-illumination UAV remote sensing images. First, YCbCr color channel separation is applied to preserve color information from visible images. Then, a Low-Rank Representation Fusion Network (LRRNet) is employed to perform structure-aware fusion between thermal infrared (TIR) and visible images, thereby enabling effective preservation of structural details and realistic color appearance. Furthermore, a weakly paired training mechanism is incorporated into HDCGAN to enhance detail restoration and structural fidelity. To achieve objective evaluation, a structural consistency assessment framework is constructed based on semantic segmentation results from the Segment Anything Model (SAM). Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches in both visual quality and application-oriented evaluation metrics. Full article

Multi-objective evolutionary optimization faces significant challenges due to guidance mismatch under complex Pareto-front geometries. This paper proposes a dynamic self-organizing map-assisted evolutionary algorithm (D-SOMA), a manifold-aware framework that harmonizes knowledge-informed priors with unsupervised objective-space characterization. Specifically, a knowledge-informed guided resampling strategy is formulated to bridge stochastic initialization and targeted exploitation. By distilling spatial distribution priors from the decision-variable boundaries of early-stage elite solutions, it establishes a high-quality starting population biased towards promising regions. To capture the intrinsic geometry of the evolving population, a self-organizing map (SOM)-based adaptive subregion characterization strategy leverages the topological preservation of self-organizing maps to extract latent modeling parameters. This strategy adaptively determines subregion centers and influence radii, enabling a data-driven partitioning that respects the underlying manifold structure. Furthermore, a density-driven phase-responsive scale adjustment strategy is introduced. By synthesizing spatial density feedback and temporal evolutionary trajectories, it dynamically modulates the characterization granularity K, thereby maintaining a rigorous balance between geometric modeling fidelity and computational overhead. Extensive experiments on 50 benchmark problems from the DTLZ, WFG, MaF and RWMOP suites demonstrate that D-SOMA is statistically superior to seven state-of-the-art algorithms, exhibiting robust convergence and superior diversity across diverse problem landscapes. Full article

Northeast China, situated in the eastern Central Asian Orogenic Belt (CAOB), marks the terminal closure zone of the Paleo-Asian Ocean (PAO) (eastern segment). At present, due to extensive Quaternary cover, the structural deformation characteristics and deep structure of the Solonker Suture Zone in the east of the Nenjiang–Balihan fault remain poorly constrained, which limits our understanding of the tectonic evolution of the PAO. This study integrates deep seismic reflection (DSR) and magnetotelluric (MT) sounding profiles to investigate the crustal structural, sedimentary framework, and tectonic evolution of the oceanic and continental crusts along the western slope of the Songliao Basin. Two regional detachment surfaces (D1 and D2) were identified. The D2 interface demarcates the upper crust’s basal boundary, overlain by multiple high-amplitude monoclinic reflections. The area below the D2 interface exhibits a network structure of arcuate and variably oriented reflections, indicating a dual-layered orogenic structure. The upper crust exhibits distinct structural domains defined by strongly contrasting monoclinal reflections: north-dipping, low-resistivity zones in the southern sector and south-dipping, high-resistivity zones in the northern sector. These oppositely oriented reflections have been interpreted as marking an Early Paleozoic accretionary wedge and oceanic island arc, respectively. Interposed between these opposing structural domains, the Paleozoic to Early Mesozoic forearc basin sequences are preserved, with a pre-Middle Permian oceanic basin identified north of the study area. By integrating characteristics of seismic reflection sequences with regional geological data, this paper clarifies the processes of closure and collision at the northern margin of the PAO (Eastern Segment). Full article

The development of efficient, stable, and sustainably fabricated photocatalysts for solar-driven hydrogen evolution remains a critical challenge in the field. Herein, we report a novel green coprecipitation strategy to synthesize calcium-doped zinc oxide (Ca-ZnO) nanosheets, utilizing cactus juice as a natural, multifunctional medium for the coprecipitation process. This method enables the in situ, tunable incorporation of 3–7% Ca²⁺ ions into the wurtzite ZnO lattice without the use of harsh chemical reagents. Comprehensive characterization confirms that Ca²⁺ substitutionally replaces Zn²⁺, which preserves the intrinsic crystal structure of ZnO well while inducing the formation of uniform nanosheet morphology. This doping strategy effectively modulates the electronic band structure, progressively narrowing the bandgap from 3.19 eV to 2.90 eV and significantly enhancing visible-light absorption. Crucially, the incorporation of Ca²⁺ also generates oxygen vacancies, which serve as efficient electron traps to suppress photogenerated charge carrier recombination. The optimized 5%Ca-ZnO photocatalyst demonstrates a favorable hydrogen evolution rate of 889 μmol·g⁻¹·h⁻¹ under full-spectrum irradiation, with stability, retaining 94.8% of its activity after four cycles. This work not only provides a high-performance material but also establishes a generalizable, sustainable paradigm for the design of advanced semiconductor photocatalysts. Full article

