Search Results (219)

High frame rate imaging of synthetic aperture radar (SAR), also known as video SAR (ViSAR), has attracted extensive research in recent years. When ViSAR system parameters are fixed, there is a technical trade-off between high frame rates and high resolution. In traditional ViSAR, the frame rate is usually increased by increasing the carrier frequency to increase the azimuth modulation frequency and reducing the synthetic aperture time. This paper attempts to propose a strip non-overlapping mode ViSAR based on Orbital Angular Momentum (OAM) beams, which uses the topological charge of vortex electromagnetic wave (VEW) to improve the azimuth modulation frequency, to improve the frame rate. By introducing the concept of VEW frame splitting, a corresponding time-varying topological charge mode is designed for ViSAR imaging. This design successfully introduces an additional azimuth modulation frequency while maintaining the original imaging resolution, thus significantly improving the frame rate performance of the ViSAR system. However, the Bessel function term in VEW causes amplitude modulation in the echo signal, while the additional frequency modulation causes the traditional matching filter to fail. To address these problems, an improved Range-Doppler algorithm (RDA) is proposed in this paper. By employing the range cell center approximation method, the negative effect of the Bessel function on imaging is reduced effectively. Furthermore, for the introduction of tuning frequency, the azimuth matched filter is specially improved, which effectively prevents the defocusing issues caused by the mismatch of tuning frequency. Finally, the computer simulation results prove that the ViSAR system and imaging algorithm based on VEW can effectively improve the frame rate of ViSAR and maintain the imaging resolution, which provides a research direction for the development of ViSAR technology. Full article

Additive manufacturing (AM) with clay and ceramic-based materials is gaining momentum as a sustainable alternative in construction, yet its advancement depends on bridging experimental practice with predictive modeling. This review synthesizes advances in mathematical formulations and numerical tools applied to clay, geopolymers, alumina, and related extrusion-based pastes. Classical rheological models, including the Bingham and Herschel–Bulkley formulations, remain central for characterizing yield stress, structuration, and flow stability. Meanwhile, finite element (FEM) and computational fluid dynamics (CFD) approaches are increasingly supporting predictions of deformation, shrinkage, drying, and sintering. Despite these advances, their application to natural clay systems remains limited due to heterogeneity, moisture sensitivity, and the lack of standardized constitutive parameters. Recent studies emphasize that validation is essential: rheometry, layer stability tests, in situ monitoring, and prototyping provide necessary calibration for reliable simulation. In parallel, parametric and generative design workflows, particularly through Rhino and Grasshopper ecosystems, illustrate how digital methods can link geometric logic, fabrication constraints, and performance criteria. Overall, the literature demonstrates a transition from isolated modeling efforts toward integrated, iterative frameworks where rheology, numerical simulation, and experimental validation converge to improve predictability, reduce trial-and-error, and advance scalable and sustainable clay- and ceramic-based AM. Full article

Orbital angular momentum (OAM) beams exhibit divergence during transmission, which constrains the capacity of communication system channels. To address these challenges, intelligent reflecting surfaces (IRSs), which can independently manipulate incident electromagnetic waves by adjustment of their amplitude and phase, are employed to construct IRS-assisted OAM communication systems. By introducing additional information pathways, IRSs enhance diversity gain. We studied the simulations of two placement methods for an IRS: arbitrary placement and standard placement. In the case of arbitrary placement, the beam reflected by the IRS can be decomposed into different OAM modes, producing various reception powers corresponding to each OAM mode component. This improves the signal-to-noise ratio (SNR) at the receiver, thereby enhancing channel capacity. In particular, when the IRS is symmetrically and uniformly positioned at the center of the main transmission axis, its elements can be approximated as a uniform circular array (UCA). This configuration not only achieves optimal reception along the direction of the maximum gain of the orbital angular momentum beam but also reduces the antenna radius required at the receiver to half or even less. Full article

