Search Results (1,155)

Pressure-sensitive adhesion arises at a specific rheological behavior of polymer systems, which should correlate with their relaxation properties, making them potentially useful for predicting and altering adhesive performance. This work systematically studied the rheology of eco-friendly pressure-sensitive adhesives based on non-crosslinked polyisobutylene ternary blends free of solvents and byproducts, which serve for reversible adhesive bonding. The ratio between individual polymer components differing in molecular weight affected the rheological, relaxation, and adhesion properties of the constituted adhesive blends, allowing for their tuning. The viscosity and viscoelasticity of the adhesives were studied using rotational rheometry, while their adhesive bonds with steel were examined by probe tack and shear lap tests at different temperatures. The adhesive bond durability at shear and pull-off detachments depended on the adhesive composition, temperature, and contact time under pressure. The double differentiation of the continuous relaxation spectra of the adhesives enabled the accurate determination of their characteristic relaxation times, which controlled the durability of the adhesive bonds. A universal linear correlation between the reduced failure time of adhesive bonds and their reduced formation time enabled the prediction of their durability with high precision (Pearson correlation coefficient = 0.958, p-value < 0.001) over at least a four-order-of-magnitude time range. The reduction in the formation/failure times of adhesive bonds was most accurately achieved using the longest relaxation time of the adhesives, associated with their highest-molecular-weight polyisobutylene component. Thus, the highest-molecular-weight polymer played a dominant role in adhesive performance, determining both the stress relaxation during the formation of adhesive bonds and their durability under applied load. In turn, this finding enables the prediction and improvement of adhesive bond durability by increasing the bond formation time (a durability rise by up to 10–100 times) and extending the adhesive’s longest relaxation time through elevating the molecular weight or proportion of its highest-molecular-weight component (a durability rise by 100–350%). Full article

With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO₂ emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems. Full article

In this study, we assessed soil pollutants and surveyed the bacterial communities using 16S rRNA sequencing to better understand how to improve rehabilitation strategies for nickel-laterite mines in the Philippines. Representative soil samples and rhizospheres from Saccharum spontaneum L. in three post-mining sites rehabilitated in 2015, 2017, and 2019 were collected and analyzed. X-ray diffraction (XRD) identified iron oxyhydroxides, silicates, and clays as major soil components. Based on the pollution load index and contamination degree, the 2015A and 2015B sites were classified as “pristine” and had a “low degree of pollution”, while the remaining sites (2017A, 2017B, 2019A, and 2019B) were considered “moderately contaminated” with nickel, chromium, cobalt, lead, zinc, and copper. An analysis of the bacterial community composition revealed that the phyla Proteobacteria and Actinobacteria, along with the genus Ralstonia, were the most abundant groups across both control and rehabilitated sites. Our results showed that the soil pH and organic matter contents were strongly linked to specific bacterial community composition. These taxa have potential for inoculation in nickel-laterite soils to promote the growth of hyperaccumulator plants. Our results also showed a significant correlation between the structure of the bacterial communities and nickel, chromium, and manganese soil contents, but not with rehabilitation time. Furthermore, we identified the genera Diaphorobacter as potential bioindicators because they are sensitive to nickel and chromium. This study provides valuable baseline data on heavy metal pollution and microbial diversity in a rehabilitated Ni-laterite mine site. Full article

