Search Results (1,021)

The seismic bearing capacity of water-saturated composite foundations adjacent to slopes is critical for engineering safety, yet it is significantly influenced by complex factors such as earthquakes and heavy rainfall. This paper establishes a failure mechanism model that involves both reinforced and non-reinforced zones, comprehensively considering the synergistic effects of seismic force, pore water pressure and group pile replacement rate, and thus addressing the issue that existing models struggle to account for the coupling effects of multiple factors. Based on the principle of virtual work, a general solution for ultimate bearing capacity is derived, and the optimal solution is obtained using the MATLAB R2023a exhaustive method. Findings reveal that pile group support substantially enhances bearing capacity: the improvement becomes more pronounced with higher soil strength parameters (φ, c) and replacement ratios. When the seismic acceleration coefficient increases from 0 to 0.3, the bearing capacity of the unreinforced foundation decreases by approximately 61.6% (from 134.71 kPa to 51.83 kPa), while group pile support can increase the bearing capacity by 433.2%. Notably, when soil strength is inherently high, the marginal benefit of pile group reinforcement diminishes. A case study in Fuzhou validates through numerical simulation that pile groups improve foundation stability by altering energy dissipation distribution, with the discrepancy between theoretical calculations and simulation results within 10%. The research results can directly guide the design of saturated composite foundations near slopes in earthquake-prone areas (such as Fujian and Guangdong) and enhance the seismic safety reserve by optimizing the replacement rate of group piles (recommended to be 0.2~0.3). Full article

This study investigates the use of artificial neural networks (ANNs) and deep neural networks (DNNs) for estimating photovoltaic (PV) energy output, with a particular focus on hyperparameter tuning. Supervised regression for photovoltaic (PV) direct current power prediction was conducted using only sensor-based inputs (PanelTemp, Irradiance, AmbientTemp, Humidity), together with physically motivated-derived features (ΔT, IrradianceEff, IrradianceSq, Irradiance × ΔT). Samples acquired under very low irradiance (<50 W m⁻²) were excluded. Predictors were standardized with training-set statistics (z-score), and the target variable was modeled in log space to stabilize variance. A shallow artificial neural network (ANN; single hidden layer, widths {4–32}) was compared with deeper multilayer perceptrons (DNN; stacks {16 8}, {32 16}, {64 32}, {128 64}, {128 64 32}). Hyperparameters were selected with a grid search using validation mean squared error in log space with early stopping; Bayesian optimization was additionally applied to the ANN. Final models were retrained and evaluated on a held-out test set after inverse transformation to watts. Test performance was obtained as MSE, RMSE, MAE, R², and MAPE for the ANN and DNN. Hence, superiority in absolute/squared error and explained variance was exhibited by the ANN, whereas lower relative error was achieved by the DNN with a marginal MAE advantage. Ablation studies showed that moderate depth can be beneficial (e.g., two-layer variants), and a simple bootstrap ensemble improved robustness. In summary, the ANN demonstrated superior performance in terms of absolute-error accuracy, whereas the DNN exhibited better consistency with relative-error accuracy. Full article

Slurry shields maintain excavation face stability by forming a sealing filter cake through pressurized slurry filtration, though cutterhead rotation inevitably compromises this integrity. This study investigates seawater-based slurry filtration behavior under cutterhead disturbance using model testing, utilizing the effective support force conversion rate to quantify the filter cake formation efficiency. Quantitative analysis evaluated key slurry constituents—bentonite, carboxymethyl cellulose (CMC), and fine sand (content/particle size)—and operational parameters including cutterhead rotation speed, advance rate, and slurry pressure. Results demonstrate enhanced conversion rate and stability with increased bentonite, CMC, and fine sand content; reduced fine sand particle size; elevated slurry pressure; and decreased cutterhead speed/advance rate. Nonlinear relationships exist between bentonite content and fine sand particle size, on the one hand, and the mean conversion rate and its fluctuation range, on the other. Stratum permeability and slurry pressure exhibit nonlinear effects on fluctuation range but linear relationships with mean value, indicating marginal impacts on support force magnitude and operational stability. Sensitivity analysis confirms bentonite as the dominant influencing factor, followed by cutterhead speed and CMC. Full article

