Search Results (914)

The finite element model (FEM) for induction motors (IM) was developed and validated through experimental testing. The validated FEM provides a reliable basis for further optimization of the electric machine. A strong sliding mode technique, in conjunction with field-oriented control (FOC), is proposed for speed control of the IM. The sliding mode controller ensures steady functioning in the face of ambiguities and disruptions, while FOC enables precise control of the motor’s magnetic field. This combination enhances both the efficiency and accuracy of speed control in IM, making it a valuable tool for industrial applications. The proposed sliding mode control (SMC) was fine-tuned using the advantages produced by the ant colony optimization algorithm. This approach aids in resolving issues and delivers optimal speed and field responses. Simulation and experimental results demonstrate the effectiveness of the proposed approach. The optimized induction motor achieved a 28% reduction in rotor Joule losses, resulting in improved energy efficiency. Additionally, using Ant Colony Optimization to adjust the SMC parameters led to a 99.74% reduction in speed tracking error and a 99.59% reduction in flux error compared to traditional manual tuning. These substantial improvements confirm the superiority of the proposed method for high-performance and energy-efficient electric vehicle applications. Full article

Background: The anatomical variability of the nasal cavity affects intranasal drug delivery, especially to the olfactory region for nose-to-brain treatments. While previous studies used average models or 2D measurements to account for inter-individual variability, 3D shape variation of the region crossed by drug particles that target the olfactory area, namely the region of interest (ROI), remains unexplored to our knowledge. Methods: A geometric morphometric analysis was performed on the ROI of 151 unilateral nasal cavities from the CT scans of 78 patients. Ten fixed landmarks and 200 sliding semi-landmarks were digitized, using Viewbox 4.0, and standardized via Generalized Procrustes Analysis. Shape variability was analyzed through Principal Component Analysis. Morphological clusters were identified using Hierarchical Clustering on Principal Components, and characterized with MANOVA, ANOVA, and Tukey tests. Results: Validation tests confirmed the method’s reliability. Three morphological clusters were identified. Variations were significant in the X and Y axes, and minimal in Z. Cluster 1 had a broader anterior cavity with shallower turbinate onset, likely improving olfactory accessibility. Cluster 3 was narrower with deeper turbinates, potentially limiting olfactory accessibility. Cluster 2 was intermediate. Notably, 31.5% of patients had at least one cavity in cluster 1. Conclusion: Three distinct morphotypes of the region of the nasal cavity that potentially influence accessibility were identified. These findings will guide future computational fluid dynamics studies for optimizing nasal drug targeting and represent a practical step toward tailoring nose-to-brain drug delivery strategies in alignment with the principles of personalized medicine. Full article

Accurate recognition of estrus behavior in sows is of great importance for achieving scientific breeding management, improving reproductive efficiency, and reducing labor costs in modern pig farms. However, due to the evident spatiotemporal continuity, stage-specific changes, and ambiguous category boundaries of estrus behaviors, traditional methods based on static images or manual observation suffer from low efficiency and high misjudgment rates in practical applications. To address these issues, this study follows a video-based behavior recognition approach and designs three deep learning model structures: (Convolutional Neural Network combined with Long Short-Term Memory) CNN + LSTM, (Three-Dimensional Convolutional Neural Network) 3D-CNN, and (Convolutional Neural Network combined with Temporal Convolutional Network) CNN + TCN, aiming to achieve high-precision recognition and classification of four key behaviors (SOB, SOC, SOS, SOW) during the estrus process in sows. In terms of data processing, a sliding window strategy was adopted to slice the annotated video sequences, constructing image sequence samples with uniform length. The training, validation, and test sets were divided in a 6:2:2 ratio, ensuring balanced distribution of behavior categories. During model training and evaluation, a systematic comparative analysis was conducted from multiple aspects, including loss function variation (Loss), accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC curves. Experimental results show that the CNN + TCN model performed best overall, with validation accuracy exceeding 0.98, F1-score approaching 1.0, and an average AUC value of 0.9988, demonstrating excellent recognition accuracy and generalization ability. The 3D-CNN model performed well in recognizing short-term dynamic behaviors (such as SOC), achieving a validation F1-score of 0.91 and an AUC of 0.770, making it suitable for high-frequency, short-duration behavior recognition. The CNN + LSTM model exhibited good robustness in handling long-duration static behaviors (such as SOB and SOS), with a validation accuracy of 0.99 and an AUC of 0.9965. In addition, this study further developed an intelligent recognition system with front-end visualization, result feedback, and user interaction functions, enabling local deployment and real-time application of the model in farming environments, thus providing practical technical support for the digitalization and intelligentization of reproductive management in large-scale pig farms. Full article

