Search Results (4,910)

Concrete is a fundamental material in structural engineering, widely used in critical infrastructure such as bridges, nuclear power plants, and dams. These structures may be subjected to extreme dynamic loads resulting from natural disasters, industrial accidents, or missile impacts. Therefore, a comprehensive understanding of concrete behavior under high strain rates is essential for safe and resilient design. Experimental investigations, particularly spalling tests, have highlighted the strain-rate sensitivity of concrete in dynamic tensile loading conditions. This study presents a macroscopic 3D discrete element model specifically developed to simulate the dynamic response of concrete subjected to extreme loading. Unlike conventional continuum-based models, the proposed discrete element framework is particularly suited to capturing damage and fracture mechanisms in cohesive materials. A key innovation lies in incorporating a physically grounded strain-rate dependency directly into the local cohesive laws that govern inter-element interactions. The originality of this work is further underlined by the validation of the discrete element model under dynamic tensile loading through the simulation of spalling tests on normalstrength concrete at strain rates representative of severe impact scenarios (30–115 s⁻¹). After calibrating the model under quasi-static loading, the simulations accurately reproduce key experimental outcomes, including rear-face velocity profiles and failure characteristics. Combined with prior validations under high confining pressure, this study reinforces the capability of the discrete element method for modeling concrete subjected to extreme dynamic loading, offering a robust tool for predictive structural assessment and design. Full article

As power grids continue to expand in scale, the occurrence of cascading failures within them can lead to significant economic losses. Therefore, assessing the criticality of grid nodes is crucial for ensuring the secure and stable operation of power systems and for mitigating losses when cascading failures occur. The classical Local Link Similarity (LLS) algorithm in complex networks evaluates the importance of network nodes from a neighborhood topology perspective, but it suffers from issues such as the excessive weighting of node degrees and the neglect of electrical parameters. Based on the classical algorithm, this paper first develops the Improved Local Link Similarity (ILLS) algorithm by substituting alternative similarity metrics and comparatively evaluating their performance. Building upon the ILLS, we then propose the Electrical LLS (ELLS) algorithm by integrating node power flow and electrical coupling connectivity as multiplicative factors, with optimal combinations determined via simulation experiments. Compared to classical approaches, ELLS demonstrates superior adaptability to power grid contexts and delivers enhanced accuracy in power system node importance assessments. These algorithms are applied to rank the node importance in the IEEE 300-bus system. Their performance is evaluated using the loss-of-load-size metric, comparing ELLS, ILLS, and the classical algorithm. The results demonstrate that under the loss-of-load-size metric, the ELLS algorithm achieves approximately 25% higher accuracy compared to both the ILLS and the classical algorithm, validating its effectiveness. Full article

Optimizing stress distribution at the bone–implant interface is critical to enhancing the long-term biomechanical performance of dental implant systems. Vertical misalignment between splinted implants can result in elevated localized stresses, increasing the risk of material degradation and peri-implant bone resorption. This study employs three-dimensional finite element analysis (FEA) to evaluate the mechanical response of peri-implant bone under oblique loading, focusing on how variations in vertical implant platform alignment influence stress transmission. Four implant configurations with different vertical placements were modeled: (A) all crestal, (B) central subcrestal with lateral crestal, (C) lateral subcrestal with central crestal, and (D) all subcrestal. A 400 N oblique load was applied at 45° simulated masticatory forces. Von Mises stress distributions were analyzed in both cortical and trabecular bone, with a physiological threshold of 100 MPa considered for cortical bone. Among the models, configuration B exhibited the highest cortical stress, exceeding the physiological threshold. In contrast, configurations with uniform vertical positioning, particularly model D, demonstrated more favorable stress dispersion and lower peak values. Stress concentrations were consistently observed at the implant–abutment interface across all configurations, identifying this area as critical for design improvements. These findings underscore the importance of precise vertical alignment in implant-supported restorations to minimize stress concentrations and improve the mechanical reliability of dental implants. The results provide valuable insights for the development of next-generation implant systems with enhanced biomechanical integration and material performance under functional loading. Full article

