Search Results (4,242)

Accurate numerical simulation of debris flows is essential for hazard assessment and early-warning design, yet high-fidelity solvers remain computationally expensive, especially when large ensembles must be explored under epistemic uncertainty in rheology, initial conditions, and topography. At the same time, field observations are typically sparse and heterogeneous, limiting purely data-driven approaches. In this work, we develop a deep-learning Fourier Neural Operator (FNO) as a fast, physics-consistent surrogate for one-dimensional shallow-water debris-flow simulations and demonstrate its application to the Rendinara–Morino system in central Italy. A validated finite-volume solver, equipped with HLLC and Rusanov fluxes, hydrostatic reconstruction, Voellmy-type basal friction, and robust wet–dry treatment, is used to generate a large ensemble of synthetic simulations over longitudinal profiles representative of the study area. The parameter space of bulk density, initial flow thickness, and Voellmy friction coefficients is systematically sampled, and the resulting space–time fields of flow depth and velocity form the training dataset. A two-dimensional FNO in the $(x,t)$ domain is trained to learn the full solution operator, mapping topography, rheological parameters, and initial conditions directly to $h(x,t)$ and $u(x,t)$, thereby acting as a site-specific digital twin of the numerical solver. On a held-out validation set, the surrogate achieves mean relative $L^2$ errors of about 6–$7\%$ for flow depth and 10–$15\%$ for velocity, and it generalizes to an unseen longitudinal profile with comparable accuracy. We further show that targeted reweighting of the training objective significantly improves the prediction of the velocity field without degrading depth accuracy, reducing the velocity error on the unseen profile by more than a factor of two. Finally, the FNO provides speed-ups of approximately $36\times$ with respect to the reference solver at inference time. These results demonstrate that combining physics-based synthetic data with operator-learning architectures enables the construction of accurate, computationally efficient, and site-adapted surrogates for debris-flow hazard analysis in data-scarce environments. Full article

2-acetylfuran is a product of the Maillard reaction and is widely found, especially in heat-processed foods such as grain products, baked goods, and dairy products. Although 2-acetylfuran contributes to flavor, high concentrations may be toxic. Its target organs and dose–response relationships remain poorly characterized. In this study, transgenic zebrafish with fluorescently labeled immune and neural systems were used to assess the effects of 2-acetylfuran on immune and neural development. Wild-type zebrafish were employed to assess the toxicity of 2-acetylfuran on locomotor ability, gastrointestinal development, and liver function. The maximum non-lethal concentration (MNLC) and the 10% lethal concentration (LC₁₀) for zebrafish embryos were 0.844 and 0.889 μL/mL, respectively. Regarding immunotoxicity, at concentrations of 0.281, 0.844, and 0.889 μL/mL, 2-acetylfuran significantly reduced the numbers of neutrophils, T cells, and macrophages. Regarding locomotor and neurotoxicity, motor speed and total locomotor distance were significantly reduced at 0.844 and 0.889 μL/mL. These findings were consistent with neurodevelopmental assessments, in which 0.844 μL/mL 2-acetylfuran resulted in a significant increase in apoptotic cells in the central nervous system and markedly shortened peripheral motor nerve lengths. Regarding gastrointestinal toxicity, 0.844 and 0.889 μL/mL 2-acetylfuran significantly reduced the gastrointestinal area, while neutrophil counts showed no significant changes, suggesting a relatively mild effect on the gastrointestinal tract. Regarding hepatic toxicity, all tested concentrations of 2-acetylfuran primarily increased the delayed yolk sac absorption area. Furthermore, at 0.844 μL/mL, histological examination revealed hepatic pathological changes characterized by hepatocyte nuclear swelling, vacuolar degeneration, and hepatocyte necrosis. In summary, this study reveals the multi-organ toxicity profile of 2-acetylfuran in the zebrafish model, with particularly high sensitivity in the immune system and liver. This research provides theoretical support for risk assessment and process control of 2-acetylfuran in foods. Full article

