Search Results (13,474)

Face super-resolution (FSR) aims to reconstruct high-quality high-resolution face images from low-resolution inputs. Although CNN–Transformer hybrid models have shown promising performance by jointly modeling local textures and global dependencies, their large parameter sizes and high computational costs hinder practical deployment in resource-constrained scenarios such as mobile devices and embedded systems. Meanwhile, existing lightweight SR models usually reduce complexity by simplifying network depth, channel dimensions, or convolutional operations, which may weaken feature representation capability and lead to insufficient recovery of fine facial structures. To address these issues, this paper proposes HCTIUNet, a lightweight CNN–Transformer hybrid network based on an inverted U-shaped architecture. Specifically, the proposed network integrates lightweight CNN branches for local facial texture extraction and Transformer branches for global dependency modeling, while introducing a multi-scale feature interaction strategy and a global feature refinement module to enhance facial structural details. Experimental results on the FFHQ, CelebA, and Helen datasets demonstrate that HCTIUNet achieves competitive performance under the ×8 face super-resolution setting, obtaining PSNR/SSIM/LPIPS values of 27.55 dB/0.765/0.225, 27.63 dB/0.761/0.212, and 27.53 dB/0.777/0.213, respectively. Moreover, HCTIUNet contains 10.5 M parameters, requires 9.9 G FLOPs, and achieves an inference time of 0.021 s. These results indicate that the proposed method achieves a favorable trade-off between reconstruction accuracy, perceptual quality, and computational efficiency, making it suitable for efficient face super-resolution applications. Full article

Accurate reconstruction of complete tree point clouds is essential for estimating ecosystem structural characteristics from LiDAR data. In urban forestry environments, however, terrestrial laser scanning (TLS) and mobile laser scanning (MLS) frequently produce incomplete observations. Occlusion caused by neighboring trees, together with interference from surrounding urban objects such as buildings and vehicles, often leads to missing regions within scanned point clouds. These defects may further affect the reliability of tree structural analysis and parameter estimation. Although recent learning-based point cloud completion methods have improved reconstruction performance, several limitations remain when they are applied to complex tree structures. Many existing networks depend on farthest point sampling (FPS) for feature extraction, which can result in the loss of fine-scale branching information. Furthermore, local feature aggregation methods based on the traditional k-nearest neighbor (KNN) strategy are highly sensitive to regions with uneven point cloud distribution, such as the canopy region where density variations are significant in tree point clouds. To alleviate these issues, this study proposes GS-TreeAttn, an attention-guided framework specifically for tree point cloud completion. This network models density and structural representation as a coupled problem and employs a structure-guided density-adaptive attention mechanism to jointly capture global structural dependencies and local geometric features. We comprehensively evaluate the proposed method using publicly available datasets and urban forestry data collected under real-world scanning conditions. Experimental results show that even in complex scenarios with severe occlusion and uneven sampling density, GS-TreeAttn generates more complete reconstruction results. This improvement is particularly evident in regions where the canopy and branches mutually occlude each other, where information loss is very common in real-world urban forestry. Full article

In processes related to the treatment of mineral materials, the crushing stage determines the ability to obtain the required particle-size fraction. At the same time, it is an exceptionally energy-intensive step (accounting for about 5% of global electricity consumption) and one that generates significant environmental impacts, particularly in the form of high noise levels and considerable dust emissions. This study focuses on acoustic issues associated with the operation of crushers equipped with materials of varying hardness. Noise level measurements were carried out and then compared with the machines’ operational parameters, such as reduction ratio, throughput, energy consumption, and grain-size distribution. The results indicate that the properties of the processed material have a significant influence on noise emission during the crushing process. The study included various types of materials, such as pebble, basalt, and granite (feed size 16–22 mm), as well as lower-strength materials, including aerated concrete, recycled concrete, and ceramic materials (average particle size of approximately 50 mm), enabling a comparative analysis under controlled operating conditions. The measured noise levels ranged from front position 105.3 dB and side position 105.2 dB, depending on the material type, with the highest values observed for [hard material, e.g., recycled concrete and basalt] and the lowest for [weak material, e.g., aerated concrete]. The differences between extreme cases reached up to the top position 107.6 dB, indicating a strong relationship between material properties and acoustic emission. These findings highlight the importance of material selection in crushing processes and provide a useful reference for reducing noise impact and improving the environmental performance of industrial aggregate production. Full article