Objective: Radical therapies are associated with significant morbidity in patients with localized prostate cancer (PCa). While advances in nuclear magnetic resonance techniques have enabled the development of focal ablation procedures that can selectively destroy tumors, preserve the gland and surrounding structures, and minimize side effects, existing vascular-targeted photodynamic therapy (VTP) and nanodrug therapies often face limitations, such as recurrence and insufficient drug concentration at the tumor site. This study investigated a novel approach that combines VTP with systemic treatment using drug-loaded nanoparticles in a murine model, demonstrating substantial advancements beyond current monotherapies. Methods: SCID (severe combined immunodeficiency) mice were engrafted with androgen-sensitive prostate tumor cells (LNCaP-AR) and treated with a combination of VTP and two different drugs linked to fucoidan nanoparticles (Enzalutamide and Paclitaxel). Experiments were performed using different cohorts: the evaluation of oncological effect, the administration time and concentration of systemic therapy, a comparison of efficacy between VTP and radiotherapy, and the induction of the abscopal effect in untreated synchronous tumors. Results: The groups that received combination therapy showed better tumor control. After eight weeks, the recurrence-free survival rates were 87.5%, 62.5%, and 50% in the VTP + N-PAC, VTP + N-ENZ, and VTP monotherapy groups, respectively (p < 0.05). There was a significant difference in the intra-tumoral concentration of nanodrugs between the groups with combined treatment and monotherapy. After two weeks, the monotherapy groups showed almost total elimination of the drugs, whereas in the combined therapy groups, this concentration remained high, starting to decrease after three weeks (p < 0.05). Treatment with nanodrugs associated with VTP showed superior oncological benefits compared to radiotherapy alone or in combination with other therapies. The abscopal effect on synchronous tumors was not demonstrated with VTP alone or in combination with nanodrugs. Conclusions: Combining vascular photodynamic therapy with nanodrugs was highly effective in treating a prostate tumor model, leading to increased survival and a reduced risk of tumor recurrence. This approach significantly advances beyond existing VTP and nanodrug therapies by improving tumor control, ensuring sustained intra-tumoral drug concentration, and yielding superior oncological outcomes. Our results suggest that this therapy is a potential treatment option for prostate tumors treated with VTP in future clinical trials. Full article

Loading and clamping schemes significantly influence the behavior of shape memory alloys, specifically, the course of their solid-state transformations. This paper presents experimental and numerical findings regarding the nonlinear response of samples of the above-mentioned type of smart materials observed during tensile tests. Hysteretic properties were studied to elucidate the superelastic behavior of the tested and modeled samples. The conducted tensile tests considered two configurations of grips, i.e., the standard one, where the jaws transversely clamp a specimen, and the customized bollard grip solution, which the authors developed to reduce local stress concentration in a specimen. The characteristic impact of the boundary conditions on the solid phase transformation in shape memory alloys, present due to the specific clamping scheme, was studied using a thermal camera and extensometer. Martensitic transformation and the plateau region in the nonlinear stress–strain characteristics were observed. The results of the numerical simulation converged to the experimental outcomes. This study explains the complex nature of the phase changes in shape memory alloys under specific boundary conditions induced by a given clamping scheme. In particular, variation in the martensitic transformation course is identified as resulting from the stress distribution observed in the specimen’s clamping area. Full article

This study investigates the failure behavior of aluminum solar panel mounting structures subjected to uplift pressure, with particular focus on conditions not typically considered in conventional design, specifically, foundation defects. To clarify critical failure modes and evaluate potential countermeasures, full-scale pressure loading tests were conducted. The results showed that when even a single column base was unanchored, structural failure occurred at approximately half the design wind pressure. Although reinforcement measures—such as the installation of uplift-resistant braces—increased the failure pressure to 1.5 times the design value, they also introduced the risk of undesirable failure modes, including panel detachment. Additionally, four-point bending tests of failed members and joints, combined with structural analysis of the frame, demonstrated that once the ultimate strength of each component is known, the likely failure location within the structure can be reasonably predicted. To prevent panel blow-off and progressive failure of column bases and piles, specific design considerations are proposed based on both experimental observations and numerical simulations. In particular, avoiding local buckling in members parallel to the short side of the panels is critical. Furthermore, a safety factor of approximately two should be applied to column bases and pile foundations to ensure structural integrity under unforeseen foundation conditions. Full article