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this paper proposes a physics-constrained hybrid prediction model that integrates a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture with wide residual connections (WRC) and a physics-constrained loss (PCL). The proposed modeling approach combines real operational measurement data with high-resolution simulation data to enhance data diversity and improve generalization capability. The CNN–BiLSTM structure captures nonlinear temporal dependencies, while the WRC preserves critical low-level transient electrical features during deep temporal modeling. In addition, multiple physical constraints, including power balance, voltage conversion relationships, and battery state-of-charge (SOC) dynamics, are incorporated into the training process to enforce physically consistent predictions. The model is validated using charging and discharging experiments on a laboratory-scale SPS under both steady-state and transient conditions. Comparative results demonstrate that the proposed approach achieves higher prediction accuracy, improved dynamic stability, and faster recovery following disturbances compared with conventional data-driven models. These results indicate that physics-constrained deep learning provides an effective and interpretable modeling framework for SPS state prediction, supporting digital twin-oriented monitoring and real-time prediction applications. Full article

This work develops a zero-trust, physics-constrained mathematical framework for detecting stealthy drift perturbations in power system dynamical models. Such perturbations constitute adversarial, statistical deviations that preserve first-order operating trends, making them difficult to identify using classical residual-based estimators or unconstrained data-driven models. We introduce ZETWIN, a spatio-temporal learning architecture formulated as a constrained optimization problem in which the nodal admittance matrix $Y_{bus}$ acts as a graph-structured linear operator embedded directly into the loss functional. This construction enforces Kirchhoff-consistent latent representations and yields a mathematically grounded zero-trust decision rule that flags any trajectory violating physical feasibility, independent of prior attack signatures. The proposed framework is evaluated using a PyPSA-based AC–DC meshed network, demonstrating an AUROC = 0.994, and F1 = 0.969. The formulation highlights how physics-informed constraints, graph operators, and spatio-temporal approximation theory can be combined to construct mathematically interpretable zero-trust detectors for complex dynamical systems. Full article

The efficient utilization of industrial waste (containing alkaline compounds, especially Ca-based species) for flue gas desulfurization (FGD) is of great importance for both environmental protection and resource recovery. In this study, paper industry white mud was modified with Ca(OH)₂ to develop a cost-effective sorbent with enhanced SO₂ removal performance. Optimization experiments identified the best preparation conditions as a 1:1 Ca(OH)₂/white mud ratio, 60 °C modification temperature, 6 h reaction time, and a liquid-to-solid ratio of 3:1. Under these conditions, the sorbent achieved nearly 100% SO₂ removal in the first 6 h and maintained >90% efficiency after 10 h, significantly outperforming raw white mud and Ca(OH)₂ alone. Characterization revealed that the superior performance originated from structural stability and abundant active sites. BET analysis showed a high surface area (24.8 m²·g⁻¹) and pore volume (0.160 cm³·g⁻¹), which were largely preserved after desulfurization, indicating resistance to pore blockage. SEM images confirmed a transition from porous aggregates to densified product layers, consistent with a shrinking-core/product-layer mechanism. XRD identified CaSO₄·2H₂O as the dominant product, while in situ FTIR demonstrated that O₂ promotes sulfite oxidation and H₂O accelerates hydrated sulfate formation, enhancing activity but causing faster pore blocking. The presence of NO extended sorbent durability by catalyzing continuous sulfite oxidation through NO/NO₂ redox cycling. Overall, Ca(OH)₂-modified white mud combines high reactivity, durability, and structural stability, offering a promising alternative to conventional sorbents. This work provides a viable route for the resource utilization of paper industry waste and practical insights for designing efficient and sustainable materials for industrial FGD systems. Full article

