Search Results (4,478)

Fast radio bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, while ultra-high-energy cosmic rays (UHECRs; $E\gtrsim {10}^{18}$ eV) remain among the most important unresolved problems in astroparticle physics. This review examines the viability of FRBs and their central engines as sources of UHECRs within a comprehensive multi-messenger framework. We summarize the observational constraints on UHECR source populations imposed by the energy spectrum, nuclear composition, anisotropy measurements, diffuse $\gamma$-ray background, and high-energy neutrino observations, which, together, favor source classes capable of accelerating heavy nuclei with hard injection spectra, modest cosmological evolution, and sufficiently high source densities. We then review the current landscape of FRB progenitor and engine models, including magnetars, supramassive neutron stars, compact-object mergers, and accretion-powered systems, emphasizing their energetics, environments, and particle-acceleration capabilities through relativistic shocks, magnetic reconnection, magnetar wind nebulae, and direct electromagnetic acceleration by ultra-relativistic FRB pulses. We discuss how these scenarios are constrained by neutrino and $\gamma$-ray observations from IceCube, KM3NeT, and Fermi-LAT, as well as by large-scale UHECR anisotropy measurements from the Pierre Auger Observatory and Telescope Array. Finally, we examine the observational tests that will become possible in the coming decade through large samples of localized FRBs, composition-resolved UHECR measurements, next-generation neutrino observatories, and wide-field $\gamma$-ray facilities. We emphasize that FRB dispersion and rotation measures provide unique probes of the baryonic and magnetic environments relevant for UHECR acceleration and propagation, enabling a new form of multi-messenger tomography of cosmic-ray source environments and allowing the FRB–UHECR connection to become a quantitatively testable astrophysical framework. Full article

This study introduces a Caputo fractional-order version of the SEIRS-V model to investigate the spreading dynamics of worms within wireless sensor networks. Traditional integer-order worm propagation models describe the instantaneous evolution of network states; however, they do not adequately account for memory and hereditary characteristics that may influence the transmission dynamics. Consequently, their ability to represent realistic network behavior can be limited in systems where past states affect current propagation patterns. The framework divides sensor nodes into susceptible, exposed, infectious, recovered, and vaccinated classes, while explicitly incorporating worm transmission rates, temporary loss of immunity, and the impact of preventive security measures under limited resource conditions. A detailed theoretical examination is performed, covering the existence, boundedness, and uniqueness of solutions of the fractional-order system. The coupled nonlinear fractional system is solved semi-analytically by means of the Fractional Reduced Differential Transform (FRDT) technique. To confirm accuracy and robustness, the identical system is also discretized and solved using the finite difference scheme (FDS). Unlike previous studies on worm propagation models in wireless sensor networks, which are mainly limited to equilibrium point analysis and qualitative investigations without deriving explicit solutions, the present work develops an approximate semi-analytical solution for the fractional-order SEIRS-V system using the FRDTM. Comparisons between the two solution sets demonstrate excellent agreement and high precision. Numerical outcomes are presented through a series of 2D graphical profiles that illustrate the time-dependent behavior of each compartment and reveal the sensitivity of worm propagation and suppression to variations in the fractional order and key model parameters. The integrated theoretical and computational findings underscore the strong protective role of vaccination in mitigating worm outbreaks and offer valuable guidelines for strengthening cybersecurity measures in wireless sensor networks. Full article

