Search Results (74)

This paper presents an innovative end-to-end framework for conformal antenna array design and beam steering in Low Earth Orbit (LEO) satellite-based IoT communication systems. We propose a multi-stage learning architecture that integrates machine learning (ML) for antenna parameter prediction with reinforcement learning (RL) for adaptive beam steering. The ML module predicts optimal geometric and material parameters for conformal antenna arrays based on mission-specific performance requirements such as frequency, gain, coverage angle, and satellite constraints with an accuracy of 99%. These predictions are then passed to a Deep Q-Network (DQN)-based offline RL model, which learns beamforming strategies to maximize gain toward dynamic ground terminals, without requiring real-time interaction. To enable this, a synthetic dataset grounded in statistical principles and a static dataset is generated using CST Studio Suite and COMSOL Multiphysics simulations, capturing the electromagnetic behavior of various conformal geometries. The results from both the machine learning and reinforcement learning models show that the predicted antenna designs and beam steering angles closely align with simulation benchmarks. Our approach demonstrates the potential of combining data-driven ensemble models with offline reinforcement learning for scalable, efficient, and autonomous antenna synthesis in resource-constrained space environments. Full article

Blazars are a class of active galactic nuclei characterized by having one of their relativistic jets oriented close to our line of sight. Their broad emission spectrum makes them exceptional laboratories for probing fundamental physics. In this review, we explore the potential impact on blazar observations of three scenarios beyond the standard paradigm: (i) the hadron beam model, (ii) the interaction of photons with axion-like particles (ALPs), and (iii) Lorentz invariance violation. We focus on the very-high-energy spectral features these scenarios induce in the blazars Markarian 501 and 1ES 0229+200, making them ideal targets for testing such effects. Additionally, we examine ALP-induced effects on the polarization of UV-X-ray and high-energy photons from the blazar OJ 287. The unique signatures produced by these models are accessible to current and upcoming instruments—such as the ASTRI Mini Array, CTAO, LHAASO, IXPE, COSI, and AMEGO—offering new opportunities to probe and constrain fundamental physics through blazar observations. Full article

This work introduces a novel variable camber mechanism that combines the high-load capacity, structural stability, and mechanical efficiency of rigid-body mechanisms with the adaptability, lightweight design, and continuous and smooth motion of compliant mechanisms. The proposed mechanism, featuring an articulated airfoil structure with revolute joints and a cantilever beam that models and controls airfoil camber morphing, employs both standard and higher kinematic pairs to constrain mobility and facilitate camber adjustments through beam deflection and coordinated kinematic interactions. Through multidisciplinary optimization, this study determined the optimal mechanism configuration and airfoil shapes for a small fixed-wing UAV (Unmanned Aerial Vehicle), meeting its morphing and mission requirements, showing the potential for drag reduction by up to 13% across various cruise conditions, thus lowering overall mission drag and energy usage. 2D (airfoil) and 3D (wing) prototypes were built to demonstrate the working principle of the proposed mechanism and to highlight its morphing capabilities. It can morph into multiple airfoil configurations, producing continuous, smooth and efficient airfoil shapes. Moreover, the mechanism is robust, simple, and easy to manufacture, effectively harnessing the strengths of both rigid-body and compliant mechanisms. Full article

Full-scale structural experiments significantly contribute to understanding reinforced concrete (RC) behavior but are often constrained by high costs, extensive time requirements, and practical spatial limitations. Alternatively, small-scale physical models offer a feasible solution, though accurately replicating nonlinear material behavior under load at reduced scales remains challenging. This research addresses these challenges by introducing a methodological approach to developing a novel scaled-down concrete material to emulate full-scale structural behavior. The developed material strictly adheres to Froude similitude criteria, ensuring an accurate representation of gravitational effects without requiring artificially induced gravity, such as centrifuge testing. Experimental validation demonstrates that this material model successfully replicates critical mechanical properties of full-scale concrete, with less than 2% variance observed in compressive strength, strain characteristics, and failure modes. Further validation through comparative testing of scaled-down and corresponding full-scale RC beams confirms the material’s capability to precisely capture structural responses. Consequently, the proposed scaled-down concrete model offers a reliable, economical, and effective approach to evaluating structural performance, overcoming traditional limitations associated with full-scale structural experimentation. Full article