To address the deterioration issues of wet joints in prefabricated steel–UHPC composite bridge decks caused by inadequate interfacial performance, an orthotropic steel–UHPC composite bridge deck system under hogging moments was investigated. A numerical study on the cracking resistance of wet joints was conducted using a cohesive zone model based on the traction–separation law to characterize the interfacial mechanical behavior. The numerical model was validated against experimental results, showing good agreement in terms of crack development and structural response. Subsequently, a parametric analysis was carried out to evaluate the influence of different reinforcement details, UHPC thickness and stud spacing. The results indicated that the adopted cohesive model was capable of accurately simulating the cracking behavior at the wet joint interface. In addition, the cracking resistance of UHPC wet joints could be significantly improved by providing additional reinforcement and reducing the longitudinal stud spacing. Moreover, the results revealed that joint reinforcement primarily enhanced local crack control performance, while having a limited effect on the global load–deflection response of the structure. These findings provide a reliable basis for the design and optimization of wet joint configurations in prefabricated steel–UHPC composite bridge decks. Full article

Selective laser melting (SLM) equipment operates as a complex cyber–physical system, wherein strong implicit coupling among internal subsystems presents significant challenges for condition monitoring and fault diagnosis. Existing deep learning methods often suffer from feature submersion when processing multi-source heterogeneous data and lack the capability for system-level topological causal inference. To address these issues, we propose a multisystem coupling modeling and anomaly traceability method based on a decoupled graph attention network (ST-DBGAE). Independent local spatiotemporal feature alignment modules are constructed to map heterogeneous sensory data into a unified latent space. This eliminates dimensional discrepancies while strictly maintaining the feature independence of underlying hardware subsystems, such as optical and gas circuits. A dynamic graph attention mechanism with sparse priors is subsequently introduced to adaptively capture time-varying coupling weights triggered by implicit interactions (e.g., thermal fluids), bypassing the need for predefined rigid physical connections. Furthermore, a dual-branch two-stage decoupled optimization architecture is designed. By blocking the cross-interference of global backpropagation, this architecture outputs a continuous equipment health index (HI) based on reconstruction errors and employs a topological difference matrix inference mechanism to reversely anchor the root-cause nodes responsible for cross-system cascading degradation. Experimental results based on over 310,000 real operational monitoring records from industrial SLM equipment demonstrate that the proposed model achieves a comprehensive diagnostic Macro-F1 score of 96.5% across eight operating states. The single-class detection rates (ACCs) of specific underlying anomalies are significantly improved. This method not only enables high-precision equipment health warnings but also provides a physically interpretable microscopic fault propagation mapping for predictive maintenance. Full article

The global change towards renewable energy presents significant challenges, particularly for local communities in developing countries. Although many of these countries have adopted various clean energy initiatives, numerous wind power projects have faced considerable delays and implementation obstacles. Key issues include conflicts with local inhabitants, environmental licensing bottlenecks, and difficulties integrating projects into national electricity grids. These setbacks highlight misaligned stakeholder interests and the lack of robust tools for impact assessment. This paper introduces a multi-criteria decision-making model designed to evaluate the sustainability impacts of wind power megaprojects, using a case study from Colombia. The model integrates stakeholder perspectives and assesses projects across four dimensions: environmental, social, economic, and institutional. Its application demonstrates the model’s effectiveness in capturing complex social dynamics and improving the understanding of stakeholder concerns. It offers a structured framework to support more inclusive and informed decision-making processes. This study proposes a practical tool for enhancing the planning and governance of renewable energy initiatives, the stakeholder-oriented multidimensional assessment framework designed to evaluate social, economic and environmental challenges associated with wind power projects. The findings underscore the importance of incorporating these dimensions into impact assessments to foster stronger alignment with local communities and increase the likelihood of project success in the energy transition. Full article

