Search Results (1,177)

Identifying the activation status of advanced driver assistance systems (ADAS) in real-world driving environments is crucial for safety, responsibility attribution, and accident forensics. Unlike prior studies that primarily rely on simulation-based settings or unsynchronized data, we collected a multimodal dataset comprising synchronized controller area network (CAN)-bus and smartphone-based inertial measurement unit (IMU) signals from drivers on consistent highway sections under both ADAS-enabled and manual modes. Using these data, we developed lightweight classification pipelines based on statistical and deep learning approaches to explore the feasibility of distinguishing ADAS operation. Our analyses revealed systematic behavioral differences between modes, particularly in speed regulation and steering stability, highlighting how ADAS reduces steering variability and stabilizes speed control. Although classification accuracy was moderate, this study provides one of the first data-driven demonstrations of ADAS status detection under naturalistic conditions. Beyond classification, the released dataset enables systematic behavioral analysis and offers a valuable resource for advancing research on driver monitoring, adaptive ADAS algorithms, and accident forensics. Full article

This study evaluates media-level filtration behaviour and short-term fuel consumption outcomes for five spin-on lubricating oil filters operated under real driving conditions at high altitude. To improve interpretability, filters are reported using parameter-based identifiers (media descriptors and equivalent circular diameter, ECD) rather than internal codes. Pore-scale morphology was quantified by microscopy and expressed as ECD, and bulk fluid cleanliness was summarised using ISO 4406 codes. Trials were conducted over representative urban and extra-urban routes at altitude; fuel consumption was analysed using ANCOVA. The results indicated clear media-level differences (tighter pore envelopes and cleaner ISO codes, particularly for two OEM units). However, fuel-consumption differences were not statistically significant (ANCOVA, p = 0.29). Accordingly, findings are reported as short-term cleanliness and media characterisation under high-altitude duty rather than durability or efficiency claims. The parameter-based framing clarifies trade-offs across metrics and avoids over-generalisation from brand or part numbers. The work highlights the value of ECD as a comparative pore metric and underscores limitations of microscopy/cleanliness data for inferring engine wear or long-term consumption. Future work will incorporate formal multi-pass testing (ISO 4548-12), direct differential-pressure instrumentation, used-oil viscosity tracking, and wear-metal spectrometry to enable cross-vendor benchmarking and causal interpretation. Findings are presented as short-term cleanliness and media characterisation; no durability claims are made in the absence of direct wear measurements. Full article

Power converters are increasingly pushing toward higher switching frequencies, with current designs typically operating between tens of kilohertz and a few megahertz. The commercialization of gallium nitride (GaN) power transistors has opened new possibilities, offering performance far beyond the limitations of conventional silicon devices. Despite this promise, the potential of GaN technology remains underutilized. This paper explores the feasibility of achieving sub-gigahertz switching frequencies using GaN-based switch-mode power converters, a regime currently inaccessible to silicon-based counterparts. To reach such operating speeds, it is essential to understand and quantify the intrinsic frequency limitations imposed by GaN device physics and associated parasitics. Existing power conversion topologies and control techniques are unsuitable at these frequencies due to excessive switching losses and inadequate drive capability. This work presents a detailed, systematic study of GaN transistor behavior at high frequencies, aiming to identify both fundamental and practical switching limits. A compact analytical model is developed to estimate the maximum soft-switching frequency, considering only intrinsic device parameters. Under idealized converter conditions, this upper bound is derived as a function of internal losses and the system’s target efficiency. From this, a soft-switching figure of merit is proposed to guide the design and layout of GaN field-effect transistors for highly integrated power systems. The key contribution of this study lies in its analytical insight into the performance boundaries of GaN transistors, highlighting the roles of parasitic elements and loss mechanisms. These findings provide a foundation for developing next-generation, high-frequency, chip-scale power converters. Full article

