Search Results (673)

This research introduces a unique optimization framework centered on the Geese V-Formation Algorithm to enhance the technical planning of distributed energy resources in renewable microgrid-oriented radial distribution systems. The proposed methodology addresses the optimal placement and sizing of photovoltaic panels, wind turbines, battery energy storage systems, and capacitor banks to provide comprehensive voltage support, minimize active power losses, and refine overall grid functionality. Drawing inspiration from the aerodynamic efficiency of migratory geese, the Geese V-Formation Algorithm integrates dynamic leader-follower coordination, adaptive role rotation, and cooperative information exchange mechanisms. These features allow the algorithm to effectively balance global exploration and local exploitation, making it uniquely suited to address the complex, nonlinear, and multi-objective nature of modern microgrid design. The effectiveness of this approach was evaluated through rigorous simulations on the IEEE-33 and IEEE-69 bus distribution systems utilizing the Python programming language. The empirical results indicate that the Geese V-Formation Algorithm achieves substantial power loss reductions, reaching 91.62% and 92.45%, respectively, when integrating solar and wind resources with energy storage and reactive power compensation. Furthermore, the optimized configurations significantly improved bus voltage profiles and enhanced substation power factors, confirming the technical effectiveness of the framework under the considered benchmark constraints. By providing a technical decision-support approach for engineers and utility planners, this framework facilitates the deployment of reliable, decentralized renewable energy systems that align with global energy transition objectives and promote sustainable infrastructure development. Full article

Capacitive electrocardiography (cECG) enables non-contact heart rate monitoring through clothing, but motion artifacts remain a critical limitation for practical applications. We present a novel motion artifact removal method using non-contact floating electrodes as noise references combined with multi-reference Normalized Least Mean Squares (NLMS) adaptive filtering. The floating electrodes, positioned without skin contact, couple primarily to ambient 50 Hz mains interference, which becomes amplitude-modulated during motion due to changes in electrode–body capacitance. Six reference signals are derived from this noise electrode: band-pass-filtered signal and its derivative (capturing baseline-type artifacts), envelope and its derivative (capturing amplitude modulation patterns), and envelope asymmetry and its derivative (capturing non-linear electrode response during motion). The NLMS algorithm adaptively combines these references to estimate and remove motion artifacts while preserving QRS morphology through low-pass filtering of the correction signal. A hysteresis-based motion detector with minimum duration constraints enables selective application of artifact removal only during motion periods, leaving rest-period ECG unmodified. We present this as a proof-of-concept validation of a novel reference-electrode architecture for motion artifact suppression in non-contact ECG. The method was validated on 7 subjects across 24 recording sessions using two electrode configurations in two environments with different electromagnetic interference levels. Controlled axial rotation motion was induced at three frequencies using a custom apparatus with IMU-based gamification for protocol adherence. Performance was evaluated using R-peak detection F1 score against gel surface-contact electrodes ground truth and RMS reduction in motion regions. Results demonstrate consistent improvement in R-peak detection accuracy during motion periods with substantial artifact energy reduction. The proposed method is designed to address motion artifacts regardless of their physical source, though the present validation focused on subject-induced motion. Full article

Traditional visual SLAM systems often suffer from localization drift in dynamic environments due to interference from moving objects. Although semantic segmentation and depth-based masking methods have improved performance, they may still suffer from boundary under-segmentation and missed detections due to truncation of dynamic objects. To address these challenges, we propose a cascaded framework, DMSG-SLAM, a cascaded visual SLAM system that fuses Depth-Mask, Semantic information and Geometry constraints for dynamic environments. A lightweight object detection network, combined with depth consistency, is first employed to generate instance-like masks for preliminary dynamic feature removal. Then, a rotation-aware local epipolar geometric filtering mechanism is introduced to suppress residual features near object boundaries and mitigate perceptual blind spots caused by occlusion or truncation. Within potential dynamic regions, the epipolar threshold is adaptively switched according to the estimated inter-frame rotation to provide a more conservative filtering effect under challenging motion conditions. In addition, a TSDF-based dense volumetric map is incorporated to reconstruct more consistent surfaces. Experiments on highly dynamic sequences from the TUM RGB-D dataset indicate that DMSG-SLAM achieves competitive accuracy in dynamic environments, with localization performance improving by up to 90% compared to ORB-SLAM2. Full article