This study investigates the effectiveness of combining helical piles (HPs) with geogrid reinforcement compared to conventional piles in improving pullout performance in dense sand, addressing a key challenge in reinforced foundation design. A comprehensive experimental program was conducted to evaluate the pullout behavior of HPs embedded in Shahriyar sand reinforced with geogrid layers. The research focused on quantifying the effects of critical parameters—pile configuration, helix pitch, and geogrid placement depth—on ultimate pullout capacity and displacement response to better understand hybrid reinforcement mechanisms. Pullout tests were performed using a Zwick/Roell Z150 universal testing machine with automated data acquisition via TestXpert11 V3.2 software. The experimental program assessed the following influences: (1) pile configurations—plain, single-helix, and double-helix; (2) helix pitch ratios of 1.00, 1.54, and 1.92 (pitch-to-shaft diameter); and (3) geogrid placement depths of 7.69, 11.54, and 15.38 (depth-to-shaft diameter) on pullout behavior. Results demonstrate that geogrid reinforcement substantially enhances pullout resistance, with single-helix HPs achieving up to a 518% increase over plain piles. Pullout resistance is highly sensitive to geogrid spacing, with optimal performance at a non-dimensional distance of 0.47 from the pile–soil interface. Additionally, double-blade HPs with geogrid placed at 0.35 exhibit a 62% reduction in displacement ratio, underscoring the role of geogrid in improving pile stiffness and load-bearing capacity. These findings provide new insights into the synergistic effects of helical pile geometry and geogrid placement for designing efficient reinforced granular foundations. Full article

With the widespread deployment of mobile imaging sensors and smart devices, the risk of image privacy leakage is increasing daily. Protecting sensitive information in captured images has become increasingly critical. Existing image privacy protection measures usually rely on manual blurring and occlusion, which are inefficient, prone to omitting privacy information, and have an irreversible impact on the usability and quality of images. To address these challenges, this paper proposes TSLEPS (Two-Stage Localization and Erasure method for Privacy protection in Sensor-captured images). TSLEPS adopts a two-stage framework comprising a privacy target detection sub-model and a privacy text erasure sub-model. This method can accurately locate and erase the private text areas in images while maintaining the visual integrity of the images. In the stage of detecting privacy targets, an inverted residual attention mechanism is designed and combined with a generalized efficient aggregation layer network, significantly improving privacy target detection accuracy. In the stage of privacy text erasure, a texture-enhanced feature attention mechanism is proposed with an adversarial generative network for the erasure task to achieve efficient erasure of privacy texts. Moreover, we introduce the half-instance normalization block to reduce the computational load and inference time so that it can be deployed on resource-constrained mobile devices. Extensive experiments on multiple public real-world privacy datasets demonstrate outstanding performance, with privacy target detection achieving 97.5% accuracy and 96.4% recall, while privacy text erasure reaches 38.2140 dB PSNR and 0.9607 SSIM. TSLEPS not only effectively solves the privacy protection challenges in sensor-captured images through its two-stage framework, but also achieves breakthrough improvements in detection accuracy, erasure quality, and computational efficiency for resource-constrained devices. Full article

Micro-hydroelectric power plants play a fundamental role in microgrid systems and rural electrification projects based on non-conventional renewable energies, where the stability of the electricity supply and load variability are critical factors for efficient operation. This work focuses on analyzing the impact of electrical load variation on the performance of a 10 kW micro hydroelectric power plant equipped with a Pelton turbine coupled to an electric generator. The main objective is to characterize the behavior of the turbine–generator system under different operating conditions, evaluating the hydraulic performance of the turbine, the electrical performance of the generator, and the overall performance of the micro power plant. Key variables such as flow rate, pressure, shaft speed, mechanical torque, current, and electrical voltage are monitored, considering the effect of electrical consumption on each of them. The experimental methodology includes tests at different electrical loads connected to the generator, using the spear system, which allows the flow rate in the injector to be modulated. The results indicate that reducing the flow rate using the spear increases the torque on the shaft, as well as the electrical current and voltage, for the same energy demand. Likewise, it is observed that the electrical efficiency of the generator remains stable for shaft speeds above 400 rpm, while the overall efficiency of the turbine–generator improves by up to 25% at this same speed. However, a voltage drop of more than 8% is recorded when the electrical power consumption increases from 3 kW to 9 kW, which demonstrates the sensitivity of the system to load variations. This work provides a comprehensive view of the dynamic behavior of micro-hydraulic power plants under realistic operating conditions, proposing an experimental methodology that can be applied to the design, optimization, and control of small-scale hydroelectric systems. These results provide novel experimental evidence on how electrical load variations affect the global performance of P -based micro hydropower systems. Full article

