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This study presents a current and extensive bibliometric analysis of pavement deterioration detection, monitoring, and assessment using various sensors alongside machine learning and deep learning algorithms. The impact of electronic sensors, machine learning, and deep learning on road pavement evaluation and monitoring within the transportation sector is highlighted. Conducting a bibliometric analysis covering research until March 1, 2024, 639 publications from 71 countries were examined. Productive countries, journals, institutions, and authors were analyzed and ranked. A standard research score and cumulative output score were calculated to normalize differences in the data. The findings reveal a significant recent increase in studies in this area. The most productive countries, journals, institutions, and authors are China, Transportation Research Record, Southeast University China, and Golroo Amir, respectively. This study serves as a valuable resource for both academic and industry researchers, offering insights into road pavement monitoring and guiding future research. In addition, accelerometer and GPS were the most used sensors, ANN and CNN were the most preferred algorithms, and cracks and potholes were the most studied topics. This study has the potential to be a good map for both academic and industrial researchers for monitoring the state of road pavements and a good guide.

The planar compact four port circularly polarized printed MIMO antenna described in this article is small and intended to support Wi-Fi and WiMAX applications. Suggested four E-shaped monopole elements make up the dual circularly polarized (CP) MIMO antenna that has two left hand CP (LHCP) elements and two right hand CP (RHCP) elements. An I shaped strip is used to join separate ground planes of designed E shaped monopole antenna to have same voltage levels. A good diversity performance is achieved with an envelope correlation coefficient (ECC) of less than 0.15. The 60 mm × 60 mm antenna is designed from FR4 material, which is inexpensive and has a 1.6 mm thickness. Using a high-frequency structure simulator (HFSS), the suggested MIMO antenna is designed and optimized. The measured findings demonstrate the circular polarization, 3-dB axial ratio beamwidth, impedance bandwidth of 2.11 GHz −2.42 GHz and 3.40 GHz-3.51 GHz, adequate axial ratio bandwidth (ARBW) of the suggested MIMO antenna along with high isolation of −17 dB, gain of 3.43 and 2.09 dBic at 2.4 GHz and 3.5 GHz respectively, TARC (Total active reflection coefficient) of less than 0 dB, a CCL (Channel capacity loss) of lesser than 0.4 bits/sec/Hz, and an MEG (Mean effective gain) value of less than 3 dB.

The seismic vibrator baseplate (SVB) is a welded structure. Ignoring welding residual stress (WRS) and only considering cyclic stress can decrease the accuracy of fatigue life assessments. This study aims to investigate the fatigue life prediction and optimization of SVB under the influence of WRS. A welding model of SVB and an SVB-ground excitation model were developed, and the stress distribution of SVB was determined using the finite element method. A fatigue life prediction study that incorporates WRS, based on a modified S-N curve, was conducted. A multi-objective fatigue life optimization analysis was performed using genetic algorithms. The results shows that the backing weld (BW) of the SVB is the most susceptible area to fatigue failure. Under the combined effects of WRS and working stress, the fatigue life of the SVB is 8.14 years. When considering WRS, the prediction results align well with field data, showing an error margin of less than 5 %. WRS is identified as a critical factor influencing the fatigue life of SVB, and optimizing welding parameters can effectively reduce WRS, thus extending the service life. The optimal welding parameters were found to be welding interpass temperature of 105 °C and welding speed of 10.23 mm/s. After optimization, the fatigue life of the SVB increased to 10.23 years, a 25.68 % improvement over the pre-optimization condition. Previous studies did not consider the influence of WRS, whereas this work integrates it into the fatigue analysis, producing a modified S-N curve that enhances the accuracy of life prediction. Furthermore, multi-objective optimization algorithms and fusion decision-making methods were applied to the fatigue life optimization of the SVB, offering novel research approaches and methodologies. This study provides theoretical guidance for the structural design and optimization of the SVB, aligning with practical engineering needs and showing substantial application value.

Wire-wrapped fuel rods are widely utilized in lead-bismuth reactors and subjected to high kinetic energy fluid flow, significantly influencing the reactor's safe operation. This study addresses the increasing interest in fuel assemblies by developing a theoretical fluid-structure interaction model to elucidate the dynamic behavior of wire-wrapped fuel rods. Newton's law is employed to derive the governing equation, with fluid force coefficients determined through widely accepted numerical simulations, while the impact and downstream nonlinear boundary condition are incorporated. Two comparative analyses robustly validate the reliability of the proposed model. Building on this model, the study investigates the nonlinear vibration characteristics (amplitude, phase space, impact forces, etc.) of the structure, considering the effects of gap size and streamlining parameter. Moreover, the theoretical derivation of this model provides a foundational and dependable mathematical framework to facilitate further research on fretting wear.

Investigating the effects of waste discharge on nanofluids using a non-Newtonian fluid model is vital for enhancing heat and mass transfer performance in engineering systems, such as cooling systems in power plants, oil, and gas drilling operations, and wastewater treatment facilities, while simultaneously mitigating the environmental impact of pollutant diffusion in these industrial processes. Therefore, this study examines the effects of porous medium, thermal radiation, magnetic effect, and external pollutants in a water-based ternary hybrid nanofluid flow within the context of the Reiner-Philippoff fluid model. The suitable similarity transformations are utilized to transform the partial differential equations (PDEs) into ordinary differential equations (ODEs). The resulting set of ODEs are solved numerically to find the solutions using the function bvp4c available in MATLAB software. The ternary hybrid nanofluid (Ag-Cu-TiO2) significantly enhances heat and mass transfer rates by about 42.72 % and 2.53 % compared to water (H2O) at around 4.36 % and 0.60 % relative to the hybrid nanofluid (Ag-TiO2), respectively. In pollutant-free conditions, the heat and mass transfer of ternary hybrid nanofluid (Ag-Cu-TiO2) progresses up to 0.34 % and 0.26 %, respectively, compared to H2O. Meanwhile, for hybrid nanofluid (Ag-TiO2), it develops by about 0.24 % and 0.31 %, respectively. This indicates that the impact of the external pollutants significantly delays mass transfer but increases the concentration field and destabilizes the flow near the shrinking sheet. Trio slip parameters reduce shear stress, heat, and mass transfer rates, while the mixed convection parameter enhances the skin friction coefficient in the assisting flow and diminishes it in the opposing flow. The magnetic parameter enlarges shear stress with the help of the Lorentz force but thermal radiation increases the heat transfer rate while reducing surface drag. Additionally, nanoparticle volume fractions and the porous medium elevate shear stress and heat transfer rate. This research provides insights into optimizing nanofluids in pollutant-laden environments, with potential applications in industrial processes involving heat exchangers and pollution control. Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

In complex scenes, small target face detection is crucial but often hampered by detection accuracy and efficiency limitations. Our method addresses these challenges by incorporating Gaussian noise, which is key in improving model robustness and generalization. By simulating real-world imperfections, Gaussian noise acts as a regularizer and makes the model more resistant to variations in lighting and texture. Traditional methods often face difficulties when dealing with small targets and complex backgrounds due to inadequate feature extraction, suboptimal loss function design, and vulnerability to noise. To overcome these issues, we propose an improved YOLOv8 model based on multi-scale feature fusion and an optimized loss function. By leveraging Gaussian noise during training, our approach enhances both detection accuracy and operating efficiency. Experiments on the FDDB and WIDER FACE datasets demonstrate that our method performs better in various complex scenarios. Our method achieved 0.780 on the WIDER FACE validation set, outperforming existing mainstream techniques.