Elasticity is a well-established field within mathematical physics, yet new formulations can provide deeper insight and computational advantages. This study explores the geometry of two- and three-dimensional elastic curves using the formalism of geometric algebra, offering a unified and coordinate-free approach. This work systematically derives the Frenet, Darboux, and Bishop frames within the three-dimensional geometric algebra and employs them to integrate the elastica equation. A concise Lagrangian formulation of the problem is introduced, enabling the identification of Noetherian, conserved, multi-vector moments associated with the elastic system. A particularly compact form of the elastica equation emerges when expressed in the Bishop frame, revealing structural simplifications and making the equations more amenable to analysis. Ultimately, the geometric algebra perspective uncovers a natural correspondence between the theory of free elastic curves and classical beam models, showing how constrained theories, such as Euler–Bernoulli and Kirchhoff beam formulations, arise as special cases. These results not only clarify foundational aspects of elasticity theory but also provide a framework for future applications in continuum mechanics and geometric modeling. Full article

The simulation analysis of a novel stay-in-place formwork (SIPF) beam reinforced with perforated steel pipe skeleton was conducted. The SIPF beam consists of a modified magnesium oxysulfide mortar (MMOM) formwork, a square steel pipe skeleton with holes dug on the sides and top, and cast-in-place concrete. The finite element (FE) analysis model of the SIPF beam was established by using the ABAQUS CAE 2021 software, and simulation analysis was conducted with the shear span ratio (SSR), the distance between the remaining steel strips, and the strength of concrete as the variation parameters. The results show that the stiffness and shear capacity of the SIPF beam decrease with the increase in SSR and increase with the decrease in strip spacing. Under the same conditions, when the concrete strength grade is increased from C30 to C50, the shear bearing capacity of the SIPF beam increases by 11.8% to 16.2%. When the spacing of the steel strips is reduced from 200 mm to 150 mm, the shear bearing capacity can be increased by 12.7% to 31.5%. When the SSR increases from 1.5 to 3.0, the shear bearing capacity decreases by 26.9% to 37.3%. Moreover, with the increase in the SSR, the influence of the steel strip spacing on the shear bearing capacity of the SIPF beam improves, while the influence of the concrete strength on the shear bearing capacity decreases. Taking parameters such as SSR, steel strip spacing, and concrete strength as variables, the influence of steel pipe constraining the core concrete on the shear bearing capacity was considered. The calculation formula for the shear bearing capacity of the SIPF beam with perforated steel pipe skeleton was established. The calculation results fit well with the laboratory test and simulation test results and can be used for the design and calculation of engineering structures. Full article

This study presents an analytical and experimental approach to enhance cantilever-based piezoelectric energy harvesters by optimizing thickness distribution. Using a gradient projection algorithm within a state-space framework, the unimorph beam’s geometry is tailored while constraining the first natural frequency. The objective is to amplify axial strain within the piezoelectric layers, thereby increasing electric polarization and maximizing the conversion efficiency of mechanical vibrations into electrical energy. The steady-state response under harmonic base excitation at resonance was modeled to evaluate the harvester’s dynamic behavior against uniform-thickness counterparts. Results show that the optimized beam achieves significantly higher output voltage and energy harvesting efficiency. Simulations reveal effective strain concentration in regions of high piezoelectric sensitivity, enhancing power generation under resonant conditions. Two independent experimental setups were employed for empirical validation: a non-contact laser vibrometry system (Polytec 3D) and a first resonant base excitation setup. Eigenfrequencies matched within 5% using a Polytec multipath interferometry system, and constant excitation tests showed approximately 30% higher in optimal shapes electrical potential value generation. The outcome of this study highlights the efficacy of geometric tailoring—specifically, non-linear thickness shaping—as a key strategy in achieving enhanced energy output from piezoelectric harvesters operating at their fundamental frequency. This work establishes a practical route for optimizing unimorph structures in real-world applications requiring efficient energy capture from low-frequency ambient vibrations. Full article