High-dynamic amusement ride conditions involving impacts, rapid rotations, and abrupt posture changes introduce severe motion artifacts that degrade vital sign quality and destabilize physiological state recognition. This study aims to develop an engineering-ready closed-loop framework for robust passenger monitoring and intelligent diagnosis. A multimodal sensing and modeling pipeline was designed to jointly leverage physiological signals such as heart rate and SpO₂ and kinematic measurements, including acceleration, angular rate, velocity, and attitude. Inertial and PPG signals were preprocessed into supervised samples through wavelet multiresolution denoising and coordinate frame unification, while a strapdown inertial navigation system was used to propagate a 12-channel physical quantity sequence. To ensure interpretability and standards compliance, constraints from GB 8408-2018 were translated into executable threshold rules, enabling standards-driven auto-labeling and rule-based early warning. Building on this foundation, three learning modules were developed: a fusion model for high-dynamic heart rate estimation, a CNN–LSTM dynamic-threshold-enhanced network TAPNet for rapid kinematic anomaly screening, and an attention-augmented hybrid model HS-BANet integrating one-dimensional residual blocks, bidirectional LSTM, and multi-head attention for fine-grained arrhythmia classification. Experimental results demonstrated accurate and consistent heart rate estimation with RMSE of 1.18 bpm on HSSH-I and 1.24 bpm on the independent HSSH-II set, strong agreement with training and testing correlations of 0.9928 and 0.9865, and near-zero bias in Bland–Altman analysis. TAPNet achieved 96.9% validation accuracy and 98.2% test accuracy for kinematic anomaly recognition, maintaining robust generalization under class imbalance. HS-BANet enabled multi-class identification of PVC, PAC, VT, SVT, and AF, achieving an accuracy of 92.37%, an F1-score of 86.87%, a precision of 88.45%, a sensitivity of 88.14%, and a specificity of 89.42%. Overall, the proposed two-stage multimodal closed-loop—fast, interpretable early warning based on physical quantity thresholds followed by fine-grained diagnosis from physiological signals—supports stable feature extraction and reliable decision-making under strong motion artifacts and non-stationary dynamics, balancing responsiveness and diagnostic credibility, while showing potential for practical safety early warning and future deployment-oriented operational support in amusement ride scenarios. Full article

Magnetostrictive patch transducers (MPTs) are highly efficient for generating and detecting ultrasonic waves for non-destructive evaluation (NDE), though their use on cementitious media and fibre-reinforced concrete has not yet been investigated. In this study, a COMSOL simulation, validated with laser-Doppler vibrometry, was first used to quantify patch deformation for use in subsequent simulation of wave propagation in samples. The MPT system was then validated on thin glass plates, producing tunable A₀, S₀, and SH₀ modes through frequency-wavelength matching. In cementitious mortar plates, SH₀ and SH₁ modes were demonstrated experimentally for the first time using MPTs. The validated COMSOL model was then used to interpret complex signals in quasi-plate and half-space cementitious mortar prisms, showing that MPTs generate Rayleigh, bulk SH, and surface-skimming SH modes. In steel fibre-reinforced concrete, surface-skimming SH wave speed correlated with increases in breaking strength even in the presence of surface features such as notches. Notably, Rayleigh wave speeds could not be measured in the presence of surface features, and the Rayleigh velocities measured in the same sample, but not in the local tested area did not correlate with SH speed. This behaviour is likely due to the non-uniform distribution of material constituents, including fibre-reinforcement and coarse aggregate, combined with the different propagation paths and depth sensitivities of the reported wave modes. Overall, racetrack-coil MPTs enable multimodal inspection of cementitious media, providing information on the presence of geometric features and material properties. Full article