Ternary hybrid nanofluids are crucial to be modeled and researched before their commercial application as a heat transfer fluid. This study investigates the stagnation point flow of a ternary hybrid nanofluid past a stretching/shrinking sheet, focusing on the influence of the melting parameter and second-order velocity slip. The governing partial differential equations (PDEs) are initially formulated and subsequently reduced to ordinary differential equations (ODEs). These ODEs are further transformed into first-order form and numerically solved using the bvp4c solver in MATLAB. Stability analysis is conducted due to the existence of two potential solutions, of which only one proves stable upon analysis. The numerical results indicate significant enhancements in heat transfer performance under conditions of elevated melting and enhanced velocity slip. Reducing the melting parameter and second-order velocity slip may expand the solution range, leading to a delay in boundary layer separation. The stable numerical solution for the heat transfer rate is then validated with the use of a logic mining model namely Permutation 2 Satisfiability Reverse Analysis (P2SATRA). The most accurate induced logic, chosen to illustrate the overall relationship between the selected parameters is achieved in the third fold of a 10-fold cross-validation, yielding an accuracy of 0.81818.

The growing digital transformation has increased the need for effective intrusion detection systems. Traditional intrusion detection systems face challenges in accurately classifying complex patterns. To address this issue, this study proposed a Hybrid Learning Model (HLM) that combines both parametric and non-parametric classifiers. The proposed HLM consist of two stages: the first stage employs a non-parametric Base Learner (np-BL) to analyze the data patterns and the second stage involves meta-modelling to generalize the overall performance of the model, named the Parametric Meta-Learning (PML) model. The proposed HLM blends the outcomes of np-BL and PML models using a stacking ensemble. As a base learning model K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), and Support Vector Classification with Radial Basis Function (SVC-RBF), are adopted from a non-parametric classifier group. The parametric classifiers Logistic Regression (LR), Naïve Bayes Classifier (NBC), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machine with linear kernel (Linear SVM) were used as meta-models. The HLM, as proposed, enhances the adaptability and robustness of the model by combining non-parametric and parametric models. To evaluate the competence of the proposed HLM, a performance analysis was conducted using the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets. The effectiveness was assessed using various metrics, including classification accuracy, precision, recall, F1-Score (F1), Receiver Operating Characteristic (ROC) curve, Detection Rate (DR), and False Alarm Rate (FAR). The proposed HLM achieves a better accuracy rate across different datasets when compared with the existing models. The achieved accuracies are 99.02 %, 99.98 % and 99.63 % for the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets respectively. Furthermore, the HLM gave a significant reduction in FAR, with values of 0.0126, 0.0001, and 0.0016 for the above-mentioned datasets.

The current study intends to predict the optimised condition to attain the objective of acquiring highest heat transfer rate to develop an efficient model. The transient flow of Carreau nanofluid within a microchannel when channel walls are susceptible to radiation is contemplated. Buongiorno model is employed, which emphasizes the repercussions of Brownian motion and thermophoresis phenomena; also, mixed-convective flow is accounted. The modelled problem gives rise to partial differential equations, which are non-dimensionalized employing non-dimensional quantities. The resultant equations are solved numerically using the finite difference method. Results of analysis demonstrate that the Weissenberg number for n1, depicts shear thickening nature, decreasing velocity. The skin friction coefficient increases when solutal Grashof number rises for the high range of the Reynolds number. The Sherwood number increases when Schmidt number is less for increased value of Reynolds number. Optimization method reveals the highest heat transfer rate of 7.3687 for the considered model. ANOVA results show that the manipulation of Reynolds number is crucial with 57.29% impact and the manipulation of Prandtl number has minor impact of 1.41%on Nusselt number. Shear thinning nature of Carreau fluid finds its application in extrudability, printability and injectability and shear thickening nature is extensively used in industrial polishing, explosion resistance.

Distinguishing unknown non-orthogonal qubits is an essential requirement for addressing various challenges in quantum computation, including quantum machine learning, quantum communications, and quantum technologies. For instance, while quantum teleportation enables the transfer of unknown individual qubits between distant parties, an algorithm is necessary to define the associated state of a teleported qubit at the receiving end. In this paper, we propose a novel quantum algorithm designed to effectively determine the state of a given unknown qubit and distinguish a subset of non-orthogonal qubits. The proposed algorithm can efficiently identify the state of an unknown qubit in the form cosθ2|0〉+sinθ2|1〉 using the Mz operator. By estimating the angle θ through the measurement of entanglement degree, the proposed algorithm can identify the state of an unknown qubit. Experimental validation of the proposed algorithm is conducted using the IBM quantum computer simulator chip ibmqx2. Furthermore, a t-test is conducted to compare the proposed algorithm with the direct measurement approach. The results indicate a significant difference between the two methods, demonstrating the superior performance of the proposed algorithm.

Harmful emissions from diesel vehicles, particularly unmodified ones, pose significant concerns for human health and the environment, underscoring the urgency to address these issues. This study investigated the effects of commercial fuels, B10, B20, and biodiesel B100, used with a metallic partial-flow catalyzed diesel particulate filter (P-CDPF), on a light-duty unmodified diesel vehicle's thermal efficiency and emissions characteristics. The initial test was conducted on a chassis dynamometer to measure the fuel flow rates at three different engine speeds, 1500, 2000, and 2500 rpm, with four loads, 84, 112, 140, and 160 Nm. This was done to evaluate brake-specific fuel consumption and brake thermal efficiency under steady-state conditions. The second test followed the new European driving cycle to examine emission factors of regulated pollutants under both urban and highway driving conditions. The results indicated that BSFC values increased with the biodiesel ratio in the blends, attributed to lower heating values. However, higher oxygen contents with increasing biodiesel ratios led to more complete combustion and improved brake thermal efficiency. Installation of a P-CDPF had a minimal impact, resulting in less than a 3.4% increase in the BSFC and a 1% decrease in brake thermal efficiency across all tested fuels, owing to its relatively low pressure drop. Increasing the biodiesel ratio from B10 and B20 to B100 resulted in reductions of up to 32% of particulate mass and 45% of particulate number in vehicle emissions. P-CDPF installation further reduced particulate mass by over 60% and particulate number by 36% across all tested fuels, demonstrating its effectiveness in trapping and passively oxidizing particulate matter. Furthermore, the P-CDPF significantly reduced harmful gases with addition of a catalytic coating. A combination of a P-CDPF and commercial biodiesel fuels emerges as an effective solution for reducing regulated emissions from unmodified diesel vehicles.

