Search Results (61,173)

Wheeled multifunctional motorized crossing frames represent a new type of crossing equipment for high-voltage transmission line construction. The initial design is too conservative, having a large safety margin and high material redundancy. Therefore, it is necessary to study a lightweight design version. However, as the structure constitutes an assembly consisting of multiple components, it also exhibits relatively high complexity. In a lightweight design, optimizing multi-component and multi-size parameters can lead to structural interference and separation, seriously affecting the smooth progress of design optimization. Therefore, an optimization design method of a multi-parameter complex assembly structure is proposed to solve this problem. Firstly, the typical stress conditions of the wheeled multifunctional motorized crossing frame were analyzed using its structural model. Then, a finite element model of the beam was established in ANSYS 2021 R1 Workbench, and the mechanical characteristics were analyzed. The results show that the arm support is the key load-bearing component and has significant optimization potential. Subsequently, functional mapping relationships were established among the 14 dimension parameters of the arm support, reducing the number of design variables to six and successfully avoiding component separation or interference during optimization. Through global sensitivity analysis, the height, thickness, and length of the arm body were screened out as the core optimization parameters from six initial design variables. Then, 29 groups of sample points were generated via central composite design (CCD), and a response surface model reflecting the relationships among the arm body’s dimensional parameters, total mass, maximum stress, and maximum deformation was established using the Kriging method. Leave-one-out cross-validation (LOOCV) was performed, and the coefficients of determination (R²) for model fitting were all higher than 0.995, indicating extremely high prediction accuracy. Taking mass and deformation minimization as the optimization objectives, the MOGA algorithm was adopted to perform multi-objective optimization and determine the optimal engineering parameters. Simulation verification was conducted on the optimized arm support, and an eigenvalue buckling analysis was performed simultaneously to verify structural stability. Finally, the proposed optimization method was experimentally verified through mechanical performance tests of the full-scale prototype under symmetric and eccentric loads. The results show that the mass of the optimized arm support is reduced from 217.73 kg to 189.8 kg, with a weight reduction rate of 12.8%. Under an eccentric load of 70,000 N, the maximum deformation of the arm support is 8.9763 mm, the maximum equivalent stress is 314.86 MPa, and the buckling load factor is 6.08, all of which meet the requirements for structural stiffness, strength, and buckling stability. The maximum error between the experimental and finite element results is only 4.64%, verifying the accuracy and reliability of the proposed method. The proposed optimization methodology, validated on a wheeled multifunctional motorized crossing frame, serves as a transferable paradigm for the lightweight design of complex assemblies with coupled dimensional constraints, thereby offering a general reference for the structural optimization of multi-component transmission line equipment, construction machinery, and other multi-component engineering systems. Full article

DC motors are widely used in mechatronic systems; however, their performance degrades significantly in the presence of nonlinear mechanical loads, parameter variations and sensing uncertainties. This paper proposes three control strategies (i.e., PID, optimal, and hybrid controllers) for discrete-time DC motor systems to overcome the disturbances caused by nonlinear mechanical loads and parameter variations. Optimal control of nonlinear discrete-time systems is formally characterized by the Hamilton–Jacobi–Bellman (HJB) equation, whose analytical solution is generally intractable. To address this challenge, a learning-based optimal control strategy based on the Heuristic Dynamic Programming (HDP) framework is developed to approximate the HJB equation, supported by a formal convergence proof. For that purpose, Neural Networks (NNs) are employed to approximate both the cost function and the optimal control policy, enabling near-optimal performance with manageable computational complexity. Although the resulting optimal control achieves fast convergence, it may introduce overshoot and steady-state offset under nonlinear disturbances. To address this limitation, a hybrid control framework is proposed, where nonlinear optimal corrections are integrated with the robustness and adaptability of Proportional–Integral–Derivative (PID) control through error-dependent gating and gain-scheduling mechanisms. A structured evaluation framework is conducted, including nominal analysis, motor-parameter stress testing across nine nonlinear scenarios, controller-design sensitivity analysis, and stochastic measurement-noise assessment under filtered sensing conditions. Results demonstrate that the hybrid controller preserves transient speeds within 5–10% of the optimal controller while effectively eliminating overshoot and steady-state offset under nominal conditions. The hybrid design reduces the accumulated tracking error by more than 95% compared to the optimal controller, while incurring only negligible additional control effort. Under aggressive supply-sag disturbances, the hybrid controller significantly limits peak deviation and reduces accumulated tracking error by over 90%, while maintaining comparable control cost. Overall, the hybrid framework provides a convergence-proven and practically deployable control solution that combines near-optimal convergence speed with robust, overshoot-free performance for intelligent motion-control and robotics applications. Full article