This article presents a method for calibration of dynamic vehicle weighing systems (WIM—Weigh-In-Motion) involving the calibration of all WIM stations operating within a given road network segment as a single process. A key assumption of the method is the presence of at least one scale with significantly higher accuracy than the calibrated systems in this part of road network. This reference scale function may be played by a static scale, slow-pass scale (LS-WIM—Low-Speed WIM) for measurement of vehicle axle load or by a selected WIM system with heightened accuracy. Both the reference scale and all systems undergoing calibration must be equipped with a system for the automatic recognition of vehicle registration number plates. The reference scale makes it possible to determine axle load values considered as benchmark values. Then, for each vehicle weighed on the reference scale and subsequently on any WIM system operating within the analysed area, the relative difference between the reference result and the WIM system measurement is calculated with respect to the reference value. This difference forms the basis for the operation of the algorithm estimating the coefficients of the static characteristic of the calibrated WIM system (so-called calibration coefficients), which are then used to determine corrected weighing results. The estimation of the coefficients is updated after each identified vehicle that has previously been weighed on the reference scale is considered. The article presents both the results of simulations and experimental studies concerning the proposed spatial method of calibration. The results obtained allow for an assessment of the effectiveness of the proposed solution. As can be seen from the analyses conducted, this method leads to a significant reduction in systematic error of vehicle weight measurement. Unfortunately, it does not eliminate random errors. The spatial calibration approach described in this paper has certain limitations. The main ones include the impact of ANPR system errors on calibration effectiveness, cases where a vehicle is unloaded or loaded between WIM stations, and the propagation of systematic errors from the reference systems to the other WIM systems. A significant advantage of the proposed spatial calibration method is that it can operate effectively using weighing data from a single reference WIM system and does not require heavy traffic volumes. Full article

To investigate the early-stage flame propagation and pressure response of premixed H₂-O₂ combustion under ultra-high-pressure constant-volume conditions, a transient CFD model was developed for a large-volume confined chamber. The numerical framework combines a density-based solver, the Peng–Robinson real equation of state, large eddy simulation, and a reduced H₂-O₂ chemical kinetic mechanism. Simulations were conducted at initial pressures of 30 and 40 MPa, H₂/O₂ molar ratios of 8:1 and 12:1, and three-, four-, and five-point ignition configurations. The results show that increasing the initial pressure from 30 MPa to 40 MPa advances the pressure rise onset from approximately 1.65 ms to 1.28 ms and increases the maximum pressure rise rate from 18.6 MPa·ms⁻¹ to 27.4 MPa·ms⁻¹ under the H₂/O₂ = 8:1 and three-point ignition condition. Under the investigated fuel-rich conditions, increasing the H₂/O₂ molar ratio from 8:1 to 12:1 delays the pressure rise onset from approximately 1.28 ms to 1.46 ms and reduces the maximum pressure rise rate from 27.4 MPa·ms⁻¹ to 21.1 MPa·ms⁻¹. For the 30 MPa and H₂/O₂ = 8:1 cases, the four-point ignition case produces the largest pressure rise rate of approximately 23.5 MPa·ms⁻¹, whereas the five-point ignition case shows a lower pressure fluctuation amplitude of approximately 3.6 MPa. The present conclusions are based on CFD quantitative engineering predictions and should be further validated using quantitative experimental measurements. Full article

Infrared cameras used in radio telescopes often suffer image degradation in complex optical and thermal environments. Solar radiation, convergent reflected light, and thermal emission from support structures can substantially impair imaging performance. To address this problem, this paper proposes a split reflective ellipsoidal baffle for suppressing infrared imaging degradation. Unlike conventional baffles, which mainly rely on structural occlusion and surface absorption, the proposed design functions as an upstream stray light regulation unit. It also establishes a computational framework integrating ellipsoidal vane geometry, realistic edge microtopography modelling, ray-tracing simulation, and detector plane irradiance response analysis. First, the reflective properties of the ellipsoidal surface are used to construct an off-axis stray light propagation constraint model. Under this model, incident stray radiation is redirected away from the effective imaging path or guided into light-trapping regions between adjacent vanes. Second, a laser confocal microscope is used to capture the true three-dimensional edge morphology of vanes with different materials and machining angles. This strategy addresses the limitations of the conventional 0.02 mm rounded edge approximation, which cannot accurately represent real scattering behaviour. The measured morphologies are then converted into high-fidelity computational models compatible with ray-tracing analysis. Furthermore, stray light suppression performance is evaluated using point source transmittance, detector plane irradiance distribution, and grey scale response in experimental images. Simulation and darkroom experiments show that the proposed baffle suppresses residual stray light more effectively than conventional absorptive baffles. The results demonstrate a computable, manufacturable, and experimentally verifiable strategy for front-end stray light control and baffle optimisation. This strategy can also support image quality enhancement in infrared imaging systems operating under complex optical and thermal environments. Full article