This work mainly focuses on the energy and exergy analysis of a single-effect absorption cooling system operating with the couple H₂O-LiBr, under different climatic conditions in Senegal and France. A simulation model was developed, using the Engineering Equation Solver V10 (EES) software. Results indicate that the system can achieve a maximum COP of 0.76 and an exergy efficiency of 56%, which decreases as the generator temperature increases. Increasing the generator temperature from 87 to 95 °C significantly improves COP, but gains become marginal beyond 100 °C. The highest exergy destruction occurs in the generator, followed by the absorber, condenser, and evaporator. A temperature difference above 44 °C between the generator and the absorber is required to maintain H₂O-LiBr solution stability. Optimal temperatures for hot climates like Senegal are 90 °C (generator), 42 °C (absorber/condenser), and 7 °C (evaporator), while maximum exergy efficiency (56%) is reached at 81 °C, typical of moderate climates (France). Evaporator exergy efficiency increases from 16 to 52% with rising ambient temperature, while absorber and condenser efficiencies drop. Increasing the cooling water flow rate from 0.2 to 1.4 kg/s reduces exergy losses in the absorber and the condenser by up to 36%. The solution heat exchanger (SHE) optimal effectiveness of 0.75 reduces exergy consumption in the absorber and the generator. Full article

This study presents the development and validation of an automated oyster classification system designed to classify oysters by size and place them into trays for freezing. Addressing limitations in conventional manual processing, the proposed system integrates a vision-based recognition algorithm and a delta robot (parallel robot) equipped with a soft gripper. The vision system identifies oyster size and optimal grasp points using image moment calculations, enhancing the accuracy of classification for irregularly shaped oysters. Experimental tests demonstrated classification and grasping success rates of 99%. A process simulation based on real industrial conditions revealed that seven units of the automated system are required to match the daily output of 7 tons achieved by 60 workers. When compared with a theoretical 100% success rate, the system showed a marginal production loss of 715 oysters and 15 trays. These results confirm the potential of the proposed system to improve consistency, reduce labor dependency, and increase productivity in oyster processing. Future work will focus on gripper design optimization and parameter tuning to further improve system stability and efficiency. Full article

One of the crucial types of information needed to guarantee the secure operation of power systems is their voltage stability condition. This is particularly true for power systems operating at peak hours or under abnormal conditions, such as contingencies. The literature shows several methods for voltage stability assessment; however, they are either accurate and computationally burdensome or less accurate and computationally efficient. The main goal of this research work is to propose methods that are both accurate and fast, features that are especially important in strict real-time operating conditions. Two new methods for computing the maximum loadability and the voltage stability margin of power systems are proposed. Both methods use a powerful, second-order, and non-divergent power flow with an optimally computed step size; however, each of them is initialized differently. Very high-quality initializations are obtained by using a linear voltage stability index and sensitivity analysis factors. This combination leads to a fast, robust, and accurate method, suited for strict real-time power system operation. The proposed methods require 90% fewer power flow runs compared with conventional methods, such as the continuation method for small systems, and tend to require even fewer power flow runs for larger systems. Computer simulations of the proposed methods use small benchmarks to large realistic power systems, showing that the requirements for real-time use—namely accuracy, robustness, and computational efficiency—are met. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article

