Search Results (717)

With the rapid advancement in laser scanning technologies, the capability to collect massive volumes of data and richer detailed features has been significantly enhanced. However, the differential representation ability of multi-source point clouds in capturing intricate structures within complex scenes, combined with the computational burden imposed by large datasets, presents substantial challenges to current registration methods. The proposed method encompasses two innovative feature point pruning techniques and two closely interconnected progressive processes. First, it identifies structural points that effectively represent the features of the scene and performs a rapid initial alignment of point clouds within the two-dimensional plane. Subsequently, it establishes the mapping relationship between the point clouds to be matched utilizing FPFH descriptors, followed by further screening to extract the maximum consensus set composed of points that meet constraints based on the intensity of graph nodes. Then, it integrates the processes of feature point description and similarity measurement to achieve precise point cloud registration. The proposed method effectively extracts matching primitives from large datasets, addressing issues related to false matches and noise in complex data environments. It has demonstrated favorable matching results even in scenarios with low overlap between datasets. On two public datasets and a self-constructed dataset, the method achieves an effective point set screening rate of approximately 1‰. On the WHU-TLS dataset, our method achieves a registration accuracy characterized by a rotation precision of 0.062° and a translation precision of 0.027 m, representing improvements of 70% and 80%, respectively, over current state-of-the-art (SOTA) methods. The results obtained from real registration tasks demonstrate that our approach attains competitive registration accuracy when compared with existing SOTA techniques. Full article

Background: Skilled human movement, such as the golf swing, emerges from coordinated rotational and translational dynamics. This study investigates pitch—a screw-theoretic invariant defined as the ratio of linear to angular velocity along the instantaneous screw axis (ISA)—as a compact metric for quantifying motor coordination. Methods: We reanalyzed a validated motion capture dataset involving a proficient and a novice female golfer. ISA trajectories and pitch values were computed from 3D marker data, and synchronized with vertical ground reaction force (GRF) signals collected via force plate. Results: The proficient golfer exhibited tightly bounded pitch oscillations (approximately ±0.0025 cm/rad) that were temporally aligned with a single, well-defined GRF peak. In contrast, the novice showed irregular pitch fluctuations (−0.025 to +0.01 cm/rad) and asynchronous GRF patterns with multiple peaks. Conclusions: These findings demonstrate that pitch can serve as a biomechanical indicator of skilled performance, reflecting the degree of intersegmental coordination and force timing. Screw theory thus offers a rigorous framework for evaluating movement efficiency in sport and rehabilitation contexts. Full article

Magnetic tunnel junctions (MTJs) are pivotal for spintronic applications such as magneto resistive memory and sensors. Two-dimensional van der Waals heterostructures offer a promising platform for miniaturizing MTJs while enabling the twist-angle engineering of their properties. Here, we investigate the impact of twisting the insulating barrier layer on the performance of a van der Waals MTJ with the structure graphene/1T-VSe₂/h-BN/1T-VSe₂/graphene, where 1T-VSe₂ serves as the ferromagnetic electrodes and the monolayer h-BN acts as the tunnel barrier. Using first-principles calculations based on density functional theory (DFT) combined with the non-equilibrium Green’s function (NEGF) formalism, we systematically calculate the spin-dependent transport properties for 18 distinct rotational alignments of the h-BN layer (0° to 172.4°). Our results reveal that the tunneling magnetoresistance (TMR) ratio exhibits dramatic, rotation-dependent variations, ranging from 2328% to 24,608%. The maximum TMR occurs near 52.4°. An analysis shows that the twist angle modifies the d-orbital electronic states of interfacial V atoms in the 1T-VSe₂ layers and alters the spin polarization at the Fermi level, thereby governing the spin-dependent transmission through the barrier. This demonstrates that rotational manipulation of the h-BN layer provides an effective means to engineer the TMR and performance of van der Waals MTJs. Full article

Magnetic gears are becoming promising devices that can replace conventional mechanical gears in several applications, where reduced maintenance, absence of lubrication and intrinsic overload protection are especially relevant. This paper focuses on the experimental analysis of a coaxial magnetic gear prototype recently developed at the Department of Industrial Engineering of the University of Padova. It is found that its efficiency is high and aligned with prototypes in the literature, its stationary response confirms the velocity ratio of the corresponding mechanical planetary gear, the overload protection is aligned with numerical prediction, while the dynamic response highlights that the intrinsic compliance of the magnetic coupling prevents the use of such device in high-frequency transients. It is concluded that the proposed architecture can be effectively employed for speed reducers applications where low-frequency modulation is sufficient, which includes many industrial applications. Nevertheless, high rotational speeds are allowed. The performance characteristics, although specific for the prototype considered, experimentally highlights the key features of coaxial magnetic gear devices. The experimental performance are also compared with estimations from the literature, when available. Full article