Background: Manual dexterity (MD) impairment is a frequent and disabling feature in patients with Parkinson’s disease (PD) and paranoid schizophrenia (PS), significantly affecting functional independence and activities of daily living. However, rehabilitation strategies specifically targeting fine motor control remain insufficiently integrated into routine physiotherapy (PT). Objective: This study investigated the effects of a structured, progressive PT program incorporating targeted MD training on upper limb function in patients with PD and PS. Methods: A prospective, exploratory, interventional study was conducted in 30 patients, allocated to either an experimental group (EG, n = 20) or a control group (CG, n = 10). Participants had PD (Hoehn and Yahr stages II–III) or chronic, clinically stable PS. MD was assessed using the Purdue Pegboard Test, Coin Rotation Task, and Kapandji opposition score. The EG completed a four-phase, 40-week dexterity-oriented rehabilitation program, while the CG received standard disease-specific PT. Between-group differences in change scores were analyzed using one-way ANOVA. Results: The EG showed significantly greater improvements than the CG in thumb opposition, psychomotor processing speed, and unilateral and bilateral fine motor performance (p < 0.001 for all), with large to very large effect sizes (η² = 0.45–0.76). No significant between-group differences were observed for complex sequential assembly tasks. Conclusions: Integrating targeted MD training into structured PT programs significantly improves fine motor performance in patients with PD and PS, supporting its inclusion in rehabilitation protocols for residential and outpatient care settings. Full article

The traditional ultrasonic bonding technique for IGBT T2 copper terminals often causes physical damage to ceramic substrates, severely compromising the reliability of power modules. Meanwhile, T2 copper laser welding faces inherent challenges including low laser absorption efficiency and unstable molten pool dynamics. To address these issues, this study targets the high-quality connection of IGBT T2 copper terminals and proposes a welding solution integrating a Fiber-Diode Hybrid Laser system with galvo-scanning technology. Comparative experiments between galvo-scanning and traditional oscillation methods CNC scanning were conducted under sinusoidal and circular trajectories to explore the regulation mechanism of welding quality. The results demonstrate that CNC scanning lacks precision in thermal input control, resulting in inconsistent welding quality. Galvo-scanning enables precise modulation of laser energy distribution and molten pool behavior, effectively reducing spatter and porosity defects. It also promotes the transition from columnar grains to equiaxed grains, significantly refining the weld microstructure. Under the sinusoidal trajectory with a welding speed of 20 mm/s, the Lap-shear strength of the galvo-scanned joint reaches 277 N/mm², outperforming all CNC-scanned joints. This research proposes a non-contact welding strategy targeted at eliminating the mechanical failure mechanism associated with conventional ultrasonic bonding of ceramic substrates. It establishes the superiority of galvo-scanning for precision welding of high-reflectivity materials and lays a foundation for its potential application in new energy vehicle power modules and microelectronic packaging. Full article

During the diesel engine start-up phase, low rotational speed and coolant temperature result in poor fuel atomization and prolonged ignition delay. This impedes the in-cylinder combustion process and directly impacts the engine’s emission performance. As the first combustion cycle during the starting process, the initial starting cycle significantly influences subsequent combustion cycles and overall starting performance. This paper proposes a target-torque-based control strategy for fuel injection quantity during the starting process. It optimally determines the target acceleration curve for the starting process, thereby calculating the optimal fuel injection quantity for the initial starting cycle. Based on this, a combustion system simulation model of the diesel engine was established using the 3D CFD software AVL FIRE v2010. The simulation investigated the impact of first injection speed on the combustion process and performance of the first firing cycle under different ambient temperatures: normal temperature (20 °C), low temperature (5 °C), and cold start (−10 °C). The results indicate that the optimal first cycle injection quantities under normal, low, and cold start conditions are 17.3 mg, 18.5 mg, and 20.4 mg, respectively. The impact of first injection speed on the first firing cycle combustion process primarily manifests in the mixture formation rate and time, and higher speeds do not necessarily yield better results. The optimal first injection speeds at normal temperature (20 °C), low temperature (5 °C), and cold start (−10 °C) were 220 r/min, 240 r/min, and 220 r/min, respectively. Corresponding indicated thermal efficiencies were 30.74%, 28.67%, and 28.7%, with relatively low emissions of pollutants such as CO, NOx, and HC. Full article

