Search Results (1,798)

This paper presents the design and evaluation of a lightweight, minimally actuated lower limb exoskeleton that emphasizes hip–knee coordination for natural and efficient gait assistance. The system adopts a hip-only motorized actuation strategy in combination with an electromagnetically controlled knee locking mechanism, ensuring rigid stability during stance while providing compliant assistance during swing. To support sit-to-stand transitions, a gas spring–ratchet mechanism is integrated, which remains disengaged in the seated position, delivers assistive torque during rising, and provides cushioning during the descent to enhance safety and comfort. The control framework fuses foot pressure and thigh-mounted IMU signals for finite state machine (FSM)-based gait phase detection and employs a fuzzy PID controller to achieve adaptive hip torque regulation with coordinated hip–knee control. Preliminary human-subject experiments demonstrate that the proposed design enhances lower-limb coordination, reduces muscle activation, and improves gait smoothness. By integrating a minimal-actuation architecture, a practical sit-to-stand assist module, and an intelligent control strategy, this exoskeleton strikes an effective balance between mechanical simplicity, functional support, and gait naturalness, offering a promising solution for everyday mobility assistance in elderly or mobility-impaired users. Full article

Beta (β)-type Ti-29Nb-13Ta-4.6Zr (TNTZ) alloys combine low modulus with biocompatibility but require improved surface properties for long-term implantation. This study aimed to enhance the surface mechanical strength and antibacterial performance of TNTZ by applying TiN and TiAlN coatings via PVD. Notably, TiAlN was deposited on TNTZ for the first time, enabling a direct side-by-side comparison with TiN under identical deposition conditions. Dense TiN (~1.06 μm) and TiAlN (~1.73 μm) coatings were deposited onto solution-treated TNTZ and characterized by X-ray diffraction, scanning probe microscopy, Vickers microhardness, Rockwell indentation test (VDI 3198), static water contact angle measurements, and a Kirby–Bauer disk-diffusion antibacterial assay against Escherichia coli (E. coli). Both coatings formed face-centered cubic (FCC) structures with smooth interfaces (Ra ≤ 5.3 nm) while preserving the single-phase β matrix of the substrate. The hardness increased from 192 HV (uncoated) to 1059 HV (TiN) and 1468 HV (TiAlN), and the adhesion quality was rated as HF2 and HF1, respectively. The surface wettability changed from hydrophilic (48°) to moderately hydrophobic (82°) with TiN and highly hydrophobic (103°) with TiAlN. Similarly, the diameter of the no-growth zones increased to 18.02 mm (TiN) and 19.09 mm (TiAlN) compared to 17.65 mm for uncoated TNTZ. The findings indicate that TiAlN, in particular, provided improved hardness, adhesion, and hydrophobicity. Preliminary bacteriostatic screening under diffusion conditions suggested a modest relative antibacterial response, though the effect was not statistically significant between coated and uncoated TNTZ. Statistical analysis confirmed no significant difference between the groups (p > 0.05), indicating that only a preliminary bacteriostatic trend— rather than a definitive antibacterial effect—was observed. Both nitride coatings strengthened TNTZ without compromising its structural integrity, making TiAlN-coated TNTZ a promising candidate for next-generation orthopedic implants. Full article

The accuracy of phase unwrapping is a decisive factor in achieving high-precision dimensional measurement using the projected fringe profilometry. However, discontinuities at truncation points inevitably lead to phase jumps, especially when measuring objects with complex hollow features, resulting in significantly increased errors. To address this issue, this paper proposes an adaptive phase correction algorithm based on row and column constraints. First, the algorithm identifies the main normal phase distribution region in each column and interpolates abnormal values deviating from this region, ensuring smooth phase distribution in the column direction. Then, it detects each continuous non-zero segment in every row, locates phase jump positions, and performs local corrections. This approach enhances the overall continuity of the phase map and effectively compensates for phase jump errors. Experimental results demonstrate that the proposed method can effectively suppress phase jumps caused by object edges and hollow regions, achieving an absolute error of less than 0.05 mm in measured step height differences in standard blocks. This provides a reliable phase preprocessing solution for the optical measurement of complex-shaped objects. Full article