Background/Objectives: Self-adhesive flowable resins (SAFR) entered the market, eliminating the adhesive system application due to their self-adhesive technology. Guided by the PICO framework (Population, Intervention, Comparison, Outcome), the aim was to conduct a systematic review of clinical studies to compare the clinical performance of Self Adhesive Flowable Resin (SAFRs) with conventional flowable resins used for direct restorations. Methods: The protocol of this systematic review was registered in the International Prospective Register of Systematic Reviews (CRD42023394297) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Five databases (PubMed, Embase, Web of Science, Scopus, and Cochrane Library) were searched from inception to July 2025. Nine randomized clinical trials were included, totaling 493 restorations in 232 patients. Clinical performance was assessed using USPHS or FDI criteria, with follow-up periods ranging from 6 months to 5 years. Data were pooled using a random-effects meta-analysis to calculate risk differences (RD) and 95% confidence intervals (CI) for marginal adaptation, retention, marginal staining, post-operative sensitivity, color stability, surface roughness, secondary caries, and anatomical form. Results: Meta-analysis showed no significant differences between SAFRs and CFRCs for in terms of: marginal adaptation (RD = 0.01; 95% CI: −0.02 to 0.04; p = 0.53; I² = 0%), retention (RD = 0.00; 95% CI: −0.02 to 0.03; p = 0.81; I² = 0%), marginal staining (RD = 0.01; 95% CI: −0.01 to 0.02; p = 0.51; I² = 0%), and post-operative sensitivity (RD = −0.01; 95% CI: −0.03 to 0.02; p = 0.62; I² = 0%). The certainty of the evidence for all outcomes was rated as moderate to high according to the GRADE assessment. Conclusions: SAFR restorations demonstrated comparable clinical performance to conventional resins; however, heterogeneity in follow-up duration and the scarcity of long-term data (>5 years) warrant caution. Full article

This paper addresses the challenges encountered by grid-connected photovoltaic (PV) systems, including the stochastic behavior of the system, harmonic distortion, and variations in grid impedance. To this end, an in-depth technical and pedagogical analysis of three linear multivariable current control strategies is performed: proportional-integral (PI), proportional-resonant (PR), and deadbeat (DB). The study contributes to theoretical formulations, detailed system modeling, and controller tuning procedures, promoting a comprehensive understanding of their structures and performance. The strategies are investigated and compared in both the rotating ($dq$) and stationary ($\alpha \beta$) reference frames, offering a broad perspective on system behavior under various operating conditions. Additionally, an in-depth analysis of the PR controller is presented, highlighting its potential to regulate both positive- and negative-sequence components. This enables the development of more effective and robust tuning methodologies for steady-state and dynamic scenarios. The evaluation is conducted under three main conditions: steady-state operation, transient response to input power variations, and robustness analysis in the presence of grid parameter changes. The study examines the impact of each controller on the total harmonic distortion (THD) of the injected current, as well as on system stability margins and dynamic performance. Practical aspects that are often overlooked are also addressed, such as the modeling of the inverter and photovoltaic generator, the implementation of space vector pulse-width modulation (SVPWM), and the influence of the output LC filter capacitor. The control structures under analysis are validated through numerical simulations performed in MatLab^® software (R2021b) using dedicated computational routines, enabling the identification of strategies that enhance performance and ensure compliance of grid-connected photovoltaic systems. Full article

Rare earth elements (REEs) and trace elements, due to their relative stability during sedimentary processes, are effective geochemical proxies for sediment provenance. In the Dongdaohaizi Depression of the eastern Junggar Basin, the provenance of the Middle Jurassic Sangonghe Formation remains contentious. In this study, representative sandstone samples were systematically collected from all three members of the Sangonghe Formation in both the Dongdaohaizi Depression and its western margin. Through comprehensive petrographic and geochemical analyses, we obtained the following results. The Sangonghe Formation is primarily composed of feldspathic lithic sandstones, lithic sandstones, and minor lithic–feldspathic sandstones. The heavy mineral assemblage includes zircon, garnet, chromite, and rutile, suggesting source rocks of intermediate to acidic igneous, metamorphic, and mafic lithologies. The total REE contents range from 101.84 to 192.68 μg/g, with an average of 161.80 μg/g. The ∑LREE/∑HREE ratios vary from 6.59 to 13.25 (average 10.96), and the average δEu values are close to 1. The δCe value ranges from 1.09 to 1.13 (average 1.11). Trace element discrimination diagrams, including La-Th-Sc, Th-Co-Zr/10, Th-Sc-Zr/10, and La/Y-Sc/Cr ternary plots, indicate that most samples fall within the continental island arc domain, with a few plotting in the passive continental margin field. Comparison with potential surrounding source regions reveals dual provenances: an eastern source from the Kalamaili Mountains and a western source from the Zhayier Mountains. During the Early Jurassic, these two orogenic belts acted as distinct sediment sources. The Zhayier Mountains provided stronger input, with fluvial and tidal processes transporting sediments into the basin, establishing the primary subsidence center in the west of the depression. By the Middle Jurassic, continued thrusting of surrounding fold belts caused a migration of the lake center and the main depocenter to the western edge of the Dongdaohaizi Depression, while the former depocenter gradually diminished. Furthermore, sustained erosion and denudation of the Mosowan Uplift during the Early–Middle Jurassic reduced its function as a structural barrier, thereby promoting increased mixing between eastern and western sediment sources. The study not only refines existing paleogeographic models of the Junggar Basin, but also demonstrates the utility of REE–trace geochemistry in deciphering complex provenance systems in tectonically active basins. Full article

