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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.

Abstract The safety, stability, and long-term performance of reinforced concrete (RC) structures depend significantly on soil-structure interaction (SSI), a critical phenomenon governing the dynamic relationship between soil and structural behaviour. SSI plays a pivotal role in seismic design, influencing the stiffness, damping, and natural frequency of structures, yet its application in practical design remains underutilized due to challenges in modelling and integrating code provisions. This review synthesizes existing knowledge on SSI, emphasizing its impact on buildings, bridges, and foundations under static and dynamic loads. It highlights advancements in analytical, numerical, and experimental modelling methods, such as finite element analysis and discrete element methods, and evaluates their effectiveness in capturing the complex interactions between soil and structural systems. The review identifies key gaps, including a lack of unified guidelines in international codes, inadequate integration of SSI in real-world design processes, and limited exploration of its role in emerging engineering challenges like sustainability and climate resilience. Historical seismic events, such as the Kobe and Loma Prieta earthquakes, are analysed to underscore the detrimental consequences of neglecting SSI considerations. Additionally, the review discusses recent innovations, including the application of machine learning and advanced computational tools, and their potential to enhance the accuracy and efficiency of SSI analysis. This study offers actionable insights for improving design practices, such as adapting SSI frameworks for structures on soft soils and incorporating dynamic interactions in seismic design codes. It concludes with a call for interdisciplinary collaboration and future research into novel SSI applications, including its integration with smart sensing technologies and sustainable infrastructure design. This review bridges the gap between theoretical advancements and practical applications of soil-structure interaction (SSI) by synthesizing current knowledge, identifying critical research gaps, and proposing innovative solutions to enhance structural resilience, sustainability, and seismic safety. Graphical Abstract

Abstract More and more research is focusing on the unmanned aerial vehicle (UAV) inspection of onshore wind/solar power stations; however, how to balance the contradiction between detection accuracy and efficiency is still a challenge for domestic and international researches. In this paper, for the sparse distribution of inspection targets in onshore wind/solar power stations, the YOLOv8 model is used to quickly extract information such as photovoltaic module contours and key points of wind turbine blades in the wide-angle image of the UAV. By connecting these key points in series, fine route planning of the UAV is realized, and the zoom lens is used to carry out efficient and fine inspection of photovoltaic (PV) modules and wind turbine blades. In order to accelerate the convergence of the PV module contour extraction model, this paper introduces a loss function for the difference in linear angular orientation, which improves the average IOU accuracy of the YOLOv8 model to 93%. In the extraction of key points such as the center and tip of wind turbine blades, this paper firstly adopts serpentine convolution to replace the traditional convolution operator in order to adapt the wind turbine blade features, and secondly incorporates the a priori information of the angle constraints between the blades into the loss function. Finally, multi-angle wind turbine imaging photos in desert, hill, rice field, and other scenes are quickly simulated by computer simulation software to improve the model generalization performance. The experimental results show that the improved model achieves 85.4% on the mAP50 metric, which is a 9.2% improvement over YOLOv8, and 6.2% on the more stringent mAP50:95 metric.

Abstract Green building (GB) projects in the Middle East face several causes of waste that occur during design and construction stages. These causes affect the objectives of GB projects (economic, environmental, and social). Therefore, this research aims to define causes of waste in GB projects and evaluate the effect of these causes on the objectives of GB projects. Forty-five causes of waste are determined and classified into five main groups as follows: (G01) green materials, (G02) green building design, (G03) sustainable site, (G04) green building technologies, and (G05) green building stakeholders. Through field surveys, including semi-structured interviews and brainstorming sessions, the probability of occurrence for each cause of waste and impact on the economic, environmental, and social objectives are evaluated, as well as the waste severity is determined based on a combined effect of probability and impacts. The correlations among the waste indices are assessed, and the highest correlation is observed between probability and economic followed by economic and social objective. The results show that the most significant cause of waste that has the highest value for economic, environmental, and social objectives is “Poor assessment of site conditions before design, such as topography, hydrology, climate, vegetation, and soil.” Group 05 has the maximum number of critical causes of waste, which is considered the most significant group, due to its high values related to all objectives. Results indicate that the economic objective is classified as the most affected one by the causes of waste, followed by the environmental objective.

