Search Results (9,301)

Photovoltaic (PV) cells play an important role in the development of green energy. However, in practical photovoltaic systems, shunting-related defects and hotspot phenomena may originate not only from manufacturing imperfections, but also from mechanical stress and environmental factors during transportation, installation, and long-term field operation. Such hotspots not only reduce the power-generation efficiency and service life of PV cells but may also pose safety risks to grid-connected photovoltaic power stations. To address this problem, a squared even-order derivative (SEOD) method based on surface temperature analysis is introduced to enable the quantitative detection of thermal defects in PV cells. In this study, typical faults in PV cells, including low-resistance defects and silicon-based deep scratches, are analyzed. A simulation model is established to correlate typical faults with their equivalent volumetric heat sources, followed by experimental validation for low-resistance defects. Based on this framework, the SEOD algorithm is developed and applied to achieve high-precision localization and quantitative characterization of thermal defects in both simulation models and experimental samples. Full article

With the increasing density of urban underground space development, the soil disturbance induced by large-section rectangular pipe jacking poses a significant threat to the safety of underlying subway tunnels. Taking the Lihe Road utility tunnel project in Wuhan, which crosses over Metro Line 4, as the engineering background, a three-dimensional finite element (FE) model was established using Midas GTS NX to simulate the entire pipe jacking process. Field monitoring data from caisson excavation, ground improvement, pipe jacking, and backfill grouting were introduced for validation, enabling a systematic investigation of the influence mechanism of pipe jacking on existing tunnels. In the numerical simulation, the modified Mohr–Coulomb constitutive model was adopted for the soil, and a “portal-type” reinforcement system was introduced. The pipe jacking process was simulated equivalently with a 1.2 m advance per cycle. The results indicate that the ground settlement induced by pipe jacking exhibits a stage-wise accumulation pattern and eventually develops into a stable settlement trough. The vertical settlement of the tunnel follows an evolutionary law of “early occurrence in the near field, delayed response in the far field, and final convergence,” with peak settlements of 2.44 mm and 2.53 mm for the left and right lines, respectively. Ground improvement significantly mitigates soil deformation, reducing the maximum surface settlement from 45.5 mm to 11.1 mm, decreasing the tunnel’s peak vertical settlement by 37%, and reducing horizontal displacement by 64%, thereby effectively suppressing lateral soil extrusion. The proposed closed-loop analysis method of “numerical simulation–monitoring validation–measure evaluation” reveals the spatiotemporal evolution law of soil–tunnel interaction during pipe jacking construction and provides valuable reference for risk control in similar engineering projects. Full article

Masonry retaining walls constitute an essential component of historic and urban infrastructure in seismic regions; however, their seismic performance remains insufficiently quantified due to material heterogeneity, limited tensile capacity, and complex soil–structure interaction. This study investigates the seismic response of historic stone masonry retaining walls using a finite element-based anisotropic macro-modeling approach. The analysis focuses on the perimeter retaining walls of Emirgan Grove in Istanbul, which represent culturally significant heritage structures constructed from natural limestone and cement–lime mortar. Material properties were defined based on experimental test results and representative values reported in the literature, while composite anisotropic behavior was incorporated into the numerical models. Static loads, earth pressures, and seismic actions were applied in accordance with the Turkish Building Earthquake Code (TBEC-2018) using the equivalent static earthquake load method. Representative wall segments with heights of 2.5 m, 3.5 m, 4.0 m, and 6.30 m were analyzed. The numerical results show that maximum compressive stresses reached approximately 0.48 MPa, remaining well below the allowable limit of 4.50 MPa, while maximum tensile stresses of about 0.28 MPa did not exceed the allowable tensile limit of 1.00 MPa. In contrast, shear stresses locally reached approximately 0.25 MPa, exceeding the allowable shear limit of 0.10 MPa, particularly along the soil–wall interface in taller walls. Sliding stability was satisfied in all cases, whereas overturning and shear behavior governed seismic vulnerability. These findings confirm that wall height is the primary parameter controlling seismic response and demonstrate the effectiveness of the proposed framework for preservation-oriented seismic safety assessment of historic masonry retaining walls. Full article