Background: Rehabilitation of atrophic posterior mandibles using dental implants is often complicated by anatomical limitations, particularly the proximity of the inferior alveolar nerve (IAN). Techniques such as IAN lateralization and transposition enable implant placement but are associated with neurosensory disturbances (NSDs). This systematic review and meta-analysis aimed to assess the incidence, duration, and predictors of NSDs following IAN repositioning for implant placement and to evaluate the effectiveness of adjunctive methods like piezo-surgery and platelet-rich fibrin (PRF) in minimizing complications. Methods: Following PRISMA 2020 guidelines, a comprehensive search of electronic databases and gray literature identified 20 studies, including randomized controlled trials, prospective cohorts, and retrospective analyses published between 2009 and 2024. Outcomes analyzed included incidence of NSDs, recovery rates, implant stability quotient (ISQ), marginal bone loss, and implant success rates. Meta-analysis was performed using RevMan 5.3 software, with heterogeneity and publication bias assessed using standard Cochrane tools. Results: Transient NSDs occurred in 15–40% of cases, with higher rates observed in transposition techniques. Most patients experienced recovery within 6 months. Piezoelectric surgery significantly reduced the incidence and duration of NSDs compared to rotary instruments. Meta-analysis revealed no statistically significant differences between lateralization and transposition techniques in ISQ, marginal bone loss, success rate, or NSDs at 3 months (p > 0.05). PRF was associated with accelerated nerve recovery. IAN repositioning is effective for implant placement in atrophic mandibles with a risk of transient NSDs. Conclusions: Lateralization combined with piezo-surgery and PRF shows favorable outcomes in minimizing nerve injury and optimizing implant success. The PROSPERO registration code is as follows: CRD420251086835. Full article

In practical applications, precise tuning of current controllers is essential for achieving desirable dynamic performance and stability margins. Traditional tuning techniques rely heavily on accurate plant parameter identification. However, this process is often challenged by inherent nonlinearities and unmodeled dynamics in motor systems. To address this issue, this paper proposes a current loop parameter tuning algorithm based on open-loop frequency sweeping. As the swept Bode diagram reveals nonlinear factors typically neglected during modeling, it provides a basis for control parameter correction. A pulse-sine voltage injection method is first introduced to identify motor parameters, serving as initial values for the controller. By analyzing the magnitude and phase characteristics of the open-loop transfer function, the delay time constant in the high-frequency range can be accurately identified, and mismatched parameters in the low-to-mid frequency range can be corrected. This method does not rely on complex model structures or extensive online adaptation mechanisms. Experimental results on a mechanical test platform demonstrate that the proposed tuning strategy significantly enhances the current loop’s closed-loop bandwidth and dynamic performance. Full article