The adoption of novel technologies such as Unmanned Aerial Vehicles (UAVs) in Search and Rescue (SAR) operations remains limited. As a result, their full potential is not yet realized. Although UAVs have been deployed on an ad hoc basis, typically under manual control by dedicated operators, assisted and fully autonomous configurations remain largely unexplored. In this study, three SAR frameworks are systematically evaluated within a unified benchmarking framework: conventional ground missions, UAV-assisted missions, and fully autonomous UAV operations. As the key performance indicator, the target localization time was quantified and used as the means of comparison amongst frameworks. The conventional and assisted frameworks were experimentally tested through physical hardware in a controlled outdoor setting, wherein simulated callouts occurred via rescue teams. The autonomous swarm framework was simulated in the form of a multi-agent Reinforcement Learning (RL) method via the use of the Proximal Policy Optimization (PPO) algorithm. This enabled the optimization of the decentralized cooperative actions that could occur for efficient exploration of a partially observed three-dimensional environment. Our results demonstrated that the autonomous swarm significantly outperformed the conventional and assisted approaches in terms of speed and coverage. Finally, a detailed depiction of the framework’s integration into an operational system is provided. Full article

The systems to manipulate a single particle in a microfluidic channel can be adopted to pharmacological and cytological experiments of single-cell observation. The common cell position systems use syringe pumps driven by piezoelectric devices, and these have a flow quantity limit. To achieve single-cell manipulation using actuators without limiting the flow quantity and with a low risk of contamination, we propose a particle control system that uses a sidewall-driven peristaltic micropump driven by pneumatic pressure. The adopted pump was integrated into a single-layer mold with a flow path and was simple to fabricate. Unlike syringe pumps, it not only pumps water forward, but also inhales from the back simultaneously, and can pump indefinitely. We developed a responsive and precise particle position control system using this pump in combination with a high-speed camera. In this system, the pumping pressure is operated by real-time adjustment of a pneumatic pressure supply to realize PID control. This approach moves the particle rapidly when it is far from a designated target position for a quick approach and slowly near the target position to position precisely. Full article

Jixin fruit (Malus domestica ‘Jixin’) is a high-value specialty fruit of significant economic importance in northeastern and northwestern China. Automatic recognition of fruit maturity is a critical prerequisite for intelligent harvesting. However, challenges inherent to field environments—including heterogeneous ripeness levels among fruits on the same plant, gradual color transitions during maturation that result in ambiguous boundaries, and occlusion by branches and foliage—render traditional image recognition methods inadequate for simultaneously achieving high recognition accuracy and computational efficiency. Although existing deep learning models can improve recognition accuracy, their substantial computational demands and high hardware requirements preclude deployment on resource-constrained embedded devices such as harvesting robots. To achieve the rapid and accurate identification of Jixin fruit maturity, this study proposes Multi-Attention DeepLabV3+ (MA-DeepLabV3+), a streamlined semantic segmentation framework derived from an enhanced DeepLabV3+ model. First, a lightweight backbone network is adopted to replace the original complex structure, substantially reducing computational burden. Second, a Multi-Scale Self-Attention Module (MSAM) is proposed to replace the traditional Atrous Spatial Pyramid Pooling (ASPP) structure, reducing network computational cost while enhancing the model’s perception capability for fruits of different scales. Finally, an Attention and Convolution Fusion Module (ACFM) is introduced in the decoding stage to significantly improve boundary segmentation accuracy and small target recognition ability. Experimental results on a self-constructed Jixin fruit dataset demonstrated that the proposed MA-DeepLabV3+ model achieves an mIoU of 86.13%, mPA of 91.29%, and F1 score of 90.05%, while reducing the number of parameters by 89.8% and computational cost by 55.3% compared to the original model. The inference speed increased from 41 frames per second (FPS) to 81 FPS, representing an approximately two-fold improvement. The model memory footprint is only 21 MB, demonstrating potential for deployment on embedded devices such as harvesting robots. Experimental results demonstrate that the proposed model achieves significant reductions in computational complexity while maintaining high segmentation accuracy, exhibiting robust performance particularly in complex scenarios involving color gradients, ambiguous boundaries, and occlusion. This study provides technical support for the development of intelligent Jixin fruit harvesting equipment and offers a valuable reference for the application of lightweight deep learning models in smart agriculture. Full article

This study quantitatively analyzed the impact of concurrent large-scale wildfires that occurred in Korea in March 2025 on air quality in agricultural regions and identified potential risks to agricultural workers. Analysis of air quality data from eight agricultural sites nationwide revealed that the average concentrations of PM₁₀ and PM_2.5 during the wildfire period increased by 47.3% and 24.9%, respectively, compared to non-fire periods. Multiple regression analysis indicated that PM₁₀ concentrations were dominated by physical dispersion and dilution effects driven by variables such as wind speed and distance. In contrast, PM_2.5 showed a strong positive correlation with relative humidity, suggesting it is significantly influenced by secondary formation and atmospheric stagnation. Notably, the potential for particulate matter accumulation was confirmed during high-humidity hours when atmospheric inversion layers form, combined with the basin topography characteristic of Korean rural areas. This implies that elderly agricultural workers may be exposed to high concentrations of hazardous substances even when smoke is not visually apparent. Therefore, this study suggests the necessity of establishing specific protective measures for agricultural workers, including the introduction of targeted, site-specific forecasting (“pinpoint forecasts”) for downwind farmlands and restrictions on outdoor work during early morning hours. Full article