The accurate detection of tea buds in outdoor environments is crucial for the intelligent management of modern tea plantations. However, this task remains challenging due to the small size of tea buds and the limited computational capabilities of the edge devices commonly used in the field. Existing object detection models are typically burdened by high computational costs and parameter loads while often delivering suboptimal accuracy, thus limiting their practical deployment. To address these challenges, we propose TeaBudNet, a lightweight and robust detection framework tailored for small tea bud identification under outdoor conditions. Central to our approach is the introduction of Weight-FPN, an enhanced variant of the BiFPN designed to preserve fine-grained spatial information, thereby improving detection sensitivity to small targets. Additionally, we incorporate a novel P2 detection layer that integrates high-resolution shallow features, enhancing the network’s ability to capture detailed contour information critical for precise localization. To further optimize efficiency, we present a Group–Taylor pruning strategy, which leverages Taylor expansion to perform structured, non-global pruning. This strategy ensures a consistent layerwise evaluation while significantly reducing computational overhead. Extensive experiments on a self-built multi-category tea dataset demonstrate that TeaBudNet surpasses state-of-the-art models, achieving +5.0% gains in AP@50 while reducing parameters and computational cost by 50% and 3%, respectively. The framework has been successfully deployed on Huawei Atlas 200I DKA2 developer kits in real-world tea plantation settings, underscoring its practical value and scalability for accurate outdoor tea bud detection. Full article

To meet the demands of deep grid integration of renewable energy and long-distance power transmission, this paper presents a hybrid multi-infeed DC system architecture that includes an AC power source (AC), a voltage source converter (VSC), and a modular multilevel converter (MMC). Addressing the limitations of traditional differential protection—such as insufficient sensitivity under high-resistance grounding and susceptibility to false operations under out-of-zone disturbances—this paper introduces an enhanced current differential criterion based on dynamic phasor analysis. By effectively decoupling DC bias and load current components and optimizing the calculation of action and braking quantities, the proposed method enables the rapid and accurate identification of typical faults, including high-resistance grounding, three-phase short circuits, and out-of-zone faults. A multi-scenario simulation platform is built using MATLAB to thoroughly validate the improved criterion. Simulation results demonstrate that the proposed method offers excellent sensitivity, selectivity, and resistance to false operations in multi-infeed complex systems. It achieves fast fault detection (~2.0 ms), strong sensitivity to high-resistance internal faults, and low false tripping under a variety of test scenarios, providing robust support for next-generation DC protection systems. Full article

To address the issues of reduced operational efficiency, shortened equipment lifespan, and significant safety hazards caused by bearing wear and blade cavitation in hydroelectric turbine generators due to prolonged high-load operation, this paper proposes a ResNet + self-attention-based acoustic fingerprint fault diagnosis algorithm for hydroelectric turbine generators. First, to address the issue of severe noise interference in acoustic signature signals, the ensemble empirical mode decomposition (EEMD) is employed to decompose the original signal into multiple intrinsic mode function (IMF) components. By calculating the correlation coefficients between each IMF component and the original signal, effective components are selected while noise components are removed to enhance the signal-to-noise ratio; Second, a fault identification network based on ResNet + self-attention fusion is constructed. The residual structure of ResNet is used to extract features from the acoustic signature signal, while the self-attention mechanism is introduced to focus the model on fault-sensitive regions, thereby enhancing feature representation capabilities. Finally, to address the challenge of model hyperparameter optimization, a Bayesian optimization algorithm is employed to accelerate model convergence and improve diagnostic performance. Experiments were conducted in the real working environment of a pumped-storage power station in Zhejiang Province, China. The results show that the algorithm significantly outperforms traditional methods in both single-fault and mixed-fault identification, achieving a fault identification accuracy rate of 99.4% on the test set. It maintains high accuracy even in real-world scenarios with superimposed noise and environmental sounds, fully validating its generalization capability and interference resistance, and providing effective technical support for the intelligent maintenance of hydroelectric generator units. Full article