The electrification of agricultural equipment is a critical pathway to address the dual challenges of increasing global food production and ensuring sustainable agricultural development. As the core power unit, the permanent magnet synchronous motor (PMSM) drive system faces severe challenges in achieving high performance, robustness, and reliable control in complex farmland environments characterized by sudden load changes, extreme operating conditions, and strong interference. This paper provides a comprehensive review of key technological advancements in PMSM drive systems for agricultural electrification. First, it analyzes solutions to enhance the reliability of power converters, including high-frequency silicon carbide (SiC)/gallium nitride (GaN) power device packaging, thermal management, and electromagnetic compatibility (EMC) design. Second, it systematically elaborates on high-performance motor control algorithms such as Direct Torque Control (DTC) and Model Predictive Control (MPC) for improving dynamic response; robust control strategies like Sliding Mode Control (SMC) and Active Disturbance Rejection Control (ADRC) for enhancing resilience; and the latest progress in fault-tolerant control architectures incorporating sensorless technology. Furthermore, the paper identifies core challenges in large-scale applications, including environmental adaptability, real-time multi-machine coordination, and high reliability requirements. Innovatively, this review proposes a closed-loop intelligent control paradigm encompassing environmental disturbance prediction, control parameter self-tuning, and actuator dynamic response. This paradigm provides theoretical support for enhancing the autonomous adaptability and operational quality of agricultural machinery in unstructured environments. Finally, future trends involving deep AI integration, collaborative hardware innovation, and agricultural ecosystem construction are outlined. Full article

With accelerating digital transformation, firms must renew how they create, deliver, and capture value to remain competitive and to advance sustainable competitiveness. This study examines how ambidextrous market orientation drives digital business model innovation (DBMI) through the mediating role of digital resource bricolage and the moderating effect of environmental turbulence. Using survey data and structural equation modeling (SEM), we find that both proactive and responsive market orientations positively affect DBMI. Digital resource bricolage partially mediates both relationships, with a stronger mediation effect for responsive orientation. Environmental turbulence strengthens the association between ambidextrous market orientation and digital resource bricolage. Complementing variable-centric tests, fuzzy-set qualitative comparative analysis (fsQCA) identifies three configurational pathways sufficient for high DBMI, revealing alternative routes to business-model renewal under different contextual conditions. The findings extend ambidextrous market orientation research to digital contexts, enrich the resource-recombination perspective on DBMI, and provide actionable guidance for firms seeking to orchestrate data, platforms, and legacy assets to reconfigure activity systems. By clarifying when and how market sensing and shaping translate into effective digital recombination, this study informs strategies for sustainable competitiveness in turbulent environments. Full article

This study investigates the structural and fracture behavior of an automotive exhaust manifold with a predefined semi-elliptical surface crack under realistic thermo-mechanical loading. A combined FEM–XFEM workflow was applied; the FEM identified the critical stress concentration zone, where the maximum Von Mises stress reached 165.6 MPa at 700 °C, and the XFEM was used to model crack growth with a refined mesh. The computed Mode I stress intensity factors ranged from 21 to 24 MPa√m, remaining below the temperature-dependent fracture toughness of AISI 321 stainless steel, which confirmed stable crack behavior under service conditions. Fatigue life was assessed using the Smith–Watson–Topper (SWT) parameter. Two scenarios were considered: a quasi-pulsating case, giving a predicted life of 3.8 × 10⁸ cycles, and a fully reversed case, reducing the life to 6.7 × 10⁷ cycles. These results confirm that the manifold operates within the high-cycle fatigue regime, while also demonstrating the strong sensitivity of life predictions to the applied stress ratio. This combined FEM–XFEM methodology provides a reliable numerical framework for assessing crack driving forces and guiding durability-based design of exhaust manifolds. Full article