Background/Objectives: This systematic review examines how artificial intelligence (AI) is transforming fingerprint and latent print identification in criminal investigations, tracing the evolution from traditional dactyloscopy to Automated Fingerprint Identification Systems (AFISs) and AI-enhanced biometric pipelines. Methods: Following PRISMA 2020 guidelines, we conducted a literature search in the Scopus, Web of Science, PubMed/MEDLINE, and legal databases for the period 2000–2025, using multi-step Boolean search strings targeting AI-based fingerprint identification; 68,195 records were identified, of which 61 peer-reviewed studies met predefined inclusion criteria and were included in the qualitative synthesis (no meta-analysis). Results: Across the included studies, AI-enhanced AFIS solutions frequently demonstrated improvements in speed and scalability and, in several controlled benchmarks, improved matching performance on low-quality or partial fingerprints, although the results varied depending on datasets, evaluation protocols, and operational contexts. They also showed a potential to reduce certain forms of examiner-related contextual bias, while remaining susceptible to dataset- and model-induced biases. Conclusions: The evidence indicates that hybrid human–AI workflows—where expert examiners retain decision making authority but use AI for candidate filtering, image enhancement, and data structuring—currently offer the most reliable model, and emerging developments such as multimodal biometric fusion, edge computing, and quantum machine learning may contribute to making AI-based fingerprint identification an increasingly important component of law enforcement practice, provided that robust regulation, continuous validation, and transparent governance are ensured. Full article

Pre-emergence control is one of the critical steps in the agricultural production of direct-seeding rice. To investigate the mechanism of pre-emergence flame control, a flame control test bench and a flame control and sowing integrated operation machine were designed and made. The experimental results demonstrate that tall fescue seeds achieved complete inactivation (100% rate) when exposed to a target temperature of 140 °C for 1 min. A temperature distribution analysis revealed that the 1 mm soil layer exhibited a lower temperature rise compared with the surface layer, while the 2 mm layer recorded the minimum temperature elevation. Among the tested nozzle–soil distances, 150 mm significantly improved the soil-heating efficacy over 200 mm, with 100 mm yielding the optimal performance. Statistical analysis confirmed that the nozzle–soil distance, seed burial depth, and operating speed exerted highly significant (p < 0.01) effects on the tall fescue seed inactivation rate. The seed burial depth emerged as the most influential factor, followed by the operating speed and nozzle–soil distance. Data from the field experiment further revealed a speed-dependent decline in the inactivation rates: 80.27% at 3 km·h⁻¹, 66.30% at 4 km·h⁻¹, and 46.10% at 5 km·h⁻¹, and SPSS analysis indicated that there were extremely significant differences between every pair of groups of data (p < 0.01). This study verified that pre-emergence flame control technology can effectively eliminate grass seeds on the soil surface and has a certain inhibitory effect on shallow-buried seeds, which contributes to the advancement of pre-emergence control technology. Full article

Background: Probable sarcopenia, indicated by low handgrip strength, is a prevalent condition among hospitalized older adults and may reflect broader functional and nutritional decline. Methods: We examined differences in nutritional, functional, and cognitive status between Alzheimer’s clinical syndrome (ACS) patients with probable sarcopenia and those without sarcopenia. A cross-sectional analysis was conducted on 194 hospitalized older adults with ACS. Probable sarcopenia was defined using European Working Group on Sarcopenia in Older People (EWGSOP2) handgrip strength thresholds. Results: Patients with probable sarcopenia (n = 137) had significantly lower Mini-Mental State Examination (MMSE) scores, Geriatric Nutritional Risk Index (GNRI), albumin, hemoglobin, and gait speed compared to those without. After age and sex adjustment, MMSE (p = 0.023), GNRI (p = 0.002), hemoglobin (p = 0.022), albumin (p = 0.003), and gait speed (p < 0.001) remained significantly different. In the sex- and age-adjusted multivariable model (adjusted R² = 0.442), higher nutritional risk (β = 0.26, p = < 0.001), lower MMSE scores (β = 0.17, p = 0.029), polypharmacy (β = −4.20, p = 0.002), and slower gait speed (β = 4.12, p = 0.010) were associated with reduced handgrip strength. In the multivariable binary logistic regression model (adjusted for age and sex), moderate or high nutritional risk and slow gait speed emerged as independent predictors of probable sarcopenia, with OR 5.14 (95% CI 1.34–19.75; p = 0.017) and OR 3.13 (95% CI 1.30–7.52; p = 0.011), respectively. Conclusions: Probable sarcopenia in hospitalized older adults with ACS is highly prevalent and is associated with higher nutritional risk, poorer cognitive and physical function, and polypharmacy; its early recognition may help to guide more targeted nutritional and functional interventions. Full article

