Search Results (2,299)

Power-quality resilience is an important component of sustainable industrial electricity use, as voltage sag events can cause production interruptions, equipment damage, and inefficient allocation of mitigation costs and benefits among stakeholders. However, high initial investment costs and the lack of a viable commercial operation scheme have hindered the large-scale deployment of mitigation devices. To support sustainable power-quality governance, this study proposes an integrated framework that connects the technical compensation performance of the mitigation device with the economic foundation of a tripartite commercial operation model. First, an adaptive switching compensation strategy dynamically shifts between different modes based on the real-time voltage sag depth, establishing a mapping relationship with avoided-loss benefits. Then, a bi-level Nash bargaining model is constructed to allocate costs and benefits among the government, the enterprise, and the user, deriving closed-form analytical solutions for both the upper- and lower-level games. Through pilot operations at a large public service facility, economic losses of 480,000 CNY caused by a single voltage sag can be effectively avoided. Meanwhile, under the proposed scheme, all three parties achieve positive net present values. Compared to the user self-funding mode, the user’s NPV increases by 21.9%. Furthermore, unlike bilateral or equal-sharing alternatives, the Nash bargaining solution ensures all parties remain within the strong feasible region. The government and enterprise recover their costs within 4.14 and 6.20 years, respectively. These results indicate that the proposed framework can enhance the economic sustainability of power-sensitive users, encourage shared public–private investment in power-quality improvement, and support more resilient and efficient industrial electricity use. Full article

This review examines neural operator architectures for learning solution operators of parametric partial differential equations (PDEs), with an emphasis on conceptual clarity and practical implementation. The work analyzes key models, including DeepONet, PCANet, and the Fourier Neural Operator, highlighting their underlying representations, computational structures, and comparative performance. These architectures are demonstrated on three canonical PDE problems: the Poisson equation, a linear elasticity problem, and a hyperelasticity problem. To make the presentation self-contained, key foundational topics are introduced, including finite-dimensional representations of function spaces, singular-value decomposition, and sampling from infinite-dimensional function spaces. Beyond forward modeling, the review discusses the use of neural operators as surrogate models within a Bayesian inverse-problem framework, including prior specification, forward-map approximation, and posterior computation. The performance of the three neural-operator architectures is evaluated on in-distribution samples, out-of-distribution samples, and Bayesian inference tasks. The review also discusses challenges related to prediction accuracy and generalization, outlining emerging strategies such as residual-based error correction and multi-level training. The review concludes by positioning neural operators within broader scientific-computing workflows and by identifying directions for reliable, scalable operator learning. Full article

Banks must balance deposit stability, loan expansion, and regulatory compliance while operating under liquidity constraints and financial risks. This study presents a mathematical model to examine the dynamics of bank deposits and loans under the influence of liquidity mechanisms and regulatory policies. The model proceeds in three stages: a deterministic nonlinear model, a dynamic optimal control model, and a stochastic model. Under the deterministic model, deposit withdrawals are liquidity-dependent, leading to a feedback mechanism in which liquidity improves deposit stability while financing loan growth. The theoretical results demonstrate the model’s positive and bounded solutions and show the existence and local stability of equilibria. Several parameters are based on regulatory policies or calibrated from Indonesian banking data, while the unknown parameters are estimated using the particle swarm optimization (PSO) algorithm. The results show that the proposed model is capable of fitting and predicting the data and has slightly lower mean absolute percentage errors for in-sample and out-of-sample compared with the benchmark model, and achieves comparable directional forecasting performance based on the index of directionality. Sensitivity analysis shows that the capital adequacy ratio supports lending, whereas an increased reserve requirement limits lending. An optimal control approach is developed by considering the reserve and capital requirements as time-varying policy variables. By applying Pontryagin’s maximum principle, we establish the necessary conditions for optimality. Numerical experiments demonstrate that the optimal control regulation enhances financial ratios, particularly the loan-to-deposit and liquidity ratios, at a reasonable cost. Finally, the stochastic model accounts for random variations in withdrawals and credit risks. Simulation-based observations reveal that although the system becomes more volatile, the mean dynamics are close to the deterministic case. Our framework offers a data-based and analytically tractable approach for studying the dynamics of banking variables and the effects of regulatory policies. The proposed model provides a mathematical tool for assessing the long-term effects of regulatory policies on banking performance and can assist bank managers and regulators in designing strategies that balance lending activity and liquidity resilience. Full article

