Search Results (870)

In recent years, the generation of circularly polarized attosecond pulses has garnered significant attention due to their potential applications in ultrafast spectroscopy and, notably, in chiral-sensitive molecular detection. The traditional methods for generating such pulses often involve complex laser configurations or specially engineered targets, limiting their experimental feasibility. In this study, we present a streamlined and effective approach to producing circularly polarized attosecond pulses by employing a linearly polarized laser field in conjunction with a stereosymmetric linear molecule, 1-butyne (C₄H₆). The generation of high-order harmonics by this molecular system reveals a distinct plateau in the perpendicular polarization component, which facilitates the generation of isolated attosecond pulses with circular polarization. Through a detailed analysis of the time-dependent charge density dynamics across atomic sites, we identify the atoms primarily responsible for the emission of circularly polarized harmonics in the plane orthogonal to the driving field. Moreover, we explore the role of multi-orbital contributions in shaping the polarization properties of the harmonic spectra. Our findings underscore the importance of molecular symmetry and the electronic structure in tailoring the harmonic polarization, and they demonstrate a viable pathway for using circularly polarized attosecond pulses to probe molecular chirality. This method offers a balance between simplicity and performance, opening new avenues for practical applications in chiral recognition and ultrafast stereochemical analysis. Full article

To enhance the aerodynamic performance of vertical axis wind turbines (VAWTs) under complex gust conditions, the design parameters of the flap were optimized using the computational fluid dynamics (CFD) method combined with orthogonal experimental design and the SHERPA algorithm, and two gust models with mainly high and low wind speeds were generated by a self-compiling program to investigate the effects of three combinations of the chordwise mounting position of the flap, the moment of inertia, and the maximum deflection angle on the aerodynamic performance of the vertical axis wind turbine. The results demonstrated that adaptive flaps reduced the flow separation region and suppressed the formation and development of separation vortices, thereby enhancing aerodynamic performance. The adaptive flap was found to be more effective in high-speed gust environments than in low-speed ones. The optimal configuration—chordwise position at 0.4C, moment of inertia at 6.12 × 10⁻⁵ kg·m², and a maximum deflection angle of 40°—led to a 57.24% improvement relative to the original airfoil. Full article

Ventilation corridors can play an important role in removing harmful air pollution in cities; however, there are social pressures to use this corridor land for new buildings. The presented study employs RANS fluid flow simulations with the k-$ϵ$ turbulence model to investigate how the addition of buildings in the historical ventilation corridor impedes CO traced pollution removal. The urban complex situated near Raclawicka Street in Warsaw is selected as a case study for which two urban layouts dating from 2006 and 2017 are compared. The investigation includes varying ambient wind speeds and direction, with a prescribed CO-air mixture source representing a supply of road pollution. The results provide aerodynamic and dispersion characteristics and identify several generic trends indicating that the orthogonal urban layouts help to remove the pollution faster, especially when compared to courtyard building configurations, and that the introduction of occasional wide gaps between buildings can also speed up the pollution removal in the direction perpendicular to the gaps. Furthermore, for this urban complex the addition of new buildings had predominantly a local impact. The results showed that for light and mild winds, ambient speeds have little impact on dispersion patterns, but the effects of a dynamic ambient wind reversal are pronounced. Full article

As face forgery techniques, particularly the DeepFake method, progress, the imperative for effective detection of manipulations that enable hyper-realistic facial representations to mitigate security threats is emphasized. Current spatial domain approaches commonly encounter difficulties in generalizing across various forgery methods and compression artifacts, whereas frequency-based analyses exhibit promise in identifying nuanced local cues; however, the absence of global contexts impedes the capacity of detection methods to improve generalization. This study introduces a hybrid architecture that integrates Efficient-ViT and multi-level wavelet transform to dynamically merge spatial and frequency features through a dynamic adaptive multi-branch attention (DAMA) mechanism, thereby improving the deep interaction between the two modalities. We innovatively devise a joint loss function and a training strategy to address the imbalanced data issue and improve the training process. Experimental results on the FaceForensics++ and Celeb-DF (V2) have validated the effectiveness of our approach, attaining 97.07% accuracy in intra-dataset evaluations and a 74.7% AUC score in cross-dataset assessments, surpassing our baseline Efficient-ViT by 14.1% and 7.7%, respectively. The findings indicate that our approach excels in generalization across various datasets and methodologies, while also effectively minimizing feature redundancy through an innovative orthogonal loss that regularizes the feature space, as evidenced by the ablation study and parameter analysis. Full article

