Search Results (35,725)

To enhance the thermo-mechanical coupling performance of heavy-duty bearings with smart sensing capability in electric shovel applications, this study proposes a multi-objective optimization methodology for sensor-embedded bearing rings incorporating smart sensor-embedded grooves. Driven by multi-physics coupling analysis and experimental validation, a coupled thermal–mechanical model integrating frictional heat generation, heat transfer, and stress response was established. Parametric finite element simulations were conducted, with varying groove depths and axial positions. A comprehensive performance index combining three metrics—maximum temperature, equivalent stress, and principal strain—was formulated to evaluate design efficacy. Experimental tests on thermal and strain responses were employed to validate the simulation model confirming its predictive ability. Among the 21 parameter combinations, the configuration featuring an 8 mm groove depth located 20 mm from the large end face exhibited relatively optimal synergy across thermal dissipation, structural strength, and strain sensitivity. The proposed framework provides a certain theoretical and practical guidance for the design and optimization of the sensor-embedded groove structure in intelligent heavy-duty bearings. Full article

This study focuses on improving the wear resistance of cutting tools and extending their service life under intense mechanical, thermal, and radiation loads in nuclear power plant environments. This research investigates the potential of electrospark alloying (ESA) using W–Zr–B system electrodes obtained from disks synthesised by spark plasma sintering (SPS). The novelty of this work lies in the use of SPS-synthesised W–Zr–B ceramics, which are promising for nuclear applications due to their high thermal stability, radiation resistance and neutron absorption, as ESA electrodes. This work also establishes the relationship between discharge energy, coating microstructure and performance. The alloying electrode material exhibited a heterogeneous microstructure containing WB₂, ZrB₂, and minor zirconium oxides, with high hardness (26.6 ± 1.8 GPa) and density (8.88 g/cm³, porosity <10%). ESA coatings formed on HS6-5-2 steel showed a hardened layer up to 30 µm thick and microhardness up to 1492 HV, nearly twice that of the substrate (~850 HV). Elemental analysis revealed enrichment of the surface with W, Zr, and B, which gradually decreased toward the substrate, confirming diffusion bonding. XRD analysis revealed a multiphase structure comprising WB₂, ZrB₂, WB₄, and BCC/FCC solid solutions, indicating the formation of complex boride phases during the ESA process. Tribological tests demonstrated significantly enhanced wear resistance of ESA coatings. The results confirm the efficiency of ESA as a simple, low-cost, and energy-efficient method for local strengthening and restoration of cutting tools. Full article

Through the principle of mechanical vibrations, coffee can be efficiently harvested. However, this process is affected by factors related to the machine and the plant. Thus, the modal properties need to be determined. The aim of this study is to characterize the dynamic behavior of coffee plants based on their modal properties under field conditions, with the fruit at different ripening stages. Fifteen randomly selected coffee plants (Coffea arabica L.), Catuaí Vermelho variety, were instrumented to collect field data and evaluate different scenarios. This study presented an innovative methodology, where coffee plants were evaluated under field conditions using experimental modal analysis at different positions along the plants and considering the immature and mature ripening stages. Based on experimental modal analysis tests, it was possible to observe that there was a higher incidence of natural frequency peaks clustered between 20 and 40 Hz. The values of the damping ratios of the plagiotropic branches of coffee plants with predominantly ripe fruits relative to the upper, middle and lower thirds were 0.145, 0.134 and 0.127, respectively. The results suggest that selectively harvesting coffee fruits solely through vibration application is not a viable option, exclusively by mechanical vibrations. This conclusion arises from the lack of significant differences observed in the pending loads of fruits at various ripening stages, coupled with the overlapping values of frequencies identified within the studied frequency bands. This work can be employed to improve existing equipment and the design of new machinery for coffee harvesting by mechanical vibrations. Full article

