Search Results (3,977)

The integration of semantic simultaneous localization and mapping (SLAM) with 3D object detection in indoor scenes is a significant challenge in the field of robot perception. Existing methods typically rely on expensive sensors and lack robustness and accuracy in complex environments. To address this, this paper proposes a novel 3D semantic SLAM framework that integrates Oriented FAST and Rotated BRIEF-SLAM2 (ORB-SLAM2), 3D object detection, and multi-object tracking (MOT) techniques to achieve efficient and robust semantic environment modeling. Specifically, we employ an improved 3D object detection network to extract semantic information and enhance detection accuracy through category balancing strategies and optimized loss functions. Additionally, we introduce MOT algorithms to filter and track 3D bounding boxes, enhancing stability in dynamic scenes. Finally, we deeply integrate 3D semantic information into the SLAM system, achieving high-precision 3D semantic map construction. Experiments were conducted on the public dataset SUNRGBD and two self-collected datasets (robot navigation and XR glasses scenes). The results show that, compared with the current state-of-the-art methods, our method demonstrates significant advantages in detection accuracy, localization accuracy, and system robustness, providing an effective solution for low-cost, high-precision indoor semantic SLAM. Full article

This work proposes a novel polarization-maintaining hollow-core anti-resonant fiber structure characterized by high birefringence and low transmission loss. To address the inherent trade-off between birefringence and confinement loss, a Pareto-front-based multi-objective optimization algorithm is introduced into the geometrical design of the ARF. The optimal fiber design achieves a birefringence exceeding $1\times {10}^{-4}$ and a confinement loss of approximately 1 dB/m at the telecommunication wavelength of 1.55 $\mathsf{\mu }$m. In particular, the asymmetric wall thickness configuration further improves the trade-off, enabling confinement loss as low as 0.15 dB/m while maintaining birefringence on the order of $1\times {10}^{-4}$. This approach significantly reduces computational cost and exhibits strong potential for applications in polarization-maintaining communications, precision sensing, and high-power laser delivery. Full article

The advancement of onshore and offshore wind turbines depends on the experimental validation of new technologies, novel component designs, and innovative concepts. However, full-scale models are typically very expensive, have limited functionality, and are difficult to adapt to diverse research needs. To address this shortcoming, this article presents the design of a low-cost, modular 3D-printed small prototype of a wind turbine. It includes a multi-hollow platform for marine environments configuration and stabilization, the turbine tower, and three blades with active pitch control, not always included in wind turbine prototypes. The modular tower design allows for easy height extensions, while the rotor incorporates custom blades optimized for the prototype geometry and experimental setup. Tests were conducted to evaluate the system’s operational response and verify the proper functioning of the assembled components at various wind speeds and blade pitch angles. The results confirm that the rotor speed with the prototype’s onshore configuration is highly pitch-dependent, reaching a maximum efficiency of approximately 5°. The tower displacement, measured with an IMU, remained within a narrow range, oscillating around 2° and reaching up to 4° at higher wind speeds due to elastic deflections of the PLA structure. These results, consistent with the prototype scale, validate its usefulness in capturing essential aerodynamic and structural behaviors of the wind turbine. They also demonstrate its relevance as a new tool for experimental studies of wind turbines and open up new research, validation, and control possibilities not considered in previous developments by incorporating blade pitch control. Full article

Quasi-two-dimensional (Q2D) perovskite films have garnered significant attention as novel gain media for lasers due to their tunable bandgap, narrow linewidth, and solution processability. Q2D perovskites endowed with intrinsic quantum well structures demonstrate remarkable potential as gain media for cost-effective miniaturized lasers, owing to their superior ambient stability and enhanced photon confinement capabilities. However, the mixed-phase distribution within Q2D films constitutes a critical determinant of their optical properties, exhibiting pronounced sensitivity to specific fabrication protocols and processing parameters, including annealing temperature, duration, antisolvent volume, injection timing, and dosing rate. These factors frequently lead to broad phase distribution in Q2D perovskite films, thereby inducing incomplete exciton energy transfer and multiple emission peaks, while simultaneously making the fabrication processes intricate and reducing reproducibility. Here, we report a novel annealing-free and antisolvent-free method for the preparation of Q2D perovskite films fabricated in ambient atmosphere. By constructing a tailored mixed-solvent vapor atmosphere and systematically investigating its regulatory effects on the nucleation and growth processes of film via in situ photoluminescence spectra, we successfully achieved the fabrication of Q2D perovskite films with large n narrow phase distribution characteristics. Due to the reduced content of small n domains, the incomplete energy transfer from small n to large n phases and the carriers’ accumulation in small n can be greatly suppressed, thereby suppressing the trap-assistant nonradiative recombination and Auger recombination. Ultimately, the Q2D perovskite film showed a single emission peak at 519 nm with the narrow full width at half maximum (FWHM) of 21.5 nm and high photoluminescence quantum yield (PLQY) of 83%. And based on the optimized Q2D film, we achieved an amplified spontaneous emission (ASE) with a low threshold of 29 μJ·cm⁻², which was approximately 60% lower than the 69 μJ·cm⁻² of the control film. Full article