This study evaluates the tribological properties of poly-DL-lactic acid (PDLLA) nanosheets attached to shark-skin surfaces with varying textures. The main goal was to assess friction reduction in samples with different surface textures and investigate the influence of PDLLA nanosheets on tribological behaviors. Biomimetic shark skin was created using a polydimethylsiloxane (PDMS)-embedded stamping method (PEES) that replicates shark skin’s unique texture, which reduces friction and drag in aquatic environments. PDLLA nanosheets, with a controlled thickness of several tens of nanometers, were fabricated and attached to the PDMS surfaces. The morphological characteristics of the materials were analyzed before and after attaching the PDLLA nanosheets using scanning electron microscopy (SEM), revealing the uniformity and adherence of the nanosheets to the PDMS surfaces. Friction tests were conducted using force transducers to measure the friction coefficients of biomimetic shark skin, biological models, and flat PDMS and silicon substrates, allowing a comprehensive comparison of frictional properties. Additionally, sliding tests with human fingers were performed to assess friction coefficients between various fingerprint shapes and sample surfaces. This aspect of the study is critical for understanding how human skin interacts with biomimetic materials in real-world applications, such as wearable devices. These findings clarify the relationship between surface texture, nanosheets, and their tribological performance against human skin, thereby contributing to the development of materials with enhanced friction-reducing properties for applications such as surface coatings, substrates for wearable devices, and wound dressings. Full article

This paper presents a hardware-in-the-loop (HIL) simulation case study on the application of High-Order Sliding Mode Control (HOSMC) to a flexible-link robotic arm. The developed HIL platform combines physical hardware components with a simulated plant model, enabling real-time testing of control algorithms under realistic operating conditions without requiring a full-scale prototype. HOSMC, an advanced nonlinear control strategy, mitigates the chattering effects inherent in conventional sliding mode control by driving the system to a reduced-order sliding manifold within a finite time, resulting in smoother actuator commands and reduced mechanical stress. Flexible-link arms, while lightweight and energy-efficient, are inherently nonlinear and prone to vibration, posing significant control challenges. In this case study, the experimental HIL environment is used to evaluate HOSMC performance, demonstrating improved trajectory tracking, reduced overshoot, and minimized steady-state error. The results confirm that HIL simulation offers an effective bridge between theoretical control design and practical implementation for advanced robotic systems. Full article

Prostate cancer (PCa) diagnosis has historically relied on the prostate-specific antigen (PSA) testing. Although the screening significantly reduces mortality rates, PSA has low specificity with risks of overdiagnosis and overtreatment. These limitations highlight the need for a more accurate diagnostic approach. Emerging technologies, such as artificial intelligence (AI), novel biomarkers, and advanced imaging techniques, offer promising avenues to enhance the accuracy and efficiency of PCa diagnosis and risk stratification. This narrative review comprehensively analyzed the current literature, focusing on new tools aiding PCa diagnosis (AI-driven image interpretation, radiomics, genomic classifiers, biomarkers, and multimodal data integration) with consideration for technical, regulatory, and ethical challenges related to clinical implementation of AI-based technologies. A literature search was performed using the PubMed and MEDLINE databases to identify relevant peer-reviewed articles published in English using the search terms “prostate cancer,” “artificial intelligence,” “machine learning,” “deep learning,” “MRI,” “histopathology,” and “diagnosis.” Articles were selected based on their relevance to AI-assisted diagnostic tools, clinical utility, and performance metrics. In addition, a separate section was developed initially to contextualize the limitations of current PSA-based screening approaches. The reviewed studies showed that AI had significant utility in prostate mpMRI interpretation (lesion detection; Gleason grading) with high accuracy and high reproducibility. For the pathologist, AI-driven algorithms improve the diagnostic accuracy of digital slide evaluation for histologic diagnosis of prostate cancer and automated Gleason score grading. Genomic tools such as the Oncotype DX test, combined with AI, could also allow for tailored and individualized risk prediction. Overall, multimodal models integrating clinical, imaging, and molecular data often outperform traditional PSA-based strategies and reduce unnecessary biopsies. Transition from PSA-centered toward AI-driven, biomarker-supported, and image-enhanced diagnosis marks a critical evolution in PCa diagnosis. Full article

