Search Results (3,741)

This study explores the use of construction robots with collaborative path planning and coordination in complex building construction tasks. Current construction processes involving robots are often fragmented due to their single-task focus, with limited research focused on employing multiple construction robots to collaboratively perform tasks. To address such a challenge, this research proposes an improved A* algorithm for global path planning and obstacle avoidance, combined with the development of a BIM-based grid map of the construction site. The leader–follower method is utilized to guide the robot group in maintaining an optimal formation, ensuring smooth collaboration during construction. The methodology includes formalizing building construction site environments into BIM-based grid maps, path planning, and obstacle avoidance, which allows robot groups to autonomously navigate and complete specific tasks such as concrete, masonry, and decoration construction. The results of this study show that the proposed approach achieves significant reductions in pathlength and operational time of approximately 9% and 10%, respectively, while maintaining safety and efficiency compared with traditional manual methods. This research demonstrates the potential of collaborative construction robot groups to enhance productivity, reduce labor costs, and provide a scalable solution for the intelligent transformation of the construction industry; extends the classical A* algorithm by incorporating obstacle density into the heuristic function; and proposes a new node simplification strategy, contributing to the literature on robot motion planning in semi-structured environments. Full article

Event cameras provide microsecond temporal resolution, high dynamic range, and low latency by asynchronously capturing per-pixel luminance changes, thereby introducing a novel sensing paradigm. These advantages render them well-suited for high-speed applications such as autonomous vehicles and dynamic environments. Nevertheless, the sparsity of event data and the absence of dense annotations are significant obstacles to supervised learning for motion segmentation from event streams. Domain adaptation is also challenging due to the considerable domain shift in intensity images. To address these challenges, we propose a two-phase cross-modality adaptation framework that translates motion segmentation knowledge from labeled RGB-flow data to unlabeled event streams. A dual-branch encoder extracts modality-specific motion and appearance features from RGB and optical flow in the source domain. Using reconstruction networks, event voxel grids are converted into pseudo-image and pseudo-flow modalities in the target domain. These modalities are subsequently re-encoded using frozen RGB-trained encoders. Multi-level consistency losses are implemented on features, predictions, and outputs to enforce domain alignment. Our design enables the model to acquire domain-invariant, semantically rich features through the use of shallow architectures, thereby reducing training costs and facilitating real-time inference with a lightweight prediction path. The proposed architecture, alongside the utilized hybrid loss function, effectively bridges the domain and modality gap. We evaluate our method on two challenging benchmarks: EVIMO2, which incorporates real-world dynamics, high-speed motion, illumination variation, and multiple independently moving objects; and MOD++, which features complex object dynamics, collisions, and dense 1kHz supervision in synthetic scenes. The proposed UDA framework achieves 83.1% and 79.4% accuracy on EVIMO2 and MOD++, respectively, outperforming existing state-of-the-art approaches, such as EV-Transfer and SHOT, by up to 3.6%. Additionally, it is lighter and faster and also delivers enhanced mIoU and F1 Score. Full article

Accurate prediction of ship motion attitude remains a significant challenge due to the inherent non-stationarity and strong stochasticity of marine environmental conditions. To address this issue, this study proposes FMD-IBKA-BTGN, a hybrid model combining Feature Mode Decomposition (FMD), Improved Black-winged Kite Algorithm (IBKA), and a Bidirectional Temporal Convolutional Network with Gated Recurrent Unit (BTGN). First, FMD decomposes motion signals into intrinsic modes. Subsequently, IBKA—enhanced with chaotic mapping and Lévy flights—optimizes BTGN hyperparameters for global search efficiency. Finally, predictions from all components are ensembled for final output. Experiments on a 240 m vessel in Sea State 4 show our model outperforms six models, reducing MAPE by 20.38%, RMSE by 7.4%, MAE by 4.2%, and MSE by 0.97% versus LSTM. The model enhances both prediction accuracy and generalization. Full article

Recent advances in 3D Gaussian Splatting (3DGS) have achieved remarkable performance in scene reconstruction and novel view synthesis on benchmark datasets. However, real-world images are frequently affected by degradations such as camera shake, object motion, and lens defocus, which not only compromise image quality but also severely hinder the accuracy of 3D reconstruction—particularly in fine details. While existing deblurring approaches have made progress, most are limited to addressing a single type of blur, rendering them inadequate for complex scenarios involving multiple blur sources and resolution degradation. To address these challenges, we propose Gaussian Splatting with Multi-Scale Deblurring and Resolution Enhancement (GS-MSDR), a novel framework that seamlessly integrates multi-scale deblurring and resolution enhancement. At its core, our Multi-scale Adaptive Attention Network (MAAN) fuses multi-scale features to enhance image information, while the Multi-modal Context Adapter (MCA) and adaptive spatial pooling modules further refine feature representation, facilitating the recovery of fine details in degraded regions. Additionally, our Hierarchical Progressive Kernel Optimization (HPKO) method mitigates ambiguity and ensures precise detail reconstruction through layer-wise optimization. Extensive experiments demonstrate that GS-MSDR consistently outperforms state-of-the-art methods under diverse degraded scenarios, achieving superior deblurring, accurate 3D reconstruction, and efficient rendering within the 3DGS framework. Full article