Stone arch bridges represent culturally significant heritage assets that exhibit distinct regional characteristics. At present digital preservation largely attends to geometric modeling and typically neglects the identification and conformance of core culture genes. This oversight has resulted in a disconnect between technological application and core heritage values, a prevalent issue globally. To address this, this study employs cultural gene theory to formulate a systematic framework for investigating the architectural cultural genes of stone arch bridges from the three dimensions: material–morphological, technical–behavioral, and cultural–symbolic. This study takes the Hongji Bridge in Handan as an example and uses literature research and 3D laser scanning and UAV oblique photogrammetry and qualitative extraction and visual presentation of the architectural genetic characteristics of stone arch bridges. This study identifies 11 core genetic indicators from the dimensions of genetic architecture, inheritance, and evolution, for the architectural cultural genes for the Chinese stone arch bridges The Zhaozhou Bridge (China) and Serranos Bridge (Europe)’s cross-cultural comparative analyses are adopted to validate the generalizability of the framework and the genetic uniqueness of the Chinese stone arch bridge. This research introduces a gene-based model of digital conservancy that fosters the transition of heritage preservation from technology-driven to value-driven. Full article

Wound infections due to antibiotic resistance pose a global public health problem. Phage therapy is a promising approach to address this issue. To improve localization, phage stability, delivery, and antibacterial performance, we propose polymer mix gel microbeads encapsulated with phages as a model for the delivery of phiKZ bacteriophage to combat Pseudomonas aeruginosa. Phages were loaded into the alginate pre-gel under magnetic stirring, with further cross-linking by chitosan and/or Ca²⁺ ions. The obtained gel microbeads were characterized using FTIR and Raman spectroscopy, and their cytotoxicity and antimicrobial properties were evaluated. This study demonstrated the efficient loading of high-titer phage lysate, achieving up to 99% encapsulation efficiency for alginate–chitosan microbeads. The key characteristics of the microbeads include stable physicochemical properties, slow but continuous phage release over 48 h in physiological saline, and low cytotoxicity. The phage-loaded microbeads demonstrated strong in vitro antimicrobial activity against P. aeruginosa PAO1, resulting in mean reductions of 6.9 log₁₀ and 4.8 log₁₀ CFU/mL for alginate and alginate–chitosan formulations, respectively. This corresponded to a decrease in bacterial concentration from approximately 1.1 × 10¹¹ CFU/mL in untreated controls to 1.1 × 10⁵ CFU/mL and 7.7 × 10⁶ CFU/mL for alginate and alginate–chitosan formulations after 3 h of incubation. Full article

In response to the issues of subjective weighting of indicators and insufficient consideration of the temporal dimension of risk factors in current operational safety risk assessments for electric utilities, this paper proposes a method for assessing operational safety risks in electric utilities based on the Accident-Calibrated Cumulative Risk Index (ACCRI). First, in accordance with current standards and operational guidelines, a multi-level indicator system covering four dimensions, namely human resources and personnel behavior, equipment and facilities, environment and conditions, and management and systems, is established to provide a systematic characterization of operational safety risks in electric utilities. On this basis, the ACCRI is defined by weighted accumulation of the average risk exposure values of tertiary indicators within their characteristic periods. Historical accident sample importance is used to calibrate and identify tertiary indicator weights and characteristic periods, thereby reducing the subjectivity of traditional expert-based weighting. Furthermore, considering the differing temporal-scale characteristics of various risk indicators, a characteristic-period identification model is established and solved using an improved sparrow search algorithm to balance the timeliness and accuracy of risk assessment. By incorporating chaotic initialization, Gaussian mutation, and adaptive weighting mechanisms, this algorithm enhances population diversity and balances global search capability with local optimization capability across different solution stages, thereby improving the solution efficiency and accuracy of the model. Finally, the case study preliminarily demonstrates that the proposed method can characterize the temporal-scale differences among risk factors and shows potential for engineering application under the available enterprise data. Full article