Introduction: Minimally invasive pancreatoduodenectomy (MIPD), including laparoscopic (LPD) and robotic approaches (RPD), has gained increasing attention as an alternative to open pancreatoduodenectomy (OPD). Despite rapid technological progress, concerns persist regarding safety, reproducibility, and oncological adequacy. The publication of randomized controlled trials (RCTs) provides essential high-level evidence to reassess the true benefits and limitations of MIPD. Methods: This narrative review synthesizes all available RCTs comparing LPD and RPD with OPD. Major domains evaluated include mortality, major morbidity, intraoperative parameters, postoperative recovery, oncological outcomes, conversion, costs, and the influence of surgeon experience and institutional volume. The objective is to contextualize RCT findings rather than perform a quantitative meta-analysis. Discussion: Across studies, LPD demonstrates comparable mortality and complication rates to OPD in high-volume centers, with consistent reductions intraoperative blood loss (IBL) and shorter recovery or length of stay (LOS). RPD shows more heterogeneous results: one large trial reported improved postoperative recovery, whereas the EUROPA trial identified higher rates of pancreatic fistula (POPF) and delayed gastric emptying (DGE) alongside significantly increased costs. Both LPD and RPD achieve oncological outcomes equivalent to OPD, and 3-year survival data confirm the long-term non-inferiority of LPD. However, operative time remains longer for all minimally invasive approaches, and conversion persists as a marker of technical difficulty and incomplete learning curve. Conclusions: Current RCT evidence indicates that MIPD is safe, feasible, and oncologically sound only when performed by surgeons who have surpassed the demanding learning curve within specialized, high-volume centers. The benefits, mainly reduced IBL and faster recovery, must be weighed against longer operative times, conversion risks, and substantially higher costs for RPD. MIPD should therefore be considered an advanced option rather than a universal standard, and its broader implementation requires structured training pathways, appropriate patient selection, and institutional readiness. Full article

Coprime arrays enable enhanced degrees of freedom through the construction of virtual array equivalent signals. However, the presence of large “holes” leads to discontinuous co-arrays, which severely hampers direction-of-arrival (DOA) estimation techniques that rely on uniform array structures. This paper explores the practical application of co-array domain signal processing for underwater acoustic coprime arrays. We propose a novel array configuration based on coprime minimum disordered pairs, enabling the formation of continuously connected co-arrays without interpolating. To address the challenge of limited snapshots in underwater environments, DOA estimation can be achieved by utilizing traditional multipath matching pursuit (MMP) algorithms under the proposed continuous co-array implementation scheme. In practical applications, physical array element failures are inevitable, and faulty elements can create holes in the originally continuous co-array. While interpolation techniques can mitigate small gaps, their performance deteriorates significantly in the presence of large holes or uneven data distribution. To overcome these limitations, we introduce a sparse signal recovery (SSR) method using a fragment array data validation technique for sparse DOA estimation with an underwater acoustic coprime array. Based on the designed continuous array expansion scheme, the resulting continuous co-array is used to map the positions of element failures, revealing the gaps in the co-array. A validation model is established for partially continuous sub-arrays within the discontinuous co-array, enabling signal direction estimation based on the fragmented array validation. Both simulation and sea trial results confirm that the proposed approach maximizes the utilization of co-array elements without relying on interpolation or prediction, offering a robust solution for scenarios involving sensor failures. Full article

The accurate estimation of the fundamental period is critical for seismic design using the Equivalent Lateral Force method. This study evaluates widely used empirical period formulae from international seismic codes and previous research by comparing them with detailed finite element method (FEM) analyses. A total of 93 reinforced concrete building models were assessed. The results show that most empirical formulae, notably the American Society of Civil Engineers Standard (ASCE 7-10), the Eurocode, the National Building Code of Canada (NBCC), and the Saudi Building Code (SBC 301), systematically underestimate the fundamental period in low- and mid-rise buildings often by more than 40% under cracked conditions, while discrepancies reduce under uncracked assumptions. Equations such as those proposed by the Building Standard Law of Japan (BSLJ) and Australian Standard (AS 11407.2) show comparatively closer agreements with FEM predictions, whereas formulae developed by Goel and Chopra and by Alguhane et al. have distinct differences, especially at greater heights. Statistical parameters, including the arithmetic mean difference and the standard deviation, were employed to enhance the comparison and assess the accuracy and dispersion of the estimated fundamental periods. The results indicate that empirical formulae, although beneficial in first-design stages, are likely to yield conservative results and suggest the use of advanced numerical computation or revised models and coefficients for RC high-rise and irregular buildings. Full article

