Search Results (243)

Transmission tower climbing is a critical specialized operation in ultra-high-voltage power maintenance and communication infrastructure servicing. However, existing lower-limb exoskeletons used for tower climbing still suffer from insufficient motion intention recognition accuracy under complex operational environments. To address this issue, this study proposes an inertial measurement unit (IMU)-based bidirectional temporal deep learning method for motion intention recognition. First, a one-dimensional convolutional neural network (1D-CNN) is employed to extract local temporal features from multi-channel IMU signals. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) is introduced to model the forward and backward temporal dependencies of motion sequences. Furthermore, a temporal attention mechanism is incorporated to emphasize discriminative features at critical movement phases, enabling the precise recognition of short-duration and transitional motions. Experimental results demonstrate that the proposed method outperforms traditional machine learning approaches and unidirectional temporal models in terms of accuracy, F1-score, and other evaluation metrics. In particular, this method demonstrates significant advantages in identifying the flexion/extension phases and transitional states. This study provides an offline method for analyzing movement intentions in lower-limb exoskeleton control for power transmission tower climbing scenarios and offers a reference for developing assistive control strategies for assisted climbing tasks in this specific context. Full article

Azimuth multi-channel (AMC) Synthetic Aperture Ladar (SAL) is a promising technique for overcoming the inherent trade-off between azimuth resolution and swath width in single-channel SAL, by replacing temporal sampling with spatial sampling. However, due to the micron-scale wavelength, AMC SAL is extremely sensitive to non-cooperative target motion: even millimeter-level radial velocities can induce significant inter-channel phase deviations, leading to severe azimuth ambiguities (false targets). To address this critical issue, a motion-compensated reconstruction framework for AMC SAL is proposed for micro-motion targets. The relationship between target radial motion and inter-channel phase deviations is theoretically derived, and a parametric strategy based on a Minimum Azimuth Ambiguity-to-Signal Ratio (MAASR) criterion is proposed to estimate the radial velocity. Simulation results demonstrate that the uncompensated processing suffers from strong ambiguities (AASR = −2.90 dB) and a notable azimuth position shift (−42 samples), whereas the proposed method suppresses false targets to the noise floor (<−40 dB) and corrects the position error. These simulation results indicate that the proposed method enables AMC SAL imaging for the non-cooperative moving target with millimeter-level radial velocity. Full article

Digital transformation is reshaping construction asset compliance, but fragmented information and weak evidence trails still constrain effective management. This systematic grey literature review (2014–2025) identifies technologies supporting asset management and compliance assurance and compares adoption maturity across the United Kingdom (UK), the United States (US), Singapore, and the Gulf Cooperation Council (GCC). Using multi-channel search strategies and the AACODS appraisal (Authority, Accuracy, Coverage, Objectivity, Date, Significance), 131 records were identified; 92 full texts reviewed; 82 eligible; and 43 sources retained. Coding identified a recurring five-technology “core digital stack”: Building Information Modelling (BIM), Digital Twins (DT), Internet of Things (IoT), Artificial Intelligence/Machine Learning (AI/ML), and Blockchain (BC). Within the retained corpus, BIM and AI/ML were the most frequently referenced technologies, whereas BC was referenced more selectively and discussed mainly for tamper-evident traceability. DT and IoT were typically discussed alongside BIM, while IoT also frequently co-occurred with AI/ML in analytics-led compliance workflows. A (Region × Technology) maturity matrix suggests higher, policy-led maturity where mandates and audit-ready information align with national frameworks (UK, Singapore), and more uneven, project-led adoption in decentralised contexts (US, GCC). Overall, the findings emphasise that effective compliance relies on integrated, evidence-focused digital stacks supported by standardised information governance rather than isolated tools. Full article

Low-dose computed tomography (LDCT) can effectively reduce ionizing radiation; however, the associated image noise and artifacts can severely compromise the accuracy of clinical diagnosis. To address the challenge of balancing noise suppression and detail preservation in LDCT images, this study proposes a deep learning (DL)-based image denoising method termed Progressive Fusion Distillation Network (PFDN). Building upon the Information Multi-distillation Network (IMDN), the proposed method incorporates a pixel attention (PA) mechanism and a progressive fusion strategy, and further designs a Pixel Parallel Extraction Block (PPEB) together with a Progressive Fusion Distillation Block (PFDB) to fully exploit multi-scale and multi-channel features, thereby optimizing the image denoising network through efficient feature separation and re-fusion. In addition, by explicitly leveraging the noise characteristics specific to LDCT images, the method establishes an end-to-end training framework suitable for medical imaging. Experimental results demonstrate that PFDN not only effectively reduces image noise and artifacts, but also enhances overall image quality while preserving diagnostically relevant image structures under the adopted evaluation setting. Full article