This article presents a simple, original data augmentation algorithm for Received Signal Strength Indicator (RSSI), dedicated to indoor localization systems. The aim of the research was to develop a synthetic data generation method to serve as a regularization technique, making models more robust against measurement noise. The proposed approach combines propagation modeling using polynomial regression with the individual statistical characteristics of each Access Point (AP), accounting for signal fluctuations and a probabilistic signal outage mechanism. The effectiveness of the proposed solution was experimentally verified by evaluating K-NN and MLP neural network models in both classification and regression variants. The study was conducted on datasets with different measurement grid granularities, demonstrating the algorithm’s ability to improve the generalization properties of estimators, even with a limited number of samples in the training set. The results showed that the use of augmentation reduced the Mean Absolute Error (MAE) by an average of approximately 20% for the dense training set and about 17% for the sparse set. Within the evaluated test environment, models trained on the augmented sparse measurement grid, which contained 67% fewer physical calibration points (30 points compared to the dense grid’s 92), reached a precision comparable to models trained on the dense real-world dataset. Analysis of histograms and Cumulative Distribution Functions (CDF) of the error confirmed the preservation of the signal’s statistical integrity and the effective mitigation of gross errors. The proposed solution constitutes an efficient and easy-to-implement alternative to complex generative models (e.g., GANs). These findings serve as a successful proof-of-concept and pilot study, laying the foundation for further development and validation in larger, more complex spatial environments. Full article

Micropropagation of Selenicereus hybrids is a key tool for breeding and conservation; however, further refining the balance between high multiplication rates and morphological quality remains a complex challenge within conventional protocols. This study explores targeted signaling modulation using nine bioactive small molecules—including three mammalian glycogen synthase kinase 3 (GSK3) inhibitors (TDZD-9, VP3.15 and VP0.7), three leucine rich repeat kinase 2 (LRRK2) inhibitors (JZ1.24, JZ1.3 and IGS4.75), and three phosphodiesterase (PDE) inhibitors—to complement traditional micropropagation. Explants were evaluated in two distinct contexts: a hormone-free basal medium (BM) and a plant growth regulator-supplemented medium (PIT2) and the response rates, yield, and quality were measured and integrated using a Global Efficiency Index (GEI). Results demonstrate that inhibitor efficacy is strictly context-dependent; while most molecules repressed budding in BM, they acted as response modulators by determining the specific type of morphogenic pathway in PIT2. Notably, the GSK3 inhibitor TDZD-9 reached the highest GEI (0.85) by maximizing productivity, whereas LRRK2 inhibitors effectively preserved architectural integrity. Flow cytometry confirmed cytogenetic stability across all treatments, with a 98.5% plantlet survival rate during acclimatization. In conclusion, the strategic integration of targeted signaling modulators and multi-parametric indices offers a refined and objective framework to enhance the efficiency of mass propagation protocols in pitahaya and other recalcitrant species. Furthermore, our findings provide new evidence of the strong potential of these small molecules as novel tools to improve plant micropropagation beyond traditional plant growth regulators. Full article