Flapping-wing flight offers a promising solution for aerial mobility in low-density environments such as the Martian atmosphere, where conventional rotorcraft faces significant performance constraints. However, the coupled aerodynamic and structural mechanisms governing lift generation at low Reynolds numbers remain insufficiently understood. This study investigates the aeroelastic and unsteady aerodynamic behaviour of a bio-inspired flapping wing using an integrated experimental–numerical framework. High-speed imaging is employed to extract representative wing kinematics, including flapping frequency, stroke amplitude, and rotational motion. A geometrically scaled wing model is developed based on Reynolds number similitude and analysed using finite element methods to characterise its dynamic response. Aeroelastic behaviour is evaluated through modal transient simulations, while aerodynamic performance is assessed using both vortex-lattice modelling and computational fluid dynamics. The results show strong coupling between bending and torsional modes, with the structural response highly dependent on excitation frequency relative to the natural modes. Near-resonant conditions lead to amplified deformation and distinct phase relationships, while aerodynamic simulations reveal vortex-dominated lift generation. These findings provide a physics-based framework for the design and analysis of flapping-wing systems operating in low-Reynolds-number and low-density flight regimes. Full article

Conventional settlement monitoring techniques are inadequate for seawall construction environments due to severe physical impacts, the absence of terrestrial communication networks, and highly dynamic disturbances. This research proposes a multi-source fusion settlement monitoring system designed specifically for the construction phase to overcome these constraints. An integrated inclinometer–magnetoresistive sensing unit is the central component of this system. The unit achieves physical isolation from the severe impact loads of rock backfilling, guarantees protection in high-salinity and high-humidity environments, and accommodates the large deformations typical of soft foundations by utilizing a structural design that includes a rigid channel steel sheath, anti-corrosion sealing, and flexible joints. In terms of computation, a cascaded attitude fusion framework is developed that combines a Multiplicative Extended Kalman Filter (MEKF) with Quaternion Estimator (QUEST) initialization. High-precision displacement inversion via quaternion rotation is made possible by the introduction of an adaptive mechanism based on the Mahalanobis distance that precisely detects and suppresses transient acceleration disturbances induced by construction machinery and waves. Additionally, data transmission issues in remote offshore areas are resolved by combining solar power and BeiDou short-message communication technologies. This adaptive technique minimizes attitude estimate errors in dynamic situations by approximately 84.56%, as demonstrated by experimental and field validation. The system was deployed as a 165 m array comprising 49 sensing units and monitored continuously for 458 days, achieving a normalized RMSE of 9.44–11.02% compared to reference settlement tubes and capturing a maximum settlement of 1.7 m in the core high-fill section. These results confirm the system’s high monitoring accuracy and resilience in harsh construction conditions. Full article

The unprecedented precision now being achieved in the measurement of the Pontecorvo–Maki–Nakagawa–Sakata (PMNS) lepton mixing matrix opens a new window onto the underlying structure of the neutrino mass matrix and the possibly associated flavor symmetries. In this work, we investigate the constraints imposed on the unitary matrix $U_{\nu }$ that diagonalizes the neutrino mass matrix, under the hypothesis that the charged-lepton mixing matrix $U_l$ consists of a single two-by-two rotation in one of the three sectors: (1,2), (1,3), or (2,3). For this analysis, we considered the latest global fit, which incorporates the precision measurement of ${\theta }_{12}$ from the JUNO experiment. For each scenario, we also derive analytical expressions for the entries of $U_{\nu }$ in terms of the measured PMNS parameters to obtain compact sum-rule-like formulae. Full article

Accurate and efficient oriented detection is critical for remote sensing images, yet remains challenging due to multi-scale distribution, arbitrary orientations, and stringent computational constraints of onboard platforms. To mitigate these challenges, we propose Partial-Ghost Shuffle Convolution and Gated Position-Sensitive Attention Wavelet Rotation Network (PGWave-RotNet), a lightweight wavelet-guided rotation detector that explicitly enhances multi-scale and arbitrarily oriented features while maintaining high efficiency. To reduce feature redundancy while preserving directional diversity, we design a Partial-Ghost Shuffle Convolution (PGSConv) module that integrates partial convolution with ghost shuffle. Next, to adaptively refine multi-scale and arbitrarily oriented contexts, we introduce a Gated Position-Sensitive Attention (GPSA) module with a learnable gating mechanism. To suppress aliasing and sharpen edges during upsampling, we propose a Directional-Biased Wavelet Transform Upsampling (DBWTU) module based on high-frequency wavelet reconstruction. Additionally, we develop a Weighted Cosine Angular Loss (WCAL) to improve orientation precision for square-like targets. Experiments on DOTAv1 and DIOR-R achieve 82.27% and 83.82% mAP50, outperforming existing methods. These innovations collectively enable efficient and accurate oriented detection in remote sensing. Full article