Waste cooking oil (WCO), a significant urban waste stream, presents untapped potential for synthesizing high-value materials. This study introduces an innovative “epoxidation-hydrolysis-blending” strategy to conveniently transform WCO into a highly flexible, photocurable elastomer suitable for 3D printing. Initially, WCO is converted into WCO-based hydroxy fatty acids (WHFA) via epoxidation and hydrolysis, yielding linear chains functionalized with multiple hydrogen-bonding sites. Subsequently, blending WHFA with hydroxyethyl acrylate (HEA) yields a novel photocurable WHFA/HEA elastomer. This elastomer exhibits excellent dimensional accuracy during vat photopolymerization 3D printing. Within the WHFA/HEA system, WHFA acts as a dual-functional modifier: its flexible alkyl chains enhance conformational freedom through plasticization while serving as dynamic hydrogen-bonding cross-linking sites that synergize with HEA chains to achieve unprecedented flexibility via reversible bond reconfiguration. Mechanical testing reveals that the optimized WHFA/HEA elastomer (mass ratio 1:3) exhibits ultra-high flexibility, with an elongation at break of 1184.66% (surpassing pure HEA by 360%). Furthermore, the elastomer demonstrates significant weldability (44.23% elongation retention after 12 h at 25 °C), physical reprocessability (7.60% elongation retention after two cycles), pressure-sensitive adhesion (glass interface adhesion toughness: 32.60 J/m²), and notable biodegradability (14.35% mass loss after 30-day soil burial). These properties indicate broad application potential in flexible electronics, biomedical scaffolds, and related fields. This research not only pioneers a low-cost route to multifunctional photocurable 3D printing materials but also provides a novel, sustainable solution for the high-value valorization of waste cooking oil. Full article

Autonomous beach-cleaning robots require reliable, low-cost navigation on sand. We study Sim-to-Real transfer of deep reinforcement learning (DRL) policies using a minimal sensor suite—wheel-encoder odometry and a single 2-D LiDAR—on a 30 kg differential-drive platform (Raspberry Pi 4). Two policies, Proximal Policy Optimization (PPO) and a masked-action variant (PPO-Mask), were trained in Gazebo + Gymnasium and deployed on the physical robot without hyperparameter retuning. Field trials on firm sand and on a natural loose-sand beach show that PPO-Mask reduces tracking error versus PPO on firm ground (16.6% ISE reduction; 5.2% IAE reduction) and executes multi-waypoint paths faster (square path: 112.48 s vs. 103.46 s). On beach sand, all waypoints were reached within a 1 m tolerance, with mission times of 115.72 s (square) and 81.77 s (triangle). These results indicate that DRL-based navigation with minimal sensing and low-cost compute is feasible in beach settings. Full article

To address the challenges of frequent path conflicts and prolonged computation times in path planning for large-scale multi-UAV operations within urban low-altitude airspace, this study proposes a conflict-free path planning method integrating 3D Jump Point Search (JPS) and an incremental update mechanism. A hierarchical algorithmic architecture is employed: the lower level utilizes the 3D-JPS algorithm for efficient single-UAV path planning, while the upper level implements a conflict detection and resolution mechanism based on a dual-objective cost function and incremental updates for multi-UAV coordination. Large-scale UAV path planning simulations were conducted using a 3D grid model representing urban low-altitude airspace, with performance comparisons made against traditional methods. The results demonstrate that the proposed algorithm significantly reduces the number of path search nodes and exhibits superior computational efficiency for large-scale UAV path planning. Specifically, under high-density scenarios of 120 UAVs per square kilometer, the proposed DOCBS + IJPS method can reduce the conflict-free path planning time by 35.56% compared to the traditional CBS + A* conflict search and resolution algorithm. Full article