Wet clutches are extensively employed in automotive transmission systems due to their benefits of smooth shift and stable operation. However, existing methodologies have not yet thoroughly analyzed the real-time dynamic temperature distribution of wet clutches, and the heating and heat transfer mechanisms during the sliding friction process of friction pairs remain underexplored. To address these gaps, this study proposes a real-time dynamic temperature prediction model for wet clutches and investigates the heat generation and transfer mechanisms in the friction pair sliding process. Specifically, the heat production and exchange dynamics of the wet clutch friction pair are systematically analyzed, followed by an examination of the real-time temperature variation of the separator plate under both high-slip and low-slip speed conditions. In the numerical simulations, the predicted temperature values from the proposed model demonstrate excellent agreement with experimental measurements, with dynamic peak temperature discrepancies remaining within ±2 °C. Furthermore, the validated temperature evolution laws are corroborated by experimental results obtained from a dedicated wet clutch performance test rig, thereby providing comprehensive empirical verification of the proposed real-time dynamic temperature prediction framework for wet clutch separator plates. In summary, the model can accurately capture the temperature variation characteristics of wet clutches under different operating conditions, providing a reliable basis for real-time thermal management of transmission systems. It holds significant practical value for optimizing cooling system design, extending clutch service life, and ensuring shifting quality in vehicles. Full article

This paper focuses on the issues of roof movement and ground pressure behavior in large-height mining extraction of shallow coal seams. By adopting a combined method of theoretical analysis and physical simulation experiments, it establishes a mechanical model for the rotational subsidence of key blocks and a physical simulation test model to conduct stability analysis on the rotational subsidence of key blocks, thereby revealing the characteristics and laws of roof movement. The findings indicate that the horizontal thrust during the rotational subsidence of key blocks increases non-linearly with the rotation angle, exhibiting a higher growth rate when the block size coefficient is less than 0.5. Two modes of instability—sliding and deformation—are observed for key blocks. To prevent sliding instability, the block size coefficient should be maintained below 0.75; however, sliding instability is likely to occur when the rotation angle exceeds 10°. Conversely, smaller rotation angles and larger block size coefficients reduce the likelihood of deformation instability. The reasonable working resistance of the support decreases with the increase in the rotation angle (it decreases sharply when the rotation angle exceeds 10°) and increases with the increase in the block size coefficient. Physical simulation indicates that roof movement is divided into three stages: immediate roof collapse, stratified fracturing and instability of the basic roof, and periodic fracturing of the basic roof. An increase in mining height accelerates the instability of the immediate roof, enlarges the opening of through-layer fissures, shortens the step distance of mining pressure, and heightens the risk of sudden pressure. The research results provide theoretical guidance for the safe and efficient mining with large mining height in shallow coal seams. Full article