We address the inverse spectral problem for a PT-symmetric Dirac operator with discontinuity conditions imposed along the entire real axis—a configuration that has not been explicitly solved in prior literature. Our approach constructs fundamental solutions via convergent recursive series expansions and establishes their linear independence through a constant Wronskian. We derive explicit formulas for transmission and reflection coefficients, assemble them into a PT-symmetric scattering matrix, and demonstrate how both spectral and scattering data uniquely determine the underlying complex-valued, discontinuous potentials. Unlike classical treatments, which assume smoothness or limited discontinuities, our framework handles full-axis discontinuities within a non-Hermitian setting, proving uniqueness and providing a constructive recovery algorithm. This method not only generalizes existing inverse scattering theory to PT-symmetric discontinuous operators but also offers direct applicability to optical waveguides, metamaterials, and quantum field models where gain–loss mechanisms and zero-width resonances are critical. Full article

Probabilistic roadmap (PRM) methods are widely used for robot navigation in both 2D and 3D environments; however, a major drawback is that the raw paths tend to be jagged. Executing a trajectory along such paths can lead to significant overshoots and tight turns, making it difficult to achieve a near-optimal solution under motion constraints. This paper presents an enhanced PRM-based path planning approach designed to improve path quality and computational efficiency. The method integrates advanced sampling strategies, adaptive neighbor selection with spatial data structures, and multi-stage path post-processing. In particular, shortcut smoothing and polynomial fitting are used to generate smoother trajectories suitable for motion-constrained robots. The proposed hybrid sampling scheme biases sample generation toward critical regions—near obstacles, in narrow passages, and between the start and goal—to improve graph connectivity in challenging areas. An adaptive k-d tree-based connection strategy then efficiently builds a roadmap using variable connection radii guided by PRM* theory. Once a path is found using an any-angle graph search, post-processing is applied to refine it. Unnecessary waypoints are removed via line-of-sight shortcuts, and the final trajectory is smoothed using a fitted polynomial curve. The resulting paths are shorter and exhibit gentler turns, making them more feasible for execution. In simulated complex scenarios, including narrow corridors and cluttered environments, the advanced PRM achieved a 100% success rate where standard PRM frequently failed. It also reduced calculation time to 30% and peak turning angle by up to 50% compared to conventional methods. The approach supports dynamic re-planning: when the environment changes, the roadmap is efficiently updated rather than rebuilt from scratch. Furthermore, the use of an adaptive k-d tree structure and incremental roadmap updates leads to an order-of-magnitude speedup in the connection phase. These improvements significantly increase the planner’s path quality, runtime performance, and reliability. Quantitative results are provided to substantiate the performance gains of the proposed method. Full article

In this study, we investigate implicit fractional differential equations subject to anti-periodic boundary conditions. The fractional operator incorporates two distinct generalizations: the Caputo tempered fractional derivative and the Caputo fractional derivative with respect to a smooth function. We investigate the existence and uniqueness of solutions using fixed-point theorems. Stability in the sense of Ulam–Hyers and Ulam–Hyers–Rassias is also considered. Three detailed examples are presented to illustrate the applicability and scope of the theoretical results. Several existing results in the literature can be recovered as particular cases of the framework developed in this work. Full article