The central purpose of this study is to develop and validate an advanced numerical model capable of simulating the vibrational behavior of the operator’s seat in a tractor-type agricultural vehicle designed for operation in protected horticultural environments, such as vegetable greenhouses. The three-dimensional (3D) model of the seat was created using SolidWorks 2023, while its dynamic response was investigated through Finite Element Analysis (FEA) in Altair SimSolid, enabling a detailed evaluation of the natural vibration modes within the 0–80 Hz frequency range. Within this interval, eight significant natural frequencies were identified and correlated with the real structural behavior of the seat assembly. For experimental validation, direct time-domain measurements were performed at a constant speed of 5 km/h on an uneven, grass-covered dirt track within the research infrastructure of INMA Bucharest, using the TE-0 self-propelled electric tractor prototype. At the operator’s seat level, vibration data were collected considering the average anthropometric characteristics of a homogeneous group of subjects representative of typical tractor operators. The sample of participating operators, consisting exclusively of males aged between 27 and 50 years, was selected to ensure representative anthropometric characteristics and ergonomic consistency for typical agricultural tractor operators. Triaxial accelerometer sensors (NexGen Ergonomics, Pointe-Claire, Canada, and Biometrics Ltd., Gwent, UK) were strategically positioned on the seat cushion and backrest to record accelerations along the X, Y, and Z spatial axes. The recorded acceleration data were processed and converted into the frequency domain using Fast Fourier Transform (FFT), allowing the assessment of vibration transmissibility and resonance amplification between the floor and seat. The combined numerical–experimental approach provided high-fidelity validation of the seat’s dynamic model, confirming the structural modes most responsible for vibration transmission in the 4–8 Hz range—a critical sensitivity band for human comfort and health as established in previous studies on whole-body vibration exposure. Beyond validating the model, this integrated methodology offers a predictive framework for assessing different seat suspension configurations under controlled conditions, reducing experimental costs and enabling optimization of ergonomic design before physical prototyping. The correlation between FEA-based modal results and field measurements allows a deeper understanding of vibration propagation mechanisms within the operator–seat system, supporting efforts to mitigate whole-body vibration exposure and improve long-term operator safety in horticultural mechanization. Full article

Quinoa (Chenopodium quinoa Willd.) is a highly nutritious crop known for its tolerance to salt stress; however, the molecular mechanisms underlying this trait remain poorly understood. This study aims to perform the in silico characterisation of calcium-dependent protein kinase (CPK) gene family sequences and to evaluate their expression profiles under salt stress conditions. Using bioinformatics tools, CPK family gene sequences were identified and in silico-characterised, including conserved domains, cis-regulatory motifs, and physicochemical properties. Experimentally, two contrasting accessions were compared: a salt-tolerant one (UNSA_VP033) and a salt-sensitive one (UNSA_VP021). Salt tolerance indices were determined during germination, gene expression levels were quantified by RT-qPCR, and antioxidant enzyme activities, along with malondialdehyde (MDA) content, were evaluated under different NaCl concentrations. Sixteen sequences with characteristic CPK family domains were identified. Promoter analysis revealed cis-elements associated with hormonal and stress responses. Physicochemical parameters predicted proteins of 50–60 kDa with variable isoelectric points. Experimentally, UNSA_VP033 showed the significant overexpression of CqCPK12, CqCPK17, CqCPK20, and CqCPK32, correlated with the higher antioxidant activity of superoxide dismutase (SOD) and peroxidase (POD), and lower MDA levels at 200 mM NaCl. In contrast, the sensitive accession exhibited significant reductions in gene expression and antioxidant activity. In conclusion CPK genes play a key role in the salt stress response in quinoa, particularly CqCPK12, CqCPK17, CqCPK20, and CqCPK32 in the tolerant accession. These findings may contribute to the development of more salt-tolerant varieties, thereby enhancing agricultural sustainability in saline soils. Full article