Abstract With rapid urbanization, the increasing density of buildings in urban areas has forced us to consider interactions between adjacent structures. However, most studies have focused on fixed foundations, neglecting soil-structure interactions (SSI). This paper addresses this gap by investigating the optimization of two adjacent structures using electromagnetic inerter dampers (EMID) under SSI. The EMID system, equipped with energy harvesting capabilities, was optimized using the Monte Carlo search method, with results demonstrating a significant reduction in dynamic response. Specifically, frequency and time domain analyses show that the EMID system reduces peak displacement and acceleration by up to 41.23% compared to uncontrolled structures. Additionally, the system efficiently converts seismic energy into electrical energy, with energy harvesting reaching peak values under intense seismic waves, such as those observed in Mexico City earthquake conditions. This innovative system not only enhances seismic performance, particularly in soft soil conditions, but also offers energy resilience by supplying emergency power, thus highlighting its potential for sustainable urban infrastructure.

Abstract Vision impairment affects nearly 2.2 billion people globally, and nearly half of these cases could be prevented with early diagnosis and intervention—underscoring the urgent need for reliable and scalable detection methods for conditions like diabetic retinopathy and age-related macular degeneration. Here we propose a distributed deep learning framework that integrates self-supervised and domain-adaptive federated learning to enhance the detection of eye diseases from optical coherence tomography images. We employed a self-supervised, mask-based pre-training strategy to develop a robust foundation encoder. This encoder was trained on seven optical coherence tomography datasets, and we compared its performance under local, centralized, and federated learning settings. Our results show that self-supervised methods—both centralized and federated—improved the area under the curve by at least 10% compared to local models. Additionally, incorporating domain adaptation into the federated learning framework further boosted performance and generalization across different populations and imaging conditions. This approach supports collaborative model development without data sharing, providing a scalable, privacy-preserving solution for effective retinal disease screening and diagnosis in diverse clinical settings.

Abstract In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.

Abstract Alkaline water electrolysers are ideal for gigawatt-scale hydrogen production due to the usage of non-precious metal and low-cost raw materials. However, their performances are modest with the separated electrode and diaphragm structure which can date back to more than 100 years ago. Here we report a catalyst-coated diaphragm assembly to improve the performance of alkaline water electrolysers. The transport resistance of OH- ions is reduced and the electrochemical surface area of catalysts is enlarged by more than forty fold, representing more than 40% increase in hydrogen production rate or as much as 16% reduction in energy consumption. The electrolyser with our catalyst-coated diaphragm assembly delivers current densities as high as 1 A cm−2 at 1.8 V or 2 A cm−2 at 2 V and shows good stability after more than 1000 hours of operation. Therefore, the catalyst-coated diaphragm assembly route is promising for the development of high-performance and efficient alkaline water electrolysers.

Abstract Surface electromyogram (sEMG) serves as a means to discern human movement intentions, achieved by applying epidermal electrodes to specific body regions. However, it is difficult to obtain high-fidelity sEMG recordings in areas with intricate curved surfaces, such as the body, because regular sEMG electrodes have stiff structures. In this study, we developed myoelectrically sensitive hydrogels via 3D printing and integrated them into a stretchable, flexible, and high-density sEMG electrodes array. This electrode array offered a series of excellent human-machine interface (HMI) features, including conformal adherence to the skin, high electron-to-ion conductivity (and thus lower contact impedance), and sustained stability over extended periods. These attributes render our electrodes more conducive than commercial electrodes for long-term wearing and high-fidelity sEMG recording at complicated skin interfaces. Systematic in vivo studies were used to investigate its efficacy to control a prosthetic hand by decoding sEMG signals from the human hand via a multiple-channel readout circuit and a sophisticated artificial intelligence algorithm. Our findings demonstrate that the 3D printed gel myoelectric sensing system enables real-time and highly precise control of a prosthetic hand.