The present work investigated numerically turbulent airflows over a hybrid Darrieus/Savonius vertical axis wind turbine. Firstly, the isolated turbines were validated in comparison to previous studies from the literature. Later, new recommendations were obtained for the simulation of a hybrid turbine subject to turbulent airflow. The numerical simulations consisted of the solution of time-averaged equations of mass and momentum in x and y directions using the finite volume method, available in the commercial code Ansys Fluent (version 2022 R1). For closure of turbulence, the k − ω SST (Shear Stress Transport) model was employed. For lower magnitudes of tip speed ratio (TSR), the hybrid turbine improved the power coefficient (C_P) compared to the Darrieus turbine (e.g., by 70% at TSR = 0.75), thereby demonstrating the self-starting capability of the hybrid configuration. Unexpectedly, at the optimal TSR = 1.5, the hybrid turbine performed about 6.5% better than the Darrieus turbine, indicating that the balance between the additional power generated by the Savonius rotor and losses caused by flow disturbances in the hybrid configuration was positive. As a novelty, results highlighted the role of each rotor (Darrieus and Savonius) for the performance of the hybrid turbine by comparing it with isolated Darrieus and Savonius turbines under the same conditions. Full article

As robotic applications rapidly expand into increasingly complex and dynamic environments, greater emphasis is being placed on the intelligence and safety of human–robot collaboration at the task execution level. In shared human–robot workspaces, even the most precise motion planning cannot fully prevent collisions. To address this critical safety concern, we propose a variational Bayesian Kalman filtering-based external torque estimation algorithm that integrates the robot’s dynamic model while avoiding additional system complexity. We begin by reviewing the robot dynamics framework and the classical external torque estimation method based on generalized momentum. We then derive a Kalman filter-based approach for external torque estimation in robotic manipulators and analyze the adverse effects arising from mismatches in process noise covariance. Finally, we introduce a sliding window-based variational Bayesian Kalman filter, which dynamically estimates the current process noise covariance while simultaneously mitigating the accumulation of recursive errors. Full article

Stock price prediction remains a challenging problem due to the inherent volatility and complexity of financial markets. This study proposes a multi-model machine learning framework for one-day-ahead stock price prediction using thirty-six features derived from technical indicators. Empirical analysis is conducted on data from Apple, Tesla, and NVIDIA, employing nine classification algorithms, including support vector machines, random forests, extreme gradient boosting, and logistic regression. Results indicate that momentum-based indicators are the most influential predictors. While support vector machines achieve the highest accuracy for Apple, extreme gradient boosting performed best for NVIDIA and Tesla. In addition, explainable AI techniques are applied to interpret individual model predictions, thereby enhancing transparency and trust in the results. The study contributes to financial analytics research by providing a comparative evaluation of diverse machine learning methods and highlighting key indicators critical for short-term stock price forecasting. Full article

The increasing demand for effective flow control in supersonic boundary layers, particularly for mitigating shock-wave boundary-layer interactions, underscores the need to explore optimized micro-vortex generator (MVG) configurations. This study investigates the aerodynamic performance of two different MVG configurations: a two-MVG setup with a pair of close parallel-positioned MVGs and a three-MVG arrangement that includes an additional upstream unit. Both are examined within a Mach 2.5 flow regime, aiming to improve mixing and energize the boundary layer. Large Eddy Simulations (LES) were performed using high-order numerical schemes. A newly developed vortex identification method was utilized to characterize vortex structures, while turbulent kinetic energy (TKE) metrics were integrated to quantify turbulence. Findings reveal that the two-MVG configuration produces regular, symmetric vortex pairs with limited interaction. This results in a steady increase in TKE and a thickened momentum boundary layer—indicative of notable energy loss. In contrast, the three-MVG setup generates more intricate and interactive vortex formations that significantly elevate TKE levels, rapidly expand the turbulent region, and reduce energy loss downstream. The peak TKE occurs before tapering slightly. Instantaneous flow analysis further highlights chaotic, hairpin-dominated vortex structures in the three-MVG case, compared to the more orderly ones observed in the two-MVG case. Overall, the three-MVG configuration demonstrates superior mixing and boundary-layer energization potential, albeit with greater structural complexity. Full article

