Search Results (205)

Aquatic product safety is closely linked to human health, yet the effects of historical residual drugs in aquaculture pond sediment remain unclear. Malachite green (MG), a prohibited aquaculture drug, is rapidly converted to leucomalachite green (LMG) after illegal use and may persist in bottom sediment, raising the question of whether residue-positive sediment can cause positive detections in fish at harvest. In order to answer this question, we developed a time-dependent predictive model for the sediment–water–fish system based on a food web bioaccumulation model and a sediment–water equilibrium model. The model examines the relationship between historical MG residues in sediment and positive detections in aquatic products, and estimates LMG residue levels in fish at harvest. The reliability of this model was further validated through comparison with measured data. The results showed that when the sediment residue concentration was 227.48 μg/kg, the maximum LMG concentration in fish during the culture period reached 0.56 μg/kg, indicating a positive correlation with fish concentration. The closer the habitat water layer was to the sediment, the higher the risk of positive detection in fish; in the middle water layer, the LMG concentration in fish could reach 0.28 μg/kg, and, under ideal conditions, a two-fold relationship existed between the middle and lower water layers. Sediment organic carbon content determined the amount of LMG released into the water; when it was set to 0.0035, the initial LMG concentration in water was 0.07 μg/kg, showing a negative correlation with fish concentration. This study confirmed that a certain amount of residual malachite green in sediment could cause positive detection in mandarin fish during the culture period, which is consistent with the validation experiment. If the model predicted no positive detection at the pollutant concentration corresponding to the sediment of the positive detection pond, this would indicate that malachite green originated from other sources during the culture process; otherwise, the sediment may lead to the risk of positive detection in fish. One limitation of the model is that it may underestimate pollutant concentrations in fish during the early stages of aquaculture. In addition, its ability to predict the effects of combined pollutant exposure remains to be validated. The model developed here can be used to estimate residue levels of aquaculture drugs during the culture period and may provide regulatory authorities with a basis for distinguishing historical residues from illicit use, thereby improving the efficiency of aquatic product quality and safety supervision. Full article

Hydrodynamic models underpin Autonomous Underwater Vehicle (AUV) design, motion control, and performance evaluation. Existing methods face two critical bottlenecks: (1) conventional explicit CFD requires predefined trajectories, which fails to capture true motion responses under combined rudder-propeller action and creates a disconnect between simulation and real operations; (2) the widely adopted Standard Submarine Motion Equations (SSME) suffer from high parameter redundancy, while high-precision non-parametric models incur prohibitive computational costs, hindering embedded deployment. To address these gaps, this paper proposes an implicit CFD-driven framework for fully appended AUVs equipped with through-body thrusters. It requires no preset trajectories, directly coupling periodic propeller thrust and rudder angle excitations to achieve 5-degree-of-freedom (5DOF) spatial motion simulations aligned with real navigation states. Parametric and non-parametric models are identified via Least Squares (LS) and Neural Networks (NN), respectively. Sobol global sensitivity analysis reduces SSME dimensionality, yielding a Basic Submarine Motion Equation (BSME) with only 25 key parameters—cutting the parameter count by 55% with negligible accuracy loss. Validation shows the non-parametric NN model reduces prediction error by over 10% compared to its parametric counterpart, while the streamlined BSME enables real-time forecasting in low-power computing scenarios. This approach balances accuracy and efficiency for rapid hydrodynamic prediction during early AUV design and embedded controller deployment. Full article

Large-scale spiking neural network simulation requires numerical integration that preserves membrane dynamics and spike timing without making fine-resolution updates prohibitively expensive. This balance is difficult for conductance-based leaky integrate-and-fire (LIF) networks because synaptic decay, threshold crossings, resets, and refractory periods form a hybrid dynamical system. To address this difficulty, we introduce a spike-aware propagation (SAP) approximation method that combines exact receptor-trace updates, analytic homogeneous membrane propagation, Gauss–Legendre quadrature, and spike localization, improving the accuracy–efficiency Pareto frontier. We establish an error bound and conditional convergence under consistent refinement for the proposed SAP. At h = 1 ms, the single-realization T = 1000 ms comparison showed a lower voltage RMSE for SAP than for Euler at the same width in the two high-activity regimes. The five-seed T = 200 ms robustness experiment likewise showed lower voltage RMSE for SAP than for NEST at the same width. At the highest drive, the paired mean reduction was 3.91 mV (95% CI, 3.85–3.97 mV). This quantified gain supports SAP as a practical route to an improved accuracy–efficiency balance in large-scale conductance-based LIF simulation while underscoring the method’s configuration-dependent and regime-dependent scope. Full article