Background/Objectives: Controlled release systems, such as polymeric microparticles (MPs), have emerged as a promising solution to extend the bioavailability and reduce dosing frequency for biologic drugs; however, the formulation of these systems to encapsulate highly sensitive, hydrophilic biologic drugs within hydrophobic polymers remains a nontrivial task. Although scalable manufacturing and FDA approval of single emulsion processes encapsulating small molecules has been achieved, scaling more complex double emulsion processes to encapsulate hydrophilic biologics remains more challenging. Methods: Here, we demonstrate that two hydrophilic, low-molecular-weight, recombinant chemokines, CCL22 and CCL2, can be encapsulated in poly(lactic-co-glycolic acid) (PLGA) MPs using a single emulsion method where the proteins are dissolved in an organic solvent during formulation. Results: As expected, we observed some differences in release kinetics from single emulsion MPs compared to double emulsion MPs, which traditionally have been used to encapsulate proteins. Single emulsion MPs exhibited a substantially reduced initial burst. Importantly, protein released from single emulsion CCL22-MPs also retained biological activity, as determined by a cell-based functional assay. Decreasing particle size or changing the polymer end group from PLGA-COOH to PLGA-OH increased the initial burst from single emulsion MPs, demonstrating tunability of release kinetics for protein-loaded, single emulsion MPs. Finally, to improve scalability and enable more precise control over MP formulations, the single emulsion process was adapted to a microfluidic, continuous manufacturing system, and the resulting MPs were evaluated similarly. Conclusions: Altogether, this study demonstrates the feasibility of using a single emulsion encapsulation method for at least some protein biologics. Full article

Organic photodetectors (OPDs) offer considerable promise for low-power, solution-processable biosensing and imaging applications; however, their performance remains limited by spectral mismatch and interfacial trap states. In this study, a highly sensitive polymer photodiode was developed via trace incorporation (0.8 wt%) of InP/ZnSe/ZnS quantum dots (QDs) into a PTB7-Th:PC₇₁BM bulk heterojunction (BHJ) matrix. This QD doping approach enhanced the external quantum efficiency (EQE) across the 540–660 nm range and suppressed the dark current density at −2 V by passivating interface trap states. Despite a slight decrease in optical absorption at the optimized composition, the internal quantum efficiency (IQE) increased significantly from ~80% to nearly 95% resulting in a net EQE improvement. This suggests that QD incorporation improved charge transport without compromising charge separation efficiency. As a result, the device achieved a specific detectivity (D*) of 1.8 × 10¹³ Jones, representing a 93% improvement over binary BHJs, along with an ultra-low dark current density of 7.76 × 10⁻¹⁰ A/cm². Excessive QD loading, however, led to optical losses and increased dark current, underscoring the need for precise compositional control. Furthermore, the enhanced detectivity led to a 4 dB improvement in the signal-to-noise ratio (SNR) of photoplethysmography (PPG) signals in the target wavelength range, enabling more reliable biophotonic sensing without increased power consumption. This work demonstrates that QD-based spectral and interfacial engineering offers an effective and scalable route for advancing the performance of OPDs, with broad applicability to low-power biosensors and high-resolution polymer–QD imaging systems. Full article