Trajectory planning for logistics UAVs in complex environments faces a key challenge: balancing global search breadth with fine constraint accuracy. Traditional algorithms struggle to simultaneously manage large-scale exploration and complex constraints, and lack sufficient modeling capabilities for multi-UAV systems, limiting cluster logistics efficiency. To address these issues, we propose a GWOP algorithm based on dual-layer fusion of GWO and GRPO and incorporate a graph attention network (GAT). First, CEC2017 benchmark functions evaluate GWOP convergence accuracy and balanced exploration in multi-peak, high-dimensional environments. A hierarchical collaborative architecture, “GWO global coarse-grained search + GRPO local fine-tuning”, is used to overcome the limitations of single-algorithm frameworks. The GAT model constructs a dynamic “environment–UAV–task” association network, enabling environmental feature quantification and multi-constraint adaptation. A multi-factor objective function and constraints are integrated with multi-task cascading decoupling optimization to form a closed-loop collaborative optimization framework. Experimental results show that in single UAV scenarios, GWOP reduces flight cost (FV) by over 15.85% on average. In multi-UAV collaborative scenarios, average path length (APL), optimal path length (OPL), and FV are reduced by 4.08%, 14.08%, and 24.73%, respectively. In conclusion, the proposed method outperforms traditional approaches in path length, obstacle avoidance, and trajectory smoothness, offering a more efficient planning solution for smart logistics. Full article

In this work, pCh-MWCNTs@Ag-TiO₂/S and pCh-MWCNTs@Ag-TiO₂ nanocomposites were synthesized through a combined phosphorylation and cross-linked polymerization method. The materials were thoroughly characterized using several analytical techniques, including SEM/EDS, FTIR, TGA, and BET analysis. SEM images revealed that the pCh-MWCNTs@Ag-TiO₂/S nanocomposite displayed a smooth, flake-like morphology with spherical, dark greenish particles. EDS analysis confirmed the presence of Si, S, P, and Ag as prominent elements, with Ti, C, and O showing the most intense peaks. The TGA curves indicated significant weight loss between 250–610 °C for pCh-MWCNTs@Ag-TiO₂ and 210–630 °C for pCh-MWCNTs@Ag-TiO₂/S, corresponding to the decomposition of organic components. FTIR spectra validated the existence of functional groups such as hydroxyl (-OH), carboxyl (-COOH), and carbonyl (-C=O) on the surface of the nanocomposites. Following characterization, the materials were evaluated for their capacity to adsorb Hg²⁺ at parts-per-billion (ppb) concentrations in contaminated water. Batch adsorption experiments identified optimal conditions for mercury removal. For pCh-MWCNTs@Ag-TiO₂, the best performance was observed at pH 4, with an adsorbent dose of 4.0 mg, initial mercury concentration of 16 ppb, and a contact time of 90 min. For pCh-MWCNTs@Ag-TiO₂/S, optimal conditions were at pH 6, a dosage of 3.5 mg, the same initial concentration, and a contact time of 100 min. Each parameter was optimized to determine the most effective conditions for Hg²⁺ removal. The nanocomposites showed high efficiency, achieving more than 95% mercury removal under these conditions. Kinetic studies indicated that the adsorption process followed a pseudo-second-order model, while the equilibrium data aligned best with the Langmuir isotherm, suggesting monolayer adsorption behavior. Overall, this research highlights the effectiveness of sulfur-modified chitosan-based nanocomposites as eco-friendly and efficient adsorbents for the removal of mercury from aqueous systems, offering a promising solution for water purification and environmental protection. Full article