Computational fluid dynamics methods range from Reynolds-Averaged Navier–Stokes (RANS) simulations to Direct Numerical Simulations (DNSs). RANS offers low computational cost at the expense of accuracy, while DNS provides high accuracy but at a prohibitive cost. The aim of this study is to evaluate whether multi-fidelity neural networks can learn a corrective mapping from RANS to DNS for a small canonical dataset and to determine how training-set composition and model architecture influence generalisation across geometries. In this study, multi-fidelity neural networks for data fusion between low-fidelity RANS and high-fidelity DNS data were applied to turbulent flow (Re = 5600) over parameterised periodic hills, defined by a geometry parameter characterising the steepness ratio. The inputs to the models were the coordinates and the corresponding RANS velocity components, and the outputs were the DNS velocity components, with data from both fidelities mapped onto the same mesh. Both a single-branch and a two-branch architecture were considered. Generalisability was assessed within a small dataset of five periodic hills defined by different values of the geometry parameter $\alpha$ (0.5, 0.8, 1.0, 1.2, 1.5). Both model architectures were trained on data from different combinations of the geometry parameter to evaluate interpolation and extrapolation capabilities. Both networks successfully corrected RANS flow fields for unseen geometries in interpolation regimes. When interpolating, the single-branch architecture achieved more than a 69% reduction in error, while the two-branch architecture achieved more than a 60% reduction, with both improving key flow features such as recirculation zones and jet structures. A key finding is that the single-branch architecture consistently outperformed the two-branch formulation, particularly in low-data regimes. The results show that multi-fidelity neural networks can improve RANS predictions using small datasets and simple inputs, provided that the training set spans the relevant geometric space. As the model does not require the geometry parameter as an explicit input, it is applicable to geometries lacking straightforward parameterisation. The demonstrated advantage of the single-branch architecture highlights the importance of architectural simplicity when training data is limited. Full article

Screen content images (SCIs) exhibit complex structural heterogeneity, rendering traditional statistics-based natural scene image quality assessment (NR-IQA) metrics ineffective. Although deep learning models achieve high prediction accuracy, their prohibitive computational demands preclude deployment in latency-sensitive industrial scenarios. While existing handcrafted lightweight SCI-IQA metrics reduce computational overhead, most rely on unsegmented global feature pooling or holistic edge statistics (e.g., edge histograms or Fisher vector coding), thereby diluting locally critical text-edge distortions in vast homogeneous backgrounds. To address this limitation, we propose an ultra-lightweight, deep-learning-free NR-IQA framework centered on human visual masking. Unlike existing lightweight methods, our approach explicitly employs dual-scale Canny edge operators to partition SCIs into edge-sensitive and flat background regions. Guided by this visual prior, structural degradations and micro-compression textures are extracted region-wise using Sobel gradients and uniform local binary patterns (LBPs) and aggregated with global Commission Internationale de I’Eclairage L*a*b*(CIELAB) color statistics into a compact 60-dimensional descriptor. A grid-search-optimized Support Vector Regression (SVR) maps these features to subjective quality scores. Extensive cross-validation on the SIQAD and SCID datasets demonstrates that our metric outperforms existing handcrafted lightweight SCI metrics and traditional NSS models, while achieving accuracy competitive with representative full-reference metrics. Consuming only 79.3 ms per image on a standard CPU, it offers a practical accuracy–efficiency trade-off for resource-constrained periodic quality monitoring. Full article

The rapid evolution of Urban Air Mobility (UAM) necessitates high-performance morphing structures capable of seamless transitions between flight and ground modes. This research presents a rigorous structural optimization framework for a wing deployment mechanism, addressing the critical challenge of minimizing stress concentrations in cantilevered revolute joints. To overcome the computational prohibitive cost of traditional multibody dynamics, a Generalized Spectral Surrogate Model (GSSM) is introduced. This novel approach maps the mechanism’s geometric parameters to its kinetic response using polynomial-modulated Fourier series, reducing the evaluation time of ${10}^5$ design configurations from 4.2 h to merely 0.8 s while maintaining a determination coefficient $R^2>0.995$. Comparative analysis demonstrates that the GSSM outperforms Artificial Neural Networks and Kriging models in capturing periodic kinematic boundaries without spurious local minima. Through a weighted topological analysis, the study identifies a global optimum ($L_2=0.5$ m, ${\theta }_2={64.2}^{\circ }$) that effectively shunts 70.3% of the aerodynamic load to the robust vehicle chassis. The proposed solution deviates from the theoretical unconstrained minimum by only 0.24%, providing a validated mathematical basis for the rapid synthesis of reliable aerospace mechanisms. Full article

