Browse Articles
Discover research articles across all indexed journals
Artificial intelligence assistance improves endoscopist accuracy for gastric cancer dysplasia and intestinal metaplasia
Microstructural and mechanical behavior analysis of magnesium matrix composites with varying Al2O3 content
Study on sound insulation performance of walls in traditional timber dwellings of the Miao ethnic group in Qiandongnan
Enhancing trustworthiness of Arabic online health information quality evaluation using an enhanced BERT architecture with PCA and ICA feature weighting
Automated measurement of hallux valgus angles using a deep learning model: validation and comparison with surgeons of varying expertise
Moving standard deviation assisted two-terminal traveling wave based fault location estimation technique for transmission system incorporated with UPFC
Abstract Modern power systems are increasingly complex and vulnerable to disturbances, with transmission line faults being the most frequent and disruptive. Accurate fault location estimation (FLE) is essential to ensure fast system restoration and reliable operation, particularly when flexible AC transmission system (FACTS) devices such as the Unified Power Flow Controller (UPFC) are present. This paper proposes a Moving Standard Deviation (MSD) assisted two-terminal traveling wave (TW) based FLE technique for UPFC-compensated transmission lines. In the proposed approach, terminal voltage signals are transformed into aerial mode signals using Clarke’s transformation, and MSD is applied to identify peak values (PMSDs). These peaks provide estimated times of arrival waves (ETAWs), which are used to compute the fault location. The method is validated on a 500 kV three-machine system with a 100 MVA UPFC under diverse scenarios, including varying fault distances, types, resistances, inception angles, close-in and far-bus faults, UPFC operating modes, sampling frequencies, and noisy environments. Results confirm that the proposed method consistently achieves high accuracy, with percentage errors maintained below 1% even at low sampling rates (60 Hz) and under severe noise (5 dB SNR). The technique is computationally simple, robust against UPFC influences, and offers practical applicability for modern power systems.
Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning
Driving sustainable innovation outcomes through employee AI collaboration with the mediating role of sustainable career capacities
Analysis of modified second-grade fluids in thin film configurations employing the asymptotic homotopy perturbation method
Abstract This article investigates the steady, incompressible and non-isothermal thin film flow of a modified second grade fluid under both conditions that is, lifting and drainage. The governing highly non-linear ordinary differential equations with appropriate boundary conditions have been solved and achieved approximate solutions employing the Asymptotic Homotopy Perturbation Method (AHPM). The velocity and temperature distributions have been obtained, moreover average velocity, shear stress and volume flow rate at the belt surface are presented graphically for both lifting and drainage configurations. Here it is worth mentioning that the flow of thin films are significantly influenced by $$m$$ (flow behavior index of modified second grade fluid), Brinkman number and Stokes number. The outcomes of this work are relevant to several industrial and engineering applications, comprising coating and drying technologies, polymer extrusion and processing, lubrication systems, chemical and food processing, and thermal control in thin-film transport processes. Additionally, graphical and numerical analyses have been provided to explain the impact of various parameters on the fluid behavior.
CFD-DEM coupling analysis of EPB screw conveyor muck discharge in water-rich sandy cobble strata
Deep recurrent neural networks for water hammer transient prediction and dynamic protection optimization in long distance pipelines
Abstract Water hammer phenomena pose significant threats to the operational safety and structural integrity of long-distance water transmission pipeline systems. This study develops an integrated intelligent system combining deep recurrent neural networks with distributed pressure sensor data fusion for water hammer transient prediction and dynamic protection optimization. A multi-layer bidirectional Long Short-Term Memory network with attention mechanism is constructed to capture spatial-temporal pressure dynamics from distributed sensor measurements. A Deep Q-Network based reinforcement learning algorithm generates optimal real-time protection strategies by coordinating multiple devices including surge tanks, relief valves, and valve closure sequences. Comprehensive validation demonstrates that the proposed system achieves superior prediction accuracy compared to conventional methods and significantly reduces maximum transient pressures while shortening stabilization duration. The intelligent decision framework provides water utilities with an adaptive tool for enhancing pipeline safety, minimizing infrastructure damage risks, and optimizing protection resource allocation in complex hydraulic systems.