Under the pressure of climate change, the international food market is facing great uncertainty. Rice is widely grown as a major worldwide food crop, and different rice seeds often influence the future merit of a country's rice growth. As a major food crop widely grown around the world, the seed type of rice plays a key role in ensuring food security and optimizing agricultural productivity. Therefore, identifying the types of rice grains is an important task in rice breeding and cultivation. To this end, this study proposes a new model based on the ConvNeXt framework for detecting rice types, aiming to improve the identification efficiency. Our improved model achieved an average accuracy of 94.69 %. Compared to the baseline model ConvNeXt, the proposed network is more lightweight and more accurate. We conducted comprehensive experiments on the rice dataset from the GrainSpace public dataset to ensure the thoroughness and rigor of the study. Compared to existing models, our proposed model achieved the highest accuracy while maintaining lower FLOPs and parameters.

The rapid expansion of Mobile Edge Computing (MEC) and the Internet of Things (IoT) has revolutionized technology by enabling real-time data processing at the network edge, which is essential for applications such as autonomous vehicles and smart cities. With the advent of 6G networks, which promise ultra-fast speeds, vast connectivity, and low-latency communication, MEC-IoT systems are becoming more powerful but also face significant security challenges. Existing authentication mechanisms (AMs) are often vulnerable to attacks like impersonation and insider threats. This paper introduces a novel lightweight AM, called RAM-MEN that employs cryptography and physically unclonable functions (PUFs) to secure IoT-enabled MEC environments in the 6G era. It protects against insider threats and fake MEC access points while ensuring efficiency and scalability. Additionally, the proposed RAM-MEN establishes a secure communication channel (session key) between IoT devices and the MEC server, enabling secure offloading of computationally intensive tasks. The security of the session is rigorously evaluated using formal methods, including Scyther and the random or real model, alongside informal approaches. Comparative performance evaluations show that the proposed RAM-MEN reduces communication costs by 21.54% to 45.53% and computational costs by 17.09% to 83.72%, while providing enhanced security features.

Fixed-section detection methods, with radar and video as representatives, frequently encounter incomplete detection data at controlled intersections because of high construction costs and insufficient maintenance. This results in ineffective signal control strategies. On the other hand, mobile detection methods, represented by floating cars, can perceive both macro and micro spatial-temporal characteristics of traffic flow. However, their current low penetration rate limits their ability to provide sufficient data support for signal control at intersections.To address this issue, this paper proposes an innovative method to obtain more accurate flow rates for each phase at an intersection through simulation approximation of calibrated parameters. This method utilizes the Webster delay theory to quantitatively describe the relationship between phase flow and vehicle delay, allowing for the inverse estimation of flow rates. These estimated flow rates are then refined using the proposed Radial Basis Function (RBF) neural network approximation method to achieve higher accuracy. Comprehensive experimental results demonstrate that the proposed method effectively improves the accuracy of inverse flow data estimation. This enables the effective utilization of low-penetration-rate floating car data (FCD) in signal control at urban intersections. By leveraging this innovative approach, signal control systems can make more informed decisions, leading to smoother traffic flow and improved traffic management in urban areas.

Heat features of a ternary nanofluid are examined in a heated flowing condition among a curvy conduit and endoscope that undergo peristalsis motion and sinusoidal variabilities. Three nanoparticles, copper, silver, and aluminum oxide were dispersed in blood as a basic fluid to study their potential effects on the flow and temperatures of the mixed fluid under thermal radiation and magnetic resonance, as well as the system's entropy optimization. The authors explored this system to understand flowing and heat diffusion in peristaltic conduits and as a medical application that may offer a future perspective for all researchers. Continuity and energy equations in their partial differential form related to the Maxwell equation due to the influence of radial magnetic force determined issue modeling based on basic regulating equations. This system was simplified by assuming a long wavelength and translated to ODEs using similarity. Closed-form solutions in magnetic fields were calculated using Mathematica software. Comparing results to earlier studies proved validity. Figures and tables showed how the issue factors affected pumping, temperature, pressure gradient, and heat transfer rate. The most significant findings record that the boluses density climbs considerably and the pressure gradient grows up as the magnetic resonance and gab ratio increases. Data Availability: Manuscript has no associated data.

Various mathematical frameworks have been developed to study dynamics of the fluid flow in stretching films. In this work, a three–dimensional flow induced by an unsteady stretching of an infinite flat sheet is analyzed through the Lie symmetry approach. Twelve Lie point symmetries for the nonlinear partial differential equations describing the considered flow and heat transfer phenomena are derived. Invariants for these Lie symmetries are obtained to construct a new class of similarity transformations. With the deduced similarity transformations, the flow equations are converted to ordinary differential equations which are solved using the Homotopy analysis method. The deduced analytic solution enables an exploration of the effects of various parameters on the flow and heat transfer, which have not been revealed previously using the Lie method as per the authors’ knowledge. The influences of the stretching rate, parameters that maintain the non-uniformity and unsteadiness of sheet temperature, and the Prandtl number, are demonstrated with the help of graphs and tables. These results show that for certain values of the system parameters, heat transfer reverses its direction and occurs from the fluid to the sheet. At these values, maximum heat transfer does not occur at the sheet but rather slightly above it.

Boron meter is commonly used for real-time monitoring of boron concentration in the primary circuit water of pressurized water reactors to ensure the safe operation of the reactor. This paper introduces the use of Am-Be neutron source and He-3 neutron detector for a boron meter. It analyzes the physical properties of a boron meter, including the energy spectrum characteristics of Am-Be neutron sources, the sensitivity of He-3 neutron detector, radiation shielding of the boron meter’s detection device, and the fitting relationship between neutron count rate and boron concentration. The boron meter was accurately physically modeled using the MCNP program, and the calculated results were comprehensively compared with experimental results. A method using correction factor μ to adjust the count rate’s calculated results was proposed. The research results show that the sensitivity of He-3 neutron detectors, the dose rate on the surface of the boron meter detection device, neutron count rate, and other calculated results are consistent with the experimental results, verifying the accuracy of the boron physical model. This physical model can be used for more refined computational analysis or to optimize the current physical design of the boron meter, thus saving experimental costs.