Olive pomace biochar, obtained through the pyrolysis of lignocellulosic biomass, has emerged as a sustainable and multifunctional additive for polymer composites. Its physicochemical properties, including porosity, surface area, and electrical conductivity, can be tailored by controlling feedstock type and pyrolysis conditions. Although mechanical reinforcement and thermal stability improvements are well documented, the influence of biochar on surface-related properties such as wettability and contact angle remains insufficiently explored for environmentally relevant composite systems. In this study, epoxy-based composites containing biochar synthesized at 750 °C were evaluated in terms of their water interaction behavior by monitoring the evaporation dynamics of ultra-pure water droplets (10 μL, 0.055 mS/cm conductivity) at eight time intervals between 20 and 580 s using high-resolution digital microscopy. Image enhancement and segmentation were performed prior to Discrete Cosine Transform (DCT) analysis to describe droplet geometry in the frequency domain. Time-dependent variations in the standard deviations of DCT coefficients were optimized using the Bat Algorithm, resulting in mathematical models capable of accurately representing droplet evolution and surface–fluid interactions. The primary novelty of this study lies in the development of a hybrid experimental–computational framework that integrates droplet-based wettability measurements with signal-domain analysis and metaheuristic optimization. Unlike conventional studies focusing solely on material characterization, this approach establishes quantitative relationships between surface behavior and numerical descriptors derived from DCT and the Bat Algorithm. The proposed methodology provides a data-driven tool for predicting wettability trends in biochar-reinforced composites and supports the development of moisture-resistant materials for coatings, packaging, and thermal insulation applications within the context of sustainable composite design. Full article

This study investigated the effects of Lycium barbarum polysaccharides (LBP) supplementation on various indicators in coral trout (Plectropomus leopardus), including growth performance, digestive enzyme activity, muscle and skin morphology, inflammatory immune gene expression, as well as immune and antioxidant responses. In the experiment, fish were fed diets supplemented with different concentrations of LBP (0%, 0.05%, 0.1%, 0.2%, 0.5%, and 1%) over a designated experimental period. The results showed that moderate supplementation of LBP significantly improved growth performance, with the optimal concentration being around 0.243%, achieving the highest specific growth rate. LBP supplementation also enhanced intestinal digestive enzyme activity, such as trypsin in the 0.1% and 1% groups, and α-amylase in the 0.5% group. Additionally, LBP improved the nutritional composition of muscle, with the 1% group showing higher crude protein content and the 0.2–1% groups having lower crude fat content. Moderate LBP supplementation improved skin color and pigmentation, increasing the brightness, redness, and yellowness of the dorsal skin, as well as boosting carotenoid and astaxanthin concentrations. It also enhanced the immune and antioxidant functions of the skin (e.g., SOD, CAT, GSH-Px, AKP, and LZ) and improved the immune functions of the mucus (e.g., C3, C4, IgM, IgT, AKP, and LZ). Furthermore, the expression of key pro-inflammatory genes, such as TNF-α and IL-1β, was reduced. These findings suggest that LBP can serve as a natural feed additive to enhance the overall quality and health of coral trout, contributing to sustainable aquaculture practices. Full article

Low-power NPUs enable on-device LLM inference through efficient integer and fixed-point algebra, yet their lack of native exponential support makes Transformer softmax a critical performance bottleneck. Existing NPU kernels approximate using uniform piecewise polynomials to enable O(1) SIMD indexing, but this wastes computation by applying high-degree arithmetic indiscriminately in every segment. Conversely, fully adaptive approaches maximize statistical fidelity but introduce pipeline stalls due to comparator-based boundary search. To bridge this gap, we propose an attention distribution-aware softmax that uses Particle Swarm Optimization (PSO) to define non-uniform segments and variable polynomial degrees, prioritizing finer granularity and lower arithmetic complexity in attention-dense regions. To ensure efficiency, we snap boundaries into a 128-bin LUT, enabling O(1) retrieval of segment parameters without branching. Inference measurements show that this favors low-degree execution, minimizing exp-kernel overhead. Using TinyLlama-1.1B-Chat as a testbed, the proposed weighted design reduces cycles per call exp kernel (CPC) by 18.5% versus an equidistant uniform Degree-4 baseline and 13.1% versus uniform Degree-3, while preserving ranking fidelity. These results show that grid-snapped, variable-degree approximation can improve softmax efficiency while largely preserving attention ranking fidelity, enabling accurate edge LLM inference. Full article

