Search Results (5,201)

Under the “Dual Carbon” strategy, high-penetration integration of distributed generators (DG) into distribution networks has triggered bidirectional power flow and reactive power-voltage violations. This phenomenon undermines the accuracy guarantee of conventional relaxation models (represented by second-order cone programming, SOCP), causing solutions to deviate from the AC power flow feasible region. Notably, ensuring solution feasibility becomes particularly crucial in engineering practice. To address this problem, this paper proposes a collaborative optimization framework integrating convex inner approximation (CIA) theory and a solution recovery algorithm. First, a system relaxation model is constructed using CIA, which strictly enforces ACPF constraints while preserving the computational efficiency of convex optimization. Second, aiming at the conservatism drawback introduced by the CIA method, an admissible region correction strategy based on Stochastic Gradient Descent is designed to narrow the dual gap of the solution. Furthermore, a multi-objective optimization framework is established, incorporating voltage security, operational economy, and renewable energy accommodation rate. Finally, simulations on the IEEE 33/69/118-bus systems demonstrate that the proposed method outperforms the traditional SOCP approach in the 24 h sequential optimization, reducing voltage deviation by 22.6%, power loss by 24.7%, and solution time by 45.4%. Compared with the CIA method, it improves the DG utilization rate by 30.5%. The proposed method exhibits superior generality compared to conventional approaches. Within the upper limit range of network penetration (approximately 60%), it addresses the issue of conservative power output of DG, thereby effectively promoting the utilization of renewable energy. Full article

This study evaluated long-term trends in the prevalence of use of atypical and typical antipsychotic drugs (APDs), both as classes of drugs and as individual drugs, among adult inpatients in the United States (US). The Health Facts^® database developed by Cerner Corporation was used to analyze the prevalence of APD use among adult inpatients aged 18 years or older who were administered at least one antipsychotic medication order during hospitalization between 1 January 2000 and 31 December 2016. The prevalence of APD use was standardized by age, sex, race, and census region. Typical and atypical antipsychotic treatment patterns in the US differed over this period. While the use of atypical APDs increased overall, the use of typical antipsychotic medications decreased, but remained more prevalent. Overall, haloperidol and prochlorperazine were the two most administered antipsychotic medications throughout the study period. From 2000 to 2011, prochlorperazine and haloperidol were the first- and second-most prescribed typical APDs, respectively; haloperidol became the most administered antipsychotic of this class as of 2012. Quetiapine was the most administered atypical antipsychotic medication, followed by risperidone and olanzapine until 2014, after which olanzapine was the second-most administered atypical APD. There was a notable decline in the use of atypical antipsychotics medications between 2005 and 2008, which may reflect the impact of the Food and Drug Administration’s warnings and the American Diabetes Association’s consensus position, but only for a short time. The usage patterns observed in this study support existing evidence of substantial off-label use of antipsychotic drugs in the US. Full article

This paper presents a novel high-order numerical method. The proposed scheme utilizes polynomial generating functions to achieve p order accuracy in time for the Grünwald–Letnikov fractional derivatives, while maintaining second-order spatial accuracy. By incorporating a short-memory principle, the method remains computationally efficient for long-time simulations. The authors rigorously analyze the stability of equilibrium points for the fractional vegetation–water model and perform a weakly nonlinear analysis to derive amplitude equations. Convergence analysis confirms the scheme’s consistency, stability, and convergence. Numerical simulations demonstrate the method’s effectiveness in exploring how different fractional derivative orders influence system dynamics and pattern formation, providing a robust tool for studying complex fractional systems in theoretical ecology. Full article