Abstract An intelligent humidity sensing system has been developed for real-time monitoring of human behaviors through respiration detection. The key component of this system is a humidity sensor that integrates a thermistor and a micro-heater. This sensor employs porous nanoforests as its sensing material, achieving a sensitivity of 0.56 pF/%RH within a range of 60–90% RH, along with excellent long-term stability and superior gas selectivity. The micro-heater in the device provides a high operating temperature, enhancing sensitivity by 5.8 times. This significant improvement enables the capture of weak humidity variations in exhaled gases, while the thermistor continuously monitors the sensor’s temperature during use and provides crucial temperature information related to respiration. With the assistance of a machine learning algorithm, a behavior recognition system based on the humidity sensor has been constructed, enabling behavior states to be classified and identified with an accuracy of up to 96.2%. This simple yet intelligent method holds great potential for widespread applications in medical assistance analysis and daily health monitoring.

Abstract Bulk Acoustic Wave (BAW) filters find applications in radio frequency (RF) communication systems for Wi-Fi, 3G, 4G, and 5G networks. In the beyond-5G (potential 6G) era, high-frequency bands (>8 GHz) are expected to require resonators with high-quality factor (Q) and electromechanical coupling ( $${k}_{t}^{2}$$ k t 2 ) to form filters with low insertion loss and high selectivity. However, both the Q and $${k}_{t}^{2}$$ k t 2 of resonator devices formed in traditional uniform polarization piezoelectric films of aluminum nitride (AlN) and aluminum scandium nitride (AlScN) decrease when scaled beyond 8 GHz. In this work, we utilized 4-layer AlScN periodically poled piezoelectric films (P3F) to construct high-frequency (~17–18 GHz) resonators and filters. The resonator performance is studied over a range of device geometries, with the best resonator achieving a $${k}_{t}^{2}$$ k t 2 of 11.8% and a $${Q}_{{\rm {p}}}$$ Q p of 236.6 at the parallel resonance frequency ( $${f}_{{\rm {p}}}$$ f p ) of 17.9 GHz. These resulting figures-of-merit are ( $${{{\rm {FoM}}}}_{1}={{k}_{t}^{2}Q}_{{\rm {p}}}$$ FoM 1 = k t 2 Q p and $${{{\rm {FoM}}}}_{2}={f}_{{\rm {p}}}{{{\rm {FoM}}}}_{1}{\times }{10}^{-9}$$ FoM 2 = f p FoM 1 × 10 − 9 ) 27.9 and 500, respectively. These and the $${k}_{t}^{2}$$ k t 2 are significantly higher than previously reported AlN/AlScN-based resonators operating at similar frequencies. Fabricated 3-element and 6-element filters formed from these resonators demonstrated low insertion losses (IL) of 1.86 and 3.25 dB, and −3 dB bandwidths (BW) of 680 MHz (fractional BW of 3.9%) and 590 MHz (fractional BW of 3.3%) at a ~17.4 GHz center frequency. The 3-element and 6-element filters achieved excellent linearity with in-band input third-order intercept point (IIP3) values of +36 and +40 dBm, respectively, which are significantly higher than previously reported acoustic filters operating at similar frequencies.

Abstract Nanoelectromechanical systems (NEMS) based on atomically-thin tungsten diselenide (WSe2), benefiting from the excellent material properties and the mechanical degree of freedom, offer an ideal platform for studying and exploiting dynamic strain engineering and cross-scale vibration coupling in two-dimensional (2D) crystals. However, such opportunity has remained largely unexplored for WSe2 NEMS, impeding exploration of exquisite physical processes and realization of novel device functions. Here, we demonstrate dynamic coupling between atomic lattice vibration and nanomechanical resonances in few-layer WSe2 NEMS. Using a custom-built setup capable of simultaneously detecting Raman and motional signals, we accomplish cross-scale mode coupling between the THz crystal phonon and MHz structural vibration, achieving GHz frequency tuning in the atomic lattice modes with a dynamic gauge factor of 61.9, the best among all 2D crystals reported to date. Our findings show that such 2D NEMS offer great promises for exploring cross-scale physics in atomically-thin semiconductors.