This review explores recent advancements in morphing structures for Unmanned Ariel Vehicles (UAVs), focusing on mechanical designs and control strategies of quadrotors that enable real-time geometric reconfiguration. Morphing mechanisms, ranging from closed-loop linkages to bioinspired and compliant structures, are evaluated in terms of adaptability, actuation simplicity, and flight stability. Control approaches, including model predictive control, reinforcement learning, and sliding mode control, are analyzed for their effectiveness in handling dynamic morphology. The review also highlights key morphing wing concepts such as GNATSpar and Zigzag Wingbox, which enhance aerodynamic efficiency and structural flexibility. A novel concept featuring an inverted slider-crank mechanism (ISCM) is introduced, enabling dual-mode UAV operation for both aerial and terrestrial missions, which is particularly useful in scenarios like wildfire suppression where stability and operation longevity are crucial. This study emphasizes the importance of integrated design approaches that align mechanical transformation with adaptive control. Critical gaps in real-world testing, swarm coordination, and scalable morphing architectures are identified, suggesting future research directions for developing robust, mission-adaptive UAV systems. Full article

Enhanced lubrication is critical for improving gear wear resistance. Current research on surface textures has overlooked the fundamental role of structural connectivity. Inspired by biological scales, a biomimetic hexagonal texture (BHT) was innovatively designed for tooth flanks, featuring dual-orientation grooves (perpendicular and inclined to the rolling-sliding direction) with bidirectional interconnectivity. This design synergistically combines hydrodynamic effects and directional lubrication to achieve tribological breakthroughs. A lubrication model for line contact conditions was established. Subsequently, the texture parameters were then optimized using response surface methodology and numerical simulations. FZG gear tests demonstrated the superior performance of the optimized BHT, which achieved a substantial 82.83% reduction in the average wear area ratio and a 25.35% decrease in tooth profile deviation variation. This indicated that the biomimetic texture can effectively mitigate tooth surface wear, thereby extending the service life of gears. Furthermore, it significantly improves thermal management by enhancing convective heat transfer and lubricant distribution, as evidenced by a 7–11 °C rise in bulk lubricant temperature. This work elucidates the dual-mechanism coupling effect of bio-inspired textures in tribological enhancement, thus establishing a new paradigm for gear surface engineering. Full article

This paper introduces a novel sensorless speed control strategy for squirrel-cage induction motors, which ensures robust operation in the presence of external disturbances by applying the state feedback technique. Based on the induction motor model, the speed controller is synthesized by defining a sliding variable that is driven to zero through the supertwisting control law, ensuring the stabilization of the tracking error. The time derivative of the error variable is estimated using a robust differentiator based on the sliding-mode twisting algorithm, thereby eliminating the need to estimate the load torque. A robust observer is employed to estimate the rotor speed and flux linkages simultaneously. The convergence of the estimated rotor flux linkages is enforced through a discontinuous first-order sliding-mode input, while the convergence of the rotor speed estimate is attained via a quasi-continuous super-twisting sliding-mode input. In the proposed model, the inductance parameters are determined from the magnetizing inductance and the leakage inductances of the stator and rotor. A procedure is also presented for adjusting the stator resistance and leakage inductances, taking into account the squirrel-cage rotor type and the skin effect in alternating current conduction. The performance of the sensorless speed control system under variations in load torque and reference speed is validated through experimental testing. The rotor speed estimation provided by the robust observer is accurate. The reference speed tracking control, evaluated using a 1600–1700 rpm pulse train phase-shifted by 4 s with respect to a 0–0.5 N·m pulse train, demonstrates high precision. Full article

This article presents an in-depth study of a dynamic wireless power transfer (DWPT) system used to charge electric vehicles (EVs), with a focus on modeling and controlling a double-D (DD) coil structure. The chosen DD coil design improves energy transfer efficiency and minimizes mutual coupling between adjacent transmit coils, a common problem in dynamic applications. A comprehensive mathematical model is developed to account for the nonlinear dynamics of the system, i.e., when the vehicle is moving and misalignments and coupling variations occur. A robust nonlinear control method based on sliding mode control (SMC) is implemented to ensure stable operation and accurate regulation of the output voltage. The controller is tested in different scenarios where the vehicle speed changes, thus ensuring its robustness and stability under all operating conditions. Particular attention is paid to the critical transition zone, in which the receiver coil is placed between two transmitter coils in order to achieve minimal magnetic coupling. The simulation results demonstrate that the proposed controller offers a fast dynamic response (~0.07 s) and stable voltage tracking, even in the event of significant variations in mutual inductance and different EV movement speeds. These results confirm the effectiveness of the control approach and its potential for real-time charging of electric vehicles in large-scale DWPT applications. Full article