The extent to which environmental regulation yields a “weak” or “strong” Porter effect is fundamentally shaped by the trade-off between industrial output and productivity improvements, a complex dynamic that requires granular, sub-national empirical evidence. This study employs a continuous multi-period DID approach to evaluate the impact of China’s prohibited breeding zone policy (PBZP) on sustainability indicators—specifically, pig green total factor productivity (PGTFP) and its components, green technology efficiency (GEC) and green technology progress (GTC)—across 371 counties in the Yangtze River Basin from 2013 to 2021. The results indicate that PBZP significantly boosts county-level PGTFP and GTC in the basin, with an effect size of 0.206 for PGTFP and GTC identified as its primary driver, thereby supporting the “strong Porter hypothesis”, as confirmed by robustness and endogeneity tests. Channel exploration shows that PBZP has enhanced PGTFP through capital factor optimization and spatial restructuring. Heterogeneity analysis identifies significant regional differences across the upper, middle, and lower reaches of the Yangtze River. Further analysis reveals a trade-off between the impact of PBZP on PGTFP and the production capacity of pigs. Finally, this study outlines several policy recommendations for fostering new quality productive forces in pig farming by improving PGTFP while safeguarding effective pig production capacity. Full article

The safe crossing of pedestrians at traffic light-regulated intersections is ensured by the permissive and prohibitive signals provided for them and by certain priority rules that drivers in traffic flows conflicting with pedestrians must comply with. This in turn leads to the occurrence of traffic delays, which are inevitable under certain traffic conditions. The present study focuses on determining the length of traffic delays when pedestrians cross at a traffic light-regulated intersection in the city of Sofia, Republic of Bulgaria. The intersection was selected due to the high intensity of pedestrian flows established in preliminary random observations, which is provoked by its location. The study includes determining the traffic delays over a period of 12 h with full readings of these indicators for each cycle of the traffic light system during the morning and evening peak periods and with partial readings for 15 min for each cycle in the remaining hours of the study period. The results show the lack of a relationship between the waiting time of vehicles and the number of pedestrians crossing. Such an influence can be sought in the behavior and types of pedestrians and the intervals at which they enter the crosswalk. This study can assist researchers in this field in developing pedestrian crossing models and determining additional measures to increase their safety when crossing light-regulated intersections. Full article

In ultra-deep well drilling, directly measuring the static formation temperature (SFT) is highly time-consuming, as it requires extended shut-in periods for the wellbore to reach full thermal equilibrium, making it impractical for routine engineering operations. To overcome this challenge, this paper establishes a wellbore–formation transient temperature model (WFTM) and proposes an SFT inversion method based on the Improved Grey Wolf Optimizer (IGWO) and Radial Basis Function (RBF) neural network. The RBF network serves as a surrogate model to replace the WFTM during iterative optimization, avoiding the prohibitive computational cost of repeated WFTM evaluations and enabling rapid prediction of the transient wellbore temperature field. Meanwhile, the IGWO algorithm uses the measured bottomhole circulating temperature (BHCT) as a constraint to optimize the geothermal gradient in SFT inversion. Multi-well validation shows that the RBF surrogate predicts BHCT with relative errors consistently below 1%, demonstrating its effectiveness as a substitute for the WFTM. Compared with the direct iterative approach (IGWO-WFTM), the IGWO-RBF method yields slightly lower SFT inversion accuracy, but this deviation remains within engineering tolerances, and the computational time is reduced by approximately 18 times. Requiring only surface temperature and routinely measured BHCT, the proposed approach offers a practical and efficient pathway for real-time assessment of formation temperature during ultra-deep oil well drilling. Full article

Illegal parking is a persistent challenge in central areas of intermediate cities, where growing demand for curb access competes with limited road space, pedestrian circulation, and public transport operations. While previous research on parking management and curbside regulation has documented links between parking behavior, congestion, and street performance, empirical evidence based on direct street-segment observations remains limited in Latin American intermediate-city contexts. This study examines the distribution across observed street segments, user profile, and urban impacts of parking events across six commercial street segments in central Loja, Ecuador. A total of 1425 parking events were recorded between 11 and 28 May 2026 and classified by maneuver type, vehicle type, apparent use, parking reason, companions, operational conditions, and impacts on congestion, pedestrian circulation, and public transport. Descriptive statistics, exact confidence intervals, chi-square tests with Cramér’s V, false discovery rate correction, standardized residuals, and adjusted logistic regression models were applied. Results show that 64.4% of observed events were infractions, mainly prohibited-zone parking (38.9%) and double parking (25.5%). Infraction type varied strongly by street segment (Cramér’s V = 0.651). Adjusted congestion probabilities were substantially higher for double parking (45.0%) and prohibited-zone parking (41.3%) than for non-infraction events (10.2%). The findings indicate substantial heterogeneity in illegal-parking prevalence and composition among the six observed street segments, together with significant associations between parking infractions and urban street performance. These results identify segment-specific differences within the study area but should not be interpreted as evidence of spatial predictability beyond the observed locations and period. Full article

