Search Results (896)

Recent advancements in deep learning have significantly improved fault diagnosis methods. However, challenges such as insufficient feature extraction, limited long-range dependency modeling, and environmental noise continue to hinder their effectiveness. This paper presents a novel mixture of multi-view convolutional (MOM-Conv) layers integrating the Mixture of Experts (MOE) mechanism. This design effectively captures and fuses both local and contextual information, thereby enhancing feature extraction and representation. This proposed approach aims to improve prediction accuracy under varying noise conditions, particularly in rolling ball bearing systems characterized by noisy signals. Additionally, we propose the Mamba-enhanced adaptive self-attention long short-term memory (MASA-LSTM) model, which effectively captures both global and local dependencies in ultra-long time series data. This model addresses the limitations of traditional models in extracting long-range dependencies from such signals. The architecture also integrates a multi-step temporal state fusion mechanism to optimize information flow and incorporates adaptive parameter tuning, thereby improving dynamic adaptability within the LSTM framework. To further mitigate the impact of noise, we transform vibration signals into denoised multi-channel representations, enhancing model stability in noisy environments. Experimental results show that our proposed model outperforms existing state-of-the-art approaches on both the Paderborn and Case Western Reserve University bearing datasets, demonstrating remarkable robustness and effectiveness across various noise levels. Full article

Roll misalignment in roll-to-roll (R2R) processes is a critical cause of lateral displacement and tension imbalance, leading to dimensional instability and surface defects in polymer films. Conventional analyses based on beam or camber models often require complex calibration and additional sensing, which can limit their applicability in real-time production environments. This study introduces a diagnostic approach that estimates web misalignment directly from side-to-side tension differences measured in roll-to-roll (R2R) systems. The method eliminates the need for additional sensors and complex geometric calibration, simplifying system setup. The correlation between tension imbalance, lateral displacement, and the equivalent misalignment angle was experimentally established. Our approach produced accurate predictions across various process conditions, including different roll misalignments and applied tensions, and we found that reducing tension “hunting” further enhances prediction stability. This study demonstrates that the proposed tension-based approach can complement existing systems and reduce the reliance on complex external sensing for diagnostic checks of misalignment. By simplifying alignment diagnostics, the method provides a practical route to enhance process setup, reduce downtime, and improve the uniformity of polymer films in continuous manufacturing. Full article

Three-wheeled vehicles are increasingly adopted as sustainable transport solutions, but their asymmetric design and lightweight structure make them vulnerable to ride discomfort and rollover instability. This study develops a high-fidelity 12-degrees-of-freedom (DOF) dynamic model in MATLAB/Simulink and MSC ADAMS to analyze and improve ride comfort, handling, and roll stability. The model captures longitudinal, lateral, vertical, roll, pitch, and yaw motions, along with tire dynamics represented through the Magic Formula, and is validated using real-world data from an instrumented test vehicle. In this research, both active and passive control strategies were separately implemented and studied. The active strategy involves an Active Vehicle Roll Dynamics Control (VRDC) system with an active rear suspension to suppress roll and yaw during aggressive maneuvers. The passive strategy focuses on improving rollover resistance by modulating throttle input based on sensor data from gyroscopes, accelerometers, and compasses. Simulation and experimental results show that each strategy, when applied independently, enhances roll stability, reduces yaw rate deviations, and improves handling performance. These findings demonstrate the effectiveness of both approaches in improving the safety and dynamic behavior of electric three-wheeled vehicles under real-world conditions. Full article

The optimization of the microstructural homogeneity and residual stress distribution in Ti sputtering targets for OLED 6G applications is essential for improving dimensional stability, durability, and deposition performance. Herein, 3N Ti plates were hot-rolled at 730 °C and then annealed at 600 °C and 700 °C for different durations to investigate the effects of annealing parameters on microstructural evolution and stress relaxation. X-ray diffraction analysis revealed that hexagonal α-Ti with progressive development of the (002) orientation was produced during annealing under all the conditions. Electron backscatter diffraction analyses showed that short-time annealing at 600 °C (≤30 min) generated heterogeneous grains, high dislocation density, and mixed grain boundary character, whereas extended annealing (≥60 min) produced a more uniform microstructure. However, residual stress differences between the plate center and edge remained significant under this condition. Conversely, annealing at 700 °C promoted progressive recrystallization, as indicated by increased high-angle grain boundary fractions and decreased kernel average misorientation values, and facilitated grain growth stabilization across the plate. Prolonged annealing improved microstructural and residual stress uniformity significantly, and near-complete homogenization was achieved after 5 h. These findings demonstrate that annealing at 700 °C for sufficient time is optimal for producing homogeneous microstructures and uniform residual stress distributions, providing valuable guidelines for Ti sputtering target processing. Full article