Radio frequency identification (RFID) is popular and attaining momentum in manifold sectors, including, but not limited to, pharmaceuticals, retail, defense, transport, healthcare and currently security. Utilizing RFID solely as a solution does not result in effective security. Conventional systems have integrated only one solution, such as GSM, cryptography, wireless sensors, biometrics or a One-Time Password (OTP); however, the security provided is limited since each incorporated technology has its disadvantages. Our paper proposes improving the conventional methods in the field by proposing an intelligent door-lock system prototype implementing two-step authentication, providing double-layered security provisions in, for instance, highly sensitive zones. The suggested technique, firstly based on RFID technology and then a password (PIN) during the authentication process, results in a hybrid system that is more accurate and efficient compared to a traditional, single-method system. The Arduino micro-controller is interfaced with RFID, with a keypad that receives the input to the micro-controller, a Liquid Crystal Display to output the authentication status and finally a motor connected to the door for automation within a limited time-frame. Adding biometric verification, such as fingerprints and face recognition, can enhance the proposed design further by providing an additional layer of security from external intruders. Full article

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

Control of buoyancy-assisted convective flow and the associated thermal behavior of nanofluids in finite-sized conduits has become a great challenge for the design of many types of thermal equipment, particularly for heat exchangers. This investigation discusses the numerical simulation of the buoyancy-driven convection (BDC) of a nanofluid (NF) in a differently heated cylindrical annular domain with an interior cylinder attached with a thin baffle. The annular region is filled with non-Darcy porous material saturated-nanofluid and both NF and the porous structure are in local thermal equilibrium (LTE). Higher thermal conditions are imposed along the interior cylinder as well as the baffle, while the exterior cylinder is maintained with lower or cold thermal conditions. The Darcy–Brinkman–Forchheimer model, which accounts for inertial, viscous, and non-linear drag forces was adopted to model the momentum equations. An implicit finite difference methodology by considering time-splitting methods for transient equations and relaxation-based techniques is chosen for the steady-state model equations. The impacts of various pertinent parameters, such as the Rayleigh and Darcy numbers, baffle dimensions, like length and position, on flow, thermal distributions, as well as thermal dissipation rates are systematically estimated through accurate numerical predictions. It was found that the baffle dimensions are very crucial parameters to effectively control the flow and associated thermal dissipation rates in the domain. In addition, machine learning techniques were adopted for the chosen analysis and an appropriate model developed to predict the outcome accurately among the different models considered. Full article

The propagation of high-frequency ultrasound waves will generate both physical and chemical effects as they propagate through a liquid medium, such as acoustic streaming, an acoustic fountain, and atomization. These phenomena are believed to be the main factors that contribute to the enhancement of mass transfer in the gas–liquid carbon dioxide (CO₂) absorption system. Computational Fluid Dynamic (CFD) simulation is one of the powerful tools that can be used to model the complex hydrodynamic behavior induced by the propagation of ultrasound waves in the liquid medium. In this study, the ultrasonic irradiation forces were simulated via the momentum source term method using commercial CFD software (ANSYS Fluent V19.1). In addition, a parametric study was conducted to investigate the influences of absorber height and ultrasonic power on the hydrodynamic mixing performance. The simulation results indicated that enhanced mixing and a higher intensification factor were achieved with increased fountain flow velocity, particularly at the lowest absorber height and highest ultrasonic power. Conversely, the energy efficiency was improved with the increase of absorber height and decrease of ultrasonic power. To determine the optimal combination of absorber height and ultrasonic power, this trade-off between the energy efficiency and intensification in the ultrasonic-assisted absorption system (UAAS) is a crucial consideration during process scale-up. Full article

In tennis matches, “momentum” describes the situation where players are inspired by positive factors during the game. This paper focuses on the quantification of momentum, the impact of momentum on match trends, and the optimisation of players’ match strategies based on momentum. In this research, the Markov chain is used to quantify the momentum, and the “momentum score” of players is obtained. The Eta and Spearman correlation coefficients are used to study the impact mechanism of momentum on match trends. The results show that, within a 95% confidence interval, momentum has a strong correlation with the results of the current game of players, but a weak correlation with the result of each current point. In addition, in this paper, we construct a match strategy optimisation model for players based on momentum scores. On the one hand, the entropy weight method and TOPSIS are combined to evaluate players’ performances. On the other hand, a BP neural network prediction model is established based on a multiple linear regression model with 13 indicators in 10 categories to predict match trends. According to the evaluation and prediction results, a series of strategy optimisation suggestions are put forward for players to cope with matches. Full article