Dynamic positioning (DP) systems play a critical role in maintaining vessel position and heading under environmental disturbances such as wind, waves, and currents. This study presents a model predictive control (MPC)-based DP system for a fireboat equipped with a rudder–propeller configuration, explicitly accounting for both environmental loads and the reaction force generated during water cannon operation. Unlike conventional DP architectures in which DP control and thrust allocation are treated as separate modules, the proposed framework integrates both functions within a unified MPC formulation, enabling real-time optimization under actuator constraints. Environmental loads are modeled by incorporating nonlinear second-order wave drift effects, while nonlinear rudder–propeller interaction forces are derived through computational fluid dynamics (CFD) analysis and embedded in a control-oriented dynamic model. This modeling approach allows operational constraints, including rudder angle limits and propeller thrust saturation, to be explicitly considered in the control formulation. Simulation results demonstrate that the proposed MPC-based DP system achieves improved station-keeping accuracy, enhanced stability, and increased robustness against combined environmental disturbances and water cannon reaction forces, compared to a conventional PID controller. Full article

In this study, the nonlinear and non-monotonic behavior of amperometric bienzyme biosensors employing an enzymatic trigger reaction is investigated analytically and computationally using a two-compartment model comprising an enzymatic layer and an outer diffusion layer. The trigger enzymatic reaction is coupled with a cyclic electrochemical–enzymatic conversion (CEC) process. The model is formulated as a system of reaction–diffusion equations incorporating nonlinear Michaelis–Menten kinetics and interlayer partitioning effects. Exact steady-state analytical solutions for substrate and product concentrations, as well as for the output current, are obtained for specific cases of first- and zero-order reaction kinetics. At the transition conditions, biosensor performance is further analyzed numerically using the finite difference method. The CEC biosensor exhibits the highest signal gain when the first enzyme has low activity and the second enzyme has high activity; however, under these conditions, the response time is the longest. When the first enzyme possesses a higher substrate affinity (lower Michaelis constant) than the second, the biosensor demonstrates severalfold higher current and gain compared to the reverse configuration under identical diffusion limitations. Furthermore, increasing external mass transport resistance or interfacial partitioning can enhance the apparent signal gain. Full article

Characterization of propeller blade vibrations is essential to ensure aerodynamic performance, minimize noise emissions, and maintain structural integrity in aerospace and unmanned aerial vehicle applications. Conventional high-fidelity finite-element and fluid–structure simulations yield precise modal predictions but incur prohibitive computational costs, limiting rapid design exploration. This paper introduces a data-driven surrogate modeling framework based on a feedforward neural network to predict natural vibration frequencies of propeller blades with high accuracy and a dramatically reduced runtime. A dataset of 1364 airfoil geometries was parameterized, meshed, and analyzed in ANSYS 2024 R2 across a range of rotational speeds and boundary conditions to generate modal responses. A TensorFlow/Keras model was trained and optimized via randomized search cross-validation over network depth, neuron counts, learning rate, batch size, and optimizer selection. The resulting surrogate achieves $R^2>0.90$ and $NRMSE<0.08$ for the second and higher-order modes, while reducing prediction time by several orders of magnitude compared to full finite-element workflows. The proposed approach seamlessly integrates with CAD/CAE pipelines and supports rapid, iterative optimization and real-time decision support in propeller design. Full article

Conventional acoustic time-of-arrival (TOA) estimation in complex indoor environments is highly susceptible to multipath reflections and occlusions, resulting in unstable measurements and limited physical interpretability. This paper presents a smartphone-based indoor localization method built on vision-assisted acoustic channel modeling, and develops a fusion anchor integrating a pan–tilt–zoom (PTZ) camera and a near-ultrasonic signal transmitter to explicitly perceive indoor geometry, surface materials, and occlusion patterns. First, vision-derived priors are constructed on the anchor side based on line-of-sight reachability, orientation consistency, and directional risk, and are converted into soft anchor weights to suppress the impact of occlusion and pointing mismatch. Second, planar geometry and material cues reconstructed from camera images are used to generate probabilistic room impulse response (RIR) priors that cover the direct path and first-order reflections, where environmental uncertainty is mapped into path-dependent arrival-time variances and prior probabilities. Finally, under the RIR prior constraints, a path-wise posterior distribution is built from matched-filter outputs, and an adaptive fusion strategy is applied to switch between maximum a posteriori (MAP) and minimum mean square error (MMSE) estimators, yielding debiased TOA measurements with calibratable variances for downstream localization filters. Experiments in representative complex indoor scenarios demonstrate mean localization errors of 0.096 m and 0.115 m in static and dynamic tests, respectively, indicating improved accuracy and robustness over conventional TOA estimation. Full article