Stone-built cultural heritage sites face significant threats from weathering and environmental stress, leading to structural damage or even total collapse. Consequently, robust monitoring and conservation strategies are essential. This study introduces the Monument Rockfall Risk Assessment (MRRA), a heuristic prioritization framework designed for the rapid ranking of detachment risks in monumental contexts. The MRRA was tested on the Piazzale Michelangelo Ramps in Florence (Italy), which are prone to rockfall hazard due to the presence of unstable blocks made of Pietraforte sandstone. The methodology employs a qualitative-heuristic risk rating approach, considering factors such as joint characteristics, centre of gravity location, and estimated kinetic energy of falling blocks. Susceptibility, vulnerability, and elements at risk were evaluated for each unstable block to calculate a relative risk index, which was then aggregated to determine the overall risk of each coping. The methodology was applied to a recent rockfall event that occurred in 2020 and compared with expert judgement to evaluate the model’s performance in identifying criticalities. Since decisions on defence and restoration works depend on geomechanical, social, and economic factors, this study explores an approach to establish optimal risk rating thresholds for the MRRA methodology, balancing false and missed alarms. Full article

An improved optimizer based on projection-iterative methods (IPIMO) is proposed to address the optimal configuration problem of multi-type energy storage systems (MT-ESS), with the objective of achieving synergistic minimization of comprehensive costs, including both investment and operational expenditures. A comprehensive energy system model is established, integrating photovoltaic power, wind power, and six typical energy storage technologies—lithium-ion battery, flywheel energy storage, supercapacitors, valve-regulated lead-acid battery, compressed air energy storage, and redox flow battery. Four typical operational scenarios are designed to validate the adaptability and robustness of the algorithm. A systematic evaluation of IPIMO’s comprehensive performance is conducted by comparing it with the weighted average method (WA), the single-energy storage optimization method (SEO), the projection-iterative-methods-based optimizer algorithm (PIMO), and the genetic algorithm (GA). Simulation results demonstrate that IPIMO exhibits superior convergence performance, achieving stable convergence rapidly and significantly outperforming PIMO and GA. Moreover, IPIMO achieves the lowest total cost across all four scenarios, with an average of $46,837, representing reductions of 6.54% compared to the benchmark weighted average method and 11.8% compared to the SEO. Additionally, IPIMO adaptively adjusts the allocation ratios of energy storage types based on scenario characteristics, prioritizing energy-type storage in stable scenarios while increasing the proportion of fast-response storage to 49.1% in fluctuating scenarios, thereby demonstrating its strong scenario adaptability. Full article

Emotion identification via computer vision has made continuous progress over the last few years. Although images have been the gold standard for the past two decades, video is increasingly common. Video is particularly suitable for the study of emotions, as it allows them to be considered as spatiotemporal phenomena. In particular, the discovery of anxiety among Mexican students is a key element for improving their learning in the classroom. In pursuit of this goal, we focused on the following challenges. First, the scarcity of specialized datasets for this task prompted us to develop an experimental protocol to generate a specific dataset; second, to conduct a thorough study of the appropriate number of emotional intensity levels; and third, to develop a suitable design for a deep learning architecture. Our pivotal results include the development of a new dataset labeled with three different emotion levels and appropriate ConvNet architectures, complemented by a study of various intensity levels. The optimal architecture achieved an F1-score of 0.7620 across five intensity levels and provides an adequate baseline for multiclass classification. Full article