Facility agricultural robots operating in greenhouse environments often encounter narrow passages, dense obstacle distributions, and frequent path-direction changes, which increase the difficulty of achieving efficient and smooth autonomous navigation. Conventional A* algorithms usually suffer from redundant node expansion, dense turning-point distributions, and insufficient path continuity under such constrained conditions. To address these issues, this study proposes an improved A*-based path-planning framework that integrates adaptive heuristic weighting, dynamic corner correction, and Bézier-curve-based path smoothing. Rather than introducing an entirely new planning paradigm, the proposed method coordinates several existing optimization strategies within a unified framework to improve search efficiency, path regularity, and path continuity for facility agricultural scenarios. The adaptive heuristic weighting strategy dynamically adjusts the contribution of the heuristic term according to the relative distance between the current node and the target node, thereby improving global search guidance while reducing unnecessary exploration. Dynamic corner correction is introduced to suppress zigzag path structures and reduce redundant turning nodes in obstacle-dense regions, while Bézier-curve-based smoothing is employed to improve path continuity and compatibility with the kinematic characteristics of agricultural mobile robots. Simulation experiments were conducted on grid maps and greenhouse-like environments with different obstacle distributions, and comparative evaluations were performed against Dijkstra, RRT, and conventional A* algorithms. Under representative simulation scenarios, the proposed framework reduced the number of turning points by up to 53.7% and decreased computation time by approximately 19.4% compared with the conventional A* algorithm, based on the average results of repeated trials under identical conditions. In addition, physical platform experiments on a ROS2-based agricultural robot demonstrated that the planned trajectories maintained relatively stable navigation performance and smoother directional transitions in constrained greenhouse-like environments. The results indicate that the proposed framework achieves a more balanced trade-off between computational efficiency, path compactness, and path smoothness than the benchmark methods considered in this study. Nevertheless, the current validation remains limited to structured or semi-structured greenhouse environments under static obstacle conditions. Future work will focus on improving adaptability to dynamic agricultural scenarios and integrating the framework with real-time perception and motion-control systems for practical greenhouse deployment. Full article

Soybean is an important food and oil crop, and rapid nondestructive identification of seed cultivars is crucial for seed purity inspection, varietal certification, breeding management and food-quality control. However, the global spectral profiles of individual soybean seeds from different cultivars are often highly similar, making it difficult for single-representation models to simultaneously capture spectral sequential dependency and inter-band relational structure. To address this issue, this study proposes a GADF–Mamba Fusion Network (GMFNet) for soybean seed hyperspectral classification. Hyperspectral images of 24,800 seeds from eight cultivars were acquired, and reflectance spectra in the range of 900–1700 nm were collected. After preprocessing, 200 effective bands were retained. The preprocessed one-dimensional spectral sequence was fed into a Mamba-based branch to model continuous wavelength dependency and global spectral evolution, while the same sequence was transformed into a GADF image, resized to 208 × 208, and input into a ResNet18-based structural branch to extract inter-band relational features. The two heterogeneous representations were then integrated through a weighted feature fusion module for final classification. Experimental results showed that Mamba achieved the best test accuracy (0.8721) among the raw spectral models, whereas ResNet18 achieved the best test accuracy (0.8737) among the GADF-based structural models. More importantly, the proposed weighted fusion strategy achieved the best overall performance, reaching validation and test accuracies of 0.9039 and 0.9011, respectively. These results demonstrate that spectral sequential information and GADF-based structural semantics are highly complementary. Overall, the proposed framework provides an effective hyperspectral solution for single-seed soybean cultivar identification and shows potential for non-destructive automated quality control in food-industry applications. Full article

Single-lead electrocardiogram (ECG) is widely used in wearable devices for atrial fibrillation (AF) screening. Nevertheless, subtle pathological characteristics like P-waves and f-waves in practical signals are vulnerable to noise contamination. Meanwhile, the scarcity of high-quality annotated abnormal data instances leads to severe class imbalance. To mitigate these issues, we present an end-to-end framework designed for arrhythmia diagnosis using single-lead ECG signals, which integrates quality-aware data augmentation with a Peak-Enhanced attention mechanism. First, to mitigate the problem of data imbalance, a Quality-Aware Generative Adversarial Network (QA-GAN) is designed. This network integrates a signal quality evaluation module based on signal kurtosis, together with a dynamic soft-label training scheme, guiding the generator to prioritize learning high-quality morphological features, thereby synthesizing high-fidelity minority class samples. Second, to accurately capture subtle pathological features in electrocardiograms, a Peak-Enhanced Attention Convolutional Network (PEAC-Net) classification model is proposed. This model incorporates a Peak-Enhanced Attention (PE-Att) module, which employs learnable derivative convolutional kernels to precisely identify the transition points in the ECG signal. Furthermore, by integrating one-dimensional multi-scale dilated convolution (DSGC1D) with bidirectional LSTM, the model achieves effective capturing of both fine-grained local morphological features and long-range global rhythm patterns. Experimental results on the PhysioNet 2017 dataset indicate that the presented model attains an accuracy of 0.902 and a macro-F1 score of 0.880, respectively, outperforming other state-of-the-art models and also exhibiting robust data adaptability on the MIT-BIH dataset. Full article