Facing the harsh offshore environment characterized by severe space constraints and continuous platform motion, this study develops an optimized rotating packed bed (RPB) for compact and robust triethylene glycol dehydration. Through integrated experimental and computational investigation, the concentric-ring rotor was identified as superior among four configurations, consistently achieving dehydration equilibrium above 80% under lean TEG conditions. CFD analysis revealed its fundamental mechanism: synergistic matching between the centrifugal force field and annular flow paths yields the most uniform liquid distribution. This enabled the establishment of a strong predictive correlation (R² = 0.935) between simulated liquid uniformity and experimental dehydration performance. Guided by flow field diagnostics, targeted structural optimizations increased dehydration equilibrium from 86.1% to 92.25% while reducing system pressure drop by 73%. Parametric studies defined an optimal operating envelope at a gas-to-liquid ratio of 60:1 and system pressure of 2 MPa, achieving peak efficiency of 96.42% with robust performance across 50–150% load variations. This work demonstrates a simulation-guided pathway for intensifying separation processes, providing a validated framework for designing marine-adapted dehydration technology. Full article

Spatial and temporal heterogeneity in pasture quantity and nutritive value remains a major constraint to efficient nutritional management in grazing-ruminant systems. This critical narrative review was based on targeted searches of peer-reviewed literature on pasture heterogeneity, forage quality assessment, grazing management, animal monitoring, and data integration in grazing-ruminant systems, with emphasis on both recent studies and conceptually foundational work. Precision technologies have emerged as complementary tools that can improve the characterization of pasture resources, animal responses, and grazing dynamics, but their value depends on whether they support nutritionally relevant decisions under field conditions. This review examines current precision approaches, such as portable near-infrared spectroscopy, proximal and remote sensing, geospatial tools, animal-mounted sensors, and grazing-control technologies, and their capacity to improve decisions related to supplementation, stocking rate, grazing rotation, and pasture allocation. Across technologies, performance and applicability vary substantially with observational scale, calibration requirements, and validation context. This review also highlights persistent constraints, including calibration robustness, transferability across systems, field validation, interoperability, economic feasibility, and barriers to routine adoption. Precision tools can improve pasture-based nutritional management, but their practical contribution depends on how effectively they are validated, integrated, and translated into decision-support logic under commercial grazing conditions. Full article

The pavement surface temperatures in Iraq are remarkably high, causing the asphalt to deteriorate quickly, shortening its service life. While a large amount of corn husk, an agricultural waste, is available for use as an asphalt modifier, researchers have not yet fully investigated this option. In this study, the use of corn husk fiber powder (CHFP) as a long-term modifier for asphalt binders and mixtures that are exposed to high-temperature conditions is evaluated. CHFP was mixed into a 40–50 penetration grade asphalt binder at concentrations ranging from 0.0% to 0.6% by weight. Performance was assessed using laboratory tests such as penetration, softening point, rotating viscosity, dynamic shear rheometer (DSR), aging (RTFOT and PAV), and wheel tracking. The findings revealed that CHFP greatly lowers penetration while increasing the softening point, indicating increased stiffness and high-temperature stability. Rheological research showed an increase in the rutting parameter (G*/sinδ) and viscosity, as well as reduced temperature susceptibility. At the mixed level, CHFP reduced rut depth while improving dynamic stability, indicating increased resistance to permanent deformation. The best performance was obtained at 0.3% CHFP, after which, improvements decreased due to probable dispersion constraints. The performance improvement is related to the creation of a reinforcing fiber network and the absorption of light asphalt components. Overall, CHFP is a promising, environmentally friendly and cost-effective addition for increasing asphalt pavement performance and promoting sustainable waste management. Full article

Human pose estimation in orbit is critical for astronaut health monitoring, task assistance, and intelligent human–robot interaction aboard space stations. However, in microgravity, human poses exhibit arbitrary orientations and are often affected by severe occlusion and complex background interference, while the scarcity of annotated in-orbit data makes it difficult to directly transfer models trained on ground-based datasets. Existing semi-supervised methods also lack explicit constraints from human structural topology and pose-related physical priors, which often leads to unreasonable pseudo-labels and limits performance gains. To address these issues, we propose a physics-inspired semi-supervised pose estimation framework for microgravity scenarios. Specifically, a Canonical Orientation Constraint is introduced to alleviate orientation ambiguity; a Structure-aware Pseudo-Label Refinement module is designed to improve pseudo-label quality; and an Uncertainty-guided Rotational Consistency Framework is proposed to adaptively weight consistency learning under multi-view rotation augmentation. Within a Mean Teacher architecture, the proposed method jointly optimizes the supervised loss, orientation constraint, pseudo-label refinement, and rotational consistency objectives. Experiments on the Astro-Pose dataset show that the proposed method consistently outperforms both fully supervised and semi-supervised baselines under various extreme poses and occlusion conditions, improving AP from 47.6 to 55.6 and AR from 52.4 to 60.1, demonstrating its potential for space-station visual monitoring. Full article