Digital Twins (DTs) have become essential tools for the design, diagnostics, and prognostics of mechanical systems. In gearbox applications, DTs are often built using physics-based simulations guided by ISO standards. However, standards-based approaches may suffer from complexity, licensing limitations, and computational costs. The concept of symmetry is inherent in gear mechanisms, both in geometry and in operational conditions, yet practical applications often face asymmetric load distributions, misalignments, and asymmetric and symmetric nonlinear behaviors. In this study, we propose a hybrid method that integrates data-driven modeling with standard-based simulation to develop efficient and accurate digital twins for gear transmission systems. A digital twin of a spur gear transmission is generated using KISSsoft^®, employing ISO standards to compute safety factors across varied geometries and load conditions. An automated MATLAB-KISSsoft^® (COM-interface) enables large-scale data generation by systematically varying key input parameters such as torque, pinion speed, and center distance. This dataset is then used to train a neural network (NN) capable of predicting safety factors, with hyperparameter optimization improving the model’s predictive accuracy. Among the tested NN architectures, the model with a single hidden layer yielded the best performance, achieving maximum prediction errors below 0.01 for root and flank safety factors. More complex failure modes such as scuffing and micropitting exhibited higher maximum errors of 0.0833 and 0.0596, respectively, indicating areas for potential model refinement. Comparative analysis shows strong agreement between the NN outputs and KISSsoft^® results, especially for root and flank safety factors. Performance is further validated through sensitivity analyses across seven cases, confirming the NN’s reliability as a surrogate model. This approach reduces simulation time while preserving accuracy, demonstrating the potential of neural networks to support real-time condition monitoring and predictive maintenance in gearbox systems. Full article

The core structure of modern power systems reflects a fundamental symmetry between electricity supply and demand, and accurate load forecasting is essential for maintaining this dynamic balance. To improve the accuracy of short-term load forecasting in power systems, this paper proposes a novel model that combines a Multi-Strategy Improved Sand Cat Swarm Optimization algorithm (MSCSO) with a Self-Attention Temporal Convolutional Network (SA TCN). The model constructs efficient input features through data denoising, correlation filtering, and dimensionality reduction using UMAP. MSCSO integrates Uniform Tent Chaos Mapping, a sensitivity enhancement mechanism, and Lévy flight to optimize key parameters of the SA TCN, ensuring symmetrical exploration and stable convergence in the solution space. The self-attention mechanism exhibits structural symmetry when processing each position in the input sequence and does not rely on fixed positional order, enabling the model to more effectively capture long-term dependencies and preserve the symmetry of the sequence structure—demonstrating its advantage in symmetry-based modeling. Experimental results on historical load data from Panama show that the proposed model achieves excellent forecasting accuracy (RMSE = 24.7072, MAE = 17.5225, R² = 0.9830), highlighting its innovation and applicability in symmetrical system environments. Full article

Inter-turn short-circuit faults in induction motors might lead to overheating, torque imbalances, and eventual motor failure. This paper presents a fault detection framework for accurately identifying ITSC faults under various operating conditions. The proposed method integrates negative-sequence current analysis utilizing wavelet-based filtering and symmetrical component decomposition. A fault detection index to effectively monitor motor health and detect faults is presented. Moreover, the fault location is determined by phase angles of fundamental components of negative-sequence currents. Experimental validations were carried out for an inverter-fed induction motor under variable speed and load cases. These showed that the proposed approach has high sensitivity to early-stage inter-turn short circuits. This makes the framework highly suitable for real-time condition monitoring and predictive maintenance in inverter-fed motor systems, thereby improving system reliability and minimizing unplanned downtime. Full article

This study investigates the wear performance of 20CrMnTi steel, a commonly used material for spiral bevel gears, after heat treatment, with a focus on the microstructural evolution and wear behavior in both the surface and gradient direction of the carburized layer. The results show that the microstructure composition in the gradient direction of the carburized layer gradually transitions from martensite and residual austenite to a martensite–bainite mixed structure, and eventually transforms to fully bainitic in the matrix. With the extension of carburizing time, both the effective carburized layer depth and the hardened layer depth significantly increase. Wear track morphology analysis reveals that the wear track depth gradually becomes shallower and narrower, and the wear rate increases significantly with increasing load. However, the friction coefficient shows little sensitivity to changes in carburizing time and load. Further investigations show that as the carburized layer depth increases, the carbon concentration and hardness of the samples gradually decrease, resulting in an increase in the average wear rate and a progressive worsening of wear severity. After the wear tests, different depths of plowing grooves, spalling, and fish-scale-like features were observed in the wear regions. Additionally, with the increase in load and carburized layer depth, both the width and depth of the wear tracks significantly increased. The research results provide a theoretical basis for optimizing the surface carburizing process of 20CrMnTi steel and improving its wear resistance. Full article