Considering the growing demand for modern facility agriculture, it is essential to develop non-destructive technologies for assessing lettuce nutritional status. To overcome the limitations of traditional methods, which are destructive and time-consuming, this study proposes a multimodal non-destructive nitrogen detection method for lettuce based on multi-source imaging. The approach integrates terahertz time-domain spectroscopy (THz-TDS) and near-infrared hyperspectral imaging (NIR-HSI) to achieve rapid and non-invasive nitrogen detection. Spectral imaging data of lettuce samples under different nitrogen gradients (20–150%) were simultaneously acquired using a THz-TDS system (0.2–1.2 THz) and a NIR-HSI system (1000–1600 nm), with image segmentation applied to remove background interference. During data processing, Savitzky–Golay smoothing, MSC (for THz data), and SNV (for NIR data) were employed for combined preprocessing, and sample partitioning was performed using the SPXY algorithm. Subsequently, SCARS/iPLS/IRIV algorithms were applied for THz feature selection, while RF/SPA/ICO methods were used for NIR feature screening, followed by nitrogen content prediction modeling with LS-SVM and KELM. Furthermore, small-sample learning was utilized to fuse crop feature information from the two modalities, providing a more comprehensive and effective detection strategy. The results demonstrated that the THz-based model with SCARS-selected power spectrum features and an RBF-kernel LS-SVM achieved the best predictive performance (R² = 0.96, RMSE = 0.20), while the NIR-based model with ICO features and an RBF-kernel LS-SVM achieved the highest accuracy (R² = 0.967, RMSE = 0.193). The fusion model, combining SCARS and ICO features, exhibited the best overall performance, with training accuracy of 96.25% and prediction accuracy of 95.94%. This dual-spectral technique leverages the complementary responses of nitrogen in molecular vibrations (THz) and organic chemical bonds (NIR), significantly enhancing model performance. To the best of our knowledge, this is the first study to realize the synergistic application of THz and NIR spectroscopy in nitrogen detection of facility-grown lettuce, providing a high-precision, non-destructive solution for rapid crop nutrition diagnosis. Full article

Strawberry cultivation faces significant challenges from pests and diseases, which are difficult to detect due to complex natural backgrounds and the high visual similarity between targets and their surroundings. This study proposes an advanced and lightweight detection algorithm, YOLO10-SC, based on the YOLOv10 model, to address these challenges. The algorithm integrates the convolutional block attention module (CBAM) to enhance feature representation by focusing on critical disease-related information while suppressing irrelevant data. Additionally, the Spatial and Channel Reconstruction Convolution (SCConv) module is incorporated into the C2f module to improve the model’s ability to distinguish subtle differences among various pest and disease types. The introduction of DySample, an ultra-lightweight dynamic upsampler, further enhances feature boundary smoothness and detail preservation, ensuring efficient upsampling with minimal computational resources. Experimental results demonstrate that YOLO10-SC outperforms the original YOLOv10 and other mainstream algorithms in precision, recall, mAP50, F1 score, and FPS while reducing model parameters, GFLOPs, and size. These improvements significantly enhance detection accuracy and efficiency, making the model well-suited for real-time applications in natural agricultural environments. The proposed algorithm offers a robust solution for strawberry pest and disease detection, contributing to the advancement of smart agriculture. Full article

Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are limited by high costs and sparse deployment. This paper presents the development of a real-time, low-cost air quality forecasting system that integrates IoT-based sensing units with predictive machine learning algorithms. The proposed system employs low-cost gas sensors and microcontroller-based hardware to monitor pollutants such as particulate matter, carbon monoxide, carbon dioxide and volatile organic compounds. A fully functional prototype device was designed and manufactured using Fused Deposition Modeling (FDM) with modular and scalable features. The data acquisition pipeline includes on-device adjustment, local smoothing, and cloud transfer for real-time storage and visualization. Advanced feature engineering and a multi-model training strategy were used to generate accurate short-term forecasts. Among the models tested, the GRU-based deep learning model yielded the highest performance, achieving $R^2$ values above 0.93 and maintaining latency below 130 ms, suitable for real-time use. The system also achieved over 91% accuracy in health-based AQI category predictions and demonstrated stable performance without sensor saturation under high-pollution conditions. This study demonstrates that combining embedded hardware, real-time analytics, and ML-driven forecasting enables robust and scalable air quality management solutions, contributing directly to sustainable development goals through enhanced environmental monitoring and public health responsiveness. Full article