Rolling bearing fault diagnosis is essential for ensuring the safety and reliability of rotating machinery. Although deep learning-based methods have achieved promising performance, they usually require sufficient labeled data, which is difficult to obtain in practical industrial scenarios where fault samples are scarce and data sharing across sites is restricted by privacy and confidentiality constraints. Federated learning enables collaborative model training without transmitting raw data, but existing federated fault diagnosis methods often degrade under few-shot conditions. Moreover, current federated meta-learning approaches mainly focus on model-level adaptation and lack explicit class-level representation alignment, leading to prototype drift across heterogeneous operating conditions. To address these challenges, this paper proposes ProtoFed, a prototype-enhanced federated meta-learning framework for few-shot rolling bearing fault diagnosis. ProtoFed converts raw vibration signals into time–frequency representations using continuous wavelet transform and performs local episodic learning with prototypical networks. A Global Prototype Calibration mechanism aggregates local class prototypes into stable global prototypes with exponential moving average smoothing, while a Prototype-Distance Aware Aggregation strategy adaptively adjusts client aggregation weights according to local–global prototype divergence. Experiments on the CWRU and Paderborn University bearing datasets under non-IID 5-shot and 10-shot settings show that ProtoFed consistently outperforms standard federated learning, prototype-based federated learning, and federated meta-learning baselines. Under the 5-shot setting, ProtoFed achieves 95.63% and 91.35% accuracy on CWRU and PU, respectively, approaching centralized few-shot upper-bound performance while preserving the federated training paradigm. Full article

The horizontal bearing mechanism of pile–soil composite foundations adjacent to retaining walls is significantly affected by asymmetric lateral constraints caused by retaining wall movement, a scenario that remains inadequately explored in conventional design. This study employs a validated three-dimensional finite element model to investigate the response of such foundations to rotational displacement of a nearby wall. A comprehensive parametric analysis quantifies the influence of pile configuration, cushion properties, soil modulus, and loading conditions. The results demonstrate that rotational displacement (RB mode) induces a highly non-uniform load distribution within the pile group. The middle-front row piles emerge as critical load-bearing components, experiencing significant load amplification (load-transfer coefficients η_p up to 2.3). Key parameters, including pile length and cushion stiffness, selectively regulate system stiffness or optimize load sharing. Increasing the pile–wall distance is identified as an effective measure to reduce load concentration on front-row piles. The findings provide quantitative insights and practical guidance for the performance-based design of composite foundations under asymmetric constraints. Full article

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment. Full article

Pose estimation of non-cooperative spacecraft remains challenging under feature degradation. Motion blur, self-occlusion, and weak texture can cause structural line disappearance, correspondence ambiguity, and localization drift, which destabilize conventional point- and line-based analytic pose estimation pipelines relying on discrete feature detection and post-hoc 2-D-to-3-D association. To address these issues, we propose a two-stage framework for line-based 6-DoF pose estimation built upon a structure-bound multi-channel spatial distance mapping (SDM), where each SDM channel is uniquely associated with one predefined 3-D model line. By explicitly binding each SDM channel to a predefined 3-D model line, the proposed representation encodes 2-D-to-3-D line correspondence directly in the network output, thereby avoiding unstable line matching after prediction and providing solver-consistent geometric constraints for Perspective-n-Line (PnL) estimation. To reduce localization blur around the SDM zero-level set, a cross-scale self-attention (CSSA) mechanism is introduced to couple high-resolution localization features with low-resolution structural context through window-level cross-scale attention. Based on the predicted SDMs, explicit 2-D structural lines are recovered through weighted robust fitting in narrow bands around the zero-level sets, enabling the completion of partially or fully occluded lines and yielding solver-ready observations for PnL pose recovery. Experiments on a close-range non-cooperative spacecraft dataset with simulated observation distances of 10–30 m show that SDMNet achieves translation/rotation errors of 0.8%/0.0372 rad, 0.91%/0.0394 rad, and 1.38%/0.0579 rad under original, motion-blur, and occlusion conditions, respectively. These results indicate that the proposed framework can robustly recover correspondence-aware structural observations from degraded images and improve the accuracy and stability of spacecraft pose estimation. Full article