Under China’s dual-carbon policy, medium-duty commercial vehicles (MDCVs)—widely used in urban distribution with high load fluctuation and long operating hours—are key to transportation energy conservation and emission reduction. Optimizing powertrain parameters and energy management is essential for fuel-cell MDCVs. However, traditional powertrain parameter selection relies on fixed thresholds and lacks optimization, while the equivalent consumption minimization strategy (ECMS) suffers from poor driving cycle adaptability despite addressing hydrogen consumption and online application challenges. To overcome these issues, this study proposes an innovative approach for fuel cell-powered MDCVs: a driving cycle model was constructed based on hydrogen consumption and fuel cell degradation rates. Subsequently, the powertrain system parameters were optimized, culminating in the development of an adaptive ECMS (A-ECMS). Specifically, the method includes: (1) a driving cycle construction approach analyzing driving cycle clustering’s impact on adaptive control parameters; (2) a powertrain parameter optimization method considering vehicle performance under synthetic driving cycles; and (3) an A-ECMS enhanced by a crayfish optimization algorithm (COA) to improve driving cycle adaptability. Simulations show that A-ECMS achieves hydrogen consumption close to the dynamic programming algorithm (DP) optimum, reducing consumption by 2.12% and 1.45% compared to traditional ECMS under synthetic and World Transient Vehicle Cycle (WTVC) cycles, significantly improving MDCV economy. Full article

Time-dependent structural systems (TDSSs) in engineering involve high dimensionality, nonlinearity, and complex uncertainties, complicating the reliability analysis compared to time-independent assessments. To address these challenges, this paper proposes an extremum-based back propagation neural network (BPNN) method for TDSS reliability analysis. The method adopts a double-loop structure. Specifically, the inner loop finds the minimum of the time-dependent performance function for a given realization of the random variables. This transformation converts the time-dependent problem into an equivalent time-invariant one. Then, the outer loop constructs a BPNN surrogate model to map the relationship between the random variables and the performance function minima. To improve computational efficiency, an adaptive sample selection strategy is integrated into the training process. This technique selects samples near the failure boundary to iteratively update the BPNN, ensuring high accuracy with a small training set. Once the stopping criterion is satisfied, the failure probability is estimated using Monte Carlo simulation (MCS). The trained BPNN model is used to rapidly predict the extremum for the large-scale sample pool. The proposed method is verified through three practical engineering cases: a four-bar mechanism, an aero-engine turbine disc, and a cantilever tube. Results show that the method remains accurate and efficient. The successful applications confirm the rationality and engineering applicability of the proposed model. Full article

To address the limitations of conventional eddy current magnetic-field-measurement techniques, this study proposes a novel measurement method for non-magnetic metals. First, the time-varying current in the Earth Field Simulator is calibrated using background magnetic sensors to obtain the coil magnetic field. This approach avoids repetitive errors caused by multiple current injections into the coil and ensures the simultaneity of current and magnetic field measurements. Additionally, the background eddy current magnetic field is approximated as a first-order RL-equivalent circuit, enabling the calculation and elimination of the background interference to improve the measurement accuracy of eddy current magnetic fields in non-magnetic metals. Next, experiments are carried out to measure the eddy current magnetic field of the non-magnetic metal plates under both ramp and sinusoidal magnetic field excitations. Finally, the eddy current magnetic simulations of the non-magnetic metal plates are conducted based on the finite element method. Under various excitation conditions, the maximum relative deviation between simulated and measured values remains below 5%, demonstrating the high precision of the proposed measurement method. This research provides a new approach for eddy current magnetic field measurement in non-magnetic metals. Full article