This work presents an automated vision-based measurement system designed for the precise dimensional characterization of high-aspect-ratio micro-holes, achieving a relative dimensional error of less than 1% for characterizing high-aspect-ratio damage geometries. The system integrates coaxial microscopic imaging with a precision motorized scanning stage. To ensure high-fidelity measurements in early-stage warning applications, depth is determined using a focus variation method driven by a robust data fusion strategy. By capturing a sequence of images along the Z-axis, the focal planes of the defect’s surface orifice and internal base are automatically identified using a data fusion algorithm based on a consensus evaluation of three parallel sharpness metrics (Tenengrad, Laplacian, and Brenner variants). The Z-axis scanning module, featuring encoder feedback and bi-directional compensation, achieves a repeated positioning error of ±0.5 µm. For lateral damage assessment, the system’s high magnification provides an effective sampling resolution of 0.09 µm. The equivalent diameter of the focused orifice image is calculated through a robust pipeline involving adaptive thresholding, morphological filtering, and sub-pixel ellipse fitting, which serves as a highly sensitive indicator for early-stage structural deformation. The entire process can be completed within five minutes, demonstrating a rapid, highly accurate, and localized optical inspection solution that generates high-precision dimensional data crucial for quality inspection in aerospace and precision engineering. Full article

Usnic acid (UA) is a secondary metabolite produced by lichens that has attracted interest because of its antimicrobial, photoprotective, and antioxidant properties, suggesting its potential use as a biostimulant in agriculture. However, its evaluation in agricultural crops is limited. In the present study, we evaluated the effect of applying (+) UA on enzymatic and non-enzymatic antioxidant systems, photosynthetic pigments, photosynthetic enzyme activity, and markers of oxidative stress in “Albion” strawberry plants. The plants were grown in a peat moss:perlite substrate (1:1, v/v) and cultivated under tunnel greenhouse conditions using a nutrient solution applied via fertigation. (+) UA was applied at 400 µg/mL via three routes (foliar, drench, and a combination of foliar and drench) on three occasions. Leaf tissue was collected 117 days after transplantation, and the biochemical parameters were quantified. (+) UA increased the activity of glutathione peroxidase (GPX) (53% via foliar-drench) and catalase (CAT) by 73.5% (via drench), and reduced glutathione (GSH) content by 58% (via foliar). β-carbonic anhydrase (βCA) activity increased by 415% and 384% (foliar and foliar-drench, respectively). Likewise, Ribulose 1,5-bisphosphate carboxylase-oxygenase (RuBisCO) activity increased by 58.23% (drench) and phosphoenolpyruvate carboxylase (PEPC) by 25% and 46% (foliar and drench), suggesting positive effects on the processes associated with CO₂ assimilation and transport. In contrast, no significant changes were observed in the levels of hydrogen peroxide (H₂O₂), malondialdehyde (MDA), or proline, indicating the absence of oxidative stress. These findings suggest that (+) UA modulates the enzymatic antioxidant system, promoting favorable physilogical responses without inducing oxidative stress. The use of (+) UA has been proposed as a potential promoter of metabolism in agricultural crops. In addition, new avenues of research are being explored to investigate the role in modulating antioxidant responses under biotic and abiotic stress conditions. Full article

To address the problem of three-dimensional (3D) localization in underwater environments, this paper proposes a 3D positioning method based on magnetic induction communication (MI in short). Dual transmitters equipped with 3D coils are used to transmit magnetic field signals, while a single receiver with 3D coils is adopted to receive signals. Three-dimensional position calculation is realized by collecting induced voltage from the receiving 3D coils, which enables any device at a known position in space to provide positioning services for other devices. To eliminate abnormal data and suppress environmental noise interference in underwater received signals and further improve positioning performance, an improved density clustering algorithm named the Density-Based Spatial Clustering Method in Magnetic Positioning is proposed to remove erroneous positioning data. In addition, Kalman filtering is introduced to jointly suppress environmental noise interference. Experimental results demonstrate that the average positioning error of the proposed localization method is 0.67 m and maximum positioning error is 0.83 m; therefore, this paper provides a novel technical solution for underwater positioning in non-line-of-sight environments. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg C ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg C ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article

