Search Results (46,119)

Most existing carbon fiber composite materials are formed by high-temperature molding of multiple layers of fiber cloth. During the manufacturing and usage processes, materials are prone to defects such as voids, delamination, and inclusions, which seriously threaten their service life and safety performance. Ultrasonic testing is currently a widely adopted method for detecting defects in carbon fiber composite materials. However, existing narrow-pulse ultrasonic transducers often have to sacrifice emission energy to achieve narrow-pulse emission, which results in their limited ability to penetrate thicker carbon fiber composite materials. To address this issue, this paper proposes the design of a focused laminated transducer. By stacking and bonding lead titanate piezoelectric wafers and using a concave lens made of organic glass to focus ultrasonic waves, the emission sound intensity of the ultrasonic transducer is enhanced. The simulation results show that the designed focused double-stack transducer has a directivity gain that is 4.49 dB higher than that of the traditional single-piezoelectric-wafer transducer. The transducer fabricated based on this design has successfully achieved effective detection of internal defects in carbon fiber composite materials. Full article

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

The accurate prediction of herb–target interactions is essential for the modernization of traditional Chinese medicine (TCM) and the advancement of drug discovery. Nonetheless, the inherent complexity of herbal compositions and diversity of molecular targets render experimental validation both time-consuming and labor-intensive. We propose a graph neural network model, MAMGN-HTI, which integrates metapaths with attention mechanisms. A heterogeneous graph consisting of herbs, efficacies, ingredients, and targets is constructed, where semantic metapaths capture latent relationships among nodes. An attention mechanism is employed to dynamically assign weights, thereby emphasizing the most informative metapaths. In addition, ResGCN and DenseGCN architectures are combined with cross-layer skip connections to improve feature propagation and enable effective feature reuse. Experiments show that MAMGN-HTI outperforms several state-of-the-art methods across multiple metrics, exhibiting superior accuracy, robustness, and generalizability in HTI prediction and candidate drug screening. Validation against literature and databases further confirms the model’s predictive reliability. The model also successfully identified herbs with potential therapeutic effects for hyperthyroidism, including Vinegar-processed Bupleuri Radix (Cu Chaihu), Prunellae Spica (Xiakucao), and Processed Cyperi Rhizoma (Zhi Xiangfu). MAMGN-HTI provides a reliable computational framework and theoretical foundation for applying TCM in hyperthyroidism treatment, providing mechanistic insights while improving research efficiency and resource utilization. Full article

This study aims to investigate the multifaceted effects of e-commerce-led digital chain engagement on the income of Chinese crop farmers, distinguishing between participation status and participation depth. The analysis uses data from the China Rural Revitalization Survey (CRRS) conducted in 2020, with 1815 crop-farming households as the sample. To estimate causal effects, treatment effect models and instrumental variable strategies are employed. Results show that e-commerce-led digital chain participation significantly enhances household income, and deeper digital chain engagement amplifies this effect. Mechanism analyses reveal that deep engagement promotes income through multiple channels, including improved digital preparedness, enhanced product sales performance, and increased participation in digital financial services. Heterogeneity analysis indicates that the income gains mainly stem from agricultural revenue, and are more pronounced among cooperative members, though marginal benefits from deeper engagement appear limited. Quantile regressions uncover a pronounced Matthew effect: higher-income households benefit more from digital chain embedding, thereby widening the income gap. Moreover, e-commerce-led digital chain participation also improves farmers’ income satisfaction and their expectations of income sustainability. These findings suggest that policymakers should not only promote basic e-commerce participation but also implement targeted support for deep digital chain embedding to foster inclusive growth while mitigating the Matthew effect. By shifting the focus from binary participation to embedded intensity, this study provides new insights into how e-commerce-led digital transformation shapes rural income structures, offering theoretical and empirical contributions to the literature on agricultural modernization and digital inclusion. Full article

To address the issue of jitter and oscillation of guidance command during multi-vehicle cooperative engagement with maneuvering platforms, this paper proposes a novel terminal guidance law with prescribed performance constraints for multiple cooperative vehicles, which explicitly considers both transient and steady-state performance. Firstly, based on the vehicle-target relative kinematics, with time and space as the main constraint indicators, a multi-vehicle cooperative guidance model is established in the inertial coordinate system. Secondly, combined with the sliding mode control theory, cooperative guidance laws are designed for both the line-of-sight (LOS) direction and the LOS normal direction, respectively, and the Lyapunov stability proof is given. Furthermore, to counteract the impact of target maneuvers on guidance performance, a non-homogeneous disturbance observer is designed to estimate target maneuver information that is difficult to obtain directly, which ensures that performance constraints are still satisfied under strong target maneuvering conditions. Simulation results demonstrate that the proposed guidance law enables multiple coordinated vehicles to successfully engage the target under different maneuvering modes, while satisfying the terminal time-space constraints. Compared with conventional sliding mode control methods exhibiting inherent chattering, the proposed approach employs a novel PPC-SMC hybrid structure to quantitatively constrain the transient convergence of cooperative errors. This structure enhances the multi-vehicle cooperative guidance performance by effectively eliminating chattering and oscillations in the guidance commands, thereby significantly improving the system’s transient behavior. Full article