This paper describes two low-cost, modular instruments developed for rapid room-temperature characterization of mainly thermoelectrics. The first instrument measures the Seebeck coefficient across diverse sample geometries and incorporates a four-point probe configuration for simultaneous electrical conductivity measurement, including disk-shaped samples. The second instrument implements the Van der Pauw method, enabling detailed investigation of charge carrier behavior within materials. Both devices prioritize accessibility, constructed primarily from 3D-printed components, basic hardware, and readily available instrumentation, ensuring ease of reproduction and modification. A unique calibration protocol using pure elemental disks and materials with well-established properties was employed for both instruments. Validation against comparable systems confirmed reliable operation. Control and data acquisition software for both devices was developed in-house and is fully documented and does not require an experienced operator. We demonstrate the utility of these instruments by characterizing the electronic properties of polycrystalline SnSe thermoelectric materials doped with Bi, Ag, and In. The results reveal highly complex charge carrier behavior significantly influenced by both dopant type and concentration. Full article

Three-dimensional object detection based on the fusion of millimeter-wave radar and cameras is increasingly gaining attention due to characteristics of low cost, high accuracy, and strong robustness. Recently, the bird’s eye view (BEV) fusion paradigm has dominated radar–camera fusion-based 3D object detection methods. In the BEV fusion paradigm, the detection accuracy is jointly determined by the precision of both image BEV features and radar BEV features. The precision of image BEV features is significantly influenced by depth estimation accuracy, whereas estimating depth from a monocular image is naturally a challenging, ill-posed problem. In this article, we propose a novel approach to enhance depth estimation accuracy by fusing camera perspective view (PV) features and radar perspective view features, thereby improving the precision of image BEV features. The refined image BEV features are then fused with radar BEV features to achieve more accurate 3D object detection results. To realize PV fusion, we designed a radar image generation module based on radar cross-section (RCS) and depth information, accurately projecting radar data into the camera view to generate radar images. The radar images are used to extract radar PV features. We present a cross-modal feature fusion module using the attention mechanism to dynamically fuse radar PV features with camera PV features. Comprehensive evaluations on the nuScenes 3D object detection dataset demonstrate that the proposed dual-view fusion paradigm outperforms the BEV fusion paradigm, achieving state-of-the-art performance with 64.2 NDS and 56.3 mAP. Full article

This study compares enhanced turbulence models in a natural river channel 3D simulation under extreme hydrometeorological conditions. Using ANSYS Fluent 2024 R1 and the Volume of Fluid scheme, five RANS closures were evaluated: realizable k–ε, Renormalization-Group k–ε, Shear Stress Transport k–ω, Generalized k–ω, and Baseline-Explicit Algebraic Reynolds Stress model. A segment of the Santa Catarina River in Monterrey, Mexico, defined the computational domain, which produced high-energy, non-repeatable real-world flow conditions where hydrometric data were not yet available. Empirical validation was conducted using surface velocity estimations obtained through high-resolution video analysis. Systematic bias was minimized through mesh-independent validation (<1% error) and a benchmarked reference closure, ensuring a fair basis for inter-model comparison. All models were realized on a validated polyhedral mesh with consistent boundary conditions, evaluating performance in terms of mean velocity, turbulent viscosity, strain rate, and vorticity. Mean velocity predictions matched the empirical value of 4.43 [m/s]. The Baseline model offered the highest overall fidelity in turbulent viscosity structure (up to 43 [kg/m·s]) and anisotropy representation. Simulation runtimes ranged from 10 to 16 h, reflecting a computational cost that increases with model complexity but justified by improved flow anisotropy representation. Results show that all models yielded similar mean flow predictions within a narrow error margin. However, they differed notably in resolving low-velocity zones, turbulence intensity, and anisotropy within a purely hydrodynamic framework that does not include sediment transport. Full article