Unmanned Aerial Vehicles (UAVs) exhibit significant potential in enhancing the wireless communication coverage and service quality of Mobile Edge Computing (MEC) systems due to their superior flexibility and ease of deployment. However, the rapid growth of tasks leads to transmission queuing in edge networks, while the uneven distribution of user nodes and services causes network load imbalance, resulting in increased user waiting delays. To address these issues, we propose a multi-UAV collaborative MEC network model based on Non-Orthogonal Multiple Access (NOMA). In this model, UAVs are endowed with the capability to dynamically offload tasks among one another, thereby fostering a more equitable load distribution across the UAV swarm. Furthermore, the integration of NOMA is strategically employed to alleviating the inherent queuing delays in the communication infrastructure. Considering delay and energy consumption constraints, we formulate a task offloading strategy optimization problem with the objective of minimizing the overall system delay. To solve this problem, we design a delay-optimized offloading strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. By jointly optimizing task offloading decisions and UAV flight trajectories, the system delay is significantly reduced. Simulation results show that, compared to traditional approaches, the proposed algorithm achieves a delay reduction of 20.2%, 9.8%, 17.0%, 12.7%, 15.0%, and 11.6% under different scenarios, including varying task volumes, the number of IoT devices, UAV flight speed, flight time, IoT device computing capacity, and UAV computing capability. These results demonstrate the effectiveness of the proposed solution and offloading decisions in reducing the overall system delay. Full article

Smart structures with topological configurations that integrate perception and actuation have complex geometric features. The simplification of these features can lead to deviations in dynamic characteristics, making it difficult to establish an accurate dynamic model. Uncertainties, such as material nonlinearity, hysteresis in elastic deformation, and external disturbances, affect the trajectory tracking accuracy of the smart structure’s actuation function. This paper proposes a modeling method that combines finite element unit bodies and orthogonal characteristic mode reduction to construct an accurate dynamic model of the smart structure and design an adaptive backstepping controller. Nonlinear dynamic equations are derived through a finite element analysis of the structure, and the orthogonal characteristic mode reduction method is employed to reduce computational complexity while ensuring model accuracy. An adaptive backstepping controller is designed to mitigate model uncertainties and achieve precise trajectory tracking control. Simulation and experimental results demonstrate that the proposed method can effectively handle the nonlinearity and modeling errors of smart structures, achieving high-precision trajectory tracking and verifying the accuracy of the dynamic model as well as the robustness of the controller. Full article

Carbon fiber-reinforced polymer (CFRP) composites are widely used in aerospace due to their excellent strength-to-weight ratio and tailorable properties. However, these properties critically depend on the CFRP curing cycle. The commonly adopted manufacturer-recommended curing cycle (MRCC), designed to accommodate the most conservative conditions, involves prolonged curing times and high energy consumption. To overcome these limitations, this study proposes an efficient and adaptable method to determine the optimal curing cycle. The effects of varying heating rates on resin dynamic and isothermal–exothermic behavior were characterized via reaction kinetics analysis using differential scanning calorimetry (DSC) and rheological measurements. The activation energy of the reaction system was substituted into the modified Sun–Gang model, and the parameters were estimated using a particle swarm optimization algorithm. Based on the curing kinetic behavior of the resin, CFRP compression molding process orthogonal experiments were conducted. A weighted scoring system incorporating strength, energy consumption, and cycle time enabled multidimensional evaluation of optimized solutions. Applying this curing cycle optimization method to a commercial epoxy resin increased efficiency by 247.22% and reduced energy consumption by 35.7% while meeting general product performance requirements. These results confirm the method’s reliability and its significance for improving production efficiency. Full article