Background: This systematic review aims to identify modifiable and non-modifiable predictors of exercise capacity (VO₂peak level or change) in stroke survivors. These insights may further optimize rehabilitation treatment and improve long-term health outcomes. Methods: PubMed (PubMed.gov), EMBASE (Elsevier), CINAHL (EBSCO), and Web of Science (Clarivate) were searched (last search on 7 October 2025). Inclusion criteria were: (1) adults (>18 years) who survived a stroke (ischemic and hemorrhagic), (2) outcome was a measurement of maximum exercise capacity (VO₂peak) measured with CPET (or equivalent), (3) predictors of exercise capacity were measured (e.g., personal factors, disease-related factors, components of rehabilitation), (4) predictors of exercise capacity were analyzed in multivariate regression models, (5) primary research, and (6) full-text available. During the data extraction phase, predictors were categorized into modifiable and non-modifiable predictors. Risk of bias was assessed with the McMaster Critical Review Form for Quantitative Studies. Results: Of 919 unique articles, seventeen were included. Modifiable factors such as BMI (4/8 articles) and fat mass (1/1), lower limb strength (3/3), cardiorespiratory fitness (e.g., baseline VO₂peak (2/4)), training intensity (2/2) and perceived fatigue (1/1) significantly predicted VO₂peak (level or change). Significant non-modifiable predictors were age (3/11), sex (1/8), diabetes (1/2), and stroke-specific (4/8) factors. Conclusions: This systematic review highlights the significant role of modifiable and non-modifiable predictors in optimizing exercise capacity (VO₂peak) for stroke survivors. In addition, considering modifiable and non-modifiable predictors allows for more personalized treatment planning. The findings can guide healthcare professionals in tailoring rehabilitation programs, though further research is needed to develop a comprehensive prediction model. Full article

Lipschitz-based classification provides a flexible framework for general metric spaces, naturally adapting to complex data structures without assuming linearity. However, direct applications of classical extensions often yield decision boundaries equivalent to the 1-Nearest Neighbour classifier, leading to overfitting and sensitivity to noise. Addressing this limitation, this paper introduces a novel binary classification algorithm that integrates probabilistic kernel smoothing with explicit Lipschitz extensions. We approximate the conditional probability of class membership by extending smoothed labels through a family of bounded Lipschitz functions. Theoretically, we prove that while direct extensions of binary labels collapse to nearest-neighbour rules, our probabilistic approach guarantees controlled complexity and stability. Experimentally, evaluations on synthetic and real-world datasets demonstrate that this methodology generates smooth, interpretable decision boundaries resilient to outliers. The results confirm that combining kernel smoothing with adaptive Lipschitz extensions yields performance competitive with state-of-the-art methods while offering superior geometric interpretability. Full article

Background: The Slovenian Nutrition Guidelines 2025 (SNG2025) provide a quantified, plant-forward framework aligned with the EAT–Lancet diet, whereas previous Slovenian FBDGs were qualitative. Objectives: (i) To compare SNG2025 with the EAT–Lancet diet and previous Slovenian FBDGs and (ii) to assess the alignment of food intake among Slovenian adults with the SNG2025. Methods: The SNG2025 food group targets were mapped to the EAT–Lancet diet and previous Slovenian FBDGs and evaluated against a nationally representative intake (Si. Menu 2017/18; 18–64 years; sex-specific). Sodium intake was corroborated by 24-h urinary sodium levels (2022). Results: The SNG2025 introduces numeric targets across more than 16 food groups, with national adaptations (e.g., potatoes, oils and fats from foods, and dairy being optional via milk-calcium equivalents and beverage specifications). The alignment reveals very low consumption of legumes; limited consumption of vegetables, whole grains, and nuts/seeds (and fruit in men); and excess consumption of total and red/processed meat, ultra-processed foods (UPFs), free sugars/sugar-sweetened beverages, sodium, and alcohol. Biomarkers indicate a mean salt intake approximately two times the <5 g/day limit. Trans fatty acid (TFA) levels ≥ 0.5% persist in a substantial percentage of adults, predominantly from ruminant-derived TFAs. Sex-specific patterns are more adverse for men (e.g., meat, SSBs, alcohol, and sodium), whereas women have a higher intake of sweet UPFs. Conclusions: Slovenian diets are misaligned with the SNG2025. Priorities include increasing the intake of legumes, whole grains, vegetables, and nuts/seeds, while shifting protein sources away from red and processed meat. Additional priorities include reducing the intake of alcohol, sodium, free sugars, and UPFs through reformulation, procurement, and pricing/marketing measures, alongside routine biomarker and UPF surveillance. The SNG2025 enable monitoring and targeted implementation. Considering the limitations of the Si. Menu 2017/18 dataset, which includes food-group aggregation and limited information on food preparation, the results should be interpreted with caution with respect to the magnitude of deviations from SNG2025 targets, while the overall direction of misalignment remains robust. Full article