The Ivy Optimization Algorithm (IVY) is a novel swarm intelligence optimization algorithm that simulates the phototropic growth mechanism of plants. To comprehensively improve the overall optimization performance, this paper proposes an enhanced Ivy Optimization Algorithm (LSIVY) integrating improved Logistics chaotic mapping, sine–cosine operator, and adaptive t-distribution mutation strategy. Firstly, an improved cascaded Logistics chaotic mapping is used for population initialization. The double arcsine transformation improves the ergodicity and uniformity of chaotic sequences, so that initial solutions are distributed more evenly in the search space, population diversity is enhanced, and premature convergence is suppressed. Secondly, the sine–cosine operator is embedded into the position update mechanisms of IVY growth, climbing, and propagation evolution. Nonlinearly decreasing control parameters realize adaptive switching between global exploration and local exploitation and accelerate convergence. Thirdly, an adaptive t-distribution mutation strategy is designed to dynamically adjust mutation intensity according to the iteration cycle and implement directional perturbation at the optimal solution position. It combines the large-scale exploration advantage of the Cauchy distribution and the local fine search merit of the Gaussian distribution, which significantly improves the ability to escape from local optima. Comparative experiments with eight mainstream metaheuristics (DE, WOA, GWO, HHO, DBO, MBWO, AOO, native IVY) are conducted with 30 independent runs on 30-dimensional CEC 2014 (30 test functions) and CEC 2020 (10 composite functions). Quantitatively, LSIVY achieves 20~30 orders of magnitude higher optimization accuracy than standard IVY on unimodal functions, and its average standard deviation across all benchmarks drops by 4–6 orders of magnitude. LSIVY ranks first on all CEC 2020 composite functions, reducing over 30% of iterations compared with native IVY. Three classical constrained mechanical design problems (three-bar truss, cantilever beam, pressure vessel) are adopted for engineering verification. In the pressure vessel case, the average manufacturing cost of LSIVY is reduced by 9.2% against standard IVY, and the standard deviation of three engineering cases decreases by 2–3 orders on average, demonstrating remarkable robustness. The proposed algorithm not only improves the theoretical system of plant-inspired swarm intelligence algorithms but also has great application prospects in mechanical structure lightweight design, industrial equipment cost optimization, and other practical engineering fields. Full article

The paper presents the concept of a turbine-based energy harvester designed to recover kinetic energy from airflow generated by a moving vehicle and integrated with a roadside acoustic barrier. The proposed solutions employ a vertical-axis aerodynamic turbine positioned within a cavity in the barrier and various airflow guiding structures intended to enhance the efficiency of energy transfer from turbulent airflow to the turbine rotor. To evaluate the effectiveness of the proposed concepts, two-dimensional CFD simulations were conducted in the ANSYS Fluent environment using the k–ε turbulence model. Three airflow deflector geometries and one reference configuration without a deflector were analyzed. The performance of each configuration was assessed based on the maximum instantaneous power and the average power generated by the turbine during a single vehicle pass-by event. The results demonstrated a significant influence of the airflow guide geometry on system performance. The most effective configuration achieved an average power output of approximately 7 W during a single vehicle pass-by event, whereas the configuration without an airflow guide exhibited significantly lower energy recovery efficiency. The obtained findings confirm the potential of the analyzed technology as a power source for autonomous low-power roadside infrastructure systems. Full article

A biological/synthetic hybrid graft copolymer was obtained by grafting poly(acrylic acid) (PAA, synthesized via reversible addition-fragmentation chain transfer (RAFT) polymerization) to amylopectin (AMP). The novel graft copolymer presents amphiphilic properties due to the inherent insolubility of AMP in water and was further utilized as a mediator for the synthesis of gold nanoparticles (AuNPs) following an environmentally friendly in situ procedure. The AMP-g-PAA copolymer formation by the interaction of the PAA end groups with the C(6)-OH groups on an AMP backbone was confirmed by Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) and 1D (proton (¹H NMR) and carbon (¹³C NMR) nuclear magnetic resonance, and Distortionless Enhancement by Polarization Transfer (DEPT)) and 2D (correlation (COSY) and heteronuclear single quantum coherence (HSQC)) spectroscopies. The calculated degree of substitution of 1.17 suggests that the grafting was done at one OH from the three in an anhydroglycosidic unit (AGU) (preferably at that in C6 position), with a mean grafting efficiency of 76%. Additional information obtained using thermogravimetric analysis shows that the thermal decomposition of AMP-g-PAA occurs in two steps, with a residual mass of ~16 wt% at 700 °C, higher than AMP or PAA, indicating increased thermal stability of the copolymer. Dynamic and electrophoretic light scattering (DLS and ELS) measurements were used to determine the hydrodynamic size and ionic charge of the AMP-g-PAA self-assemblies in aqueous solution as well as their stability. The AMP-g-PAA was subsequently tested as a reducing agent in the environmentally friendly synthesis of AuNPs in aqueous solution, at different incubation temperatures, reaction duration, and inorganic/polymer weight ratios. The development of the surface plasmon resonance band of AuNPs, observed in UV–vis spectra, was consistently monitored over the reaction time. DLS analysis indicated time-dependent changes in the AuNPs’ particle size distributions, while scanning transmission electron microscopy confirmed that the AuNPs formed at the inorganic/polymer weight ratio of 0.36 and at 60 °C were predominantly well-dispersed, spherical-shaped nanoparticles. The AuNPs synthesized in situ within the copolymer matrix did not introduce additional cytotoxicity compared to the parent copolymer alone, with the composites representing a promising safety baseline for further investigation in biomedical applications. Full article