Optimizing the location and capacity of flexible resources is critical. The increase in the permeability of distributed generation (DG) causes the net load to drastically fluctuate, which sharply increases flexibility demand for the distribution network. The asymmetric access of distributed generators, asymmetric line parameters, and unbalanced loads aggravate the three-phase voltage unbalance of distribution systems. Energy storage systems (ESSs) and soft open points (SOPs) can flexibly adjust power flow direction, which can address the problem. This study analyzed the characteristics of flexibility resources based on the flexibility theory. Furthermore, the potential loss of flexibility demand is quantitatively assessed by conditional value-at-risk, and the coordination planning model of DG, SOP, and ESS for the unbalanced distribution network is constructed considering flexibility and economy. Because the resulting mathematical formulation belongs to a nonconvex quadratic programming problem, an adaptive linear relaxation programming method is proposed to improve computational efficiency. The adaptive linear relaxation technique is constructed based on the properties of quadratic functions and the mean value theorem, and the non-convex quadratic programming problem is converted to a series of parametric linear relaxation programming problems by the technique. Finally, the effectiveness of the proposed model is verified on IEEE 33-bus and IEEE 123-bus systems.

This paper investigates the existence and approximate controllability (ACA) of fractional neutral-type stochastic differential inclusions (NTSDIs) characterized by non-instantaneous impulses within a separable Hilbert space (HS) framework. Employing the Atangana–Baleanu–Caputo (ABC) derivative, we transform the system into an equivalent fixed-point (FP) problem through an integral operator. Subsequently, the Bohnenblust–Karlin FP theorem is leveraged to establish existence results. By assuming ACA of the corresponding linear system, we derive sufficient conditions for the ACA of the nonlinear stochastic impulsive control system. Our analysis relies on concepts from stochastic analysis, fractional calculus, FP theory, semigroup theory, and the theory of multivalued maps (MVMs). The theoretical findings are illustrated through a concrete example.

Accurate and timely traffic flow predictions are essential for effective traffic management and congestion reduction. However, most traditional prediction methods often fail to capture the complex dynamics and correlations within traffic flows due to insufficient processing of spatiotemporal data. Specifically, these methods struggle to integrate and analyze the multi-layered spatial and temporal interactions inherent in traffic data, leading to suboptimal prediction accuracy and robustness. To address this limitation, this paper presents a Multi-Graph Structures and Hypergraph Convolutional Network (MGHCN) that combines diverse graphs and hypergraphs. The MGHCN simplifies the predictive framework by integrating key components that improve its robustness and accuracy. One of the most critical components is the dual hypergraph structure, which captures edge correlations by converting traditional graph edges into hypergraph nodes. To better capture the spatiotemporal correlation of traffic data, a Graph Convolutional Network (GCN) is employed to analyze these hypergraphs in depth. Finally, a novel adjacency matrix and a dynamic graph module are used to accurately simulate interactions between spatiotemporal features, thereby enhancing the accuracy and robustness of predictions. Experimental validation on four distinct real-world traffic datasets shows that MGHCN outperforms existing state-of-the-art traffic prediction methods.

The current investigation emphasizes the mathematical modeling of the hydrodynamics associated with the oscillating water column wave energy converter (OWC-WEC) device situated above undulating bottom of the ocean in the presence of ocean currents. The mathematical problem is investigated within the framework of the two-dimensional Cartesian coordinate system by incorporating the classical linear wave-structure theory. The boundary element method is used to address the corresponding boundary value problem. A significant attention is devoted to the analysis of the effectiveness and horizontal wave force exerted on the frontal barrier of the oscillating water column device by considering various values of incident wave and current parameters, as well as structural parameters related to both the oscillating water column device and the undulated seabed. Moreover, the study in the time domain is carried out in order to illustrate the variations in free surface elevations at different time instances by considering the influence of both following and opposing currents. The results demonstrate that the “Doppler Shift” effect of the frequency due to the presence of ocean currents significantly influences the resonating patterns of the efficiency and force curves. As the current velocities increase, the resonating frequencies occur for higher values of incident wavenumbers.

In this article an anticipated reflection-mode cross-polarization converters (CPCs) are designed on a polydimethylsiloxane (PDMS) based flexible substrate operating in the GHz region. The proposed CPC metasurface comprises three layers where a flexible substrate is endwise between the resonating layer and ground layers. The converter can convert linear to circular and circular to cross-polarized waves at the frequency range of 5–8 GHz, as evidenced by the identical amplitude of Rxx and Ryx and a phase difference of 90°. More than 99 % of the polarization conversion ratio (PCR) was performed between the 6.75 and 9.07 GHz frequency range, while more than 80 % of the PCR was performed between the 6.0 and 10.73 GHz frequency range. The highest PCR values show nearly flawless energy conversion from an x-polarized incident wave to a y-polarized reflected wave. The PCR ratio of 80 % is achieved by this ultra-wideband linear-to-cross- and circular-to-cross-polarization converter. The anticipated converter's spectrum for all incidence angles between 0◦ and 60°. The PCR remained higher than 99 % over a wide incidence angle range to 60° without much performance degradation. The bending stability of the PDMS-based flexible substrate is also investigated up to 60°. The structure is fabricated on a flexible substrate, and the measured performance is compared with simulated results. Radar, antenna, and communications systems, as well as other cross-polarization converters, can utilize the transmitted wave's very high polarization purity result.

Collision, rollover, swerving, and vehicle fire are road traffic accidents (RTAs) caused by driver, vehicle, and environment-related factors, resulting in diverse severity levels, such as property damages, injuries, and fatality. In addition, vagueness in detectability, e.g., close circuit television and driver admittance, culminates RTAs' risk analysis into a multifaceted problem. The present study performed root cause analysis to investigate RTAs, where the fishbone diagram categorized RTAs (e.g., collision and swerving) and identified primary (e.g., driver and environment) and secondary (e.g., overspeeding and tire burst) root causes. For 1158 km of intercity highways in Qassim (Saudi Arabia), fuzzy-failure mode and effect analysis evaluated 171 failure modes using 3500 RTAs' recorded over three years and knowledge of practice and experts. The study highlights collision, driver-related issues, and property damage as the leading category, root cause, and consequence. Comparing the baseline with the first realistic mitigation (speed camera installation) acknowledged a 53 % reduction in risk probability number. Automated driver assistance systems, rumble strips, variable speed limits, and increasing penalties for traffic violations hypothetically dropped 76 % of cumulative risk. The methodology handles limited data and subjective risk factors to help decision-makers minimize RTAs in Saudi Arabia and other jurisdictions.