Background/Objectives: Nurses’ turnover intentions are strong predictors of actual turnover, which increases costs, reduces care quality, and destabilies the workforce. This study aimed to identify the key predictors of nurses’ turnover intentions using advanced machine learning methods and to explore how demographic, well-being, and work environment factors contribute to these intentions. Methods: Cross-sectional data were collected from 2459 nurses employed across various healthcare institutions. We used multichannel invitation and snowball sampling. An artificial neural network regression model was applied, combined with iterative feature selection and SHAP analysis, to identify the most important predictors of turnover intentions and to examine nonlinear and context-dependent relationships among variables. Results: Seven predictors explained 49.8% of the variance in turnover intentions, outperforming traditional linear models. Age was the strongest predictor, with younger nurses demonstrating a substantially higher likelihood of intending to leave; this association was nonlinear, with intentions decreasing more sharply at older ages. Job satisfaction and burnout were also strong predictors, particularly among younger nurses. Four work environment factors further contributed to turnover intentions: managerial support functioned as a protective factor, interpersonal conflict increased intentions to leave, limited professional development opportunities were strongly associated with higher turnover intentions, and role conflict showed heterogeneous effects. Conclusions: Machine learning approaches enhance understanding of complex workforce dynamics and enable more precise identification of high-risk groups. The findings support age-sensitive retention strategies, proactive monitoring of nurse well-being, and organizational interventions to strengthen managerial support and professional development, ensuring workforce stability and sustainable healthcare service delivery. Full article

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation. Full article

In structured orchard environments, harvesting robots operate where rigid bodies (e.g., trunks, poles, and wires) coexist with flexible foliage. Strict avoidance of all obstacles significantly compromises operational efficiency. To address this, this study proposes an end-to-end autonomous harvesting framework characterized by an “avoid-rigid, push-through-soft” strategy. This framework explicitly propagates uncertainties from sensor data and reconstruction processes into the planning and policy phases. First, a multi-task perception network acquires 2D semantic masks of fruits and branches. Class probabilities and instance IDs are back-projected onto a 3D Gaussian Splatting (3DGS) representation to construct a decision-oriented, semantically enhanced 3D scene model. The policy network accepts multi-channel 3DGS rendered observations and proprioceptive states as inputs, outputting a continuous preference vector over eight predefined motion primitives. This approach unifies path planning and action decision-making within a single closed loop. Additionally, a dynamic action shielding module was designed to perform look-ahead collision risk assessments on candidate discrete actions. By employing an action mask to block actions potentially colliding with rigid obstacles, high-risk behaviors are effectively suppressed during both training and execution, thereby enhancing the robustness and reliability of robotic manipulation. The proposed method was validated in both simulation and real-world scenarios. In complex orchard scenarios, the proposed AE-TD3 algorithm achieves a harvesting success rate of 77.1%, outperforming existing RRT (53.3%), DQN (60.9%), and TD3 (63.8%) methods. Furthermore, the method demonstrates superior safety and real-time performance, with a collision rate reduced to 16.2% and an average operation time of only 12.4 s. Results indicate that the framework effectively supports efficient harvesting operations while ensuring safety. Full article

Multichannel synthetic aperture radar (SAR) achieves high-resolution imaging while significantly enhancing the spatial freedom of the SAR system. As SAR hardware performance continues to improve, observed scenes often transition from simple to complex scenes. The increasingly complex clutter components introduced by complex scenes make clutter suppression increasingly challenging. Traditional multichannel clutter suppression algorithms usually assume that the observed scene, as a whole, satisfies the independent and identical distribution (IID) condition. However, in actual complex scenes, this assumption often proves difficult to uphold. Therefore, how to achieve more effective clutter suppression for complex scenes is a challenge for SAR. In this paper, we propose a knowledge-aided (KA) multichannel SAR clutter suppression algorithm for complex scenes. First, the single-channel image is processed at the superpixel level and a superpixel fusion algorithm is proposed, which adaptively realizes the refined classification of the complex scene. Then, a two-step clutter suppression processing method with multi-strategy clutter suppression preprocessing and sparse Bayesian residual clutter suppression is proposed. This method not only provides effective classification information for complex scenes but also achieves more efficient clutter suppression in complex scenes based on this classification information. Finally, the clutter suppression performance of this algorithm in complex scenes was validated through measured data. Full article