The inverse kinematics (IK) problem of the UR3 collaborative manipulator is addressed through a singularity-aware optimization framework and a statistically grounded benchmarking methodology. The IK task is formulated as a full-pose optimization problem minimizing a physically scaled residual combining Cartesian position and orientation errors. Emphasizing consistency between error formulation and optimization paradigms, a matrix-based pose-error representation is adopted as a numerically stable residual for stochastic search. Simultaneously, a smooth Jacobian-conditioning penalty is incorporated to mitigate instability near ill-conditioned configurations. Five metaheuristic solvers (PSO, GWO, GA, JADE, ALO) are implemented under a unified, reproducible experimental protocol with common maximum search settings. The Levenberg–Marquardt (LM) numerical method is included as a deterministic baseline to compare gradient-based precision against derivative-free global exploration. Performance is evaluated across nominal, industrial, and near-singular poses using 1000 Monte Carlo runs per configuration. Final-solution accuracy, variability, and computational time are analyzed directly from the Monte Carlo outcome distributions, descriptive statistics, and nonparametric rank-based tests. Results indicate that LM achieves superior numerical precision and computational speed. Among the metaheuristics, GA provides the lowest mean objective values and the smallest objective dispersion across the three tested poses, whereas JADE is the fastest solver. GWO provides an intermediate solution profile, with competitive objective values and substantially shorter execution times than GA and ALO. The optimized solutions are first verified in a RoboDK virtual environment. Subsequently, representative GWO-based configurations are experimentally validated on a physical UR3 robot through both isolated static poses and a continuous multi-pose trajectory tracking task, confirming practical kinematic feasibility and sequential stability. The proposed framework establishes a reproducible benchmark for statistically robust evaluation of metaheuristic-based IK optimization in collaborative robotics. Full article

Background: The rehabilitation of patients who have undergone extensive maxillectomy for neoplastic lesions is a significant clinical challenge. The resulting anatomical and functional defects severely impact quality of life, and traditional removable prostheses often lack stability. Zygomatic implants offer a viable solution by providing stable anchorage in the zygomatic bone, bypassing the need for bone reconstruction. Methods: This case report details the rehabilitation of a 62-year-old female patient with a history of recurrent oral squamous cell carcinoma. A fully digital workflow, including CBCT and CAD/CAM technology, was used for meticulous surgical and prosthetic planning. The surgical procedure involved a guided maxillectomy, a free forearm flap reconstruction, and the simultaneous placement of two zygomatic implants and one conventional implant. The procedure was done with EZGOMA guided surgery, which, starting from the EZPLAN software design of zygomatic and traditional implants, allowed us to determine the implant’s position in the three-dimensional axes and also the position of the internal hexagon. This allowed us to design the implant beneath the diagnostic wax-up in the three axes, and also to calculate the degrees of inclination of the multi-unit abutment. Results: All implants achieved primary stability with a torque exceeding 45 Ncm. The patient received an immediate provisional prosthesis, which allowed for the rapid restoration of phonetic and esthetic function. The post-operative course was uneventful, with no complications. Follow-up imaging confirmed the successful integration of the implants and the absence of any prosthetic or surgical issues at 24-month successful follow-up. Conclusions: This case suggests that implant-supported rehabilitation with zygomatic implants can be a highly effective treatment for patients with severe maxillary defects following cancer surgery. By using an integrated surgical and prosthetic strategy, along with advanced digital technology, we can achieve fast, safe, and predictable results. This approach successfully restores both function and esthetics, even in challenging anatomical situations. The auxilium of guided plates is a helpful aid for both implant placement and managing bone resection during cancer surgery. Full article