Fused deposition modeling (FDM) provides a flexible manufacturing route for continuous fiber-reinforced thermoplastic composites, but weak interlaminar bonding and the trade-off between load-bearing capacity and deformation capability still limit their structural applications. In this study, multi-scale carbon/Kevlar fiber hybridization was introduced into acrylonitrile butadiene styrene (ABS)-based composites by combining continuous carbon fiber (CCF) or continuous Kevlar fiber (CKF) with short carbon fiber-filled ABS (ABS/SCF) or short Kevlar fiber-filled ABS (ABS/SKF). Four hybrid configurations and two continuous-fiber baseline composites were fabricated by FDM and evaluated through three-point bending tests, floating roller peel tests, peeled-surface SEM observations, and Rule-of-Mixtures-based hybrid effect analysis. The flexural results showed that short-fiber-filled matrices improved the flexural properties of both CCF- and CKF-based composites, but the degree of improvement depended on the fiber combination. Among the investigated configurations, CCF + ABS/SCF exhibited the highest flexural modulus and strength, which were 34.31% and 27.26% higher than those of CCF + ABS, respectively. For the CKF-based composites, CKF + ABS/SCF increased the flexural modulus and strength by 31.51% and 26.78%, compared with CKF + ABS, while maintaining the progressive deformation behavior associated with Kevlar reinforcement. The peel results showed that all hybrid composites had higher interlaminar peel resistance than their corresponding baselines, with increases ranging from 18.66% to 54.42%. The peeled-surface SEM observations indicated that the short-fiber-filled matrices changed the crack-propagation features, with more matrix tearing, fiber pull-out, and irregular peeling areas. The RoM-based comparison showed that the measured flexural properties of all hybrid configurations were higher than the corresponding RoM reference values. Overall, CCF + ABS/SCF was more suitable for improving stiffness and load-bearing capacity, whereas CKF + ABS/SCF showed a more balanced response in terms of flexural performance, interlaminar peel resistance, and progressive deformation behavior. Full article

The pronounced heterogeneity of tight sandstone reservoirs in the Sulige Gas Field poses significant challenges to the uniform propagation of multi-cluster hydraulic fractures during horizontal well staged fracturing, often leading to uneven stimulation and compromised productivity. To address this issue, a coupled fluid–solid fracture propagation model based on the displacement discontinuity method (DDM) was developed, incorporating dynamic fluid distribution, rock deformation, and temporary plugging mechanisms. The model was validated against microseismic monitoring data from the Sulige field and subsequently employed to investigate the effects of reservoir heterogeneity—including porosity, permeability, and in situ stress—on multi-cluster fracture growth. Results indicate that permeability and stress heterogeneity exert the most significant influence on fracture non-uniformity, as reflected by increased coefficients of variation in fracture length. Engineering measures such as the use of high-viscosity guar gum fracturing fluids, variable perforation strategies (e.g., 6, 10, and 16 holes per cluster), and optimized temporary plugging parameters (timing of 0.5 with 12 balls) were shown to effectively mitigate these effects and promote more balanced fracture propagation. This study provides a quantitative framework for optimizing fracturing design in heterogeneous tight gas reservoirs and offers practical guidance for enhancing stimulation uniformity and gas recovery efficiency in the Sulige Gas Field. Full article

Radio-frequency identification (RFID)-based asset tracking in cabinet environments often encounters unpredictable detection caused by multipath fading, metal-induced interference, and tag placement sensitivity, which can render single-band systems unreliable under real-world conditions. This paper proposes a dual-band detection approach combining 915 MHz and 2.45 GHz to address these challenges through frequency diversity. Unlike designs confined to closely spaced UHF bands, this method uses a larger spectral gap to benefit from uncorrelated fading and distinct propagation properties. Theoretical analysis shows that dual-band detection significantly reduces joint failure probability under independent fading. The proposed framework is implemented using commercially available passive UHF tags at 915 MHz and an active RFID tag/reader at 2.45 GHz. The two systems are operated sequentially along the same guided scan path, and their detected tag-ID sets are combined offline using an OR-fusion rule without hardware-level synchronization. Across trials with varied scan speeds, power levels, reader distances, and tag placements, single-band detection fell below 50% under double-speed scanning at 200 cm, while the dual-band method remained above 70% and, in many cases, reached 100% reliability. Performance trends are further analyzed across individual scenarios, showing that 2.45 GHz links are less affected by metallic shadowing at close range, whereas 915 MHz links maintain more stable detection at longer distances. These findings are discussed in terms of deployment feasibility, indicating that the additional hardware and configuration requirements are offset by the measurable improvement in detection consistency, making the approach applicable for inventory tracking in logistics, warehousing, and industrial automation. Full article