This paper details the development and implementation of an intelligent energy efficiency system for an electrical grid that incorporates renewable energy sources, specifically photovoltaic systems. The system is applied in a small locality of approximately 8000 inhabitants and aims to optimize energy consumption in public institutions by scheduling electrical appliances during periods of surplus PV energy production. The proposed solution employs a hybrid neuro-fuzzy approach combined with scheduling techniques to intelligently shift loads and maximize the use of locally generated green energy. This enables appliances, particularly schedulable and schedulable non-interruptible ones, to operate during peak PV production hours, thereby minimizing reliance on the national grid and improving overall energy efficiency. This directly reduces the cost of electricity consumption from the national grid. Furthermore, a comprehensive power quality analysis covering variables including harmonic distortion and voltage stability is proposed. The results indicate that while photovoltaic systems, being switching devices, can introduce some harmonic distortion, particularly during peak inverter operation or transient operating regimes, and flicker can exceed standard limits during certain periods, the overall voltage quality is maintained if proper inverter controls and grid parameters are adhered to. The system also demonstrates potential for scalability and integration with energy storage systems for enhanced future performance. Full article

Based on the engineering background of a new type of segmental-assembled steel temporary beam buttress, the fatigue strength evaluation method of the steel box girders with open longitudinal ribs was taken as the research objective. The fatigue stress calculation analysis and the full-scale fatigue loading test for the steel box girder local component were carried out. The accuracy of the finite-element model was verified by comparing it with the test results, and the rationality of the fatigue strength evaluation methods for welded joints was deeply explored. The results indicate that the maximum nominal stress occurs at the weld toe between the transverse diaphragm and the top plate at the edge of the loading area, which is the fatigue-vulnerable location for the steel box girder local components. The initial static-load stresses at each measuring point were in good agreement with the finite-element calculation results. However, the static-load stress at the measuring point in the fatigue-vulnerable position shows a certain decrease with the increase in the number of cyclic loads, while the stress at other measuring points remains basically unchanged. According to the finite-element model, the fatigue strengths obtained by the nominal stress method and the hot-spot stress method are 72.1 MPa and 93.8 MPa, respectively. It is reasonable to use the nominal stress S-N curve with a fatigue life of 2 million cycles at 70 MPa and the hot-spot stress S-N curve with a fatigue life of 2 million cycles at 90 MPa (FAT90) to evaluate the fatigue of the welded joints in steel box girders with open longitudinal ribs. According to the equivalent structural stress method, the fatigue strength corresponding to 2 million cycles is 94.1 MPa, which is slightly lower than the result corresponding to the main S-N curve but within the range of the standard deviation curve. The research results of this article can provide important guidance for the anti-fatigue design of welded joints in steel box girders with open longitudinal ribs. Full article

The U.S. Environmental Protection Agency (EPA) developed the concept of Water Quality Volume (WQv) as a Best Management Practice (BMP) to treat the first 25.4 mm of rainfall in urban areas, aiming to capture approximately 90% of annual runoff. However, applying this urban-based standard—designed for areas with over 50% imperviousness—to rural regions with higher infiltration and pervious surfaces may result in overestimated facility capacities. In Korea, a uniform WQv criterion of 5 mm is applied nationwide, regardless of land use or hydrological conditions. This study examines the suitability of this 5 mm standard in rural catchments using the Hydrological Simulation Program–Fortran (HSPF). Eight sub-watersheds in the target area were simulated under varying cumulative runoff depths (1–10 mm) to assess pollutant loads and runoff characteristics. First-flush effects were most evident below 5 mm, with variation depending on land cover. Nature-based treatment systems for constructed wetlands were modeled for each sub-watershed, and their effectiveness was evaluated using Flow Duration Curves (FDCs) and Load Duration Curves (LDCs). The findings suggest that the uniform 5 mm WQv criterion may result in overdesign in rural watersheds and highlight the need for region-specific standards that consider local land-use and hydrological variability. Full article