To address the issues of high electricity costs for industrial loads in enterprise parks, significant peak-valley price differences, and insufficient utilization of renewable energy, a multi-objective capacity optimization method for photovoltaic and energy storage systems has been proposed, incorporating price-based demand response (PDR) and cycle life constraints. Firstly, a multi-objective function was constructed by integrating the aforementioned constraints, aiming to minimize the equivalent annualized comprehensive cost and the energy imbalance rate. Then, to overcome the limitations of the traditional sparrow search algorithm (SSA), such as low convergence speed, limited precision, and the tendency to fall into local optima, an improved SSA was proposed. This improved algorithm was enhanced by the integration of chaotic mapping, adaptive inertia weight, Harris Hawks encircling, and predation strategies. Through these improvements, both the convergence speed and accuracy in solving high-dimensional problems were significantly improved. Finally, a case study was conducted using real load data from an enterprise park in Zhuzhou City. The proposed algorithm achieves a maximum economic benefit improvement of 7.32% over conventional intelligent algorithms while further enhancing power supply reliability. Full article

Objectives: To compare artificial intelligence (AI) crown design with expert or non-AI computer-aided (CAD) design for single-unit tooth and implant-supported crowns across efficiency, marginal and internal fit, morphology and occlusion, and mechanical performance. Materials and Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. PubMed MEDLINE, Scopus, Web of Science, IEEE Xplore, and Dentistry and Oral Sciences Source were searched from 2016 to 2025 with citation chasing. Eligible studies directly contrasted artificial intelligence-generated or artificial intelligence-assisted crown designs with human design in clinical, ex vivo, or in silico settings. Primary outcomes were design time, marginal and internal fit, morphology and occlusion, and mechanical performance. Risk of bias was assessed with ROBINS-I for non-randomized clinical studies, QUIN for bench studies, and PROBAST + AI for computational investigations, with TRIPOD + AI items mapped descriptively. Given heterogeneity in settings and endpoints, a narrative synthesis was used. Results: A total of 14 studies met inclusion criteria, including a clinical patient study, multiple ex vivo experiments, and in silico evaluations. Artificial intelligence design reduced design time by between 40% and 90% relative to expert computer-aided design or manual workflows. Marginal and internal fit for artificial intelligence and human designs were statistically equivalent in multiple comparisons. Mechanical performance matched technician designs in load-to-fracture testing, and modeling indicated stress distributions similar to natural teeth. Overall risk of bias was judged as some concerns across tiers. Conclusions: Artificial intelligence crown design delivers efficiency gains while showing short-term technical comparability across fit, morphology, occlusion, and strength for single-unit crowns in predominantly bench and in silico evidence, with limited patient-level feasibility data. Prospective clinical trials with standardized, preregistered endpoints are needed to confirm durability, generalizability, and patient-relevant outcomes, and to establish whether short-term technical advantages translate into clinical benefit. Full article

Background. The rapid expansion of esports within higher education, accelerated by the COVID-19 pandemic, has raised important questions regarding their impact on students’ physical and psychological development. While traditional sports are well known for their benefits on motor and physical skills, esports primarily engage cognitive processes through sustained interaction with digital environments. This study compares motor skills and cognitive performance among higher education male students participating in esports and traditional sports in a post-pandemic context. Methods. The present study employs a quantitative, comparative, cross-sectional design to examine differences in motor skills (using standardized physical tests) and cognitive performance (focused attention, short-term memory, and information processing speed) between higher education male students engaged in esports and those participating in traditional sports. Results. Male students engaged in traditional sports demonstrated superior motor outcomes, particularly in muscle strength and postural control. Cognitive performance was comparable between groups, with a slight advantage for traditional sports participants in focused attention and processing speed. Conclusions. Although esports may support certain aspects of cognitive performance to a degree comparable with traditional sports, they do not provide equivalent benefits in terms of motor and postural development. These results highlight the importance of maintaining physical activity within university settings and suggest that esports should complement rather than replace traditional sports in higher education programs. Full article