This work reviews self-guided (SG) molecular simulation methods and illustrates the characteristics and applications of these methods through several example simulations. The main characteristic of SG methods is that past motion in simulations is used to guide future motion. Two forms of these methods are self-guided molecular dynamics (SGMD) and self-guided Langevin dynamics (SGLD). SG methods achieve an enhanced conformational search through promoting low-frequency motion. A simple local averaging scheme is used to extract low-frequency properties from past simulation trajectories to promote low-frequency motion, which significantly enhances conformational search efficiency with little overhead in computing cost. Based on a generalized Langevin equation (GLE), an SGLD-GLE simulation method is developed, which has enhanced conformational searching ability and at the same time can vigorously sample the canonical ensemble. A reformulation of the SG methods leads to a quantitative relation between the guiding parameters and the conformational distribution, which allows the SG methods to be combined with the replica exchange scheme to perform replica-exchanging self-guided simulations (RXSGMD/RXSGLD). RXSGMD/RXSGLD are much more efficient than temperature-based replica exchange methods, especially for large systems. Full article

The study investigates the impact of motions of floating offshore wind turbine platforms on wake evolution and overall wind farm performance, employing large-eddy simulation (LES) and dynamic wake modeling method. First, the differences between wakes of floating and bottom-fixed wind turbines under forced motion are examined. Subsequently, a systematic comparative analysis is performed for four representative floating platform configurations—Spar, Semi-submersible, Tension-Leg Platform (TLP), and Monopile (Mnpl)—to assess wake dynamics and downstream turbine responses within tandem-arranged arrays. Results indicate that platform pitch motion, by inducing periodic variations in the rotor’s relative inflow angle, significantly enhances wake unsteadiness, accelerates kinetic energy recovery, and promotes vortex breakdown. Tandem-arrange turbines simulations further reveal that platform-dependent motion characteristics substantially influence wake center displacement, velocity deficit, downstream turbine thrust, and overall power fluctuations at the wind farm scale. Among the examined configurations, the Spar platform exhibits the most pronounced wake disturbance and the largest downstream load and power oscillations, with rotor torque and thrust increasing by 10.2% and 10.6%, respectively, compared to other designs. This study elucidates the coupled mechanisms among 6-DOFs (Six Degrees Of Freedom) motions, wake evolution, and power performance, providing critical insights for optimizing floating wind farm platform design and developing advanced cooperative control strategies. Full article

To navigate dynamic environments safely, UAVs require accurate, real time onboard perception, which relies on ego motion compensation to separate self-induced motion from external dynamics and enable reliable obstacle detection. Traditional ego-motion compensation techniques are mainly based on optimization processes and may be computationally expensive for real-time applications or lack the precision needed to handle both rotational and translational movements, leading to issues such as misidentifying static elements as dynamic obstacles and generating false positives. In this paper, we propose a novel approach that integrates an event camera-based perception pipeline with an ego-motion compensation algorithm to accurately compensate for both rotational and translational UAV motion. An enhanced warping function, integrating IMU and depth data, is constructed to compensate camera motion based on real-time IMU data to remove ego motion from the asynchronous event stream, enhancing detection accuracy by reducing false positives and missed detections. On the compensated event stream, dynamic obstacles are detected by applying a motion aware adaptive threshold to the normalized mean timestamp image, with the threshold derived from the image’s spatial mean and standard deviation and adjusted by the UAV’s angular and linear velocities. Furthermore, in conjunction with a 3D Artificial Potential Field (APF) for obstacle avoidance, the proposed approach generates smooth, collision-free paths, addressing local minima issues through a rotational force component to ensure efficient UAV navigation in dynamic environments. The effectiveness of the proposed approach is validated through simulations, and its application for UAV navigation, safety, and efficiency in environments such as warehouses is demonstrated, where real-time response and precise obstacle avoidance are essential. Full article