Pile–raft foundations are widely used in soft soil engineering due to their good integrity and high stiffness. However, traditional design methods independently design pile–raft foundations and superstructures, ignoring their interaction. This leads to significant deviations from actual conditions when the superstructure height increases, resulting in excessive costs and adverse effects on building stability. This study experimentally investigates the interaction characteristics of pile–raft foundations and superstructures in soft soil under different working conditions using a 1:10 geometric similarity model. The superstructure is a cast-in-place frame structure (beams, columns, and slabs) with bamboo skeletons with the same cross-sectional area as the piles and rafts, cast with concrete. The piles in the foundation use rectangular bamboo strips (side length ~0.2 cm) instead of steel bars, with M1.5 mortar replacing C30 concrete. The raft is also made of similar materials. The results show that the soil settlement significantly increases under the combined action of the pile–raft and superstructure with increasing load. The superstructure stiffness constrains foundation deformation, enhances bearing capacity, and controls differential settlement. The pile top reaction force exhibits a logarithmic relationship with the number of floors, coordinating the pile bearing performance. Designers should consider the superstructure’s constraint of the foundation deformation and strengthen the flexural capacity of inner pile tops and bottom columns for safety and economy. Full article

This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms. Full article

Millimeter-wave antennas have become more important recently due to the diversity of applications in 5G and upcoming 6G technologies, of which automotive systems constitute a significant part. Two crucial indices, detection range and angular resolution, are used to distinguish the performance of the automotive antenna. Strong gains and narrow beamwidths of highly directive radiation beams afford longer detection range and finer spatial selectivity. Although conventionally used, patch antennas suffer from intrinsic path losses that are much higher when compared to the waveguide antenna. Designed at 77 GHz, presented in this article is an 8-element slot array on the narrow side wall of a rectangular waveguide, thus being readily extendable to planar arrays by adding others alongside while maintaining the element spacing requirement for grating lobe avoidance. Comprising tilted Z-shaped slots for higher gain while keeping constrained within the narrow wall, adjacent ones separated by half the guided wavelength are inclined with reversed tilt angles for cross-polar cancelation. An open-ended external waveguide is placed over each slot for polarization purification. Equivalent circuit models of slotted waveguides aid the design. An approach for sidelobe suppression using the Chebyshev distribution is adopted. Four types of arrays are proposed, all of which show potential for different demands and applications in automotive radar. Prototypes based on designs by simulations were fabricated and measured. Full article

As demand for high-capacity, low-latency communication rises, mmWave systems are essential for enabling ultra-high-speed transmission in fifth-generation mobile communication technology (5G) and upcoming 6G networks, especially in dynamic, data-scarce environments. However, deploying mmWave systems in dynamic environments presents significant challenges, especially in beam selection, where limited training data and environmental variability hinder optimal performance. In such scenarios, computation offloading has emerged as a key enabler, allowing computationally intensive tasks to be shifted from resource-constrained edge devices to powerful cloud servers, thereby reducing latency and optimizing resource utilization. This paper introduces a novel cloud–edge collaborative approach integrating few-shot learning (FSL) with multimodal fusion to address these challenges. By leveraging data from diverse modalities—such as red-green-blue (RGB) images, radar signals, and light detection and ranging (LiDAR)—within a cloud–edge architecture, the proposed framework effectively captures spatiotemporal features, enabling efficient and accurate beam selection with minimal data requirements. The cloud server is tasked with computationally intensive training, while the edge node focuses on real-time inference, ensuring low-latency decision making. Experimental evaluations confirm the model’s robustness, achieving high beam selection accuracy under one-shot and five-shot conditions while reducing computational overhead. This study highlights the potential of combining cloud–edge collaboration with FSL and multimodal fusion for next-generation wireless networks, paving the way for scalable, intelligent, and adaptive mmWave communication systems. Full article

This paper explores the vibration behaviour of an elastically constrained graphene origami-enabled auxetic metamaterial beam subject to a harmonic external force. The effective mechanical properties of the metamaterial are approximated using a micromechanical model trained via a genetic algorithm provided in the literature. The three coupled equations of motion are solved numerically; a set of trigonometric functions is used to approximate the displacement components. The accuracy of the proposed model is confirmed by comparing it with the natural frequencies of a simplified non-metamaterial structure available in the literature. Following this validation, the investigation extends to investigate the forced vibration response of the metamaterial beam, examining the influence of the graphene origami distribution pattern and content, graphene folding degree, linear and shear layer stiffness, and geometrical parameters on the dynamic behaviour of the structure. The results generally highlight the considerable effect of the shear layer, modelled as a Pasternak foundation, on the vibration behaviour of the elastically constrained metamaterial beams. Full article