The study focused on the development and optimization of plastic deformation of pure M1E copper using an unconventional hydrostatic extrusion (HE) process aimed at improving the performance of electrodes used in electrical discharge machining (EDM). The process was designed to refine the microstructure while maintaining the high electrical conductivity required for EDM applications. Optimization of a three-stage HE process (cumulative strain ε = 2.51) resulted in the formation of an ultrafine-grained structure (d₂ ≈ 370 nm), leading to a significant increase in mechanical strength (UTS ≈ 400 MPa) while preserving very high electrical conductivity (~99% IACS). This combination of properties is particularly important for EDM electrodes, as it allows improved wear resistance without compromising electrical performance. Due to the application-oriented nature of the study, the HE-processed copper was tested under industrial EDM conditions. Wear tests were conducted using seven electrodes of different geometries required for the production of a sample injection mold. The results demonstrated a substantial reduction in electroerosion wear of HE-processed electrodes (30–90%) compared with undeformed copper, together with up to 25% improvement in surface quality. These findings indicate that hydrostatic extrusion is an effective method for producing high performance EDM electrode materials with improved durability and machining quality. Full article

This study uses Shenyang as a case to integrate multi-source dynamic data with spatial modeling. A comprehensive resistance surface was planned using 12 indicators across the natural, built, and socio-economic dimensions, with objective weighting via the CRITIC method. A hierarchical corridor network was generated based on the MCR model and circuit theory, validated by chi-square goodness-of-fit tests and network structural analysis. The results indicate that socio-economic factors, particularly path activity frequency, dominate the spatial patterns of the corridors, confirming that the network captures connectivity rooted in human activity rather than simply replicating transportation infrastructure. The distribution of national, provincial, and municipal heritage sites across the three higher-importance tiers (L1–L3) shows no significant deviation from the regional baseline, validating the network’s inherent de-hierarchization capacity. Network structure analysis further confirms that this equitable network simultaneously exhibits robust connectivity. The resultant network displays a distinct core–periphery structure with a monocentric-multinuclear radial pattern, forming a four-tier corridor system (core, primary, secondary, and local) that provides an actionable framework for graded protection and targeted interventions. This study advances cultural heritage conservation from passive isolation towards proactive systemic network governance, offering a transferable pathway for the sustainable preservation of heritage in high-density urban environments. Full article

Object detection in UAV-based fire rescue scenarios faces multiple challenges, including densely distributed small targets, severe occlusion, and interference from smoke and flames. Existing mainstream detection models, such as the YOLO series, often prioritize inference speed at the expense of modeling global context and spatial positional information, resulting in limited performance in such complex environments. To address these limitations, this paper proposes FirePM-YOLO, an object detection architecture optimized for fire rescue applications. Based on the YOLO framework, the proposed model introduces two key innovations: first, a Position-Aware Enhanced Mamba module (PEMamba) is designed, which incorporates a compact positional encoding mechanism, lightweight spatial enhancement, and an adaptive feature fusion strategy to significantly improve scene perception while maintaining computational efficiency. Second, a PEMBottleneck structure is constructed, which dynamically balances local convolutional features and global PEMamba features via learnable weights. This module is embedded into the shallow layers of the backbone network, forming an enhanced PEM-C3K2 module that captures long-range dependencies with linear complexity while preserving fine local details, thereby enabling holistic contextual understanding of fireground environments. Experimental results on the self-built “FireRescue” dataset demonstrate that compared with the original YOLOv12 and other mainstream detectors, the proposed model achieves improvements in both mean average precision (mAP) and recall while maintaining real-time inference capability. Notably, it exhibits superior detection performance on challenging samples, such as small-scale and partially occluded professional firefighting vehicles. Full article

This paper first identifies and analyzes the phenomenon of output-voltage collapse under step load perturbations in dual-bridge converters where an extremum seeking control (ESC) optimization algorithm is employed. Although ESC is an effective online duty-cycle optimization method under steady-state power transfer conditions, it can result in severe output-voltage degradation during large-signal transients. This degradation is primarily caused by the following two factors: the reduced power transfer capability associated with the optimized duty cycles, and the limited dynamic capability of the ESC structure to rapidly adjust the duty cycles. To overcome this limitation, an enhanced extremum seeking control (EESC) structure is proposed for the first time, which enables fast output-voltage reference tracking under dynamic operating conditions, while preserving ESC’s capability for online duty-cycle optimization to minimize losses and improve efficiency. The proposed method extends the applicability of ESC from steady-state optimization to large-signal dynamic scenarios. Comparative experimental results on a dual active half-bridge (DAHB) converter reveal that the conventional ESC structure can cause dynamic collapse, corresponding to a 100% output voltage and current drop under a sudden increase in reference power from 25% to 50% of the rated power with resistive loads. In contrast, the proposed EESC structure not only maintains the same efficiency optimization as the conventional ESC but also exhibits only a brief 5% drop in output voltage and current under the same dynamic conditions, immediately recovering and thus avoiding dynamic collapse. Full article