The phosphodiesterase 1 genes PDE1A, PDE1B, and PDE1C encode calcium-regulated cyclic nucleotide phosphodiesterases that mediate the interplay between calcium and cyclic nucleotide signaling in the brain, heart, and vasculature. While an inhibitory domain and a calmodulin-binding domain have been identified in PDE1, the mechanism of regulation is not understood. In this study, we investigated the regulatory mechanism through a series of experiments. The experimental data, supported by AlphaFold structure predictions, consistently point to the following model of PDE1 regulation: In the absence of calcium, the inhibitory domain of PDE1 binds to and blocks the catalytic site via molecular interactions that closely resemble those observed in autoinhibited PDE4. Upon calcium/calmodulin binding to PDE1’s calmodulin-binding domain, steric constraints prevent the inhibitory domain from reaching the catalytic site, thereby activating PDE1. Understanding this mode of PDE1 regulation may open new avenues for pharmacological intervention. Moreover, it establishes PDE1 and PDE4 as a second mechanistic class of phosphodiesterase regulation in addition to the GAF-domain-mediated regulation known to control the activity of several other PDEs. Full article

Point cloud quality assessment remains a critical challenge due to the high dimensionality and irregular structure of 3D data, as well as the need to align objective predictions with human perception. To solve this, we suggest a novel graph-based learning architecture that integrates perceptual features with advanced graph neural networks. Our method consists of four main stages: First, key perceptual features, including curvature, saliency, and color, are extracted to capture relevant geometric and visual distortions. Second, a graph-based representation of the point cloud is created using these characteristics, where nodes represent perceptual clusters and weighted edges encode their feature similarities, yielding a structured adjacency matrix. Third, a novel Graph Attention Network Transformer Fusion (GATF) module dynamically refines the importance of these features and generates a unified, view-specific representation. Finally, a Graph Convolutional Network (GCN) regresses the fused features into a final quality score. We validate our approach on three benchmark datasets: ICIP2020, WPC, and SJTU-PCQA. Experimental results demonstrate that our method achieves high correlation with human subjective scores, outperforming existing state-of-the-art metrics by effectively modeling the perceptual mechanisms of quality judgment. Full article

Driving fatigue poses a serious threat to road safety. To detect fatigue accurately and thereby improve vehicle safety, this paper proposes a novel semi-dry electrode with the ability to automatically replenish the conductive fluid for monitoring driving fatigue. This semi-dry electrode not only integrates the advantages of both wet and dry electrodes but also incorporates an automatic conductive fluid replenishment mechanism. This design significantly extends the operational lifespan of the electrode while mitigating the limitations of manual replenishment, particularly the risk of signal interference. Additionally, this study adopts a transfer learning approach to detect driving fatigue by analyzing electroencephalography (EEG) signals. The experimental results indicate that this method effectively addresses the issue of data sparsity in real-time fatigue monitoring, overcomes the limitations of traditional algorithms, shows strong generalization performance and cross-domain adaptability, and achieves faster response times with enhanced accuracy. The semi-dry electrode and transfer learning algorithm proposed in this study can provide rapid and accurate detection of driving fatigue, thereby enabling timely alerts or interventions. This approach effectively mitigates the risk of traffic accidents and enhances both vehicle and road traffic safety. Full article

Direct power control (DPC) is a widely accepted control scheme utilized in renewable energy applications owing to its several advantages over other control mechanisms, including its simplicity, ease of implementation, and faster response. However, DPC suffers from inherent drawbacks and limitations that constrain its applicability. These restrictions include notable ripples in active power and torque, as well as poor power quality brought on by the usage of a hysteresis regulator for capacity management. To address these issues and overcome the limitations of DPC, this study proposes a novel approach that incorporates artificial neural networks (ANNs) into DPC. The proposed technique focuses on doubly fed induction generators (DFIGs) and is validated through experimental testing. ANNs are employed to recompense for the deficiencies of the hysteresis controller and switching table. The intelligent DPC technique is then compared to three other strategies: classic DPC, backstepping control, and integral sliding-mode control. Various tests are conducted to compare the ripple ratio, current quality, durability, response time, and reference tracking. The validity and robustness of the proposed intelligent DPC for DFIGs are verified through both simulation and experimental results obtained from the MATLAB/Simulink environment and the Real-Time Interface (RTI) of the dSPACE DS1104 controller card. The results confirm that the intelligent DPC outperforms conventional control strategies in terms of stator current harmonic distortion, dynamic response, power ripple minimization, reference tracking accuracy, robustness, and overshoot reduction. Overall, the intelligent DPC exhibits superior performance across all evaluated criteria compared to the alternative approaches. Full article