Abstract Precise and long-term electroanalysis at the single-cell level is crucial for the accurate diagnosis and monitoring of brain diseases. The reliable protection in areas outside the signal acquisition points at sharp ultramicroelectrode (UME) tips has a significant impact on the sensitivity, fidelity, and stability of intracellular neural signal recording. However, it is difficult for existing UMEs to achieve controllable exposure of the tip functional structure, which affects their ability to resist environmental interference and shield noise, resulting in unsatisfactory signal-to-noise ratio and signal fidelity of intracellular recordings. To address this issue, we chose a dense and electrochemically stable diamond-like carbon (DLC) film as the UME protection coating and developed a method to precisely control the exposed degree of the functional structure by directly fixed-point processing of the UME tip by the strong site-selectivity and good controllability of the atmospheric microplasma jet. By analyzing the interaction between the microplasma jet and the UME tip, as well as the changes in the removal length and microstructure of UME tips with processing time, the exposed tip length was precisely controlled down to the submicron scale. Biocompatibility experiments, electrochemical aging tests and real-time intracellular pH recording experiments have demonstrated that the DLC-UME with effective tip protection processed by microplasma jet has the potential to enable long-term detection of intracellular high-fidelity signals.

Abstract Nonlinear dynamic simulations of mechanical resonators have been facilitated by the advent of computational techniques that generate nonlinear reduced order models (ROMs) using the finite element (FE) method. However, designing devices with specific nonlinear characteristics remains inefficient since it requires manual adjustment of the design parameters and can result in suboptimal designs. Here, we integrate an FE-based nonlinear ROM technique with a derivative-free optimization algorithm to enable the design of nonlinear mechanical resonators. The resulting methodology is used to optimize the support design of high-stress nanomechanical Si3N4 string resonators, in the presence of conflicting objectives such as simultaneous enhancement of Q-factor and nonlinear Duffing constant. To that end, we generate Pareto frontiers that highlight the trade-offs between optimization objectives and validate the results both numerically and experimentally. To further demonstrate the capability of multi-objective optimization for practical design challenges, we simultaneously optimize the design of nanoresonators for three key figure-of-merits in resonant sensing: power consumption, sensitivity and response time. The presented methodology can facilitate and accelerate designing (nano) mechanical resonators with optimized performance for a wide variety of applications.

Abstract Flexible electromagnetic metamaterials are a potential candidate for the ideal material for electromagnetic control due to their unique physical properties and structure. Flexible electromagnetic metamaterials can be designed to exhibit specific responses to electromagnetic waves within a particular frequency range. Research shows that flexible electromagnetic metamaterials exhibit significant electromagnetic control characteristics in microwave, terahertz, infrared and other frequency bands. It has a wide range of applications in the fields of electromagnetic wave absorption and stealth, antennas and microwave devices, communication information and other fields. In this review, the currently popular fabrication methods of flexible electromagnetic metamaterials are first summarized, highlighting the electromagnetic modulation capability in different frequency bands. Then, the applications of flexible electromagnetic metamaterials in four aspects, namely electromagnetic stealth, temperature modulation, electromagnetic shielding, and wearable sensors, are elaborated and summarized in detail. In addition, this review also discusses the shortcomings and limitations of flexible electromagnetic metamaterials for electromagnetic control. Finally, the conclusion and perspective of the electromagnetic properties of flexible electromagnetic metamaterials are presented.

Abstract We present a system-level model with an on-chip temperature compensation technique for a CMOS-MEMS monolithic calorimetric flow sensing SoC. The model encompasses mechanical, thermal, and electrical domains to facilitate the co-design of a MEMS sensor and CMOS interface circuits on the EDA platform. The compensation strategy is implemented on-chip with a variable temperature difference heating circuit. Results show that the linear programming for the low-temperature drift in the SoC output is characterized by a compensation resistor R c with a resistance value of 748.21 Ω and a temperature coefficient of resistance of 3.037 × 10−3 °C−1 at 25 °C. Experimental validation demonstrates that within an ambient temperature range of 0–50 °C and a flow range of 0–10 m/s, the temperature drift of the sensor is reduced to ±1.6%, as compared to ±8.9% observed in a counterpart with the constant temperature difference circuit. Therefore, this on-chip temperature-compensated CMOS-MEMS flow sensing SoC is promising for low-cost sensing applications such as respiratory monitoring and smart energy-efficient buildings.