Active Debris Removal (ADR) missions demand precise and rapid controllers that lower collision risks specifically in the capture phase of tumbling objects. Sliding Mode Control (SMC), in general, offers robustness against model uncertainties. However, traditional reaching laws often face slow convergence when the chaser is too far from the target state. In this paper, we address this particular limitation and present the first application of Power Rate Reaching Law Sliding Mode Control (PRRL-SMC) to the 6-DOF coupled dynamics of a CubeSat-based debris capture mission in both the pre-capture tracking and post-capture stabilization phases in the case of tumbling debris. To show the strength of our work, we evaluate the proposed controller against Proportional–Derivative (PD), Linear Quadratic Regulator (LQR), second-order SMC (SOSMC), and terminal SMC (TSMC) for the pre-capture tracking and post-capture stabilization phases. By numerical simulations we show that PRRL-SMC reduces convergence time extremely and achieves stable capture in 7.6 s. This time it is 24.6 s for LQR and 28.1 s for SOSMC. The controller also handles the abrupt inertia variations of the combined stack post-capture successfully. This is efficient for proximity operations because of their importance in timing and fuel conservation. Full article

This paper studies the stationary incompressible Navier-Stokes equations with mixed boundary conditions using a velocity-pressure finite element formulation. We first establish a variational framework and prove existence of solutions under suitable regularity assumptions, followed by a Galerkin discretization with error estimates. Three iterative algorithms (the Stokes, Newton, and Oseen schemes) are then analyzed, with stability conditions and error bounds derived for each. Numerical experiments confirm the theoretical results: all methods achieve second-order convergence for velocity and pressure. Among the three schemes, the Newton iteration is the most efficient in terms of computational time, while the Oseen iteration exhibits the strongest robustness with respect to decreasing viscosity coefficients. Full article

Rainstorm floods constitute one of the major natural hazards threatening the safe and stable operation of power grid facilities. Constructing a rapid and accurate prediction model is of great significance in order to enhance the disaster prevention capacity of the power grid. This study proposes a rapid prediction model for urban rainstorm flood targeting power grid facilities based on deep learning. The model utilizes computational results of high-precision mechanism models as data-driven input and adopts a dual-branch prediction architecture of space and time: the spatial prediction module employs a multi-layer perceptron (MLP), and the temporal prediction module integrates convolutional neural network (CNN), long short-term memory network (LSTM), and attention mechanism (ATT). The constructed water dynamics model of the right bank of Liangshui River in Fengtai District of Beijing has been verified to be reliable in the simulation of the July 2023 (“23·7”) extreme rainstorm event in Beijing (the July 2023 event), which provides high-quality training and validation data for the deep learning-based surrogate model (SM model). Compared with traditional high-precision mechanism models, the SM model shows distinctive advantages: the R² value of the overall inundation water depth prediction of the spatial prediction module reaches 0.9939, and the average absolute error of water depth is 0.013 m; the R² values of temporal water depth processes prediction at all substations made by the temporal prediction module are all higher than 0.92. Only by inputting rainfall data can the water depth at power grid facilities be output within seconds, providing an effective tool for rapid assessment of flood risks to power grid facilities. In a word, the main contribution of this study lies in the proposal of the SM model driven by the high-precision mechanism model. This model, through a dual-branch module in both space and time, has achieved second-level high-precision prediction from rainfall input to water depth output in scenarios where the power grid is at risk of flooding for the first time, providing an expandable method for real-time simulation of complex physical processes. Full article