Electroencephalography (EEG) provides insights into the neural mechanisms underlying attention, response inhibition, and distraction in cognitive tasks. This dataset was collected to examine neural activity in young drivers and non-drivers performing Go/No-Go tasks under visual and auditory distraction conditions. A total of 40 university students (20 drivers, 20 non-drivers; balanced by sex) completed eight experimental blocks combining visual or auditory stimuli with realistic distractions, such as text message notifications and phone call simulations. EEG was recorded using a 16-channel BrainAccess MIDI system at 250 Hz. Experiments 1, 3, 5, and 7 served as transitional blocks without participant responses and were excluded from behavioral and event-related potential analyses; however, their EEG recordings and event markers are included for baseline or exploratory analyses. The dataset comprises raw EEG files, event markers for Go/No-Go stimuli and distractions, and metadata on participant demographics and mobile phone usage. This resource enables studies of attentional control, inhibitory processes, and distraction-related neural dynamics, supporting research in cognitive neuroscience, brain–computer interfaces, and transportation safety. Full article

In the field of industrial fault detection, accurate and timely fault identification is crucial for ensuring production safety and efficiency. Effective feature selection (FS) methods can significantly enhance detection performance in this process. However, the recently proposed Black-winged Kite Algorithm (BKA) tends to suffer from premature convergence and local optima when handling high-dimensional feature spaces. To address these limitations, this paper proposes an improved Black-winged Kite Algorithm (IBKA). This algorithm integrates two novel enhancement mechanisms: First, the Stagnation-Triggered Diversification Mechanism monitors the algorithm’s convergence state and applies mild perturbations to the worst-performing individuals upon detecting stagnation, effectively preventing traps in local optima. Second, the Adaptive Weak Guidance Mechanism employs a conditional elite guidance strategy during the late optimization phase to provide subtle directional guidance to underperforming individuals, thereby improving convergence efficiency. We comprehensively evaluated the proposed IBKA across 26 benchmark functions. Results demonstrate superior performance in solution quality, convergence speed, and robustness compared to the original BKA and other advanced meta-heuristics. Furthermore, fault detection applications on public datasets validate the practical applicability of the binary version of the IBKA (bIBKA), showcasing significant improvements in detection accuracy and reliability. Experimental results confirm that these enhancement mechanisms effectively balance exploration and exploitation capabilities while preserving algorithmic simplicity and computational efficiency. Full article

The complex structures and diverse object categories in indoor environments pose significant challenges for point cloud semantic segmentation. To address the insufficient capability of extracting local features in complex scenes, this paper proposes a point cloud segmentation network based on neighborhood feature enhancement termed PKA-Net. First, to obtain richer and more discriminative feature representations, we design a local feature encoding module that extracts geometric features, color information, and spatial information from local regions of the point cloud for joint feature encoding. Furthermore, we enhance the hierarchical feature extraction by integrating Kolmogorov–Arnold Networks (KAN) to form the SAPK module, improving the network’s ability to fit complex geometric structures. A residual structure is also adopted to optimize feature propagation and alleviate the problem of gradient vanishing. Finally, we propose the dual attention mechanism C-MSCA, which dynamically selects and strengthens key features through the synergistic action of channel and spatial attention, enhancing the network’s perception of local details and global structure. To evaluate the performance of the proposed PKA-Net, extensive experiments were conducted on the S3DIS dataset. Experimental results demonstrate that PKA-Net improves OA by 2.1%, mAcc by 2.9%, and mIoU by 4% compared to the baseline model. It outperforms other mainstream models, delivering enhanced overall segmentation performance. Full article