The creep failure of open-pit mine slopes with weak interlayers is one of the main types of slope instability in open-pit mines. The scientific and reasonable treatment of this type of landslide is of great significance for improving the quality of open-pit mining. In this study, we study a gently inclined and creep-type slope with weak interlayers in an open-pit mine in Inner Mongolia, China, and conduct systematic on-site engineering geological investigations, laboratory tests, and numerical simulations. The particle swarm optimization algorithm is introduced, and the creep model combining Burgers and Mohr–Coulomb is selected. Combined with triaxial compression creep test data, the creep model parameters of the weak interlayer soil are intelligently inverted. A typical profile is selected to analyze the stability of the slope. The results show that the creep of the weak interlayer is the main controlling factor for the deformation and failure of the slope. Under natural conditions, a clear continuous plastic zone appears at the front edge of the weak interlayer and the rear edge of the sliding body, resulting in slope instability and large deformation. Our results are in good agreement with the reality of engineering. Furthermore, we study the effectiveness of the local reinforcement treatment method for the weak interlayer. This study shows that local reinforcement of the weak interlayer is one of the most economical and effective means of preventing and controlling landslides. After reinforcement, the plastic zone of the slope only appears near the rear edge of the sliding body and the reinforced rock mass, with a poor connection, and the stability of the slope is good. Our results provide effective technical support for the treatment of this slope and offer a reference for the disaster prevention and mitigation of gently inclined and creep-type open-pit mine slopes with weak interlayers. Full article

To address the suboptimal leveling performance and insufficient slope stability of existing agricultural machinery chassis in hilly and mountainous regions, this study proposes an innovative omnidirectional leveling system based on a “double-layer frame” crawler-type agricultural chassis. The system employs servo-electric cylinders as its actuation components. A control model for the servo-electric cylinders has been established, accompanied by the design of an adaptive sliding mode controller (ASMC). A co-simulation platform was developed utilizing Matlab/Simulink and Adams to evaluate system performance. Comparative simulations were conducted between the ASMC and a conventional PID controller, followed by comprehensive machine testing. Experimental results demonstrate that the proposed double-layer frame crawler chassis achieves longitudinal leveling adjustments of up to 25° and lateral adjustments of 20°. Through structural optimization and the application of ASMC (in contrast to PID), both longitudinal and lateral leveling response times were reduced by 1.12 s and 0.95 s, respectively. Furthermore, leveling velocities increased by a factor of 1.5 in the longitudinal direction and by a factor of 1.3 in the lateral direction, while longitudinal and lateral angular accelerations decreased by 15.8% and 17.1%, respectively. Field tests confirm the system’s capability for adaptive leveling on inclined terrain, thereby validating the enhanced performance of the proposed omnidirectional leveling system. Full article

In lateral-joint-widening projects of multi-span continuous concrete box girder bridges, significant discrepancies in longitudinal shrinkage, creep deformation, and vertical displacement between the existing and newly added bridge sections can lead to stress concentration and subsequent concrete cracking. Notably, such incompatibility often results in pronounced overall lateral bending deformation, which compromises the structural safety and service reliability of the widened bridge. To address these challenges, this study proposes a novel sliding-type transverse connection structure. This innovative connection enables the independent development of longitudinal shrinkage and creep deformation in the new bridge superstructure relative to the old one through a sliding mechanism, thereby effectively mitigating stress concentration and minimizing overall bending deformation caused by differential deformations. To validate the feasibility and elucidate the load transfer mechanism of the proposed structure, both scaled model tests and finite element simulations were conducted. The results indicate that the connection not only effectively coordinates longitudinal deformation differences and accommodates vertical deformation between the flange plates of the new and old bridges, but also ensures efficient transverse load transfer through shear force transmission. The structural behavior is primarily governed by shear stress distribution. These findings demonstrate that the sliding-type transverse connection significantly improves deformation compatibility in bridge widening applications, thereby enhancing the mechanical performance and safety reliability of the overall structure. Full article