Background: Gastrointestinal discomfort affects up to 70% of individuals with spinal cord injury (SCI), largely due to gut dysbiosis caused by altered transit time and reduced gastrointestinal motility from autonomic disruption. Emerging evidence links prebiotics and probiotics to improved microbiome balance and reduced inflammation, yet data in SCI remain limited. Methods: Individuals aged ≥ 18 years, with a chronic SCI (≥1 year) experiencing significant gastrointestinal symptoms, will be invited to participate in this single-center randomized controlled crossover trial. Persons currently taking antibiotics, who have relevant eating or digestive disorders, or who have undergone a recent diet change will be excluded from the study. Participants will be randomized (1:1) into two groups. The first group will take a probiotic (Biotics-G, Burgerstein AG, Rapperswil-Jona, Switzerland) supplement for eight weeks, then after a four-week washout period, they will take a prebiotic (Oat Bran, Naturaplan, manufactured by Swissmill, Zurich, Switzerland) supplement for another eight weeks. The second group will receive the supplements in reverse order. The primary outcome is the Gastrointestinal Quality of Life Index, a questionnaire to assess quality of life related to gastrointestinal disorders. Secondary outcomes consist of gastrointestinal transit time, inflammatory blood markers, and gut microbiome composition. Ethics: The study will be conducted in accordance with the Declaration of Helsinki. The study was approved by the Ethics Committee for Northwest/Central Switzerland (EKNZ, ID: 2025-00238, 24.02.2025, Version 2.0). The study is registered at ClinicalTrials.gov (ID: NCT06870331, 02.04.2025). Written informed consent will be obtained from all participants involved in the study. Full article

An inorganic–organic hybrid nano-adsorbent was prepared by chemical immobilisation of an organic Schiff base Cu (II) ion receptor, DHB ((E)-N-(1-(2-hydroxy-6-methyl-4-oxo-4H-pyran-3-yl) ethylidene) benzohydrazide), a selective dehydroacetic acid-based chemosensor, onto a mesoporous silica support. In order to prepare the sorbent, the silylating agent was anchored onto the silica. During this procedure, 3-Chloropropyl trimethoxy silane (CPTS) was attached to the surface, increasing hydrophobicity. By immobilising DHB onto the CPTS platform, the silica surface was activated, and as a result the coordination chemistry of the Schiff base generated a hybrid adsorbent with the capability to rapidly sequestrate Cu (II) ions from wastewater, as an answer to combat growing Waste Electrical and Electronic Equipment (WEEE) contamination in water supplies, in the wake of a prolonged consumerism mentality and boom in cryptocurrency mining. The produced hybrid materials were characterised by FTIR, proximate and ultimate analysis, nitrogen physisorption, PXRD, SEM, and TEM. The parameters influencing the removal efficiency of the sorbent, including pH, initial metal ion concentration, contact time, and adsorbent dosage, were optimised to achieve enhanced removal efficiency. Under optimal conditions (pH 7.0, adsorbent dosage 3 mg, contact time of 70 min, and 25 °C), Cu (II) ions were quantitatively sequestered from the sample solution; 93.1% of Cu (II) was removed under these conditions. The adsorption was found to follow pseudo-second-order kinetics, and Langmuir model fitting affirmed the monolayer adsorption. Full article

Progressive rock slope destabilization poses significant geohazard risks, necessitating advanced monitoring frameworks to detect precursory failure signals. This study presents a comprehensive time-dependent evaluation of the displacement probability (CTEDP) model, which integrates GNSS-derived spatiotemporal data with multi-parameter reliability indices to enhance landslide risk assessment. Five monitoring points on a destabilizing rock slope were analyzed from mid-November 2024 to early January 2025 using kinematic metrics (velocity, acceleration, and jerk), statistical measures (e.g., moving averages), and reliability indices (RI₀, RI₁, RI₂, and RI_combined). Point 1 exhibited the most critical behavior, with a cumulative displacement of ~60 mm, peak velocities of 34.5 mm/day, and accelerations up to 1.15 mm/day². The CTEDP for active points converged to 0.56–0.61, indicating sustained high risk. The 90th percentile displacement threshold was 58.48 mm for Point 1. Sensitivity analysis demonstrated that the GNSS-derived reliability indices dominated the RI_combined variance (r = 0.999, explaining 99.8% of variance). The first- and second-order reliability indices (RI₁, RI₂) at Point 1 exceeded the 60-index threshold, indicating a transition to Class B (“Low Risk—Trend Surveillance Required”) status, while other points showed coherent deformation of 37–45 mm. Results underscore the framework’s ability to integrate spatiotemporal displacement, kinematic precursors, and statistical variability for early-warning systems. This approach bridges gaps in landslide prediction by accounting for spatial heterogeneity and nonlinear geomechanical responses. Full article