The reclamation of exhausted open-pit coal mines is an energy-intensive and costly process. Traditional methods offer no economic return. This study explores the feasibility of using autonomous electric dump trucks (EDTs) to fill the pit, leveraging regenerative braking during descent to generate energy and reduce operational costs. A comprehensive energy balance model was developed based on the operational cycle of the Komatsu HD605-7 (E-Dumper) in the unique downhill-loaded logistics of the Pery quarry. The model incorporates vehicle dynamics equations, including rolling resistance, gradient, and aerodynamic forces, to calculate net energy consumption per cycle. Three energy storage system (ESS) configurations were compared: NMC/NCA batteries, LiFePO₄ (LFP) batteries, and a hybrid LFP + supercapacitor (SC) system. Simulation results demonstrate that the net energy per cycle decreases with increasing payload capacity, even becoming negative (net energy generation) for loads above 110 tons due to powerful regenerative braking on the 13% descent grade. The hybrid LFP + SC system proved most efficient, achieving the lowest specific energy consumption (kWh/ton) by effectively capturing high-power regenerative currents. While LFP batteries have a lower energy density, their superior cycle life, thermal stability, and safety make them the optimal choice for the harsh mining environment. The proposed operation strategy, utilizing EDTs in a downhill-loaded cycle, transforms mine reclamation from a cost center into a potentially energy-neutral or even energy-positive process. A hybrid ESS with LFP batteries and supercapacitors is recommended as the most reliable and efficient solution for this specific application. Full article

Changes in nearshore water quality directly influence ecosystem stability and the sustainability of aquaculture production. Among these factors, rapid fluctuations in dissolved oxygen (DO) can compromise the physiological functions of aquatic organisms, often leading to mass mortality events and significant economic losses. To enhance the predictive capability of DO in marine ranching areas, this study evaluates multiple forecasting approaches, including AutoARIMA, XGBoost, BlockRNN-LSTM, BlockRNN-GRU, TCN, Transformer, and an ensemble model that integrates these methods. Using hourly DO observations from coastal buoys, we performed multi-step rolling forecasts and systematically assessed model performance across multiple evaluation metrics (MAPE, RMSE, and R²), complemented by residual and error distribution analyses. The results show that the ensemble model, based on deep learning techniques, consistently outperforms individual models, achieving higher forecast robustness and more effective variance control, with MAPE values maintained below 4% across all three buoys. Building upon these findings, we further developed and deployed a DO forecasting and early-warning system centered on the ensemble framework. This system enables end-to-end functionality, including automatic data acquisition, real-time prediction, hypoxia risk identification, and alert dissemination. It has already been applied in marine ranching operations, providing 1–3 day forecasts of DO dynamics, facilitating the early detection of hypoxia risks, and significantly improving the scientific support and responsiveness of aquaculture management. Full article

Crystal grain size control in steel is critical for achieving mechanical properties. This study investigates the effectiveness of microalloying with titanium, niobium, zirconium, and yttrium to inhibit grain growth with the pinning effect. The comparison of selected microalloying elements in the exact same conditions is crucial for understanding their effect and is novel. Hot-rolled samples were annealed across a wide range of temperatures (1050 to 1200 °C) for up to eight hours. Microstructural analysis confirmed the presence of stable precipitates and non-metallic inclusions such as Nb(C,N), Ti(C,N), ZrO₂, and Y₂O₃ acting as obstacles to grain boundary migration. All microalloying elements significantly outperformed the reference steel, but their effectiveness was highly dependent on the annealing temperature. Titanium was the most effective inhibitor at lower temperatures (1050 °C), while zirconium maintained control up to 1150 °C. Critically, at the highest temperature of 1200 °C, only the yttrium-alloyed steel retained a fine-grain structure, demonstrating superior thermal stability. Niobium, conversely, only showed a minimal effect at 1050 °C, though this grade also exhibited the highest hardness (up to 165 HB) due to precipitation hardening. The kinetics of grain growth were successfully modeled using the Arrhenius-type Sellars–Whiteman equation, accurately describing the behavior for up to four hours of annealing. The findings provide critical insight for selecting optimal microalloying strategies based on maximum operating temperature. Full article