Abstract In this article, a CMOS-compatible Pirani vacuum gauge was proposed featuring enhanced sensitivity, lower detection limit, and high-temperature stability, achieved through the implementation of a surface micromachining method coupled with a temperature compensation strategy. To improve performance, a T-type device with a 1 µm gap was fabricated resulting in an average sensitivity of 1.10 V/lgPa, which was 2.89 times larger than that (0.38 V/lgPa) of a L-type device with a 100 µm gap. Additionally, FEA simulations were conducted, analyzing the influence of heater temperature on sensitivity and the attenuation of sensitivity across varying ambient temperatures. A semi-empirical theoretical mode was derived for performance prediction, demonstrating strong alignment with experimental results, underscoring its effectiveness in compensating for sensitivity attenuation. Building on the foundation, the device’s performance under different ambient temperatures was characterized and effectively compensated in two distinct operational modes: constant temperature mode and constant temperature difference mode (the whole range temperature compensation error can be controlled within 2.5%). Finally, the short-time stability (variation level is approximately 1 mV), noise floor (Vrms=384 μV) and detection limit (0.07 Pa @1 Hz) of the device were characterized, confirming its suitability for practical implementation.

Accurate dense reconstruction of unknown spatial environments is crucial for applications such as underground exploration and planetary missions. Existing methods face challenges like observing blind spots and the difficulty of edge feature extraction in point clouds with non-repetitive scanning LiDARs. This paper first uses a novel odometry and mapping system integrating two solid-state LiDARs and an IMU to obtain distortion-compensated point clouds and corresponding poses, which are utilized to generate submaps. Our approach then leverages these accumulated submaps to efficiently extract edge features. Experimental results demonstrate that our submap-based method effectively identifies edge features within point clouds, which can be used for association with panoramas for joint optimization in the future.

In engineering practice, a common problem is the lack of space on the construction site for the proper storage of building materials. The consequence of this is that building materials are stored in open spaces, where they are exposed to external atmospheric factors. This study analyses the effect of storing mineral wool for 18 months under the external conditions of moderate climate occurring in Poland on its thermal conductivity. Thirty-five samples were non-destructively tested in terms of maintaining the thermal conductivity coefficient declared by the manufacturer. The tests were performed for material with a thickness of 50 and 80 mm, and the effect of wrapping pallets in foils in maintaining thermal conductivity was also examined. The thermal conductivity coefficient was measured in standard non-destructive tests using the FOX314 plate apparatus. Despite the impact of the external environment on the mineral wool for a period of 18 months, the tests confirmed that there was no significant effect of atmospheric conditions on the thermal conductivity coefficient. The measured parameter was within the limit values specified by the manufacturer and included in the technical data sheets of the products.

Unconfined compressive strength (UCS) is one of the rocks’ most valuable mechanical properties in constructing an accurate geo-mechanical model. It has traditionally been determined through laboratory core sample testing or by analysis of well-log data. After a great deal of effort and growing investment in time, the proper adoption of machine learning methods, especially the radial basis function (RBF), opens a route to promising alternatives against empirical methods for better real-time prediction of UCS. The current study considers the RBF-based machine learning model, whose parameters have been optimized using two enhanced metaheuristic frameworks: Improved Arithmetic Optimization Algorithm (IAOA) and Flying Foxes Optimization (FFO). Based on an extensive dataset already used in previous studies and applying some soft computing techniques, vigorous performance metrics such as RMSE, R², MAE, U95, and MNB were used to test the developed frameworks. The outcomes indicate a significant outperformance of the hybrid RBFF technique over the solo RBF and RBF-IA frameworks. Specifically, the RBFF model resulted in an R² of 0.998, an RMSE of 1.313, and an MNB of -0.003, reflecting its better performance in UCS prediction. This study indicates the efficiency of integrating RBF with meta-heuristic optimization to enhance UCS predictions in geotechnical studies.

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.