Global shifts in energy policy have contributed to an increase in electricity generation from renewable sources, which introduces unique issues with volatility and grid reliability. Robust grid-scale energy storage methods must fill the gap between generation and consumption. Flywheel energy storage (FES) is a mechanical technology that utilizes the stored kinetic energy of a rotating body, but is typically only suited for shorter-term frequency regulation due to significant windage losses. In this work, a novel Python 3.13-based simulation and optimization tool is presented and used to optimize geometric design parameters for efficiency, energy density, and other metrics. The simulation utilizes a 1 degree-of-freedom, multi-regime fluid friction model with a time-marching algorithm. The optimization functionality utilizes pyswarms, a particle swarm optimization package, with adjustable search parameters and cost functions to evaluate simulation results. Optimization parameters include geometric parameters of rotor radius, shaft radius, airgap width, and airgap height; material properties of mass and moment of inertia; and initial angular velocity. An optimal initial angular velocity is found for a particular geometry, lasting 30 times longer until self-discharge versus the worst values. This work can inform the design of flywheel systems to minimize windage losses and promote the technology’s utility for longer-term energy storage. Full article

Dynamic hospital corridor environments are characterized by complex corridor environments, diverse target-scale variations, frequent occlusions, and dense small-object distribution, posing significant challenges to the accuracy and efficiency of the existing methods on resource-constrained platforms. To effectively address these challenges, a high-precision framework CSAWOA (Cross Search Adaptive Whale Optimization Algorithm)-YOLOv8 (You Only Look Once version 8) model for complex medical environments was introduced in this work. By jointly modelling high-level semantic information and low-level cues such as texture and colour, the proposed model achieved a more discriminative and informative feature representation. The T-CBS (Transformer-Convolutional Bottleneck Structure) module, capable of extracting shallow-level features and integrating global contextual information to address target occlusion issues, was also proposed. Furthermore, the integration of the BiFormer module yielded an enhanced feature discriminability, improving small-target recognition while reducing sensitivity to background noise. The classification function was modified, effectively solving the problem of class imbalance in complex corridor environments. The combination of these two concepts achieved an effective balance of diversity in detection and convergence speed, leading to improved optimization performance and greater resistance to local-optimum stagnation. Meanwhile, an improved version of the WOA was developed, termed CSAWOA, enabling automatic hyperparameter optimization for the improved YOLOv8 model. From the experimental results, improvements of 4.9%, 6.1%, and 8.3% in mAP, precision, and recall, respectively, compared to YOLOv8 were demonstrated, while also exhibiting better generalization. Overall, the proposed method provides a reliable and efficient approach for object detection in complex hospital corridors, offering a valuable foundation for future research and real-world healthcare applications. Full article

Background: The integration of internationally educated nurses (IENs) into healthcare workforces is expanding globally, yet organization-led support models remain understudied. Successful IEN integration requires ethical recruitment, structured onboarding, workforce support, and stakeholder engagement in policy discussions related to transition and retention. Objective: To examine the conceptualization, implementation, and policy implications of the Philippine Nurses Association of America Cy Pres Task Force’s national initiative to support IEN onboarding and transition into U.S. healthcare. Methods: This descriptive program evaluation utilized governance documents, program planning records, policy summit materials, aggregated survey findings, PNAA Human Rights Committee resources, and the Handbook for Filipino Nurses Immigrating to the United States to examine initiative development, implementation processes, and program outputs. A descriptive narrative synthesis was used to characterize program structure, stakeholder engagement, and policy priorities. Findings: The PNAA Cy Pres governance model was built around ethical recruiting, workforce integration, and advocacy. The work began with policy summits with nurse leaders, health care organizations, recruitment agencies, and policy experts, focusing on hiring, onboarding, legal issues, and staff retention. Stakeholder engagement, interdisciplinary collaboration, and appreciative inquiry were used to identify best practices and goals. Key outputs included the establishment of a national governance structure, implementation of national and regional policy summits, and identification of policy priorities related to ethical recruitment, onboarding, workforce integration, and governance. Conclusions: The PNAA Cy Pres initiative provides an implementation-informed approach that may help guide future workforce integration efforts. The study illustrates how ethical recruitment, workforce integration, and stakeholder engagement can help translate workforce policy principles into practice. Policy & Practice Implications: Healthcare institutions, policymakers, and professional organizations need to work together to standardize onboarding, ethical recruitment, and support mechanisms to facilitate the integration and sustainability of the IEN workforce. Full article