Crack segmentation in images plays a pivotal role in the monitoring of structural surfaces, serving as a fundamental technique for assessing structural integrity. However, existing methods that rely solely on RGB images exhibit high sensitivity to light conditions, which significantly restricts their adaptability in complex environmental scenarios. To address this, we propose a structure-aware progressive multi-modal fusion network (SPMFNet) for RGB-thermal (RGB-T) crack segmentation. The main idea is to integrate complementary information from RGB and thermal images and incorporate structural priors (edge information) to achieve accurate segmentation. Here, to better fuse multi-layer features from different modalities, a progressive multi-modal fusion strategy is designed. In the shallow encoder layers, two gate control attention (GCA) modules are introduced to dynamically regulate the fusion process through a gating mechanism, allowing the network to adaptively integrate modality-specific structural details based on the input. In the deeper layers, two attention feature fusion (AFF) modules are employed to enhance semantic consistency by leveraging both local and global attention, thereby facilitating the effective interaction and complementarity of high-level multi-modal features. In addition, edge prior information is introduced to encourage the predicted crack regions to preserve structural integrity, which is constrained by a joint loss of edge-guided loss, multi-scale focal loss, and adaptive fusion loss. Experimental results on publicly available RGB-T crack detection datasets demonstrate that the proposed method outperforms both classical and advanced approaches, verifying the effectiveness of the progressive fusion strategy and the utilization of the structural prior. Full article

This study investigated the influence of the cooling process on the microstructure and mechanical properties of high-strength, high-ductility ship plate steel. The transformation temperature ranges for ferrite (F) and bainite (B) for the experimental steel were determined through thermal simulation experiments. Based on these findings, hot-rolling experiments in laboratory were designed to elucidate the influence of three different cooling paths on the resultant microstructure and mechanical properties. The results demonstrate that the two-stage (air cooling + water cooling) and three-stage (water cooling + air cooling + water cooling) processes after rolling enhance the strength through phase transformation and precipitation strengthening mechanisms. The three-stage process provides an additional fine-grain strengthening effect. Compared to the F+Pearlite (P) or B microstructures produced by single-stage cooling, the F+B dual-phase steel obtained through these multi-stage cooling routes exhibits superior ductility at a comparable yield strength grade. Notably, the two-stage cooling mode proves particularly effective in enhancing ductility. These findings provide a theoretical foundation for designing cooling processes for high-strength, high-ductility ship plate steel. Full article

Counter-current, spontaneous imbibition of brine into oil-saturated rocks is a critical process for recovery of bypassed oil in carbonate reservoirs. However, the classic Amott-cell test introduces experimental artifacts that distort the true dynamics of oil recovery, complicating the interpretation and modeling of recovery histories. In this study, we applied a modified Amott procedure to eliminate these artifacts, producing smooth and reproducible recovery histories for both water-wet and mixed-wet carbonate core plugs saturated with brine and oil. By applying Generalized Extreme Value (GEV) statistics, we modeled cumulative oil production and showed that a GEV model is able to capture the essentially non-equilibrium nature of spontaneous imbibition. Our results demonstrate that water-wet systems exhibit faster recovery rates and shorter induction times due to favorable capillary forces, while mixed-wet samples have slower dynamics and longer induction times, reflecting the influence of wettability alterations. We demonstrate that the GEV fitting parameters systematically correlate with key rock–fluid properties, such as wettability, oil viscosity, and pore network characteristics, offering a semi-quantitative approach to analyze recovery behavior. This study demonstrates the potential of a GEV-based statistical model to deepen understanding of the spontaneous imbibition mechanisms and to enhance predictive capabilities for oil production dynamics. Full article