Custom-made subperiosteal implants are increasingly used in clinical cases where significant bone loss due to trauma or disease renders conventional endosseous implant placement unfeasible. This study investigated how different internal structural designs affect the deformation and stress distribution in mandibular subperiosteal implants under clinically relevant loading conditions. An idealized implant geometry was defined based on average human mandibular dimensions, and four configurations with identical outer shape and connection features were created, differing only in sidewall architecture (solid, top-relieved, top-relieved with lateral perforations, and top-relieved lattice framework). All specimens were manufactured by metal additive manufacturing and evaluated using cone-beam computed tomography (CBCT). Mechanical testing was performed in two stages: (i) cyclic loading consisting of 500 bite cycles at an overall force of ~326–350 N and (ii) a single static high-load event of 2000 N, applied parallel to the fixation pin axes. CT datasets acquired before and after each stage were compared to detect permanent deformation. No measurable residual deformation was identified in any configuration; the only observed macroscopic change was an adhesive-bond limitation in one case, rather than structural yielding of the implant. Finite element analysis further supported these findings by identifying localized stress concentrations mainly at the implant–prosthetic interface and by revealing the load-transfer zones that govern the mechanical response. Overall, the results indicate that lightweight, perforated, and lattice-based internal designs can preserve global structural integrity across physiological and supra-physiological load ranges while enabling design optimization to improve stress distribution. Full article

This study explored the dual chemical modification of starch isolated from red lentils (Lens culinaris) to develop a biodegradable polymer with enhanced functionality for multifaceted applications. Native starch was isolated via combined salt–alkali treatment and sequentially modified through epichlorohydrin-mediated crosslinking, followed by cationization using glycidyl trimethylammonium chloride (GTAC). Utilizing a Quality by Design (QbD) strategy through Response Surface Methodology (RSM), the cationization endured fine-tuning to reach an optimal degree of substitution (DS = 0.572) under foremost conditions (GTAC: 2.1 mol, NaOH: 0.09 mol, reaction time: 18 h). Structural and functional characterization using FTIR, XRD, TGA, SEM, and zeta potential analysis confirmed the successful modification, indicating enhanced thermal stability, a transition to a more amorphous structure, and a moderately positive surface charge (+7.24 mV). The dual modified cationic lentil starch (CLS) demonstrated effective flocculation of kaolin suspensions, achieving a transmittance of up to 94%. Additionally, CLS showed significantly improved emulsion stability, maintaining over 70% stability after 24 h, compared to native starch, which dropped below 30%. These results emphasize the promising potential of CLS as an eco-friendly and high-performance alternative to synthetic polymers for water treatment and stabilization of emulsion-based formulations. Full article

This study investigates the hydrodynamic responses and energy harvesting performance of a hemispherical point-absorber wave energy converter (WEC) in uniform current. A frequency-domain Rankine source method (RSM) is developed to rigorously account for current-modified free-surface conditions, and an approximate free-surface Green-function method (AFSGM) is implemented to assess practical applicability under weak-current assumptions. The numerical settings for body, free-surface, and radiation-boundary discretizations are determined through convergence tests. Model validation is performed by comparing motion responses against published benchmark results under both zero-current and current conditions. The effects of current and motion constraints are examined for surge–heave free and heave-only cases. Results show that current can amplify the heave response and that surge freedom enhances heave motion through coupling effects, leading to increasing discrepancies between RSM and AFSGM as current strengthens. For heave-only motion, AFSGM provides practically acceptable predictions within |Fr| ≤ 0.045, while noticeable differences appear near resonance beyond this range, for which RSM is recommended. Energy harvesting is evaluated using a linear PTO damping model, revealing that current alters the capture width ratio (CWR) and shifts the optimal PTO damping and frequency, indicating the necessity of considering current in performance assessment and PTO design. Full article

Sustainable xanthan gum (XG) production is increasingly prioritized as global demand rises, and conventional processes face economic and environmental constraints. Traditional manufacturing depends heavily on refined sugars, intensive fermentation control, and solvent-based purification, which elevate production costs and ecological impact. This review highlights recent advancements designed to improve sustainability across the XG value chain, focusing on alternative substrates, optimized fermentation, and greener extraction methods. Agricultural residues, food-processing waste, lignocellulosic biomass, and industrial effluents have emerged as promising low-cost substrates that reduce reliance on refined sugar sources while supporting waste valorization. Pretreatment strategies, such as acid hydrolysis, enzymatic processing, and integrated biological–chemical methods, significantly enhance the accessibility of complex biomass for microbial fermentation. Concurrently, improvements in strain selection, metabolic engineering, and process control have increased XG yield, molecular weight, and rheological performance. Environmentally friendly extraction technologies, including ultrasound-assisted extraction, pulsed electric fields, membrane filtration, and electro-dewatering, further reduce solvent consumption and energy demand in downstream processing. However, challenges persist, including substrate variability, formation of inhibitory compounds, strain instability, and regulatory considerations for waste-derived substrates or genetically modified strains. Future progress will rely on integrating bioprocess intensification, genetic engineering, and techno-economic assessment to build scalable, low-impact, and circular XG production systems. Full article