High-speed data acquisition systems based on field-programmable gate arrays (FPGAs) often face synchronization challenges when interfacing with commercial analog-to-digital converters (ADCs), particularly under constrained hardware routing conditions and vendor-specific clocking assumptions. This work presents a vendor-independent FPGA–ADC synchronization architecture that enables reliable and repeatable high-speed data acquisition without relying on clock-capable input resources. Clock and frame signals are internally reconstructed and phase-aligned within the FPGA using mixed-mode clock management (MMCM) and input serializer/deserializer (ISERDES) resources, enabling time-sequential phase observation without the need for parallel snapshot or delay-line structures. Rather than targeting absolute metrological limits, the proposed approach emphasizes a reproducible and transparent data acquisition methodology applicable across heterogeneous FPGA–ADC platforms, in which clock synchronization is treated as a system-level design parameter affecting digital interface timing integrity and data reproducibility. Experimental validation using a custom Kintex-7 (XC7K325T) FPGA and an AFE7225 ADC demonstrates stable synchronization at sampling rates of up to 125 MS/s, with frequency-offset tolerance determined by the phase-tracking capability of the internal MMCM-based alignment loop. Consistent signal acquisition is achieved over the 100 kHz–20 MHz frequency range. The measured interface level timing uncertainty remains below 10 ps RMS, confirming robust clock and frame alignment. Meanwhile, the observed signal-to-noise ratio (SNR) performance, exceeding 80 dB, reflects the phase–noise-limited measurement quality of the system. The proposed architecture provides a cost-effective, scalable, and reproducible solution for experimental and research-oriented FPGA-based data acquisition systems operating under practical hardware constraints. Full article

To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats. Full article

Printed circuit boards (PCBs) are critical in the electronics industry. As PCB layouts grow increasingly complex, defect detection processes often encounter challenges such as low image contrast, uneven brightness, minute defect sizes, and irregular shapes, making it difficult to achieve rapid and accurate automated inspection. To address these challenges, this paper proposes a novel object detector, YOLO-UMS, designed to enhance the accuracy and speed of PCB surface defect detection. First, a lightweight plug-and-play Unified Multi-Scale Feature Fusion Pyramid Network (UMSFPN) is proposed to process and fuse multi-scale information across different resolution layers. The UMSFPN uses a Cross-Stage Partial Multi-Scale Module (CSPMS) and an optimized fusion strategy. This approach balances the integration of fine-grained edge information from shallow layers and coarse-grained semantic details from deep layers. Second, the paper introduces a lightweight RG-ELAN module, based on the ELAN network, to enhance feature extraction for small targets in complex scenes. The RG-ELAN module uses low-cost operations to generate redundant feature maps and reduce computational complexity. Finally, the Adaptive Interaction Feature Integration (AIFI) module enriches high-level features by eliminating redundant interactions among shallow-layer features. The channel-priority convolutional attention module (CPCA), deployed in the detection head, strengthens the expressive power of small target features. The experimental results show that the new UMSFPN neck can help improve the AP50 by 3.1% and AP by 2% on the self-collected dataset PCB-M, which is better than the original PAFPN neck. Meanwhile, UMSFPN achieves excellent results across different detectors and datasets, verifying its broad applicability. Without pre-training weights, YOLO-UMS achieves an 84% AP50 on the PCB-M dataset, which is a 6.4% improvement over the baseline YOLO11. Comparing results with existing target detection algorithms shows that the algorithm exhibits good performance in terms of detection accuracy. It provides a feasible solution for efficient and accurate detection of PCB surface defects in the industry. Full article