The diversity of sources and uneven distribution of medical data contributes to the statistical heterogeneity within the Medical Internet of Things (MIoT) networks. In this context, comprehensive analysis of patient data is imperative to provide more precise diagnoses and treatment strategies, rendering personalized medical treatment indispensable. Moreover, the transmission of medical data over networks raises concerns regarding data privacy, necessitating thorough consideration. To address these challenges, we propose FedWFC, a federated learning method that combines a novel importance weight with fuzzy k-means clustering to effectively handle the heterogeneous medical data in MIoT networks. Firstly, we utilize fuzzy k-means for clustering and partitioning local model parameters from MIoT devices, enabling centralized updates of multiple global models based on these clusters. This cluster-centric approach streamlines personalized updates for local models. Secondly, the introduction of the new importance weight allows us to tighten the optimization error bound for global model updates. Experiments show that FedWFC improves the macro F1 score by 4.24% and the micro accuracy by 4.99% compared with existing methods, highlighting its effectiveness in MIoT data processing.

A novel crossover model for monkeypox disease that incorporates Ψ-Caputo fractional derivatives is presented here, where we use a simple nonstandard kernel function Ψ(t). We can be obtained the Caputo and Caputo–Katugampola derivatives as special cases from the proposed derivative. The crossover dynamics model defines four alternative models: fractal fractional order, fractional order, variable order, and fractional stochastic derivatives driven by fractional Brownian motion over four time intervals. The Ψ-nonstandard finite difference method is designed to solve fractal fractional order, fractional order, and variable order mathematical models. Also, the nonstandard modified Euler Maruyama method is used to study the fractional stochastic model. A comparison between Ψ-nonstandard finite difference method and Ψ-standard finite difference method is presented. Moreover, numerous numerical tests and comparisons with real data were conducted to validate the methods’ efficacy and support the theoretical conclusions.

This paper addresses the critical need to characterize traffic flow driven by the reaction time of evolving Intelligent Vehicles (IVs). Macroscopic traffic models play a vital role in understanding traffic conditions, however, the IVs behavior is ignored. Thus, a new traffic model for IVs based on safe reaction velocity, reaction time, and braking time is proposed, incorporating the IVs reaction times. The findings demonstrate a trade-off between the reaction time and braking time, significantly explaining traffic dynamics. Specifically, reducing the reaction time improves traffic operations. Results from three distinct traffic scenarios highlighted the significance of reaction time in shaping traffic safety. In the first scenario, smoother traffic flow demonstrates the impact of reaction time on safety. The shorter reaction time showed improved outcomes. In the second scenario, changes in traffic patterns near the ramp highlighted the importance of smaller reaction times in mitigating safety risks. In the third scenario, chaotic traffic conditions emphasized the role of reaction time in ensuring overall safety. The proposed traffic model offers a more realistic characterization of traffic flow. By understanding the relation between reaction time and braking time, this approach contributes to the development of safer and more efficient automated traffic mobility.

The velocity slippage with combined effects of buoyancy force plays an important role in the extraction of crude oil since it reduces the pressure drop along the pipelines and well-bore. Due to this, it becomes difficult to push the oil out of the reservoir. So, the main goal is to analyse the velocity slip and no-slip on two immiscible viscous fluids flowing in a curved channel with viscous dissipation. To the best of the author's knowledge, there is no study given on two non-miscible laminar Newtonian layers of viscous-viscous fluids. The curved channel is divided into two regions and both regions are occupied with viscous fluids. The flow is caused by the constant pressure gradient and buoyancy force. The analytical solutions of highly nonlinear mathematical equations are obtained using perturbation series by taking ε(=PrEc)≪1. The impact of the curvature ratio parameter, velocity slips, Reynolds number, buoyancy force, viscosity ratio parameter, and height ratio parameter on heat and mass transfer are examined and presented through graphs. Moreover, the behaviour of shear stresses, skin drag and Nusselt number on both walls of the channel is analyzed and presented through bar charts. It is concluded that the velocities of the immiscible fluids augment by increasing the curvature parameter whereas the temperatures and shear stresses fall. It is also observed that the shear stress at the lower wall is more significant with a rise in curvature parameter in the case when velocity slippage is not considered as compared to velocity slippage.

Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.

The ''node anchor weaving method'' is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the ''anchor nodes'' to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Scoliosis is among the most prevalent diseases affecting teenagers. However, traditional scoliosis screening methods often resort to physical examination or radiographic imaging. The two ways both rely on experts with high costs, which are not suitable for wide-range screening. Besides, estimating Cobb angle level only using natural images are challenging. To tackle these issues, we propose a multi-grained scoliosis detection framework by jointly estimating severity level and Cobb angle level of scoliosis from a natural image instead of a radiographic image, which has not been explored before. Specifically, we regard scoliosis estimation as an ordinal regression problem, and transform it into a series of binary classification sub-problems. Besides, we adopt the visual attention network with large kernel attention as the backbone for feature learning, which can model local and global correlations with efficient computations. The feature learning and the ordinal regression is put into an end-to-end framework, in which the two tasks of scoliosis severity level estimation and scoliosis angle level estimation are jointly learned and can contribute to each other. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods as well as human performance, which provides a promising and economical solution to wide-range scoliosis screening. Particularly, our approach achieves accuracies of 94.90% and 79.62% in estimating severity level and Cobb angle level, improving large margins of 4.90% and 12.15% over existing natural image based scoliosis detection performance, respectively. The code is available at https://github.com/RuiChen-stack/MGScoliosis.

The research focuses on exploring the characteristics of the Lane Emden equation, which is a second-order ordinary differential equation that has its roots in astrophysics. To approximate this equation, a technique that employs non-dyadic Haar wavelets in conjunction with quasi-linearization is utilized. By utilizing non-dyadic wavelets, the ordinary differential equation can be simplified into a system of algebraic equations. Error estimates are provided to assess the accuracy of the produced data. A comparison between the existing solutions and the numerical results obtained using the suggested approach is performed to showcase its efficiency and advantages. The non-dyadic Haar wavelet approach is found to be a rich structure for numerous solutions that span a wide range of physical parameter.

The electrochemical reduction method is an effective approach for the harmless treatment of toxic components in aluminum alloy dross (AAD) and the high-value utilization of valuable elements. By combining theoretical calculations and experiments, this paper reveals the migration behavior and transformation laws of toxic and valuable elements in AAD during hydrolysis, calcination, and electrolysis. The removal rates of N and Cl in hydrolysis were 88.01 % and 93.84 %, respectively. During calcination, Al(OH)3 was transformed into γ-Al2O3, and part of the aluminum alloy phase and Si monomers were oxidized into oxides. During electrolysis, all positive elements (such as Fe, Si, Cu, and Zn, which are more electronegative than aluminum) were incorporated into the aluminum alloy. A very small portion of Mg and Ca entered the aluminum alloy, but most of the Mg and Ca were respectively transformed into MgF2 and CaF2, gradually accumulating in the electrolyte. The toxic components N and Cl were converted into useful by-products throughout the entire process. Moreover, other valuable components entered the aluminum alloy, no other pollutants were generated, and the overall recovery rate was more than 95 %. The results of this study provide theoretical support for developing similar electrochemical disposal techniques for solid waste.