In the hot strip rolling process, inter-stand speed coordination directly affects product quality and production stability. Traditional linear speed compensation strategies perform poorly under extreme conditions such as strip tension and strip piling, making it difficult to maintain stable loop control. This study proposes a speed compensation strategy that integrates Particle Swarm Optimization (PSO) with Model Predictive Control (MPC). Based on the mechanism of hot rolling, a nonlinear state-space model is constructed, in which the compensation parameter is treated as an optimization variable to formulate a rolling optimization problem. PSO is employed to globally solve the nonlinear MPC problem, yielding an optimal compensation sequence that adapts to disturbance variations. The proposed algorithm can adaptively adjust the speed compensation parameter under typical strip piling and strip tension conditions, thereby achieving stable loop regulation and maintaining the looper angle within the desired range. This effectively addresses the speed coordination problem under abnormal conditions in hot strip rolling, improving the control performance. The experimental results verify the effectiveness of the proposed method. Full article

As an innovative advancement beyond cross-laminated timber (CLT), cross-laminated bamboo and timber (CLBT) combines sustainability with enhanced structural performance. This review critically assesses the current state of CLBT research, focusing on its failure mechanisms, mechanical properties, and predictive theoretical models. Key findings indicate that CLBT exhibits superior rolling shear strength, bending stiffness, and stability compared to conventional CLT, achieved through optimized hybrid layering and manufacturing techniques. The integration of bamboo not only improves mechanical performance but also promotes diversification of raw materials and more efficient use of regional biomass. This paper highlights the potential of CLBT as a high-performance, eco-friendly construction material and identifies key research gaps and future directions to facilitate its standardized application. Full article

This study presents a self-supervised learning framework for detecting frequency disturbances in power systems using high-resolution time series data. Employing data from the UK National Grid, we apply the Temporal Contrastive Self-Supervised Learning (TC-TSS) approach to learn task-agnostic embeddings from unlabelled 60-s rolling window segments of frequency measurements. The learned representations are then used to train four traditional classifiers, Logistic Regression (LR), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and Random Forest (RF), for binary classification of frequency stability events. The proposed method is evaluated using over 15 million data points spanning six months of system operation data. Results show that classifiers trained on TC-TSS embeddings performed better than those using raw input features, particularly in detecting rare disturbance events. ROC-AUC scores for MLP and SVM models reach as high as 0.98, indicating excellent separability in the latent space. Visualisations using UMAP and t-SNE further demonstrate the clustering quality of TC-TSS features. This study highlights the effectiveness of contrastive representation learning in the energy domain, particularly under conditions of limited labelled data, and proves its suitability for integration into real-time smart grid applications. Full article

This paper introduces a new approach to obtain robust tracking performance, disturbance resistance, and input variation resistance, and eliminate chattering phenomena in the control signal and output responses of an unmanned aerial vehicle (UAV) quadcopter with parametric uncertainty. This method involves a modified exponential reaching law (ERL) of the sliding mode control (SMC) based on a Gaussian kernel function with a continuous nonlinear Smoother Signum Function (SSF). The smooth continuous signum function is proposed as a substitute for the signum function to prevent the chattering effect caused by the switching sliding surface. The closed-loop system’s stability is ensured according to Lyapunov’s stability theory. Optimal trajectory tracking is attained based on particle swarm optimization (PSO) to select the controller parameters. A comparative analysis with a classical hierarchical SMC based on different ERLs (sign function, saturation function, and SSF) is presented to further substantiate the superior performance of the proposed controller. The outcomes of the simulation prove that the suggested controller has much better effectiveness, unknown disturbance resistance, input variation resistance, and parametric uncertainty than the other controllers, which produce chattering and make the control signal range fall within unrealistic values. Furthermore, the suggested controller outperforms the classical SMC by reducing the tracking integral mean squared errors by 96.154% for roll, 98.535% for pitch, 44.81% for yaw, and 22.8% for altitude under normal flight conditions. It also reduces the tracking mean squared errors by 99.05% for roll, 99.26% for pitch, 40.18% for yaw, and 99.998% for altitude under trajectory tracking flight conditions in the presence of external disturbances. Therefore, the proposed controller can efficiently follow paths in the presence of parameter uncertainties, input variation, and external disturbances. Full article