The continuous growth of traffic in metro networks is increasing the need for cost-effective, scalable, and power-efficient optical solutions. Filterless optical networks (FONs) have emerged as a promising architecture for metro-aggregation and metro-access domains, thanks to their low complexity and reliance on passive optical components. However, their inherent broadcast nature introduces key challenges, including spectrum waste, limited power equalization, and significant noise accumulation, particularly when coherent pluggable transceivers are employed. This work provides a detailed assessment of FON performance using state-of-the-art multi-source agreement (MSA)-compliant coherent modules, evaluating both point-to-point (P2P) and digital subcarrier multiplexing (DSCM)-based point-to-multipoint (P2MP) architectures. A novel optical amplifier (OA) optimization algorithm is proposed to balance expressed and added signal power in FON, accounting for optical power saturation effects and optical performance degradation due to limited power at the receiver input. The analysis highlights the substantial impact of transmitter out-of-band (OB) noise in FONs and its detrimental accumulation during multi-channel colorless aggregation, which can limit network capacity. In scenarios with lower capacity requirements, P2MP architectures demonstrate superior performance, benefiting from reduced insertion loss and lower OB noise accumulation while offering enhanced scalability compared with P2P solutions. Overall, the study highlights that FONs combined with coherent pluggables can support cost-efficient and scalable metro solutions, provided that OB noise, power imbalance, and amplifier operation are properly addressed through optimized design strategies. Full article

The South African Government prioritises Small, Micro-, and Medium Enterprises (SMMEs) as catalysts for employment creation, in alignment with Sustainable Development Goal 8 (SDG 8), Decent Work and Economic Growth, which advocates for sustained, inclusive, and sustainable economic growth. However, the extent to which agro-processing SMMEs translate this policy ambition into measurable socio-economic gains remains contested due to persistent structural, financial, and operational constraints. This study develops a comprehensive, data-driven business support framework tailored to agro-processing SMMEs in the Free State province of South Africa. Employing a mixed-methods approach, survey data from 88 agro-processing SMMEs were analysed across 18 business performance dimensions. Average agreement scores and performance gaps were utilised to diagnose strengths and vulnerabilities within the sector. While overall performance was relatively strong (average agreement score: 86.7%), a critical weakness emerged in operational cost management (76.1%), revealing a 14.2% gap relative to the highest-performing dimension, equipment selection (90.3%). Based on these empirical insights, the study proposes a three-tiered business support architecture: (i) maintaining and leveraging high-performing dimensions (≥85% agreement), (ii) targeted enhancement for moderate-performing areas (80–84.9%), and (iii) crisis intervention for critical weaknesses (<80%). The framework integrates cross-cutting support services, including financing, regulatory guidance, and technology access, delivered through a phased implementation strategy comprising crisis intervention, system establishment, and optimisation and scaling. A multi-channel delivery mechanism, combining a hub-and-spoke model, mobile support units, and a digital platform, ensures provincial accessibility. By translating performance diagnostics into differentiated policy action, the framework promotes efficient resource allocation, supports both high-potential and vulnerable agro-processing SMMEs, and embeds a robust monitoring and evaluation system to track key performance indicators. The study contributes to the SMME development literature by demonstrating how structured, tiered, and context-specific support models can strengthen resilience, competitiveness, and sustainable agro-industrial growth in developing-country settings. Full article

The high penetration of photovoltaic and wind power introduces strong non-stationarity and multi-scale fluctuations into power system load profiles, challenging the accuracy of short-term load forecasting (STLF). To address this, we propose a hybrid forecasting framework, IMVMD-XLSTM, which synergistically integrates an optimized multivariate decomposition with an advanced neural network. First, to address the critical issue that MVMD performance is highly sensitive to its parameter settings, which impacts decomposition quality, a multi-strategy Improved Fruit Fly Optimization Algorithm (IFOA) is developed to task-oriented adaptively tune the key parameters of MVMD, forming an Improved MVMD (IMVMD). This optimization aims to ensure decomposition stability and maximize the relevance for the subsequent forecasting task. Second, to fully leverage the characteristics of the frequency-aligned, multi-channel sub-sequences generated by IMVMD, an Extended LSTM (XLSTM) network is designed. Its serially arranged BisLSTM and mLSTM units are specifically tailored to capture the bidirectional long-term dependencies within each stable sub-sequence and the complex high-dimensional interactions across the aligned sub-sequences, respectively. Evaluated on 15 min resolution data from the Austrian grid, the proposed IMVMD-XLSTM framework achieves a day-ahead forecasting Mean Absolute Percentage Error (MAPE) of 2.45% (±1.41%). This study provides a verifiable and effective solution that couples data-adaptive signal processing with a purpose-built neural architecture to enhance forecasting reliability in renewable-rich power systems. Full article

This study evaluates the dynamics of the human pupillary reflex in response to a stepped blue light stimulus (465 nm) in young adults residing at high altitude (3400 m above sea level). High-resolution video sequences of three participants were analyzed using four classical image segmentation techniques: K-Means, Otsu, fixed binary threshold, and multi-channel RGB threshold. Rather than proposing new algorithms, this work evaluates the technical feasibility and stability of computationally lightweight segmentation approaches under controlled lighting conditions and with low-cost hardware constraints. Among the methods evaluated, fixed binary thresholding showed stable temporal behavior and minimal computational complexity within the experimental setup. The results show a consistent contraction–plateau–recovery pattern across all participants, with representative contraction, stabilization, and recovery times of 1.89 s, 0.41 s, and 2.33 s, respectively. Although limited by the small sample size, these findings support the feasibility of implementing simplified segmentation strategies for pupillometry in resource-limited settings. Full article