Wireless Sensor Networks (WSNs) play a vital role in modern intelligent systems, with localization techniques critical for determining the precise positions of sensor nodes. Time Difference of Arrival (TDOA) is a widely utilized method for node localization. However, its accuracy suffers in noisy environments. This study introduces an innovative hybrid methodology that integrates Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) with Gradient Descent Distance (GDD) optimization to enhance the accuracy of TDOA-based localization. The hybrid approach utilizes the robust search capabilities of CMA-ES for initial location estimation, thereafter employing GDD to iteratively decrease the mean squared error (MSE) between estimated and real node locations. Simulations performed on several network settings showed substantial improvements. The TDOA-CMAES with GDD strategy demonstrated a maximum enhancement of 24.8 % in localization accuracy, a 36 % decrease in mean localization error (MLE), and an 18 % reduction in computation time relative to traditional approaches such as JAYA and Particle Swarm Optimization (PSO) and other current techniques. The hybrid method produced an Average Localization Error (ALE) much lower than existing techniques, illustrating its resilience in noisy conditions. Our hybrid TDOA-CMAES-GDD localization system is good for real-time WSN applications that need to quickly and accurately locate nodes because it reduces the time it takes to calculate and improves its accuracy. The method thus offers a strong, effective approach for improving TDOA-based localization in noisy settings.

In this research, a new fractional low-pass electrical transmission lines model (LPETLM) described by the local fractional derivative (LFD) is derived for the first time. By defining the Mittag-Leffler function (MLF) on the Cantor set (CS), two special functions, namely, theLTδ-function and LCδ-function, are extracted to develop an auxiliary function, which is employed to look for the non-differentiable (ND) exact solutions (ESs) together with Yang’s non-differentiable transformation. Eight sets of the ESs are obtained and the corresponding dynamic performances on the CS for γ=ln2/ln3 are displayed. As expected, for γ→1, the ESs of the local fractional LPETLM become the ESs of the classic LPETLM and the outlines are also depicted graphically. The outcomes confirm that our new method is a promising tool to handle the local fractional PDEs in the electrical and electronic engineering.

In this research endeavor, the authors introduce a novel lifetime probability model. This distribution is meticulously examined and characterized, offering insights into its behavior and applicability in various contexts. The proposed new density function of this distribution has various heavy tail forms that are useful in the field of reliability, insurance and statistical modeling. This allows for the representation and modeling of a wide range of data sets that are diverse in their form and nature. The new distribution is characterized by having different patterns of risk or failure rates. The researchers extend the new distribution to the bivariate domain through different methods, including the Morgenstern-Farley-Gumbel distribution, the modified Morgenstern-Farley-Gumbel distribution, the famous Clayton mathematical versions, and the Rennie versions. These extensions enhance the usefulness of the proposed distribution in modeling multivariate age and reliability data and dependencies between variables. The study presents some statistical modeling experiments on reliability data and some important comparisons are presented within the framework of some statistical comparison criteria.

The rapid growth of Internet of Things (IoT) applications generates vast volumes of high-speed data streams from numerous devices. Cloud computing solutions often handle and manage this data; however, for certain applications, the latency introduced by transmitting data from edge devices to the cloud may be unacceptable. This issue is exacerbated by the bandwidth constraints of public networks, which become significant barriers in large-scale IoT implementations. Consequently, effective resource management, service management, data storage, and power management require more robust infrastructure and complex protocols. An ''intelligent gateway'' based on fog computing can enhance the efficient utilization of cloud and network resources. Resource planning and management in a fog-cloud environment significantly impact system performance, particularly latency. This problem is known to be NP-hard. This paper addresses the challenge of lifetime optimization for scheduling data-intensive tasks in fog and cloud-based IoT systems. Initially, we propose an Integer Linear Programming (ILP) optimization model to frame the problem. Subsequently, we introduce a heuristic method named Data-Locality Aware Job Scheduling in Fog-Cloud (DLSFC), which is derived from the proposed formulation. The effectiveness of DLSFC is evaluated across various system characteristics, with results demonstrating that DLSFC achieves solutions within 85 % of the ideal outcome provided by the LP solver.

The city of Xi’an, Shaanxi, China, is the transportation hub in western China, which has planned to establish an underground utility tunnel system. However, this city has faced a series of fissure disasters with a wide area of 250 km2, which is hazardous to the safety of the operation and maintenance of tunnels. The results of finite element simulation suggest that prefabricated utility tunnels can better adapt to the activity of the ground fissures. Based on the results, this paper quantifies the maximum ground fissure settlement that the tunnel can bear and compares and analyzes several different working conditions such as crossing angles, and prestressing-pretensioning values. The results show that the main form of damage in the utility tunnel under the activity of surface cracks is the failure of the rubber waterproofing of the joints. To minimize this risk, this paper proposes a method of increasing the prestress to improve the rubber waterproofing performance. This conclusion can effectively increase the operation safety of the utility tunnel.

Recent progress in additive manufacturing (AM) has transformed how complex bioinspired engineering structures are designed and produced. Nonetheless, these structures present substantial difficulties in mathematical modeling and computational analysis. The main goal of this paper is to develop an effective computational method to predict the dynamic response of graphene platelets (GPLs)-reinforced functionally graded triply periodic minimal surface (FG-TPMS) plates (referred to as GPLR-FG-TPMS plates) supported by PF. To achieve this, we employ TrSDT within the IGA framework for efficiency and accuracy. The study establishes advanced plate models by combining three sheet-based TPMS configurations with three porosity distributions and three GPL distribution variations through the plate thickness. Then, we consider the effects of geometrical parameters, material properties, and BCs on free vibration and the dynamic response of GPLR-FG-TPMS plates supported by PF. The findings greatly improve our understanding of the complex dynamic responses of GPLR-FG-TPMS plates, paving the way for applications in various fields in the future.

In this research paper, we study a semi analytical technique to solve the nonlinear partial differential equations. This technique is good combination of homotopy analysis method with Kharrat-Toma transform. Also, we present the numerical solution of nonlinear fractional coupled Whitham-Broer-Kaup equation using studied technique. The Whitham-Broer-Kaup model is broadly considered to study the tsunami wave dynamics under gravity. The regularized version of Hilfer-Prabhakar fractional derivative is used to model the problem. Some qualitative properties, existence and uniqueness of the considered model and its solution are also discussed.The obtained solutions are presented graphically to show the efficiency of studied technique. Error analysis tables are also given to demonstrate the accuracy of obtained results.