The development of stable and efficient nanolubricants remains one of the main challenges in tribology due to particle agglomeration, poor long-term stability, and inconsistent frictional behavior under boundary lubrication. This study investigates the tribological performance of SAE 10W-40 engine oil enhanced with titanium dioxide (TiO₂) nanoparticles subjected to thermal treatments. TiO₂ powders (Degussa P25, ~30 nm) were calcined at 450 °C, 550 °C, 650 °C, and 750 °C, and incorporated into the base oil at a constant concentration of 0.05 wt%. Tribological tests were conducted using a four-ball tribometer under ASTM D4172 conditions (396 N, 1200 rpm, 30 min) at both ambient (23 °C) and elevated (75 °C) temperatures. The coefficient of friction (COF) and wear scar area (WSA) were measured, while the surface morphology was analyzed via 3D optical profilometry, SEM, and EDS. The results indicate that TiO₂ nanoparticles thermally treated at 550 °C offered the best tribological behavior, exhibiting the lowest COF and smallest WSA at both test temperatures. The improved performance is attributed to optimized crystalline structure and enhanced dispersion stability after calcination. Although no Ti-based tribofilm was detected, smoother wear scars suggest physical surface protection mechanisms, such as rolling and asperity smoothing. These findings highlight the critical influence of thermal treatment on nanoparticle effectiveness and demonstrate the potential of optimized nanoadditized lubricants for advanced friction and wear reduction under boundary lubrication conditions, providing practical guidance for developing next generation nanolubricants with improved durability and efficiency under boundary lubrication conditions. Full article

This study proposes a hybrid method that combines a Genetic Algorithm (GA) with Digital Twin (DT) technology to address nighttime reactive power backfeed in large-scale photovoltaic (PV) power plants. First, the GA is employed to optimize the location and number of multitask inverters to minimize line losses and eliminate the reactive power backfeed. Subsequently, the DT continuously monitored the grid conditions and performed rolling dispatch to mitigate the residual reactive power caused by nighttime voltage fluctuations. Simulation results show that GA-based optimization reduces line losses from 0.346 to 0.2818 kW (18.6% reduction) and helps alleviate inverter thermal stress. When integrated with DTs, the method further improves voltage stability and demonstrates a strong adaptive control capability. The proposed GA–DT strategy can also be regarded as a potential AIoT application in PV plants, with the potential to reduce operational and maintenance costs and enhance the system reliability in the future. Full article

Background and Objectives: Adequate peri-implant soft tissue dimensions are essential for health, hygiene, and esthetics. When ridge volume is sufficient, phenotype modification may avoid bone grafting. This case report describes a pedicled roll flap performed concurrently with single-stage implant placement after spontaneous socket healing, without bone substitute, and assesses soft-tissue stability with serial intraoral scans. Clinical case: A single-tooth edentulous site underwent prosthetically driven, fully guided implant placement. A modified roll flap with vertical and palatal incisions was prepared; the de-epithelialized crestal connective tissue was elevated and rolled into a buccal envelope to augment thickness. No graft material was used. A provisional crown conditioned the emergence profile. Follow-up included photographs, radiographs, and intraoral scan superimpositions at 2 weeks, 3–4 months, 8 months, and 14 months after implant treatment. Healing was uneventful. Buccal soft-tissue thickness increased, keratinized mucosa was preserved, and midfacial levels remained stable. Emergence profile and papillae integrated harmoniously. Crestal bone levels were stable radiographically. Digital scans corroborated soft-tissue thickness maintenance. No donor-site morbidity occurred. Conclusions: In healed sockets with adequate bone, a modified pedicled roll flap at implant placement can thicken the peri-implant phenotype and achieve stable esthetic integration without bone substitutes. Full article

The high salt content, low permeability, and fragile structure of saline–alkali land severely constrain the construction and development of irrigation channels. Compaction is an effective means of improving the soil’s engineering performance. Previous studies in this field have mostly been limited to two-dimensional numerical simulations and generally lack systematic physical experiments to support their findings, resulting in an insufficient understanding of the three-dimensional deformation mechanism and macroscopic mechanical response of soil during compaction. In view of the above limitations, this study adopts a comprehensive research framework of “physical experiment–numerical simulation”. Conducting indoor rolling model tests of control variables and simultaneously constructing the corresponding 2D and 3D discrete element models based on the MatDEM platform revealed the influence of curing agent dosage (10% and 25%), loosely laid sample thickness (10 cm and 30 cm), and number of rolling passes on the compaction effect. The test results show that the degree of compaction increases in a typical three-stage pattern of “rapid rise–slow growth–gradual stabilization” with the number of rolling passes, and the number of economic rolling passes is from 4 to 6. Increasing the dosage of the curing agent and reducing the thickness of application both significantly improve the uniformity of compaction and the final density. Numerical simulation further reveals that the 3D model can more accurately reflect the three-dimensional stress state of the soil and the spatial movement of particles, and that the simulation results are in higher agreement with the experimental data. The 2D model has greater computational efficiency and can capture the main compaction trends under specific simplified conditions, but it has deficiencies in quantitative accuracy. This study verified the effectiveness and advantages of MatDEM in simulating complex geotechnical compaction processes, providing theoretical support for an in-depth understanding of compaction mechanisms and the optimization of construction parameters using discrete element methods. Full article