The spatial distribution of plant species is a crucial indicator of the mechanisms driving competition or coexistence both within and between populations and communities. Analyzing these patterns provides essential insights into fundamental ecological processes and aids in evaluating ecological hypotheses. To study the spatial distribution of dominant tree species and their associations both within and among species, we established a 25-hectare forest plot in Lushan Mountain. We employed the g(r) function alongside three null models—complete spatial randomness (CSR), heterogeneous Poisson (HP), and antecedent condition (AC)—to analyze spatial patterns and assess species interactions at various life stages. Additionally, we examined the relationships between spatial distributions and environmental factors such as soil properties and topography using Berman’s test. Our results showed that all 12 dominant tree species exhibited significant aggregation under the CSR model; however, the scales of aggregation were reduced under the HP model. We also found evidence of aggregation among multiple species across different life stages and tree layers under CSR. Notably, this pattern persisted under the AC model but was limited to specific spatial scales. Furthermore, elevation, topographical convexity, and the total content of soil nitrogen (N) and carbon (C) were identified as statistically significant predictors of species distributions. Overall, these findings highlight that both biological and environmental factors play a vital role in shaping plant spatial patterns across different scales. Full article

Plant functional traits (PFTs) serve as key predictors of plant survival and adaptation to environmental gradients. Studies on intraspecific variation in PFTs are crucial for evaluating species’ adaptation to projected climate change and developing long-term conservation strategies. This study systematically investigated PFT responses in Polyspora chrysandra (Theaceae, Yunnan, China) through an integrated multivariate analysis of 20 leaf functional traits (LFTs) and 33 environmental factors categorized into geographical conditions (GCs), climate factors (CFs), soil properties (SPs), and ultraviolet radiation factors (UVRFs). To disentangle complex environmental–trait relationships, we employed redundancy analysis (RDA), hierarchical partitioning (HP), and partial least squares structural equation modeling (PLS-SEM) to assess direct, indirect, and latent relationships. Results showed that the intraspecific coefficient of variation (CV) ranged from 7.071% to 25.650%. Leaf tissue density (LTD), specific leaf area (SLA), leaf fresh weight (LFW), leaf dry weight (LDW), and leaf area (LA) exhibited moderate intraspecific trait variation (ITV), while all other traits demonstrated low ITV. Reference Bulk density (RBD) and Silt emerged as significant factors driving the variation. Latitude (Lat), altitude (Alt), and mean warmest month temperature (MWMT) were also identified as key influences. HP analysis revealed Silt as the most important predictor (p < 0.05). Latent variable analysis indicated descending contribution rates: SPs (31.51%) > GCs (11.52%) > CFs (11.04%) > UVRFs (10.29%). Co-effect analysis highlighted significant coupling effects involving RBD and cation exchange capacity of clay (CECC), as well as organic carbon content (OCC) and UV-B seasonality (UVB2). Path analysis showed SPs as having the strongest influence on leaf thickness (LT), followed by GCs and UVRFs. These findings provide empirical insights into the biogeographical patterns of ITV in P. chrysandra, enhance the understanding of plant environmental adaptation mechanisms, and offer a theoretical foundation for studying community assembly and ecosystem function maintenance. Full article

Coriander is a significant crop, playing an essential role in daily life for various purposes, including flavouring curries and medicinal uses, among others. Despite its importance, coriander is still harvested manually. To address this, developed a self-propelled battery-operated coriander harvester, designed with ergonomics, environmental sustainability and affordability for small and marginal farmers in mind. The harvester is equipped with a main frame, a lead-acid battery, a BLDC motor, a reciprocating cutter bar, a PU conveyor belt, a collection bag, a handle, and transport wheels. The harvester was tested on the coriander crop, and the results were analyzed using Design Expert software to optimize various operational parameters. The harvester’s performance was evaluated at three forward speeds: 1.5 km/h, 2 km/h, and 2.5 km/h, resulting in covered areas of 0.114 ha, 0.164 ha, and 0.22 ha, with field efficiency values of 76%, 82%, and 88%, respectively. Optimal harvesting conditions were identified by design expert software at a forward speed of 1.64 km/h, with a conveyor driving pulley at level 3 (50.8 mm) and a cutting height at level 2 (75 mm). Under these conditions, the harvester achieved a harvesting efficiency of 97.24% and a cutting efficiency of 98.2%, with minimal conveying loss of 0.96%. The theoretical field capacity was 0.16 ha/h, the actual field capacity was 0.131 ha/h, and the overall field efficiency was 81.8%. Full article