Decision-making is a critical module for intelligent vehicles to achieve safe and efficient autonomous driving. However, most existing reinforcement learning-based decision-making methods optimize policies by maximizing the expected return, which may inadequately account for low-probability but high-cost safety risks in complex traffic interactions. To address this issue, this paper proposes a Risk-Sensitive Distributional Proximal Policy Optimization (PPO) method, termed Risk-Sensitive Distributional Proximal Policy Optimization (RSDPPO), for highway lane-changing decision-making. Within the PPO framework, a distributional state-value function is introduced to model the return distribution under the current policy, and a Wang distortion-based risk measure is further incorporated to construct a risk-sensitive advantage function. In this way, risk information contained in the return distribution can be propagated into the policy gradient update, guiding the learned policy to avoid high-risk driving behaviors while maintaining training stability. Simulation experiments are conducted in a highway lane-changing scenario with heterogeneous surrounding vehicles. The results show that, under medium-density traffic, the proposed method outperforms representative baseline algorithms in cumulative reward, success rate, and safety reward. Further evaluation under higher-density traffic demonstrates that RSDPPO maintains better overall performance, indicating stronger adaptability to denser traffic conditions. Ablation studies further show that risk-averse distortion improves the balance between safety and efficiency by increasing safety margins during car-following and lane-changing maneuvers. These results indicate that RSDPPO provides an effective risk-sensitive policy optimization framework for safety-oriented highway lane-changing decision-making. Full article

This paper proposes a passive in-orbit calibration method for phased array antennas using GNSS carrier-phase measurements. By performing synchronous observation and exploiting the short-baseline property between the positioning antenna and array elements, the first differencing operation eliminates space propagation errors and clock biases. By further utilizing receiver channel consistency, the second differencing operation cancels out the receiver channel errors, thereby extracting the relative receive-chain phase error of the element under test. Under typical operating conditions, the calibration accuracy can reach an RMS error of approximately $3.02\mathrm{mm}$, corresponding to a phase accuracy of $5.72°$ in the GPS L1 band. This accuracy is close to the $5.625°$ minimum phase step of a 6-bit digital phase shifter, and can be further improved under higher $C/N_0$ and well-controlled residual error conditions. Without requiring a dedicated GNSS band excitation signal, this method avoids co-frequency self-interference with the positioning antenna, which provides an auxiliary approach for in-orbit calibration of phased array receive chains. Full article

This paper investigates the feasibility of 3D self-localization and tracking using chipless radio frequency identification (RFID) tags operating in the terahertz (THz) frequency band. The primary objective is to achieve sub-millimeter (sub-mm) localization and tracking accuracy while minimizing reliance on external infrastructure. To this end, a hybrid localization framework is proposed that jointly exploits round-trip time-of-flight (RToF) and angle-of-arrival (AoA) measurements to enhance localization performance. Although near-field propagation effects are inherently significant in the considered THz operating regime, a simplified far-field approximation is adopted to facilitate tractable system modeling and analytical development. The proposed framework is further extended to dynamic scenarios through an extended Kalman filter (EKF)-based tracking algorithm, which incorporates temporal state evolution to improve estimation robustness under noisy measurements. Furthermore, the Cramér–Rao lower bound (CRLB) for the hybrid RToF-AoA system is derived to establish the fundamental limits of localization accuracy under varying system configurations and measurement conditions. Simulation results demonstrate that the proposed approach is capable of achieving sub-mm localization and tracking accuracy with a highly constrained anchor infrastructure, including operation with a single anchor in the considered scenario. These findings highlight the potential of THz chipless RFID technology as a promising enabling solution for next-generation high-accuracy localization and tracking applications. Full article