Under conservation tillage in the Huang-Huai-Hai wheat–maize rotation area, the ridge-clearing no-till seeder for strip tillage mitigates the adverse impacts of surface residues on seeding quality by clearing stubble specifically within the seed rows, demonstrating significant potential for application and promotion. However, the inadequate understanding of the seeder’s operational performance and governing mechanisms under varying field conditions hinders its high-quality and efficient implementation. To address this issue, this study selected the stubble height, forward speed, and stubble knife rotational speed (PTO speed) as experimental factors. Employing a three-factor quasi-level orthogonal experimental design, coupled with response surface regression analysis, this research systematically elucidated the interaction mechanisms among these factors concerning the seeding depth consistency and seed spacing uniformity of the seeder. An optimized parameter-matching model was subsequently derived through equation system solving. Field trials demonstrated that a lower forward speed improved the seed spacing uniformity and seeding depth consistency, whereas high speeds increased the missing rates and spacing deviations. An appropriate stubble height enhanced the seed spacing accuracy, but an excessive height compromised depth precision. Higher PTO speeds reduced multiple indices but impaired depth accuracy. Response surface analysis based on the regression models demonstrated that the peak value of the seed spacing qualification index occurred within the forward speed range of 8–9 km/h and the stubble height range of 280–330 mm, with the stubble height being the dominant factor. Similarly, the peak value of the seeding depth qualification index occurred within the stubble height range of 300–350 mm and the forward speed range of 7.5–9 km/h, with the forward speed as the primary factor. Validation confirmed that combining stubble heights of 300−330 mm, forward speeds of 8−9 km/h, and PTO speeds of 540 r/min optimized both metrics. This research reveals nonlinear coupling relationships between operational parameters and seeding quality metrics, establishes a stubble–speed dynamic matching model, and provides a theoretical foundation for the intelligent control of seeders in conservation tillage systems. Full article

The 2020 southern Puerto Rico earthquake sequence highlighted the severe seismic vulnerability of informally constructed two-story reinforced concrete (RC) houses. This study examines the failure mechanisms of these structures and assesses the effectiveness of first-floor RC shear-wall retrofitting. Nonlinear pushover and dynamic time–history analyses were performed using fiber-based distributed plasticity models for RC frames and nonlinear macro-elements for second-floor masonry infills, which introduced a significant inter-story stiffness imbalance. A bi-directional seismic input was applied using spectrally matched, near-fault pulse-like ground motions. The findings for the as-built structures showed that stiffness mismatches between stories, along with substantial strength and stiffness differences between orthogonal axes, resulted in concentrated plastic deformations and displacement-driven failures in the first story—consistent with damage observed during the 2020 earthquakes. Retrofitting the first floor with RC shear walls notably improved the performance, doubling the lateral load capacity and enhancing the overall stiffness. However, the retrofitted structures still exhibited a concentration of inelastic action—albeit with lower demands—shifted to the second floor, indicating potential for further optimization. Full article

Nanofiltration (NF) is considered a competitive purification method for acidic stream treatments. However, conventional thin-film composite NF membranes degrade under acid exposures, limiting their applications in industrial acid treatment. For example, wet-process phosphoric acid contains impurities of multivalent metal ions, but NF membrane technologies for impurity removal under harsh conditions are still immature. In this work, we develop a novel strategy of acid-resistant nanofiltration membranes based on interfacial polymerization (IP) of polyethyleneimine (PEI) and cyanuric chloride (CC) with the s-triazine ring. The IP process was optimized by orthogonal experiments to obtain positively charged PEI-CC membranes with a molecular weight cut-off (MWCO) of 337 Da. We further applied it to the approximate industrial phosphoric acid purification condition. In the tests using a mixed solution containing 20 wt% P₂O₅, 2 g/L Fe³⁺, 2 g/L Al³⁺, and 2 g/L Mg²⁺ at 0.7 MPa and 25 °C, the NF membrane achieved 56% rejection of Fe, Al, and Mg and over 97% permeation of phosphorus. In addition, the PEI-CC membrane exhibited excellent acid resistance in the 48 h dynamic acid permeation experiment. The simple fabrication procedure of PEI-CC membrane has excellent acid resistance and great potential for industrial applications. Full article