Microencapsulation is a fundamental technology for protecting active compounds from environmental degradation by factors such as light, heat, and oxygen. This process significantly improves their stability, bioavailability, and shelf life by entrapping an active core within a protective matrix. Therefore, a thorough understanding of the physicochemical interactions between these components is essential for developing stable and efficient delivery systems. The composition of the microcapsule and the encapsulation method are key determinants of system stability and the retention of encapsulated materials. Recently, the application of computational tools to predict and optimize microencapsulation processes has emerged as a promising area of research. In this context, molecular dynamics (MD) simulation has become an indispensable computational technique. By solving Newton’s equations of motion, MD simulations enable a detailed study of the dynamic behavior of atoms and molecules in a simulated environment. For example, MD-based analyses have quantitatively demonstrated that optimizing polymer–core interaction energies can enhance encapsulation efficiency by over 20% and improve the thermal stability of active compounds. This approach provides invaluable insights into the molecular interactions between the core material and the matrix, ultimately facilitating the rational design of optimized microstructures for diverse applications, including pharmaceuticals, thereby opening new avenues for innovation in the field. Ultimately, the integration of computational modeling into microencapsulation research not only represents a methodological advancement but also pivotal opportunity to accelerate innovation, optimize processes, and develop more effective and sustainable therapeutic systems. Full article

The Messara Basin, a critical agricultural region in Crete, Greece, faces escalating geohazards, particularly land subsidence driven by intensive groundwater abstraction. Historical radar interferometry (1992–2009) indicated subsidence up to 20 mm·yr⁻¹, while recent European Ground Motion Service data (2016–2021) show mean vertical velocities reaching −31.2 mm·yr⁻¹. This study provides the first integrated hydrogeological assessment for the Basin, based on systematic field surveys, borehole inventories, and four coordinated campaigns (2021–2023) that established a basin-wide monitoring network of 767 stations. The dataset supports delineation of recharge zones, identification of potentiometric depressions, and mapping of aquifer-stress areas. Results show strong seasonality and extensive cones of depression, with local heads declining to ~−50 m below sea level. Land-use change (1990–2018 CORINE data; 2000–2020 agricultural censuses) combined with updated geological mapping highlights the vulnerability of post-Alpine formations, especially Quaternary and Plio–Pleistocene deposits, to deformation. The combined evidence links pumping-induced head decline with spatially coherent subsidence, delineates hotspots of aquifer stress, and identifies zones of elevated compaction risk. These findings provide a decision-ready baseline to support sustainable groundwater management, including enhanced monitoring, targeted demand controls, and managed aquifer-recharge trials. Full article

The rising demand for elderly care amid ongoing nursing shortages has highlighted the limitations of conventional alert systems, which frequently generate excessive alerts and contribute to alarm fatigue. The objective of this study is to develop a hybrid, context-aware nurse alerting framework for long-term care (LTC) facilities that minimizes redundant alarms, reduces alarm fatigue, and enhances patient safety and caregiving balance during multi-person care scenarios such as mealtimes. To do so, we aimed to intelligently suppress, delay, and validate alerts by integrating rule-based logic with Large Language Model (LLM)-driven semantic reasoning. We conducted an experimental study in a real-world LTC environment involving 28 elderly residents (6 high, 8 medium, and 14 low care levels) and four nurses across three rooms over seven days. The proposed system utilizes video-derived skeletal motion, care-level annotations, and dynamic nurse–elderly proximity for decision making. Statistical analyses were performed using F1 score, accuracy, false positive rate (FPR), and false negative rate (FNR) to evaluate performance improvements. Compared to the baseline where all nurses were notified (100% alarm load), the proposed method reduced average alarm load to 27.5%, achieving a 72.5% reduction, with suppression rates reaching 100% in some rooms for some nurses. Performance metrics further validate the system’s effectiveness: the macro F1 score improved from 0.18 (baseline) to 0.97, while accuracy rose from 0.21 (baseline) to 0.98. Compared to the baseline error rates (FPR 0.20, FNR 0.79), the proposed method achieved drastically lower values (FPR 0.005, FNR 0.023). Across both spatial (room-level) and temporal (day-level) validations, the proposed approach consistently outperformed baseline and purely rule-based methods. These findings demonstrate that the proposed approach effectively minimizes false alarms while maintaining strong operational efficiency. By integrating rule-based mechanisms with LLM-based contextual reasoning, the framework significantly enhances alert accuracy, mitigates alarm fatigue, and promotes safer, more sustainable, and human-centered care practices, making it suitable for practical deployment within real-world long-term care environments. Full article