Multi-rotor ducted propellers, which integrate the high-efficiency characteristics of ducted propellers with the layout flexibility and safety advantages of distributed propulsion, are extensively utilized in the propulsion systems of low-altitude transport systems and large-scale unmanned aerial vehicles. This study numerically investigates the effects of spanwise distance, streamwise distance, rotational consistency, and rotational phase gap on the unsteady aerodynamic characteristics of multi-rotor ducted propellers under hovering conditions. A parameterized numerical computation model and an Aligned Rank Transform Analysis of Variance (ART-ANOVA) method suitable for small datasets exhibiting regular patterns were developed. Initially, numerical simulations investigated the aerodynamic performance of multi-rotor ducted propeller models with varying layout parameters. The aerodynamic coefficients of the propellers monotonically decrease as the layout spacing increases; however, the change trends differ. Aerodynamic interference reduces the airflow velocity and influences the distribution of high-pressure zones, consequently impacting thrust and efficiency. Subsequently, this paper examined the coupled effects of two rotational characteristics. The relationship between propeller aerodynamic performance and rotational phase gap exhibits distinct trigonometric function characteristics. The presence of the duct mitigates the mutual interference between blades, thereby altering the amplitude and phase of these characteristics. Finally, an ART-ANOVA method was employed to quantify the main and interaction effects, revealing that rotational consistency has a dominant influence on all aspects of aerodynamic performance. Insights into aerodynamic performance are crucial for advancing low-altitude transport systems that utilize ducted propeller propulsion systems. Full article

Regenerative agriculture has emerged as a new organic farming movement, initially difficult to distinguish from similar approaches. Its core concerns, such as ecosystem degradation caused by intensive farming, align with those of many other organic systems. However, regenerative agriculture prioritizes soil health, biodiversity, and social equity, setting itself apart through its scalability and flexibility. Unlike other ecological farming methods, often limited to smaller scales, regenerative agriculture aims to be implemented on large farms, typically major contributors to pollution due to reliance on external inputs like fertilizers and pesticides. Notably, regenerative certification standards are more flexible, allowing the use of industrially synthesized inputs under specific conditions, provided that regenerative principles are upheld. This review systematically examines seven core regenerative practices: no-tillage farming, crop rotation, cover cropping, green manures, intercropping, perennial cover systems, and integrated crop-livestock systems. It outlines the practical advantages and ecological benefits of each, while identifying key adoption challenges, including costs, farm size, and institutional barriers. The paper argues that addressing these issues, particularly concerning scale and socio-economic constraints, is essential for broader adoption. By synthesizing recent evidence, this review clarifies the distinctiveness of regenerative agriculture and highlights pathways for its scalable implementation. Full article

Industrial tree plantations (ITPs) are increasingly recognized as a sustainable response to deforestation and the decline in native wood resources in the Philippines. This study presents LUNTIAN (Labor, UNiversity, Timber Investment, and Agent-based Nexus), an agent-based model that simulates an experimental ITP operation within a mountain forest managed by University of the Philippines Los Baños. The model integrates biophysical processes—such as tree growth, hydrology, and stand dynamics—with socio-economic components such as investment decision making based on risk preferences, employment allocation influenced by local labor availability, and informal harvesting behavior driven by job scarcity. These are complemented by institutional enforcement mechanisms such as forest patrolling, reflecting the complex interplay between financial incentives and rule compliance. To assess the model’s validity, its outputs were compared to those of the 3PG forest growth model, with results demonstrating alignment in growth trends and spatial distributions, thereby supporting LUNTIAN’s potential to represent key ecological dynamics. Sensitivity analysis identified investor earnings share and community member count as significant factors influencing net earnings and management costs. Parameter calibration using the Non-dominated Sorting Genetic Algorithm yielded an optimal configuration that ensured profitability for resource managers, investors, and community-hired laborers while minimizing unauthorized independent harvesting. Notably, even with continuous harvesting during a 17-year rotation, the final tree population increased by 55%. These findings illustrate the potential of LUNTIAN to support the exploration of sustainable ITP management strategies in the Philippines by offering a robust framework for analyzing complex social–ecological interactions. Full article

Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTEC_euc) and a data assimilation module (InTEC_DA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTEC_euc model when driven by different datasets: carbon density simulation showed improvements in R² (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha⁻¹), reductions in RMSE (8.1–31.85 Mg C ha⁻¹), and improvements in rRMSE (12.37–51.82%), excluding NPP_Lin. The carbon density-driven InTEC_euc model outperformed the NPP-driven model, with improvements in R² (0.28), MAE (−8.15 Mg C ha⁻¹), RMSE (−9.43 Mg C ha⁻¹), and rRMSE (−15.34%). When the InTEC_DA model was employed, R² values for carbon density improved by 0–0.03 (excluding ACD_Yan), with MAE reductions between 0.17 and 7.22 Mg C ha⁻¹, RMSE reductions between 0.33 and 12.94 Mg C ha⁻¹ and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTEC_DA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha⁻¹ yr⁻¹, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article

Objectives: Surface-Guided Radiation Therapy (SGRT) has been widely adopted in breast cancer radiotherapy, particularly for improving setup accuracy and motion management. Recently, its application in lung cancer has attracted growing interest due to similar needs for precision. This study investigates the feasibility and clinical utility of SGRT in lung cancer treatment, focusing on its effectiveness in patient setup and real-time motion monitoring under frameless immobilization conditions. Materials and Methods: A total of 204 treatment records from 17 patients with primary lung cancer who underwent radiotherapy at Korea University Guro Hospital between October 2024 and April 2025 were retrospectively analyzed. Patients were initially positioned using the Identify system (Varian) in the CT suite, with surface data transferred to the treatment room system. Alignment was performed to within ±1 cm and ±2° across six degrees of freedom. Cone-beam CT (CBCT) was acquired prior to treatment for verification, and treatment commenced when the Distance to Correspondence Surface (DCS) was ≤0.90. Setup deviations from the Identify system were recorded and compared with CBCT in three translational axes to evaluate positioning accuracy and PTV displacement. Results and Conclusions: The Identify system was shown to provide high setup accuracy and reliable real-time motion monitoring in lung cancer radiotherapy. Its ability to detect patient movement and automatically interrupt beam delivery contributes to enhanced treatment safety and precision. In addition, even though the maximum longitudinal (Lng) shift reached up to −1.83 cm with surface-guided setup, and up to 1.78 cm (Lat) 5.26 cm (Lng), 9.16 cm (Vrt) with CBCT-based verification, the use of Identify’s auto-interruption mode (±1 cm in translational axes, ±2° in rotational axes) allowed treatment delivery with PTV motion constrained within ±0.02 cm. These results suggest that, due to significant motion in the longitudinal direction, appropriate PTV margins should be considered during treatment planning. The Identify system enhances setup accuracy in lung cancer patients using a surface-guided approach and enables real-time tracking of intra-fractional errors. SGRT, when implemented with systems such as Identify, shows promise as a feasible alternative or complement to conventional IGRT in selected lung cancer cases. Further studies with larger patient cohorts and diverse clinical settings are warranted to validate these findings. Full article

This study investigates a modified five-dimensional chaotic system by incorporating structural term adjustments and Caputo fractional-order differential operators. The modified system exhibits significantly enriched dynamic behaviors, including offset boosting, phase trajectory rotation, phase trajectory reversal, and contraction phenomena. Additionally, the system exhibits bidirectional transitions—conservative-to-dissipative transitions governed by initial conditions and dissipative-to-conservative transitions controlled by fractional order variations—along with a unique chaotic-to-quasiperiodic transition observed exclusively at low fractional orders. To validate the system’s physical realizability, a signal processing platform based on Digital Signal Processing (DSP) is implemented. Experimental measurements closely align with numerical simulations, confirming the system’s feasibility for practical applications. Full article

Introduction: Hip abductor strength is essential for pelvic stability, lower limb alignment, and injury prevention. Weaknesses of the gluteus medius and minimus contribute to various musculoskeletal conditions. Lateral band walks and monster walks are elastic resistance exercises commonly used to target the hip abductors and external rotators in functional, weight-bearing tasks. Therefore, the aim was to summarize the current evidence on the biomechanics, muscle activation, and clinical applications of lateral and monster band walks. Methods: This narrative review was conducted following the SANRA guideline. A comprehensive literature search was performed across PubMed, Scopus, Web of Science, and SPORTDiscus up to April 2025. Studies on the biomechanics, electromyography, and clinical applications of lateral band walks and monster walks were included, alongside relevant evidence on hip abductor strengthening. Results: A total of 13 studies were included in the review, of which 4 specifically investigated lateral band walk and/or monster walk exercises. Lateral and monster walks elicit moderate to high activation of the gluteus medius and maximus, especially when performed with the band at the ankles or forefeet and in a semi-squat posture. This technique minimizes compensation from the tensor fasciae latae and promotes selective gluteal recruitment. Proper execution requires control of the trunk and pelvis, optimal squat depth, and consistent band tension. Anatomical factors (e.g., femoral torsion), sex differences, and postural variations may influence movement quality and necessitate tailored instruction. Full article