Physicochemical characterization of a recently developed resin-based calcium silicate cement compared to established pulp capping materials
Experimental study on settling, transport, and packing mechanisms of proppants with different shapes in a physical model
Enhanced solution absorption of free-base over protonated nicotine in aerosols
Abstract Elucidating the pharmacokinetic profiles of different chemical forms of nicotine (free-base versus protonated)—particularly their differences in biological absorption—is critically important for balancing its potential clinical applications in neurodegenerative disease therapy with effective management of its inherent addiction risk. Current empirical observations regarding which form exhibits superior absorption remain significantly divergent. This study employs an aerosol delivery model to compare the permeability (simulated absorption) of the two nicotine forms in solution by quantifying the amount absorbed and the residual nicotine after aerosol penetration through a simulated solution barrier. The results demonstrate that free-base nicotine exhibits higher solution absorption than its protonated counterpart. The superior absorption of free-base nicotine is primarily attributed to its diffusion from the particulate phase to the gas phase within aerosols, facilitating efficient mass transfer across the gas–liquid interface. These findings provide an in vitro experimental basis for further evaluation of the potential differences in human absorption efficiency between nicotine forms, thereby supporting the development of nicotine-based pharmaceuticals.
Early fecal metabolomic profiling for predicting acute graft-versus-host disease following allogeneic hematopoietic stem cell transplantation
Application of modified multi-verse optimization for temperature control in thermal power plant condensers
Abstract The surface condenser exhibits nonlinear dynamics and inherent time delays, making precise temperature regulation essential for stable operation in thermal power systems. In this study, a Modified Multi-Verse Optimizer (MMVO) is employed to tune the parameters of a PID controller for improved temperature control of a shell-and-tube condenser. The methodology involves formulating the PID tuning task as an optimization problem, applying MMVO with defined search bounds, and evaluating its performance using 23 standard benchmark functions and repeated simulation runs. Statistical indicators including best, worst, average, and standard deviation values across 30 independent executions are used to assess robustness. Comparative analyses with Ziegler–Nichols (ZN), Genetic Algorithm (GA), and the original Multi-Verse Optimizer (MVO) demonstrate that the modified approach achieves lower integral error indices and reduced overshoot, while providing more consistent performance across trials. The results indicate that MMVO-based PID tuning offers enhanced control capability for systems with strong nonlinearities and delay characteristics.
Cannabidiolic acid as a modulator of lipid metabolism in the liver of rats with metabolic-associated steatotic liver disease
Isogeometric analysis of wing structures using multipatch parametrization and penalty-based coupling method
Abstract Geometric parametrization and structural analysis play a pivotal role in aircraft structural design. Isogeometric analysis utilizes high-order, high-continuity spline basis functions for field variable discretization, offering superior solution accuracy compared to traditional finite element methods while streamlining data exchange between CAD and CAE systems. In this study, an analysis-suitable geometric parametrization is developed for classical aircraft wing structures, comprising two skins, twenty three ribs, and two spars. The wing geometry is represented using multiple NURBS patches. Within the wingbox formed by ribs and spars, all patch interfaces maintain conformity. Conversely, interfaces between wing skins and the wingbox are deliberately nonconforming to reduce modeling complexity while preserving the skin’s high continuity. Reissner–Mindlin shell theory is applied within the isogeometric framework to model structural behavior, and a penalty-based method is implemented to enforce continuity of the displacement field across the nonconforming skin-wingbox interface. A static bending analysis of the complete wing structure is performed, and the results are compared with those obtained from a conventional finite element analysis in ABAQUS for validation. The IGA results show excellent agreement with the FEA reference solution, achieving comparable accuracy with significantly fewer degrees of freedom.