A Hybrid-Frequency Sampling Tactile Sensing System Based on a Flexible Piezoresistive Sensor Array is presented for reliable and real-time tactile perception under dynamic loading conditions. While recent studies have developed multi-channel tactile arrays, most systems remain limited by time-dependent drift in channel responses, inconsistent dynamic behavior, or insufficient temporal resolution under simultaneous loading. In this work, a system-level design integrating a flexible piezoresistive sensor array with a real-time data acquisition module is developed, incorporating a hybrid-frequency sampling strategy to reduce system complexity while preserving reliable dynamic response in key sensing channels. Register-Transfer Level (RTL) simulation verified that the hardware scheduler rigorously executed the deterministic scanning logic, demonstrating a strict one-to-one correspondence with the physical hardware signals. The array consists of 34 piezoresistive sensing nodes embedded in an elastomeric substrate. Under the implemented hybrid-frequency sampling scheme, the system achieves an overall effective acquisition bandwidth of approximately 36.9 kHz, while maintaining a repeatability better than 4.9% and robust mechanical durability under cyclic bending deformation. Dynamic loading validation was performed using a self-developed pressure comparison platform for measuring the normal contact force applied on the tactile surface, serving as ground-truth data to verify that the voltages acquired by the proposed system accurately correspond to the actual applied force. Quantitative analysis shows a strong linear correlation (R² ≈ 0.98) between the e-skin outputs and the reference forces. The recorded responses exhibit clear intensity-dependent trends and good temporal correspondence among sensing nodes, successfully distinguishing tactile stimuli such as gentle tapping, moderate pressing, and firm contact. The system also captures dynamic tactile responses during finger stroking, showing characteristic multi-unit activation patterns under spatiotemporally varying contact conditions. Compared with previously reported tactile systems typically operating below 100 Hz, the proposed design achieves an approximately 10× enhancement in effective sampling capability while significantly reducing system complexity through hybrid-frequency sampling, thereby supporting reliable dynamic tactile sensing in multi-unit arrays. These results demonstrate that the proposed system provides a practical and scalable hardware platform for dynamic tactile sensing in robotics, human–machine interaction, and wearable tactile systems. Full article

In the context of the transformation of welfare systems, and following the impact of the COVID-19 pandemic, communication and social marketing have taken on a strategic role in community social services. This study aims to analyse communication and social marketing campaigns developed from 2020 to the present, in order to identify the social issues addressed, the communication objectives and the strategies employed, as well as their link to Community Social Services. A qualitative, exploratory and descriptive design is adopted based on analysing the content of a corpus of 60 campaigns selected through intentional sampling from international public sources. The results show a clear intensification of campaigns in the post-COVID-19 period, with a predominance of themes such as gender violence, mental health, unwanted loneliness and social exclusion. The communication strategies are characterised by the use of emotional narratives, storytelling, audiovisual formats and multi-channel dissemination, combining awareness-raising, prevention and guidance towards social resources. It is concluded that community social marketing is consolidating its position as a transformative tool that reinforces access to rights, social cohesion and the role of Community Social Services as active agents of social change. Full article

High-resolution marine gravity fields are critical for interpreting seafloor structure, investigating marine geodynamics, and enabling gravity-aided navigation. However, sparse shipborne observations, heterogeneous multi-source geodetic datasets, and the inability of conventional methods to handle nonlinear inversion limit accurate gravity recovery. To overcome these limitations, we propose a spectral physics-informed constraint deep-learning framework based on a multi-channel Swin Transformer to reconstruct high-resolution marine gravity anomaly fields. The model ingests multi-source geodetic inputs organized as 64 × 64 grid patches centered near each computation point and fuses them to predict the target gravity anomaly. We adopt a remove–compute–restore (RCR) strategy that isolates residual gravity signals, which improves numerical stability and accelerates training. Inputs include satellite-altimetry-derived vertical gravity gradients, vertical deflections, mean sea surface height, and topography; the model is trained on over 430,000 shipborne gravity samples from the South China Sea (0–30° N, 105–125° E). To enforce physical consistency, we embed a spectral-domain physics constraint derived from potential-field theory into the loss function; this constraint helps recover short-wavelength gravity signals. We also introduce an adaptive multi-domain multi-scale feature fusion module (AMAMFF) to improve the integration of heterogeneous inputs, and we demonstrate its benefits in experiments across complex terrain. Validation against independent shipborne gravity checkpoints yields an RMS error of 3.09 mGal, indicating a substantial performance advantage over existing deep-learning approaches and conventional gravity-field models. Full article