Image denoising is a quintessential challenge in computer vision, intending to produce high-quality, clean images from degraded, noisy counterparts. Infrared imaging holds a pivotal position across many research domains, attributed to its inherent benefits such as concealment and noninvasiveness. Despite these advantages, infrared images are often plagued by hardware-related imperfections resulting in poor contrast, diminished quality, and noise contamination. Extracting and characterizing features amidst these unique feature patterns in infrared imagery are taxing tasks. To surmount these obstacles, we introduce the Infrared image Denoising Transformer (IDTransformer), encapsulated in a symmetrical encoder–decoder architecture. Central to our approach is the Convolutional Transposed Self-Attention Block (CTSAB), which is ingeniously conceived to capture long-range dependencies via channel-wise self-attention, while simultaneously encapsulating local context through depth-wise convolution. In addition, we refine the conventional feed-forward network by integrating Convolutional Gated Linear Units (CGLU) and deploy the Channel Coordinate Attention Block (CCAB) during the feature fusion phase to dynamically apportion weights across the feature map, thereby facilitating a more nuanced representation of pattern features endemic to infrared images. Through rigorous experimentation, we establish that our IDTransformer attains superior visual enhancement across five infrared image datasets, compared with the state-of-the-art methods. The source codes are available at https://github.com/szw811/IDTransformer.

The basic relationships and intrinsic laws in the data envelopment analysis (DEA) are the basis for designing fast solutions for big data DEA models. By delving into these relationships, it becomes feasible to address super-scale DEA problems more swiftly, aligning with the need for timeliness in real-world efficiency evaluations and reducing time costs. This paper leverages the intrinsic characteristics of DEA to formulate a DEA model scale reduction approach, offering reliable theoretical support for solving super- scale DEA problems. Initially, the partially ordered set theory to prove the relationship between the efficient decision making units (DMUs) of DEA and the maximal element of the partially ordered set. Building on this, a DEA model based on maximal element set (DEA-PS) is constructed based on the theory of partially ordered set theory, and the equivalence between DEA-PS model and the original DEA model is proved. Furthermore, it is proved that the dominant theorem exists in the solution of multiple classical DEA models, and a method of super-scale DEA models based on dominant theorem is given. The conclusions of this paper theoretically reveal the relationship between efficient DMUs and maximal elements, from this relationship, two key insights emerge: (1) Regardless of how huge the number of DMU is, the proposed method in this paper can realize the scale reduction of the DEA model quickly, (2) The algorithm has good compatibility with all current big data DEA model algorithms.

IoT is the technology that aids the interconnection of all kinds of devices over the internet to replace data, monitor devices and enhance actuators to create outcomes. Cyber-physical systems (CPS) contain control and computation components, which are compactly assured with physical procedures. The internet plays a prominent role in modern lives, and the cybersecurity challenge caused by phishing attacks is significant. This research presents a novel approach to address this problem using machine learning (ML) methods for phishing website classification. Leveraging feature extraction and innovative algorithms, the projected method aims to distinguish between malicious and legitimate websites by features of phishing attempts and analyzing inherent patterns. Phishing is a significant threat that causes extensive financial losses for internet users yearly. This fraudulent act includes identity hackers utilizing clever approaches to deceive individuals into revealing sensitive data. Generally, phishers use strategies such as advanced phishing software and fake emails to illegally acquire confidential details like usernames and passwords from financial accounts. This article develops an Explainable Artificial Intelligence with Aquila Optimization Algorithm in Web Phishing Classification (XAIAOA-WPC) approach on secure Cyber-Physical Systems. The developed XAIAOA-WPC approach mainly emphasizes the effectual classification and recognition of web phishing based on CPS. In the first phase, preprocessing is carried out on three levels: data cleaning, text preprocessing, and standardization. Furthermore, the Harris' Hawks optimization-based feature selection (HHO-FS) method is applied to derive feature subsets. The XAIAOA-WPC method utilizes a multi-head attention-based long short-term memory (MHA-LSTM) model for web phishing recognition. Besides, the detection outcomes of the MHA-LSTM approach are enhanced by using the Aquila optimization algorithm (AOA) model. At last, the XAIAOA-WPC method incorporates the XAI model LIME for superior perception and explainability of the black-box process for precise identification of intrusions. The simulation outcome of the XAIAOA-WPC method is examined on a benchmark database. The experimental validation of the XAIAOA-WPC method exhibited a superior accuracy value of 99.29 % over existing techniques.

In this paper, laser cladding technology and ultrasonic radiation technology are combined to eliminate defects such as cracks and porosity in the coating. Firstly, the influence of the ultrasonic amplitude parameter on the macroscopic morphology and microstructure of the coating was investigated, and the dissolution law of WC was analyzed. It was found that under ultrasonic radiation with an amplitude of 14 μm, the surface of the coating was smoother and the average grain size was 16.52 μm2, which was reduced by 66.47 % compared to that of the coating without applied ultrasonic. Secondly, the effect of the ultrasonic radiation process on the mechanical properties of the coating was analyzed by comparative experiments. The results showed that when the amplitude of ultrasound was 14 μm, the number of cracks in the coating was effectively reduced, the amount of wear was reduced by 96.49 %, and the average microhardness value was 510.92 HV0.025, which was a relative increase of 28.10 %. Finally, the longitudinal residual stress of the coating was measured and the residual stress at the bottom of the coating was reduced by 95.31 % at an amplitude of 14 μm.

When the cementing casing string system (CCSS) is subjected to a large-scale equal-scale model test, the huge aspect ratio of the system makes the wall thickness of the model casing string too thin to be manufactured. Therefore, this paper proposes a design method for model test with unequal scale of axial size and radial size of the CCSS. Firstly, the dynamic model of the CCSS is established and solved by the transfer matrix method (TMM), and the modal test is carried out and the results show that the numerical calculation error of the first five natural frequencies of the system is less than 4 %. Then, based on the dynamic model, the similarity laws of the complete similarity model and the partial similarity model of the CCSS are derived by equation analysis method, and the design process of the similarity model test is given. The advantages of the research are elaborated in details through examples. To verify the correctness of the similarity model’s similarity laws of the CCSS, similarity models with different parameters are designed, and the prediction errors of prototype response are analyzed based on numerical calculations. The results show that the maximum prediction error of the first eight natural frequencies of the prototype system is less than 3 %, and the response prediction results of the system are also accurate. Finally, considering uncertain factors of the partial similarity test, the similarity model test with the axial size of double scale is designed, and the results show that the maximum prediction error of the first three natural frequencies is 7.06 %, which meets the engineering requirements. This study can provide useful theoretical guidance for the large-scale model test design of the CCSS.