Given the suboptimal responses to medication and cognitive behavioral therapies in the treatment of post-traumatic stress disorder (PTSD), new approaches are needed. Background/Objectives: Therapies that include a somatic component such as Emotional Freedom Techniques (EFT) and Eye Movement Desensitization and Reprocessing (EMDR) have demonstrated efficacy in the treatment of PTSD in numerous clinical trials. This case series introduces the Trauma Reintegration Process (TRP), a psychotherapeutic process developed by the author that can be combined with somatic therapies to enhance their effectiveness, especially in patients with dissociation. Methods: This case series describes the use of TRP in combination with EFT, an energy-based somatic treatment that engages the meridian system of the body through gentle tapping on acupressure points. TRP uses EFT in combination with a focused guided imagery sequence. This case series describes the treatment of two patients: a 20-year-old woman who experienced PTSD and somatic symptoms following a serious motor vehicle accident (MVA) and a 45-year-old woman with a history of severe abuse as a child as well as adult trauma who had also been in a serious MVA. The cases contrast the way TRP can be applied in patients with single versus multiple traumas and who experience dissociation. Results: In both cases, EFT treatment stalled when the patient dissociated. After TRP was introduced, however, the EFT treatment regained momentum, leading to significant improvement in PTSD symptoms including a reduction of nightmares and flashbacks and resolution of other somatic symptoms. Conclusions: The trauma reintegration process (TRP) in combination with EFT has the potential to assist in the memory processing of patients with dissociation and complicated trauma presentation without retraumatizing the client and causing further distress or dissociation. In addition, it provides the patient with a self-empowering method to alleviate any additional traumatic sequelae. Full article

Sports analytics is a fast-evolving domain using advanced data science methods to find useful insights. This study explores the way NBA player performance metrics evolve from quarter to quarter and affect game outcomes. Using Association Rule Mining, we identify key offensive, defensive, and overall impact metrics that influence success in both regular-season and playoff contexts. Defensive metrics become more critical in late-game situations, while offensive efficiency is paramount in the playoffs. Ball handling peaks in the second quarter, affecting early momentum, while overall impact metrics, such as Net Rating and Player Impact Estimate, consistently correlate with winning. In the collected dataset we performed preprocessing, applying advanced anomaly detection and discretization techniques. By segmenting performance into five categories—Offense, Defense, Ball Handling, Overall Impact, and Tempo—we uncovered strategic insights for teams, coaches, and analysts. Results emphasize the importance of managing player fatigue, optimizing lineups, and adjusting strategies based on quarter-specific trends. The analysis provides actionable recommendations for coaching decisions, roster management, and player evaluation. Future work can extend this approach to other leagues and incorporate additional contextual factors to refine evaluation and predictive models. Full article

This research introduces the first design concept for a ducted coaxial-rotor amphibious spherical robot (BYQ-A1), utilizing the principle of variable mass control. It investigates whether the BYQ-A1’s variable-mass slider has a certain regularity in its impact on the aerodynamic properties of the BYQ-A1. Utilizing the Blade Element Momentum Theory (BEM) and Wall Jet Theory, an aerodynamic calculation model for the BYQ-A1 is established. An orthogonal experimental method is used to conduct tests on the impact of the variable-mass slider on the aerodynamic properties of the ducted coaxial-rotor system and validate the effectiveness of the aerodynamic calculation model. The results show that the slider generates an internal ground effect and ceiling effect within the BYQ-A1 that enhance the lift of the upper and lower rotors when the robot is equipped with it. The increased total lift compensates for the additional aerodynamic drag caused by the presence of the slider. This novel finding provides guidance for the subsequent optimization design and control method research of the BYQ-A1 and also offers valuable references for configuration schemes that incorporate necessary devices between coaxial dual rotors. Full article