Data centers are facing rapidly increasing electricity demand and carbon emissions, while the intermittency of renewable energy creates a significant temporal mismatch between renewable generation and data center load demand. To bridge this temporal mismatch, we propose a coordinated optimization strategy that integrates shared hydrogen energy storage facilities with load scheduling mechanisms. A multi-objective MILP model is formulated to minimize annualized cost, renewable energy curtailment, and carbon emissions. Simulation results show that, compared with the no-shared-station case, the proposed electricity–hydrogen coordination strategy with load shifting yields significant benefits: the annualized total cost decreases from 13.65 to 4.74 million yuan; annual carbon emissions are reduced from 6297 to 1432 tons; and peak-period electricity purchases are reduced from 5373 to 906 MWh. Under the representative daily forecast condition, Scenario S4 achieves zero renewable curtailment when grid export is permitted; therefore, the renewable-electricity utilization rate reaches 100.00% within the model boundary. When grid export is prohibited, the utilization rate decreases to 98.59%, with 179,100 kWh of annualized renewable curtailment. The research findings indicate that integrating shared hydrogen energy storage with the load flexibility of data centers can effectively reduce the system’s overall operating costs, promote the integration of renewable energy, and achieve low-carbon operation. Full article

Background: The use of cognitive enhancers (CEs) is becoming increasingly widespread among students, especially in health fields, with significant implications for both academic performance and student well-being. The aim of the study was to investigate the use of CEs among healthcare university students in relationship with stimulant intake, academic progression, and academic anxiety. Material and Methods: The cross-sectional study included 402 students enrolled in medical studies (general medicine, pharmacy, dentistry, and physiotherapy). Socio-demographic characteristics, health-related and academic data, lifestyle behaviors, and the consumption of different CEs were collected. The Academic Anxiety Scale was used to evaluate university students’ perceived stressors that contribute to academic anxiety. Data processing analysis was performed using IBM Statistical Package for Social Sciences. Results: The frequency of cognitive compound intake exhibits a positive correlation with the consumption of group B vitamins (r = 0.170, p = 0.001), tea (r = 0.158, p = 0.003), and coffee (r = 0.113, p = 0.031). Lecithin and Ginkgo biloba were significant independent predictors (p < 0.05) of enhanced alertness, memory, and academic performance. The only significant independent predictor of cardiovascular events (palpitations, tachycardia) is caffeine consumption, which increases the risk by nearly three times (OR = 2.96; p = 0.001). In terms of addictive risk, drinking caffeine raises the likelihood of being addicted by 4.78 times (p = 0.014). It has been demonstrated that the probability of using synthetic nootropics (piracetam/cerebrolysin) increases by 1.86 times (OR = 1.86, p = 0.021) as university education progresses (nN= 335). Academic anxiety had a mean score of 23.27 ± 7.38. Unemployed students exhibit markedly elevated anxiety levels compared to employed students. Respondents who justify the use of stimulants by feeling stressed or overwhelmed present the highest levels of academic distress, in contrast to those who use them to combat fatigue or out of curiosity. A large majority (95.0%) do not consider use of CEs as a form of cheating on examinations, and 88.9% are against its prohibition. Conclusions: The findings of this study highlight that many university students in the healthcare field use cognitive stimulants during periods of intellectual overload, with more than 40% of participants utilizing CEs daily or weekly in periods of academic stress. Overload schedule, academic stress, the knowledge about their positive effects, and psychosocial contexts influence consumption. Full article