Aiming at the control performance degradation of permanent magnet synchronous motor (PMSM) drive systems caused by uncertainties of internal and external disturbances, a robust control algorithm integrating an improved fast terminal sliding mode (IFTSM) surface with a novel adaptive reaching law (NARL) is proposed. A dynamic model of PMSM with disturbances is established, and an improved fast terminal sliding mode surface is designed. By introducing nonlinear terms and error derivative feedback mechanisms, the finite-time rapid convergence of system states is achieved, while solving the singularity problem of traditional terminal sliding mode control. Combined with the novel adaptive reaching law strategy, a state-dependent gain adjustment function is used to dynamically optimize the balance between reaching speed and chattering, enhancing the smoothness of the system′s dynamic response. Through the synergy of the finite-time convergence characteristic of the improved sliding mode surface and the novel adaptive reaching law, the proposed algorithm significantly enhances the system′s anti-interference capability against load mutations and parameter time variations. Experiment results demonstrate that under complex working conditions, the algorithm achieves superior speed tracking accuracy and current stability, providing a control solution with strong anti-interference capability and fast response for PMSM speed control systems. Full article

The 5th International Symposium on Frontiers in Molecular Science was held on 26–29 August 2025 in Kyoto (Japan), with the support of Kyoto Prefectural University and Kyoto Prefectural University of Medicine. It is evident that the event has proven to be significant, showcasing presentations of pioneering research achievements by internationally renowned researchers and fostering numerous stimulating discussions. The symposium’s objective was to identify and select key research themes within the domain of molecular science. Three plenary lecturers and numerous researchers of outstanding merit were invited by chairs to deliver keynote and invited lectures across six fields: S1. Protein Structure and Molecular Dynamics; S2. Enzymes; S3. Membrane Proteins; S4. Cancer Target Proteins; S5. Drug Design and Solution to Drug Resistance Problem; S6. Physiological Functions of Proteins and Organ Interactions. A total of 185 scientists from 31 countries/regions participated in the symposium with 139 presentations. We would like to express our sincere gratitude to the 31 members of the Scientific Committee and the seven members of the Local Organizing Committee who contributed to enhancing the quality of this symposium, ensuring its smooth operation, and dedicating considerable effort to the selection of each award. Full article

We present a hybrid parallel scheme for efficiently solving Caputo time-fractional partial differential equations (CTFPDEs) with integer-order spatial derivatives on multicore CPU and GPU platforms. The approach combines a second-order spatial discretization with the $L1$ time-stepping scheme and employs MATLAB parfor parallelization to achieve significant reductions in runtime and memory usage. A theoretical third-order convergence rate is established under smooth-solution assumptions, and the analysis also accounts for the loss of accuracy near the initial time $t=t_0$ caused by weak singularities inherent in time-fractional models. Unlike many existing approaches that rely on locally convergent strategies, the proposed method ensures global convergence even for distant or randomly chosen initial guesses. Benchmark problems from fractional biological models—including glucose–insulin regulation, tumor growth under chemotherapy, and drug diffusion in tissue—are used to validate the robustness and reliability of the scheme. Numerical experiments confirm near-linear speedup on up to four CPU cores and show that the method outperforms conventional techniques in terms of convergence rate, residual error, iteration count, and efficiency. These results demonstrate the method’s suitability for large-scale CTFPDE simulations in scientific and engineering applications. Full article

This paper investigates boundary value problems for a class of elliptic equations exhibiting uniform and non-uniform degeneracy, including cases of non-monotonic degeneration. A key objective is to identify conditions on the coefficients under which solutions maintain ultimate smoothness, even in the presence of degeneracy. The analysis is grounded in several fundamental aspects of symmetry. Structural symmetry is reflected in the formulation of the differential operators; functional symmetry emerges in the properties of the associated weighted Sobolev spaces; and spectral symmetry plays a critical role in the behavior of the eigenvalues and eigenfunctions used to characterize solutions. By employing localization techniques, a priori estimates, and spectral theory, we establish new coefficient conditions ensuring smoothness in both semi-periodic and Dirichlet boundary settings. Moreover, we prove the boundedness and compactness of certain weighted operators, whose definitions and properties are tightly linked to underlying symmetries in the problem’s formulation. These results are not only of theoretical importance but also bear practical implications for numerical methods and models where symmetry principles influence solution regularity and operator behavior. Full article