Accurate localization plays a critical role in enabling underwater vehicle autonomy. In this work, we develop a robust infrastructure-based localization framework that estimates the position and orientation of underwater vehicles using only range measurements from long baseline (LBL) acoustic beacons to multiple on-board receivers. The proposed framework integrates three key components, each formulated as a convex optimization problem. First, we introduce a robust calibration function that unifies multiple sources of measurement error—such as range-dependent degradation, variable sound speed, and latency—by modeling them through a monotonic function. This function bounds the true distance and defines a convex feasible set for each receiver location. Next, we estimate the receiver positions as the center of this feasible region, using two notions of centrality: the Chebyshev center and the maximum volume inscribed ellipsoid (MVE), both formulated as convex programs. Finally, we recover the vehicle’s full 6-DOF pose by enforcing rigid-body constraints on the estimated receiver positions. To do this, we leverage the known geometric configuration of the receivers in the vehicle and solve the Orthogonal Procrustes Problem to compute the rotation matrix that best aligns the estimated and known configurations, thereby correcting the position estimates and determining the vehicle orientation. We evaluate the proposed method through both numerical simulations and field experiments. To further enhance robustness under real-world conditions, we model beacon-location uncertainty—due to mooring slack and water currents—as bounded spherical regions around nominal beacon positions. We then mitigate the uncertainty by integrating the modified range constraints into the MVE position estimation formulation, ensuring reliable localization even under infrastructure drift. Full article

Based on the new high-resolution multibeam bathymetry data collected by the “Dongfanghong 3” vessel in 2023 in the Parece Vela Basin (PVB) and previous magnetic anomaly data, we systematically analyze the seafloor topographical changes of abyssal hills and oceanic core complexes (OCCs) in the “Chaotic Terrain” region, and the revised seafloor spreading model is constructed in the PVB. Using detailed analysis of the seafloor topography, we identify typical geomorphological features associated with seafloor spreading, such as regularly aligned abyssal hills and OCCs in the PVB. The direction variations of seafloor spreading in the PVB are closely related to mid-ocean ridge rotation and propagation. The formation of OCCs in the “Chaotic Terrain” can be explained by links to the continuous and persistent activity of detachment faults and dynamic adjustments controlled by variations of deep magma supply in the different segments in the PVB. We use 2D discrete Fourier image analysis of the seafloor topography to calculate the aspect ratio (AR) values of abyssal hills in the western part of the PVB. The AR value variations reveal a distinct imbalance in magma supply across various regions during the basin spreading process. Compared to the “Chaotic Terrain” area, the region with abyssal hills indicates a higher magma supply and greater linearity on seafloor topography. AR values fluctuated between 2.1 and 1.7 of abyssal hills in the western segment, while in the “Chaotic Terrain”, they dropped to 1.3 due to the lower magma supply. After the formation of the OCC-1, AR values increased to 1.9 in the eastern segment, and this shows the increase in magma supply. Based on changes in seafloor topography and variations in magma supply across different segments of the PVB, we propose that the seafloor spreading process in the magnetic anomaly linear strip 9-6A of the PVB mainly underwent four formation stages: ridge rotation, rift propagation, magma-poor supply, and the maturation period of OCCs. Full article

Ubiquitous digital array radar (UDAR) extends the integration time of moving targets by deploying a wide transmitting beam and multiple narrow receiving beams to cover the entire observed airspace. By exchanging time for energy, it effectively improves the detection ability for weak targets. Nevertheless, target motion introduces severe across-range unit (ARU), across-Doppler unit (ADU), and across-beam unit (ABU) effects, dispersing target energy across the range–Doppler-beam space. This paper proposes a beam domain angle rotation compensation and keystone-matched filtering (BARC-KTMF) algorithm to address the “three-crossing” challenge. This algorithm first corrects ABU by rotating beam–domain coordinates to align scattered energy into the final beam unit, reshaping the signal distribution pattern. Then, the KTMF method is utilized to focus target energy in the time-frequency domain. Furthermore, a special spatial windowing technique is developed to improve computational efficiency through parallel block processing. Simulation results show that the proposed approach achieves an excellent signal-to-noise ratio (SNR) gain over the typical single-beam and multi-beam long-time coherent integration (LTCI) methods under low SNR conditions. Additionally, the presented algorithm also has the capability of coarse estimation for the target incident angle. This work extends the LTCI technique to the beam domain, offering a robust framework for high-speed weak target detection. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article