Habitat complexity plays a critical role in coral reef ecosystems by enhancing habitat availability, increasing ecological resilience, and offering coastal protection. Structure-from-motion (SfM) photogrammetry has become a standard approach for quantifying habitat complexity in reef monitoring programs. However, a major bottleneck remains in the two-dimensional (2D) classification of benthic cover in three-dimensional (3D) models, where experts are required to manually annotate individual colonies and identify coral species or taxonomic groups. With recent advances in deep learning and computer vision, automated classification of benthic habitats is possible. While some semi-automated tools exist, they are often limited in scope or do not provide semantic segmentation. In this investigation, we trained a convolutional neural network with the ResNet101 architecture on three years (2015, 2017, and 2019) of human-annotated 2D orthomosaics from Kiritimati, Kiribati. Our model accuracy ranged from 71% to 95%, with an overall accuracy of 84% and a mean intersection of union of 0.82, despite highly imbalanced training data, and it demonstrated successful generalizability when applied to new, untrained 2023 plots. Successful automation depends on training data that captures local ecological variation. As coral monitoring efforts move toward standardized workflows, locally developed models will be key to achieving fully automated, high-resolution classification of benthic communities across diverse reef environments. Full article

The severe vertical motion of high-speed multihull vessels significantly impairs their seakeeping performance, making the design of effective anti-motion controllers crucial. However, existing controllers, predominantly designed based on deterministic dynamic models, suffer from limitations such as insufficient robustness, reliance on empirical knowledge, structural complexity, and suboptimal performance, which hinder their practical applicability. To address this, this paper proposes a robust decoupled vertical motion controller based on the step response inversion method and incorporating an Extended State Observer (ESO) uncertainty compensation term. The control algorithm is designed leveraging the equivalent noise bandwidth theory to account for the stochastic characteristics of pitch/heave motion, with ESO compensation introduced to enhance robustness. The stability of the closed loop system is rigorously proven through theoretical analysis. Simulation results demonstrate that the proposed algorithm significantly suppresses the amplitudes of both pitch and heave motions. Full article

The purpose of this study was to characterize the inter-segmental coordination of hip, knee, and ankle movement of the kicking leg during instep kicking for experienced athletes and novices, using vector coding as a non-linear technique. Motion capture and electromyographic data were collected for 14 soccer-majored college students and 32 novices performing the instep kicking task. The percentage of time spent on the coordination patterns, defined based on hip–knee and knee–ankle coupling angles, was calculated and compared. The agonist–antagonist activity ratio was calculated and compared. The time percentages of the knee–ankle shank dominance of the experienced athletes during the whole kicking movement were significantly greater than those of the novices (p < 0.050). Athletes achieving greater maximum ball speed had more knee flexion dominant coordination patterns in the back swing and leg-cocking, and knee extension dominant coordination patterns in the leg acceleration phase. The lower activity ratio of the tibialis anterior and gastrocnemius muscles contributed significantly to increasing kicking accuracy. These results underscore the value of vector coding in identifying key inter-segmental coordination features and directly support targeted soccer kick training. The dynamic stability exercises involving knee flexion and extension to optimize power transfer for speed, as well as activation and relaxation control exercises of the lower leg muscles to improve the kicking accuracy, may be effective ways to enhance instep kicking motor control ability and performance for soccer athletes. Full article

Magnetic nanoparticles (MNPs) perturb magnetic field homogeneity, influencing transverse relaxation and the full width at half maximum (FWHM) of nuclear magnetic resonance (NMR) spectra. In Nuclear Magnetic Resonance (NMR), this appears as decay of the free induction decay (FID) signal, whose relaxation rate determines spectral FWHM. In D₂O containing MNPs, both nanoparticles and solvent molecules undergo Brownian motion and diffusion. Under a vertical main field ($B_0$), MNPs respond to their magnetization behavior, evolving toward a dynamic steady state in which the time-averaged distribution of local field fluctuations remains stable. The resulting spatial magnetic field can thus characterize field homogeneity. Within this framework, Monte Carlo simulations of spatial field distributions approximate the dynamic environment experienced by nuclear spins. NMR experiments confirm that increasing MNP concentration and particle size significantly broadens FWHM, while stronger $B_0$ enhances sensitivity to MNP-induced inhomogeneities. Full article

Triboelectric materials can convert irregular mechanical stimuli from human motion or environmental sources into high surface charge densities and instantaneous electrical outputs. Their intrinsic properties, such as flexibility, stretchability, chemical tunability, and compatibility with diverse substrates, play a critical role in determining the efficiency and reliability of triboelectric devices. In the context of active health, triboelectric materials not only serve as the core functional layers for self-powered sensing but also enable real-time physiological monitoring, motion tracking, and human–machine interaction by directly transducing biomechanical signals into electrical information. Soft triboelectric sensors exhibit high sensitivity, wide operational ranges, excellent biocompatibility, and wearability, making them highly promising for active health monitoring applications. Despite these advantages, challenges remain in enhancing surface charge density, achieving effective signal multiplexing, and ensuring long-term stability. This review provides a comprehensive overview of triboelectric mechanisms, working modes, influencing factors, performance enhancement strategies, and wearable health applications. Finally, it systematically summarizes the key improvement approaches and future development directions of triboelectric materials for active health, offering valuable guidance for advancing wearable self-powered biosensors. Full article