Background/Objectives: Personalized 3D-printed (3DP) metallic implants delivery systems are being explored to repair bone fractures, allowing the customization of medical implants that respond to individual patient needs, making it potentially more effective and of greater quality than mass-produced devices. However, challenges associated with postsurgical infections caused by bacterial adhesion remain a clinical issue. To address this, local antibiotic therapies are receiving extensive attention to minimize the risk of implant-related infections. This study investigated the use of amikacin (AMK), a broad-spectrum aminoglycoside antibiotic, incorporated onto 3D-printed 316L stainless steel implants using biodegradable polymer coatings of chitosan and poly lactic-co-glycolic acid (PLGA). Methods: This research examined different approaches to coat 3DP implants with amikacin. Various polymer-based coatings were studied to determine the optimal formulation based on the characteristics and release profile. The optimal formulation was performed on the antibacterial activity studies. Results: AMK-chitosan with PLGA coating implants controlled the rate of drug release for up to one month. The 3DP drug-loaded substrates demonstrated effective, concentration-dependent antibacterial activity against common infective pathogens. AMK-loaded substrates showed antimicrobial effectiveness for one week and inhibited bacteria significantly compared to the uncoated controls. Conclusions: This study demonstrated that 3DP metal surfaces coated with amikacin can provide customizable drug release profiles while effectively inhibiting bacterial growth. These findings highlight the potential of combining 3D printing with localized delivery strategies to prevent implant-associated infections and advance the development of personalized therapies. Full article

As streaming platforms adopt artificial intelligence (AI)-powered subtitle systems to satisfy global demand for instant localization, the cognitive impact of these automated translations on viewers remains largely unexplored. This study used a web-based eye-tracking protocol to compare the cognitive load that GPT-4o-generated Arabic subtitles impose with that of professional human translations among 82 native Arabic speakers who viewed a 10 min episode (“Syria”) from the BBC comedy drama series State of the Union. Participants were randomly assigned to view the same episode with either professionally produced Arabic subtitles (Amazon Prime’s human translations) or machine-generated GPT-4o Arabic subtitles. In a between-subjects design, with English proficiency entered as a moderator, we collected fixation count, mean fixation duration, gaze distribution, and attention concentration (K-coefficient) as indices of cognitive processing. GPT-4o subtitles raised cognitive load on every metric; viewers produced 48% more fixations in the subtitle area, recorded 56% longer fixation durations, and spent 81.5% more time reading the automated subtitles than the professional subtitles. The subtitle area K-coefficient tripled (0.10 to 0.30), a shift from ambient scanning to focal processing. Viewers with advanced English proficiency showed the largest disruptions, which indicates that higher linguistic competence increases sensitivity to subtle translation shortcomings. These results challenge claims that large language models (LLMs) lighten viewer burden; despite fluent surface quality, GPT-4o subtitles demand far more cognitive resources than expert human subtitles and therefore reinforce the need for human oversight in audiovisual translation (AVT) and media accessibility. Full article