The Traveling Salesman Problem (TSP) is a fundamental NP-hard combinatorial optimization problem with broad applications in logistics, scheduling, and satellite mission planning. While Ant Colony Optimization (ACO) offers distributed search and positive feedback, conventional variants suffer from premature convergence and quadratic construction costs that limit scalability. We propose the Globally Adaptive Ant Colony System (GACS), which integrates three synergistic mechanisms: (1) K-nearest neighbor candidate-list pruning that reduces per-step construction complexity from $O\left(n\right)$ to $O\left(K\right)$; (2) a globally adaptive pheromone weighting scheme that dynamically calibrates reinforcement intensity as the search matures; and (3) an adaptive stagnation recovery mechanism that applies pheromone smoothing to escape local optima. Numerical experiments demonstrate that GACS consistently outperforms four traditional ACO baselines under an equivalent time budget. On a large benchmark set from TSPLIB, GACS achieves highly competitive results against various state-of-the-art metaheuristics, with non-parametric statistical tests confirming its significant superiority in both solution quality and convergence rank. Ablation and sensitivity analyses verify that all three mechanisms are individually indispensable and that the framework is robust to parameter perturbation. Specifically, the evaporation rate and stagnation threshold are identified as the most critical parameters affecting performance, while the smoothing and adaptive range parameters exhibit low sensitivity. These results establish GACS as a lightweight, scalable, and adaptable framework for the TSP. Full article

Background: Image-based dietary assessment provides a more intuitive approach for nutritional monitoring and health management. However, in multi-category bowl-based meals, food boundary adhesion, spatial stacking, and staple-food occlusion by upper-layer dishes still affect the accuracy of volume, weight, and nutritional composition prediction. Methods: This study proposes a nutrition prediction method for bowl-based foods by integrating semantic segmentation, multi-view three-dimensional reconstruction, and occlusion compensation. The improved DBP-FDSNet was used to extract food-category masks from top-view RGB images, while detail enhancement, boundary-assisted supervision, and spatial position encoding were incorporated to improve the segmentation quality of food boundaries and adhesion regions. The visible food surface inside the bowl was reconstructed using a bowl instance model and RGB-TSDF-based multi-view fusion, and the two-dimensional semantic results were mapped into the height-field parameter domain for category-level volume integration. For partially occluded, severely occluded, or completely invisible staple foods, a layered compensation strategy was introduced to reduce staple-food volume prediction errors and the erroneous assignment of upper-layer food volume. Food weight and whole-bowl Calories, Fat, Carbohydrate, and Protein were finally predicted using food density and a nutritional composition database. Results: DBP-FDSNet achieved a meanIntersectionoverUnion ($mIoU$) of 80.51% and a BoundaryF1 Score ($bF1$) of 85.73%. At the whole-bowl level, the MeanAbsolutePercentageError ($MAPE$) values for Calories, Fat, Carbohydrate, Protein, and total food mass were 13.23%, 18.51%, 14.18%, 13.35%, and 10.85%, respectively. Conclusions: The method improves the stability of category-level volume and nutritional composition prediction in complex bowl-based meal scenarios, providing a feasible solution for image-based dietary assessment and intelligent nutrition management. Full article

To address the energy efficiency optimization problem in power wireless private networks caused by fixed node positions, strong coupling between relay selection and power allocation, and strict quality of service (QoS) constraints, an intelligent reflecting surface (IRS)-assisted hybrid multi-hop relay network model is proposed. An IRS is deployed on the surface of an obstacle located between the source node and the first-hop relay to specifically enhance the first-hop link. By integrating path planning and cooperative power control, a joint optimization problem is formulated to maximize the system energy efficiency. To tackle the coupling issues in resource allocation, a joint optimization algorithm based on the block coordinate descent framework is developed, where the original problem is decomposed into three subproblems: relay selection, power allocation, and IRS phase shift configuration. These subproblems are solved using a greedy strategy, the Dinkelbach method, and a closed-form phase alignment solution, respectively. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes in terms of system energy efficiency, reliability, and latency, making it suitable for power communication scenarios with extremely stringent QoS requirements. Full article