Induction motors (IM) play essential tasks in distinct production sectors because of their low cost and robustness. Considering that most of the energy demand in industry is allocated for powering up IM, recent research has focused on detecting and predicting faults to avoid severe disturbances. Broken rotor bars (BRB) in IM cause a significant deficit of energy, above all in those applications where constant changes in speed are required, increasing the probability of a catastrophic failure. Variable speed drives (VSD) introduce harmonic components to the power supply current controlling the IM rotating speed, which make it difficult to identify BRB. Therefore, in this work, an innovative methodology is proposed for detecting BRB in VSD-fed IM with a wide rotating-speed bandwidth during their start-up transient. The introduced procedure performs a statistical analysis for computing the mean, median, mode, variance, skewness, and kurtosis, to identify slight changes on the acquired current signal. These values are fed into an artificial neural network (ANN), which carries out the IM operational condition classification as healthy (HLT) or with BRB. Experimentally obtained results corroborate the effectiveness of the proposed approach to detecting BRB even for dynamically varying rotating speed, reaching a high accuracy of 99%, similar to recently reported techniques. Full article

Loss of calcium homeostasis, a shared feature of Alzheimer’s Disease (AD) and Traumatic Brain Injury (TBI), activates enzyme-dependent cascades that promote protein misfolding, degrade synaptic architecture, impair axonal transport, and lead to neuronal death. Epidemiological studies identify TBI as a major risk factor for AD, yet the mechanistic basis for this association remains incompletely understood. Evidence from human and experimental studies implicate calcium dysregulation as a central link, triggering interconnected kinase, phosphatase, and protease networks that drive AD hallmark pathology, including amyloid-β (Aβ) accumulation and tau hyperphosphorylation. The calcium-dependent protease calpain is a key node in this network, regulating downstream enzyme activity, and cleaving essential scaffolding and signaling proteins. Selective vulnerability of the hippocampus and white matter to calcium-mediated damage may underlie cognitive deficits common to both conditions. In preclinical TBI and AD models, pharmacological inhibition of calcium-dependent enzymes confers neuroprotection. Recognizing disrupted calcium signaling as an upstream driver of post-traumatic neurodegeneration may enable early interventions to reduce AD risk among TBI survivors. Full article

Background: Liquidity crises pose significant risks to financial stability, and missing data in predictive models increase the uncertainty in decision-making. This study aims to develop a robust Bayesian Model Averaging (BMA) framework using decision trees (DTs) to enhance liquidity crisis prediction under missing data conditions, offering reliable probabilistic estimates and insights into uncertainty. Methods: We propose a BMA framework over DTs, employing Reversible Jump Markov Chain Monte Carlo (RJ MCMC) sampling with a sweeping strategy to mitigate overfitting. Three preprocessing techniques for missing data were evaluated: Cont (treating variables as continuous with missing values labeled by a constant), ContCat (converting variables with missing values to categorical), and Ext (extending features with binary missing-value indicators). Results: The Ext method achieved 100% accuracy on a synthetic dataset and 92.2% on a real-world dataset of 20,000 companies (11% in crisis), outperforming baselines (AUC PRC 0.817 vs. 0.803, p < 0.05). The framework provided interpretable uncertainty estimates and identified key financial indicators driving crisis predictions. Conclusions: The BMA-DT framework with the Ext technique offers a scalable, interpretable solution for handling missing data, improving prediction accuracy and uncertainty estimation in liquidity crisis forecasting, with potential applications in finance, healthcare, and environmental modeling. Full article