Reinforcing concrete beams with adhesive steel plates is a widely adopted method for enhancing structural performance. However, its ability to significantly improve the load-carrying capacity of reinforced concrete (RC) beams is constrained and often leads to “over-reinforced” failure. To overcome these limitations, this study introduces a novel composite reinforcement strategy that integrates steel plates in the tensile zone with Engineered Cementitious Composite (ECC) layers in the compression zone of RC beams. Static and fatigue tests were conducted on the reinforced beams, and a finite element model was developed to perform nonlinear analyses of their structural behavior under cyclic loading. The model incorporates the nonlinear material properties of concrete and rebar, enabling accurate simulation of material degradation under cyclic conditions. The model’s accuracy was validated through comparison with experimental data, demonstrating its effectiveness in analyzing the structural performance of RC beams under cyclic loading. Furthermore, a parametric study demonstrated that increasing the thickness of steel plates and ECC layers substantially improves the beams’ ductility and load-carrying capacity. These findings provide effective reinforcement strategies and offer valuable technical insights for engineering design. Full article

Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm. Full article

Background: Radiation-induced liver disease (RILD) is a severe complication arising from radiotherapy, particularly when treating abdominal malignancies such as hepatocellular carcinoma. The liver’s critical role in systemic metabolism and its proximity to other abdominal organs make it highly susceptible to radiation-induced damage. This vulnerability significantly limits the maximum safe therapeutic dose of radiation, thereby constraining the overall efficacy of radiotherapy. Among the various modalities, electron beam therapy has gained attention due to its ability to precisely target tumors while minimizing exposure to surrounding healthy tissues. However, despite its advantages, the long-term impacts of electron beam exposure on liver tissue remain inadequately understood, particularly concerning chronic injury and fibrosis driven by sustained oxidative stress. Objectives: to investigate the molecular and cellular mechanisms underlying the radioprotective effects of vitamin C in a model of radiation-induced liver disease. Methods: Male Wistar rats (n = 120) were randomly assigned to four groups: control, fractionated local electron irradiation (30 Gy), pre-treatment with vitamin C before irradiation, and vitamin C alone. The study evaluated the effects of electron beam radiation and vitamin C on liver tissue through a comprehensive approach, including biochemical analysis of serum enzymes (ALT, AST, ALP, and bilirubin), cytokine levels (IL-1β, IL-6, IL-10, and TNF-α), and oxidative stress markers (MDA and SOD). Histological and morphometric analyses were conducted on liver tissue samples collected at 7, 30, 60, and 90 days, which involved standard staining techniques and advanced imaging, including light and electron microscopy. Gene expression of Bax, Bcl-2, and caspase-3 was analyzed using real-time PCR. Results: The present study demonstrated that fractional local electron irradiation led to significant reductions in body weight and liver mass, as well as marked increases in biochemical markers of liver damage (ALT, AST, ALP, and bilirubin), inflammatory cytokines (IL-1β, IL-6, and TNF-α), and oxidative stress markers (MDA) in the irradiated group. These changes were accompanied by substantial histopathological alterations, including hepatocyte degeneration, fibrosis, and disrupted microvascular circulation. Pre-treatment with vitamin C partially mitigated these effects, reducing the severity of the liver damage, oxidative stress, and inflammation, and preserving a more favorable balance between hepatocyte proliferation and apoptosis. Overall, the results highlight the potential protective role of vitamin C in reducing radiation-induced liver injury, although the long-term benefits require further investigation. Conclusions: The present study highlights vitamin C’s potential as a radioprotective agent against electron beam-induced liver damage. It effectively reduced oxidative stress, apoptosis, and inflammation, particularly in preventing the progression of radiation-induced liver fibrosis. These findings suggest that vitamin C could enhance radiotherapy outcomes by minimizing liver damage, warranting further exploration into its broader clinical applications. Full article