This study aimed to develop a 100 mg immediate-release (IR) tablet containing dasatinib monohydrate, a tyrosine kinase inhibitor, using a Quality by Design (QbD) framework at laboratory scale. The development strategy focused on systematic identification and control of critical process parameters (CPPs) affecting tablet quality during wet granulation. Preformulation studies were conducted to evaluate key physicochemical properties of the active pharmaceutical ingredient (API), including solubility, particle size distribution, and crystallinity, which may influence dissolution behavior. A risk assessment approach based on preliminary hazard analysis (PHA) and failure mode and effects analysis (FMEA) was applied to identify high-risk process variables. Based on the risk assessment results, chopper speed during wet granulation and compression force during tableting were identified as critical process parameters. These factors were further investigated using a Design of Experiments (DoE) approach based on Define Custom Design (DCD) and response surface methodology (RSM) to evaluate their effects on critical quality attributes (CQAs), including dissolution performance, disintegration time, and tablet friability. Response surface analysis established a design space in which chopper speed ranged from approximately 2300–2500 rpm and compression force ranged from 11 to 13 kN, ensuring consistent tablet quality within the investigated operating range. The optimized process conditions produced tablets that satisfied predefined quality targets. Comparative dissolution studies demonstrated dissolution profiles comparable to the reference product across pH 1.2, 4.0, and 6.8 media, with similarity factor (f₂) values ranging from 51.18 to 85.23. The experimentally established design space demonstrated reproducible in vitro performance and physicochemical stability under accelerated storage conditions. Overall, this study demonstrates the practical application of a QbD-based development strategy integrating risk assessment and response surface optimization to improve process understanding and manufacturing robustness in wet granulation-based tablet production. Full article

Reactive power compensation devices (RPCDs) are crucial for improving the efficiency of energy systems. Distribution systems are commonly modeled under the simplifying assumption of balanced operation, which does not accurately represent real operating conditions. Motivated by the need to develop an effective computational tool for the proper selection of RPCDs, this paper proposes the application of the experience exchange strategy (EES) to the coordinated design of RPCDs. To the best of the authors’ knowledge, this is the first study to employ EES for this purpose. The proposed methodology is validated through two case studies. In the first case, an extensive exploration of the search space is performed by repeating the optimization process, resulting in a solution with a high probability of being the global optimum. Under this scenario, a comparative analysis shows that EES outperforms the genetic algorithm by 7.4%. In the second case, EES is compared with other popular heuristic techniques, including particle swarm optimization (PSO), without performing a deep exploration of the search space, observing that EES ranks in the middle, with a difference of 11.9% relative to PSO. Overall, the results confirm that the proposed EES-based framework constitutes a reliable and efficient approach. Full article

Urban riverfronts, as integral components of the urban built environment, serve as essential blue–green infrastructure that offers restorative opportunities to residents in high-density areas. However, the mechanisms through which specific spatial qualities influence well-being outcomes remain underexplored. Guided by Attention Restoration Theory (ART) and Stress Recovery Theory (SRT), this study investigates the associations among spatial perception, perceived restorativeness, environmental sensitivity, and subjective well-being along the Yangtze Riverfront in Nanjing, China. A cross-sectional survey (N = 551) was conducted across six riverfront segments, using a 96-item questionnaire to assess five spatial perception dimensions, four restorativeness dimensions, and four well-being dimensions. Structural equation modeling (SEM) results indicate that spatial perception is positively associated with perceived restorativeness (β = 0.320, p < 0.001), with aesthetic perception demonstrating the strongest relative contribution (β = 0.265). Perceived restorativeness, in turn, significantly contributes to well-being (β = 0.540, p < 0.001), partially mediating the relationship between spatial perception and well-being (indirect effect (β = 0.173; 41.69% of total effect). Notably, environmental sensitivity moderated the spatial–restorative link (β = 0.799, p < 0.001), with restorative benefits being significantly amplified for individuals with higher sensitivity. These findings highlight aesthetics, accessibility, and perceived safety as priority targets for urban design. This study offers actionable insights for optimizing riverfront landscapes as vital urban health resources. Full article