The objective is to address the issue of excessive vibration in the cab of the combine harvester. This study addresses excessive cab vibration in the Linhai 4LZ-7.0 combine harvester by analyzing vibration signals under two working conditions using the Short-Time Fourier Transform. The results identified the vibrating screen and grass crusher as primary resonance sources, with maximum vibration along the X-axis. Simulation revealed that their first-order modal frequencies coincided with external excitation frequencies, causing resonance transmission to the cab. To resolve this, the driving pulleys of both components were redesigned and replaced. Post-modification testing showed a 90% reduction in the cab vibration level index from 1215 to 112, a 26% decrease in root mean square values, and the elimination of resonance peaks in frequency spectra. By modifying excitation frequencies to avoid structural resonance, cab vibration was effectively mitigated, significantly improving operational comfort. This paper is the first to pinpoint the primary resonance source and avert harvester resonance by altering its external excitation, delivering an effective, low-cost engineering fix for agricultural-machinery manufacturers; the abstract has been updated accordingly. Full article

Reliable LiDAR point clouds are essential for perception in robotics and autonomous driving. However, adverse weather conditions introduce substantial noise that significantly degrades perception performance. To tackle this challenge, we first introduce a novel, point-wise annotated dataset of over 800 scenes, created by collecting and comparing point clouds from real-world adverse and clear weather conditions. Building upon this comprehensive dataset, we propose the Spatial Context-Aware Point Cloud Encoder Network (SCOPE), a deep learning framework that identifies noise by effectively learning spatial relationships from sparse point clouds. SCOPE partitions the input into voxels and utilizes a Voxel Spatial Feature Extractor with contrastive learning to distinguish weather-induced noise from structural points. Experimental results validate SCOPE’s effectiveness, achieving high Intersection-over-Union (mIoU) scores in snow (88.66%), rain (92.33%), and fog (88.77%), with a mean mIoU of 89.92%. These consistent results across diverse scenarios confirm the robustness and practical effectiveness of our method in challenging environments. Full article

Under large-scale farming conditions, automated sow estrus detection is crucial for improving reproductive efficiency, optimizing breeding management, and reducing labor costs. Conventional estrus detection relies heavily on human expertise, a practice that introduces subjective variability and consequently diminishes both accuracy and efficiency. Failure to identify estrus promptly and pair animals effectively lowers breeding success rates and drives up overall husbandry costs. In response to the need for the automated detection of sows’ estrus states in large-scale pig farms, this study proposes a method for detecting sows’ vulvar status and estrus based on multi-dimensional feature crossing. The method adopts a dual optimization strategy: First, the Bi-directional Feature Pyramid Network—Selective Decoding Integration (BiFPN-SDI) module performs the bidirectional, weighted fusion of the backbone’s low-level texture and high-level semantic, retaining the multi-dimensional cues most relevant to vulvar morphology and producing a scale-aligned, minimally redundant feature map. Second, by embedding a Spatially Enhanced Attention Module head (SEAM-Head) channel attention mechanism into the detection head, the model further amplifies key hyperemia-related signals, while suppressing background noise, thereby enabling cooperative and more precise bounding box localization. To adapt the model for edge computing environments, Masked Generative Distillation (MGD) knowledge distillation is introduced to compress the model while maintaining the detection speed and accuracy. Based on the bounding box of the vulvar region, the aspect ratio of the target area and the red saturation features derived from a dual-threshold method in the HSV color space are used to construct a lightweight Multilayer Perceptron (MLP) classification model for estrus state determination. The network was trained on 1400 annotated samples, which were divided into training, testing, and validation sets in an 8:1:1 ratio. On-farm evaluations in commercial pig facilities show that the proposed system attains an 85% estrus detection success rate. Following lightweight optimization, inference latency fell from 24.29 ms to 18.87 ms, and the model footprint was compressed from 32.38 MB to 3.96 MB in the same machine, while maintaining a mean Average Precision (mAP) of 0.941; the accuracy penalty from model compression was kept below 1%. Moreover, the model demonstrates robust performance under complex lighting and occlusion conditions, enabling real-time processing from vulvar localization to estrus detection, and providing an efficient and reliable technical solution for automated estrus monitoring in large-scale pig farms. Full article