This study investigates the mechanical behavior and failure characteristics of chain slings under standard and non-standard fastening methods. Through dimensional inspections, fracture tests, and finite element analysis, we identified critical factors influencing chain failure. Chains exhibiting over 10% diameter reduction or increased pitch exceeded discard criteria and showed significant strength loss. Fracture loads in aged chains dropped by more than 35% compared to standards. Structural analysis revealed that standard fastening (using master links) ensures uniform stress distribution and higher load capacity, whereas non-standard fastening (direct wrapping on eyebolts) caused stress concentration, reduced tensile capacity by over 15%, and led to localized failure near contact areas. These results validate the structural soundness of international standards (DIN EN 818-4, ISO 3056) and highlight the risks of improper fastening. Practical recommendations include strict adherence to standard fastening methods, avoidance of direct wrapping, and implementation of regular inspections. The findings emphasize the need for design considerations regarding fastening geometry and suggest further research into fatigue life prediction and contact condition optimization. Full article

To enhance the environmental sustainability and structural performance of concrete-filled steel tubes (CFSTs), this study experimentally investigates the eccentric compression behavior of short CFST columns incorporating alkali-activated concrete (AAC) and internal stiffeners. Fifteen specimens were tested, varying in steel tube thickness, stiffener thickness, and eccentricity. The results show that increasing eccentricity reduces load-bearing capacity and stiffness, while stiffeners delay local buckling and improve stability. Based on the experimental findings, the applicability of the GB 50936-2014, Design of Steel and Composite Structures Specification, and the American AISC-LRFD specification to the design of ACFST columns is further evaluated. Corresponding design recommendations are proposed, and a regression-based predictive model for eccentric bearing capacity is developed, showing good agreement with the test results, with prediction errors within 10%.providing technical references for the development of low-carbon, high-performance CFST members. Full article

The generation of realistic electrical load profiles is essential for advancing smart grid analytics, demand forecasting, and privacy-preserving data sharing. Traditional approaches often rely on large, high-resolution datasets and complex recurrent neural architectures, which can be unstable or ineffective when training data are limited. This paper proposes a data-driven framework based on a lightweight 1D Convolutional Wasserstein GAN with Gradient Penalty (Conv1D-WGAN-GP) for generating high-fidelity synthetic 24 h load profiles. The model is specifically designed to operate on small- to medium-sized datasets, where recurrent models often fail due to overfitting or training instability. The approach leverages the ability of Conv1D layers to capture localized temporal patterns while remaining compact and stable during training. We benchmark the proposed model against vanilla GAN, WGAN-GP, and Conv1D-GAN across four datasets with varying consumption patterns and sizes, including industrial, agricultural, and residential domains. Quantitative evaluations using statistical divergence measures, Real-vs-Synthetic Distinguishability Score, and visual similarity confirm that Conv1D-WGAN-GP consistently outperforms baselines, particularly in low-data scenarios. This demonstrates its robustness, generalization capability, and suitability for privacy-sensitive energy modeling applications where access to large datasets is constrained. Full article

In the context of the “double carbon” goals and energy transformation, the integration of energy and transportation has emerged as a crucial trend in their coordinated development. Wind power prediction serves as the cornerstone technology for ensuring efficient operations within this integrated framework. This paper introduces a wind power prediction methodology based on an NGBoost–GRU fusion model and devises an innovative dynamic charging optimization strategy for electric vehicles (EVs) through deep collaboration. By integrating the dynamic feature extraction capabilities of GRU for time series data with the strengths of NGBoost in modeling nonlinear relationships and quantifying uncertainties, the proposed approach achieves enhanced performance. Specifically, the dual GRU fusion strategy effectively mitigates error accumulation and leverages spatial clustering to boost data homogeneity. These advancements collectively lead to a significant improvement in the prediction accuracy and reliability of wind power generation. Experiments on the dataset of a wind farm in Gansu Province demonstrate that the model achieves excellent performance, with an RMSE of 36.09 kW and an MAE of 29.96 kW at the 12 h prediction horizon. Based on this predictive capability, a “wind-power-charging collaborative optimization framework” is developed. This framework not only significantly enhances the local consumption rate of wind power but also effectively cuts users’ charging costs by approximately 18.7%, achieving a peak-shaving effect on grid load. As a result, it substantially improves the economic efficiency and stability of system operation. Overall, this study offers novel insights and robust support for optimizing the operation of integrated energy systems. Full article