We report the study of seven commercially available rosehip oils (Rosa canina L.) using GC-MS, colorimetry (CIELab), UV-VIS, FTIR, and 3D EEM fluorescence spectroscopy, including using a smartphone spectrometer. GC-MS revealed two groups of oil samples with different chemical constituents: ω-6-dominant with 45–51% α-linolenic acid (samples S1, S2, and S5–S7) and ω-3-dominant with 47–49% α-linolenic, 7.3–19.1% oleic, 1.9–2.8% palmitic, 1.0–1.8% stearic, and 0.1–0.72% arachidic acid (S3, S4). In S1 PUFA content was found to be ~75% with ω-6/ω-3 ≈ 2:1. Favorable lipid indices of AI 0.0197–0.0302, TI 0.0208–0.0304, and h/H 33.0–50.6 were observed. The highest h/H (50.55) was observed in S5 and the lowest TI (0.0208) in S3. FTIR showed characteristic lines at ~3021, 2929/2853, 1749, and ~1370 cm⁻¹, and PCA yielded 60–80% variation and separated S1 from the rest of the samples, while the clusters grouped S5 and S6. The smartphone spectrometer also reproduced the individual differences in sample volumes ≤ 1 µL under 355–395 nm UV excitation. The non-destructive optical markers reflect the fatty acid profile and allow fast low-cost identification and quality control. An integrated control method including routine optical screening, periodic CG-MS verification, and chemometric models to trace oxidation and counterfeiting is suggested. Full article

Background. Physical activity enhances cognitive performance in adolescents, yet the role of circadian timing within the school day remains poorly understood. Purpose. This study examined whether the timing of school-based physical activity (morning, midday, afternoon) influences cognitive performance, subjective alertness, and mood states in early adolescents. Methods. A 12-week crossover intervention was conducted with 102 students (aged 12–13 years) from southern Italy. Each class participated in three 4-week conditions of structured physical activity scheduled in the morning (8:10–9:10), midday (12:10–13:10), and afternoon (15:10–16:10), separated by one-week washouts. Cognitive outcomes (d2-R, Digit Span backward, TMT-A), subjective alertness (KSS), and mood (PANAS-C) were assessed at baseline and after each condition. Analyses employed linear mixed-effects models and repeated-measures ANOVAs, adjusting for sex, BMI, chronotype, and sleep duration. Results. Morning activity produced the strongest improvements in attention (d2-R, η²p = 0.16), working memory (Digit Span backward, η²p = 0.06), processing speed (TMT-A, η²p = 0.08), alertness (KSS, η²p = 0.19), and positive affect (PANAS-C, η²p = 0.05). Midday sessions yielded moderate benefits (d2-R, η²p = 0.09; Digit Span backward, η²p = 0.05; TMT-A, η²p = 0.07; KSS, η²p = 0.09), while afternoon activity showed the weakest or nonsignificant changes (all η²p < 0.05). Chronotype moderated the effects on attention and working memory, with morning types deriving the largest gains. Conclusions. The timing of physical activity is a critical determinant of its cognitive and affective benefits. Incorporating morning exercise into school timetables may represent a low-cost, scalable strategy to optimize both learning readiness and well-being in adolescents. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

Transformer-based 2D-to-3D lifting methods have demonstrated outstanding performance in 3D human pose estimation from 2D pose sequences. However, they still encounter challenges with the relatively poor quality of 2D joints and substantial computational costs. In this paper, we propose a CGFusionFormer to address these problems. We propose a compact spatial representation (CSR) to robustly generate local spatial multihypothesis features from part of the 2D pose sequence. Specifically, CSR models spatial constraints based on body parts and incorporates 2D Gaussian filters and nonparametric reduction to improve spatial features against low-quality 2D poses and reduce the computational cost of subsequent temporal encoding. We design a residual-based Hybrid Adaptive Fusion module that combines multihypothesis features with global frequency domain features to accurately estimate the 3D human pose with minimal computational cost. We realize CGFusionFormer with a PoseFormer-like transformer backbone. Extensive experiments on the challenging Human3.6M and MPI-INF-3DHP benchmarks show that our method outperforms prior transformer-based variants in short receptive fields and achieves a superior accuracy–efficiency trade-off. On Human3.6M (sequence length 27, 3 input frames), it achieves 47.6 mm Mean Per Joint Position Error (MPJPE) at only 71.3 MFLOPs, representing about a 40 percent reduction in computation compared with PoseFormerV2 while attaining better accuracy. On MPI-INF-3DHP (81-frame sequences), it reaches 97.9 Percentage of Correct Keypoints (PCK), 78.5 Area Under the Curve (AUC), and 27.2 mm MPJPE, matching the best PCK and achieving the lowest MPJPE among the compared methods under the same setting. Full article

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article