Manufacturing in modern industrial sectors involves the machining of components where the undeformed chip thickness inevitably decreases to values comparable to the tool edge radius. Under such conditions, the ploughing effect between the workpiece and the tool becomes dominant, followed by the noticeable formation of a stagnation zone. This paper presents research focused on the analysis of the cutting process for small cross-sections of the removed layers, based on cutting force components. This study investigated the machining of two titanium alloy grades—Ti Grade 5 (Ti-6Al-4V) and Ti Grade 2—with the main focus on process stability. A material separation model was analyzed to demonstrate the mechanism of material flow within the cross-section of the machined layer. It was found that the material has a limited ability to flow sideways at the boundary of the chip thickness, thus determining the probable size of the stagnation zone in front of the cutting edge. Orthogonal cutting experiments enabled the determination of the minimum chip thickness coefficient for constant temperature conditions, independent of the tool edge radius, as $h_{min0}=$0.313. In oblique cutting tests, the sensitivity of thin-layer machining was demonstrated for the determined values of minimum undeformed chip thickness. By applying the 0–1 test for chaos, the measurement time (parameter $T·dt$) was determined for both titanium alloys to determine the range of observable chaotic behavior. The analyses confirmed that Ti Grade 2 enters chaotic dynamics much more rapidly than Ti Grade 5 and displays local cutting instabilities independent of the uncut chip thickness. Full article

Aiming at the problem of accuracy degradation caused by the mixing of spatiotemporal disturbance patterns in sound speed profile (SSP) inversion using the traditional geographic grid division method, this study proposes an acoustic stability pre-clustering framework that integrates principal component analysis and machine learning clustering. Disturbance mode principal component analysis is first used to extract characteristic parameters, and then a machine learning clustering algorithm is adopted to pre-classify SSP samples according to acoustic stability. The SSP inversion experimental results show that: (1) the SSP samples of the South China Sea can be divided into three clusters of disturbance modes with statistically significant differences. (2) The regression inversion method based on cluster attribution reduces the average error of SSP inversion for data from 2018 to 1.24 m/s, which is more than 50% lower than what can be achieved with the traditional method without pre-clustering. (3) Transmission loss prediction verification shows that the proposed method can produce highly accurate sound field calculations in environmental assessment tasks. The acoustic stability pre-clustering technology proposed in this study provides an innovative solution for the statistical modeling of marine environment parameters by effectively decoupling the mixed effect of SSP spatiotemporal disturbance patterns. Its error control level (<1.5 m/s) is 37% higher than that of the single empirical orthogonal function regression method, showing important potential in underwater acoustic applications in complex marine dynamic environments. Full article

In industrial wireless networks beyond 5G and toward 6G, combining uplink non-orthogonal multiple access (NOMA) with the Kalman filter (KF) effectively reduces interruption risks and transmission delays in remote state estimation. However, the complexity of wireless environments and concurrent multi-sensor transmissions introduce significant interference and latency, impairing the KF’s ability to continuously obtain reliable observations. Meanwhile, existing remote state estimation systems typically rely on oversimplified wireless communication models, unable to adequately handle the dynamics and interference in realistic network scenarios. To address these limitations, this paper formulates a novel dynamic wireless resource allocation problem as a mixed-integer nonlinear programming (MINLP) model. By jointly optimizing sensor grouping and power allocation—considering sensor available power and outage probability constraints—the proposed scheme minimizes both estimation outage and transmission delay. Simulation results demonstrate that, compared to conventional approaches, our method significantly improves transmission reliability and KF estimation performance, thus providing robust technical support for remote state estimation in next-generation industrial wireless networks. Full article

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios. Full article

Orthogonal frequency division multiplexing (OFDM) has been regarded as an attractive waveform for integrated sensing and communication (ISAC). However, suffering from its high peak-to-average power ratio (PAPR), sensitivity to phase noise (PN), and spectral efficiency saturation, the performance of OFDM in ISAC is limited. Against this background, this paper proposes a constellation-optimized index-modulated OFDM (CO-IM-OFDM) framework that leverages neural networks to design a constellation suitable for subcarrier activation patterns. A correlation model between index modulation and constellation is established, enabling adaptive constellation mapping in IM-OFDM. Then, Adam optimizer is employed to train the constellation tailored for ISAC, enhancing spectral efficiency under PN and PAPR constraints. Furthermore, a weighting factor is defined to characterize the joint communication–sensing performance, thus optimizing the overall system performance. Simulation results demonstrate that the proposed method can achieve improvements in bit error rate (BER) by over 4 dB and in Cramér–Rao bound (CRB) by 2% to 8% compared to traditional IM-OFDM constellation mapping. It overcomes fixed constellation constraints of conventional IM-OFDM systems, offering theoretical innovation waveform design for low-power communication–sensing systems in highly dynamic environments. Full article