This study characterizes Phoradendron nervosum, a hemiparasitic mistletoe species prevalent in Ecuador, using morphological, molecular, and ecological modeling approaches. Morphological analysis revealed that P. nervosum possesses green-yellowish cylindrical stems, lanceolate leaves with entire margins, and berry-like fruits with mucilaginous pulp. DNA sequencing of the internal transcribed spacer (ITS) region confirmed a 99.43% identity with P. nervosum (GenBank: AH009776.2), supporting the taxonomic classification. A maximum entropy (MaxEnt version 3.4.4) model was developed using 36 occurrence points and 19 bioclimatic variables to assess potential distribution across the Tumbaco region in Ecuador. Key environmental factors influencing the species’ distribution were precipitation during the warmest quarter (BIO_18), temperature seasonality (BIO_4), and mean diurnal temperature range (BIO_2). The model showed good predictive performance (AUC = 0.736), identifying areas with high suitability for P. nervosum, particularly in habitats with adequate water availability and thermal stability. Findings suggest that this mistletoe parasitizes both native and exotic tree species, potentially impacting biodiversity and forest health. This research provides a baseline for monitoring mistletoe spread under climate change scenarios and emphasizes the need for management strategies in agroforestry systems where host trees are vulnerable. Full article

The widespread deployment of electric vehicles (EVs) has introduced substantial challenges to electricity pricing, grid stability, and renewable energy integration. This paper proposes a real-time pricing optimization framework for large-scale EV charging networks incorporating renewable intermittency, demand elasticity, and infrastructure constraints within a high-dimensional optimization model. The core objective is to dynamically determine spatiotemporal electricity prices that simultaneously reduce system peak load, improve renewable energy utilization, and minimize user charging costs. A rigorous mathematical formulation is developed integrating over 40 system-level constraints, including power balance, transmission capacity, renewable curtailment, carbon targets, voltage regulation, demand-side flexibility, social participation, and cyber resilience. Real-time electricity prices are treated as dynamic decision variables influenced by charging station utilization, elasticity response curves, and the marginal cost of renewable and grid-supplied electricity. The problem is solved over 96 time intervals using a hybrid solution approach, with benchmark comparisons against mixed-integer programming (MILP) and deep reinforcement learning (DRL)-based baselines. A comprehensive case study is conducted on a 500-station EV charging network serving 10,000 vehicles integrated with a modified IEEE 118-bus grid model and 800 MW of variable renewable energy. Historical charging data with ±12% stochastic demand variation and real-world solar and wind profiles are used to simulate realistic operational conditions. Results demonstrate that the proposed framework achieves a 23.4% average peak load reduction per station, a 17.9% improvement in renewable energy utilization, and user cost savings of up to 30% compared to baseline flat-rate pricing. Utilization imbalances across the network are reduced, with congestion mitigation observed at over 90% of high-traffic stations. The real-time pricing model successfully aligns low-price windows with high-renewable periods and off-peak hours, achieving time-synchronized load shifting and system-wide flexibility. Visual analytics including high-resolution 3D surface plots and disaggregated bar charts reveal structured patterns in demand–price interactions, confirming the model’s ability to generate smooth, non-disruptive pricing trajectories. The results underscore the viability of advanced optimization-based pricing strategies for scalable, clean, and responsive EV charging infrastructure management in renewable-rich grid environments. Full article

Gas hydrate-bearing sediments in marine environments represent both a future energy source and a geohazard risk, prompting increasing international research attention. In the Shenhu area of the South China Sea, a large volume of drilling and laboratory data has been acquired in recent years, yet a comprehensive framework for evaluating the characteristics of key reservoir parameters remains underdeveloped. This study presents a spatially integrated and statistically grounded framework that captures regional-scale heterogeneity using multi-source in situ datasets. It incorporates semi-variogram modeling to assess spatial variability and provides statistical reference values for geological and geotechnical properties across the Shenhu Area. By synthesizing core sampling results, acoustic logging, and triaxial testing data, representative probability distributions and variability scales of hydrate saturation, porosity, permeability, and mechanical strength are derived, which are essential for numerical simulations of gas production and slope stability. Our results support the development of site-specific reservoir models and improve the reliability of early-phase hydrate exploitation assessments. This work facilitates the rapid screening of hydrate reservoirs, contributing to the efficient selection of potential production zones in hydrate-rich continental margins. Full article