The global environmental challenges of solid waste accumulation and aquatic eutrophication demand innovative and sustainable strategies. This study introduces a circular “waste-treats-waste” approach by converting dolomite-rich phosphate tailings (PT), a widespread industrial by-product, into a high-value adsorbent for phosphorus (P) removal. Thermal modification at 950 °C for 1 h dramatically enhanced the adsorption capacity by approximately 45 times, from 2.52 mg/g (raw PT) to 112.41 mg/g. This performance is highly competitive with, and often superior to, many engineered adsorbents. The calcination process was pivotal, decomposing carbonates into highly active CaO and MgO while developing a porous structure. Using a multi-technique characterization approach (X-ray diffraction (XRD), Fourier transform infrared spectra (FTIR), TESCAN VEGA3 tungsten filament scanning electron microscope (SEM), the Brunauer–Emmett–Teller method (BET)), the key immobilization mechanism was identified as hydroxyapatite formation, driven by Ca²⁺/Mg²⁺-phosphate precipitation and surface complexation. Nonlinear regression analysis revealed that the adsorption kinetics obeyed the pseudo-second-order model, and the equilibrium data were best described by the Freundlich isotherm. This indicates a chemisorption process occurring on a heterogeneous surface, consistent with the complex structure created by thermal modification. Notably, post-adsorption pore structure expansion suggested synergistic pore-filling and surface reorganization. This work not only demonstrates a circular economy paradigm for repurposing industrial solid waste on a global scale but also offers a cost-effective and high-performance pathway for controlling phosphorus pollution in aquatic systems, contributing directly to resource efficiency and sustainable environmental remediation. Full article

Porous photocatalysts from agricultural waste, i.e., apricot and peach shell, with titanium dioxide were prepared by a carbonaceous method, the adsorption and photocatalytic degradation and its kinetics about methylene blue (MB) were studied systematically. The properties of the prepared composite sorbents were characterized using Brunauer–Emmett–Teller, surface area, scanning electron microscopy, and energy dispersive spectroscopy analyses. Several key factors, including radiation, pH, temperature, initial MB concentration, contact time, and sorbent dosage, as well as photocatalytic activity were investigated. All the waste-TiO₂ adsorbents showed improved adsorption and photodegradation performance compared to commercial charchoal-TiO₂. The produced materials presented high specific surface areas especially those derived from apricot shell-TiO₂ with a combination of type I and IV adsorption isotherms with a hysteresis loop indicating micro and mesopore structures. In addition, under UV radiation, the composite sorbents exhibited greater MB removal efficiency than non-radiated composite sorbents. The examined conditions have shown the best MB adsorption results at pH greater than 7.5, temperature 30 °C, contact time 120 min, initial concentration 0.5 mg/L MB, and sorbent dosage equal to 2.0 g/L C/MB. The total removal rate of MB is 98.5%, while the respective amount of commercial charcoal-TiO₂ is equal to 75.0%. The kinetic model that best describes the experimental data of MB degradation from the photocatalytic materials is the pseudo-second order model. In summary, this work highlights the effectiveness and feasibility of transforming agricultural waste into carbonaceous composite sorbent for the removal of cationic dyes from wastewater. Future work will involve scaling up the synthesis of the catalyst and evaluating its performance using bed reactors for industrial processes. Full article