Existing CNN-based approaches face challenges in effectively and efficiently managing diverse scales of small infrared objects within intricate scenes, primarily as a result of the aggregation effect induced by pooling layers. As a consequence, crucial deep targets may be lost. To tackle this challenge, This research proposes an infrared deep dense connection network, termed ID-UNet. Specifically, this research devises a feature extraction module, named Infrared Small Target Feature Extraction (ISTFE), that is embedded within the ID-UNet architecture to enable cross-layer and continuous interaction between deep high-level and shallow low-level features. Consecutive connections within ISTFE’s fusion facilitate the preservation of semantic information for infrared small targets in deep layers, as well as the resolution information in shallow layers. Additionally, the UNet structure parameters were compressed, reducing the parameters by 81% compared to the traditional UNet configuration. Upon evaluating the proposed technique on three typical public datasets, the results demonstrate that the proposed method surpasses all other methods in segmentation metrics, including Intersection over Union (IoU), normalized IoU (nIoU), and F1 score. The proposed method achieves a double-win between high-precision segmentation and low computation requirements. The code is available from https://github.com/AngryWaves/ID-UNet.

This study investigates the corrosion resistance of Al2O3-TiO2 composite coatings reinforced with multi-walled carbon nanotubes (MWCNTs) on carbon steel pipe surfaces. Coatings were deposited using atmospheric plasma spraying (APS) and high velocity oxy-fuel (HVOF) techniques. Microstructural analysis revealed that HVOF-sprayed coatings exhibited denser and more homogeneous structures compared to APS-sprayed coatings. Electrochemical tests in 3.5 wt% NaCl solution showed that the HVOF-sprayed Al2O3-13 wt% TiO2-1.5 wt% MWCNT coating demonstrated the lowest corrosion current density of 7.6 × 10−9 A/cm2 and the most positive corrosion potential. Hot corrosion tests in a simulated boiler environment (500°C, 1000 h) revealed that this coating also exhibited minimal weight gain (0.9 mg/cm2) and thickness loss (2 μm). The corrosion rate of the optimal coating was 0.05 mm/year, significantly lower than the bare carbon steel (2.45 mm/year). XRD analysis of corroded samples showed that HVOF-sprayed coatings maintained their original phase composition without detectable substrate corrosion products. The superior corrosion resistance of the HVOF-sprayed Al2O3-13 wt% TiO2-1.5 wt% MWCNT coating is attributed to its optimized composition, dense microstructure, and the reinforcing effect of MWCNTs. These findings provide valuable insights for developing advanced coating solutions to mitigate corrosion-related challenges in the oil and gas industry and other harsh industrial environments.

This article uses a fixed point method to connect with Chua’s attractor model, incorporating the Atangana–Baleanu derivative using a two-step Lagrange polynomial. We introduce novel fixed point theorems using some special contractions followed by graphical representations of the convergence behavior of the iterative process. The uniqueness and existence of fixed points are demonstrated through theoretical proofs and numerical simulations. This approach of its first kind substantiates the existence and uniqueness of the model, enriching the understanding of its chaotic dynamics. Furthermore, numerical investigations explore distinct scenarios of Chua’s model, encompassing the classic Chua’s attractor, the double scroll attractor, chaos in Chua’s circuit, and behaviors such as cycle behavior and higher parametric values. Each case is analyzed graphically, providing insights into the system’s complex dynamics and validating theoretical predictions.

This study presents a photo sensor for caffeine (CA) detection, employing gold nanoparticles (Au NPs) immobilized on bismuth vanadate on indium tin oxide substrate (Au-BiVO4/ITO). The characterization of the Au-BiVO4/ITO electrode was investigated by surface analysis, crystallography and electrochemical methods. The incorporation of Au NPs into the BiVO4 structure has significantly increased the photocatalytic performance of the material, especially its photocurrent response. This modification has increased the photocurrent from the initial value of 200 µA to more than 300 µA. The Au-BiVO4/ITO PEC sensor demonstrated enhanced photocurrent responses to CA under visible light, compared to BiVO4/ITO. Under optimized conditions, the PEC sensor showed a linear correlation between analytical signal and CA concentration from 0.3 to 270 μM, with obtained detection limit of 0.08 μM. Using the standard addition way, it measured CA in serum and urine samples from athletes, achieving average recovery rates of 99.4 % in serum and 97.8 % in urine, which indicates high accuracy. Moreover, the results showing strong agreement with standard HPLC analysis and demonstrating high recovery efficiency. Furthermore, the sensor demonstrated excellent performance with acceptable figure of merit for CA measurement, making it suitable for reliable analytical applications in biological samples.

This study investigates the dynamics of scabies infestation using a mathematical model, analyzing its stability, sensitivity, and optimal control. We analyze the model for stability, and the results demonstrate the system’s stable behavior under specific conditions, suggesting the possibility of effective control. The model is subjected to sensitivity analysis, revealing the most influential parameters affecting the infestation’s dynamics. These parameters are identified as key targets for intervention. The optimal control strategy is derived using Pontryagin’s Maximum Principle, minimizing the cost of intervention while effectively controlling the infestation. The results demonstrate the importance of early intervention and sustained control efforts. This study provides a comprehensive understanding of scabies infestation dynamics, highlighting the critical factors influencing its spread and the most effective control strategies. The findings inform public health policy and guide resource allocation to combat scabies infestations effectively. The major contributions include improved understanding of scabies transmission dynamics, identification of critical parameters, development of evidence-based control strategies, evaluation of resource allocation effectiveness, and assessing intervention impact. It may also contribute to public health policy, resource allocation, disease surveillance, and intervention strategies. Numerical simulations are carried out to support our analytical findings.

In this paper, a two Degrees of freedom manipulator in the form of Quanser QUBE-Servo 2 system is used. It is an unstable system having non-minimum phase with right half s-plane pole and zero. The conventional controllers did not achieve the loop robustness for these types of systems. In this work, a Fractional-order proportional derivative (FOPDD) controller is proposed for the system whose mathematical model is made up of two loops where inner and outer loops are used for pendulum and rotary arm position respectively. The method of partial cancellation having a pre-compensator is used for non-minimum phase zeros of the system. Further, the parameters are selected using global stability region plots in which the stability of the system is shown using Riemann surface. The integral error criterion including Integral Absolute Error (IAE), Integral Square Error (ISE) and Integral Time Absolute Error (ITAE); and other performance metrics such as rise time, settling time, overshoot and undershoot are used to compare the robustness of the controllers under nominal environment, trajectory tracking and external disturbances. The simulation results indicate that the proposed FOPDD controller outperforms other existing controllers in terms of performance specifications which further justifies its usage in real-time applications.