Sicily is a Mediterranean island region whose economy is based especially on tourism, with tourists being attracted to its beaches. The whole coastline of the island, including its minor islands, is 1745 km. At the moment, considering the whole period analyzed by the Coastal Plan of Sicilian Region (2008–2024), about 115 km of the 683 km of the main island’s sandy coastline present erosion problems that affect 23% of its unprotected coastline (506 km). Some of these problems are threatening Sicily’s economic and important historical assets as well as its cultural heritage; 177 km of protected beaches, using hard structure, have lost their original beauty. In the last fifty years, about 2.5 km² of beaches were lost due to erosion, causing damages worth approximately 5 billion Euros. Current coastal management guidelines identify artificial beach nourishment as the most sustainable strategy for protecting the insular economy against the accelerating impacts of climate change. Successful nourishment, however, hinges on the availability of vast quantities of borrow material that must be granulometrically, compositionally, and chromatically compatible with native beach sediments. While subaerial quarries are being phased out due to their irreversible environmental degradation and logistical inefficiency, as well as local “ephemeral” sources (such as harbor dredging or over-alluvial deposits) providing insufficient volumes, the research has shifted toward Remote/Relict Marine Sediment Deposits (RMSDs). This study evaluates the strategic potential of RMSDs as a high-volume, low-impact resource for coastal defense. By integrating the geological, morphological, and sedimentological characteristics of the Sicilian continental shelf within a GIS framework, we have delineated potential dredging sectors. These areas are bounded by the −30 m isobath (the lower limit of Posidonia oceanica meadows) and the −200 m isobath, which represents the current operational limit of Jumbo Trailer Suction Hopper Dredgers (TSHDs). A multi-criteria constraint analysis was performed, categorizing environmental and infrastructural overlaps into fatal flaws (prohibitive) and non-prohibitive constraints. This subtractive spatial analysis reveals that approximately 6500 km² of the Sicilian shelf may be eligible for resource exploitation concessions, pending site-specific, high-resolution surveys. Full article

In the severely cold regions of northern China, large-scale clean heating retrofits in rural areas face critical problems, including substandard indoor thermal environments, excessive energy consumption, and prohibitive operating costs. To address these challenges, this study focuses on rural residences in Hohhot as the research subject. Field measurements were conducted throughout the heating season in a typical rural house in Hohhot, a representative city with severe cold weather, to collect indoor/outdoor thermal parameters and real-time operational data of an air-source heat pump (ASHP). A dynamic simulation platform was established using TRNSYS 18. The optimization scheme integrates passive envelope retrofitting (ground insulation improvement and energy-efficient windows) with the active optimized control of the ASHP system. Indoor thermal comfort was evaluated using the Predicted Mean Vote (PMV) index. The results show that the ASHP exhibits excellent heating effectiveness and economic viability, making it the preferred technology for rural residences in Hohhot and similar regions. After implementing the active–passive scheme, the proportion of time with comfortable indoor conditions in rural houses surges from 34.1% to 84.1%, while during the severe cold period, this proportion increases from 16.97% to 61%. The indoor thermal comfort index shifts from its previous state to the baseline comfort range of −1.0 to 0. The total heating energy consumption decreased from 18,646 kWh to 15,861 kWh, and the seasonal operating cost dropped from 3207 to 2579.3 RMB, achieving an overall reduction of 19.6% in both energy and costs. The proposed active–passive synergistic optimization scheme simultaneously improves the indoor thermal environment and reduces heating energy consumption, overcoming the limitations of single-measure retrofits. This study fills the research gap on the quantitative evaluation of active–passive synergy for rural clean heating in severely cold regions, providing a theoretical basis and technical support for clean heating retrofits in Hohhot and Inner Mongolia, facilitating low-carbon and efficient rural clean heating in northern China. Full article

A numerical investigation of forced convection heat transfer in a three-dimensional T-shaped bifurcating channel with an upstream rotating cylinder and a downstream vibrating wavy wall is presented. The working fluid is a ternary hybrid nanofluid (Fe₂O₃, CuO, MoS₂ in water) exhibiting Casson rheology under an inclined magnetic field. The novelty of this work lies in the first integrated configuration combining these simultaneous mechanical, magnetic, and non-Newtonian effects. Using COMSOL Multiphysics, 413 parametric combinations of Reynolds number, Hartmann number, Casson parameter, nanoparticle shape and volume fraction, magnetic field angle, cylinder rotation speed, wall amplitude (Am), and period were solved. Average Nusselt and Bejan numbers quantified heat transfer enhancement and thermodynamic irreversibility. To interpret the high-dimensional parameter space and to circumvent the prohibitive computational cost of additional 3D magnetohydrodynamics simulations, machine learning (XGBoost) models were developed to rank feature importance and provide fast, accurate surrogate predictions (R² > 0.99). Cylinder rotation dominates heat transfer, increasing the Nusselt number by over 980% (feature importance 0.42) with a modest entropy penalty. Nanoparticle volume fraction reduces the Nusselt number via viscous damping. Magnetic field parameters negligibly affect heat transfer but strongly influence entropy generation; a perpendicular field recovers up to 97% thermal efficiency at high Hartmann numbers. Full article