To address the challenges of characterizing commingled production from multiple channel sand layers with varying boundaries and shapes in tight gas reservoirs, a novel Rate Transient Analysis (RTA) model was established based on the principle of equivalent seepage volume (ESV). This model enables the determination of boundary sizes and permeabilities of individual channel sand layers within commingled tight reservoirs using modern production decline analysis theory. The production decline behavior under different channel sizes, numbers, and configurations was systematically investigated through type curve analysis. The results reveal the existence of five distinct stages in the production decline curves for unequal-width channel sands. The intermediate transient flow stage serves as a diagnostic indicator for identifying boundary disparities among layers. Furthermore, reservoirs with smaller boundary distances, fewer wide channel sand layers, and lower thickness proportions of wider channels exhibit poorer productivity and tend to experience accelerated production decline during early and middle transient flow stages. The proposed method provides an effective approach for characterizing boundary parameters of commingled tight reservoirs and offers a theoretical foundation for evaluating individual layer contributions and productivity. Full article

Feature selection is essential for enhancing classification accuracy, reducing overfitting, and improving interpretability in high-dimensional datasets. Evolutionary Feature Selection (EFS) methods employ a threshold parameter $\theta$ to decide feature inclusion, yet the widely used static setting $\theta =0.5$ may not yield optimal results. This paper presents the first large-scale, systematic evaluation of threshold adaptation mechanisms in wrapper-based EFS across a diverse number of benchmark datasets. We examine deterministic, adaptive, and self-adaptive threshold parameter control under a unified framework, which can be used in an arbitrary bio-inspired algorithm. Extensive experiments and statistical analyses of classification accuracy, feature subset size, and convergence properties demonstrate that adaptive mechanisms outperform the static threshold parameter control significantly. In particular, they not only provide superior tradeoffs between accuracy and subset size but also surpass the state-of-the-art feature selection methods on multiple benchmarks. Our findings highlight the critical role of threshold adaptation in EFS and establish practical guidelines for its effective application. Full article

Acute myeloid leukemia is characterized by the presence of malignant cells and their uncontrolled growth in bone marrow. Recent studies have been focused on the ability of curcumin, a polyphenol derived from the Curcuma longa plant. The role of curcumin is currently under investigation, due to its antitumor properties and action on several pathways, including Nuclear Factor kappa-light-chain-enhancer of activated B cells, Signal Transducer and Activator of Transcription 3, Phosphatidylinositol 3-kinase/protein kinase B, and mitogen-activated protein kinase. The aim of this review is to demonstrate the possible anti-leukemic effect of curcumin, thus its ability to induce apoptosis, inhibit cell proliferation, and modulate angiogenesis. Nowadays, although multiple synergistic effects have been observed and curcumin’s efficacy has been demonstrated through several in vivo and in vitro studies, further broad and exhaustive scientific research is needed to confirm the considerable results. In fact, the low bioavailability of curcumin has limited its clinical applications, a challenge that is currently being addressed through the development of nanoformulations to enhance its stability and absorption within the body. In conclusion, curcumin exhibits antitumor properties with a favorable profile, suggesting its potential as a supportive adjunct in the treatment of patients with acute myeloid leukemia. Full article

Nepal is currently facing critical water quality challenges due to urbanization, water management and governance issues, as well as natural disasters. This has resulted in the presence of harmful contaminants (e.g., pathogens, nitrates, arsenic) across multiple water sources, subsequently leading to waterborne disease risks (e.g., cholera and typhoid). In response to these environmental and public health concerns, we conducted a scoping review to assess microbial and chemical contaminants in drinking and irrigation water in Nepal, as well as their potential impacts on public health. Following the JBI Manual for Evidence Synthesis and the PRISMA-SCR guidelines, we systematically searched for peer-reviewed literature on Nepal’s water quality in seven databases. Of 3666 unique records screened using predefined inclusion criteria, 140 met our criteria. The studies encompassed a variety of methodological designs, with the majority focusing on water sources in the Bagmati province. Bacteria and arsenic emerged as the most prevalent contaminants. Additionally, diseases such as arsenicosis and typhoid remain widespread and may be linked to contaminated water sources. The review identified key gaps in Nepal’s water quality management, including limited geographic research coverage, inconsistent testing protocols, weak regulatory enforcement, and a lack of integration of water quality with public health planning. Our findings underscore the urgent need for effective surveillance systems and a robust regulatory framework to promptly respond to water contamination events in Nepal. Full article

The autonomous navigation of inspection robots in complex forest environments heavily relies on accurate trunk detection. However, existing detection models struggle to achieve both high accuracy and real-time performance on resource-constrained edge devices. To address this challenge, this study proposes a lightweight algorithm named YOLOv11-TrunkLight. The core innovations of the algorithm include (1) a novel StarNet_Trunk backbone network, which replaces traditional residual connections with element-wise multiplication and incorporates depthwise separable convolutions, significantly reducing computational complexity while maintaining a large receptive field; (2) the C2DA deformable attention module, which effectively handles the geometric deformation of tree trunks through dynamic relative position bias encoding; and (3) the EffiDet detection head, which improves detection speed and reduces the number of parameters through dual-path feature decoupling and a dynamic anchor mechanism. Experimental results demonstrate that compared to the baseline YOLOv11 model, our method improves detection speed by 13.5%, reduces the number of parameters by 34.6%, and decreases computational load (FLOPs) by 39.7%, while the average precision (mAP) is only marginally reduced by 0.1%. These advancements make the algorithm particularly suitable for deployment on resource-constrained edge devices of inspection robots, providing reliable technical support for intelligent forestry management. Full article

Competitive programming has emerged as a critical benchmark for evaluating large language models (LLMs) in solving algorithmic problems under competitive conditions. Existing methods, such as the Sequential One-Agent Pipeline (SOP) approach, suffer from significant limitations, including the inability to effectively manage semantic drift across multiple stages, a lack of coordinated adversarial testing, and suboptimal final solutions. These issues lead to high rates of wrong answer (WA) and time-limit exceeded (TLE) errors, especially on complex problems. In this paper, we propose the Cross-Verification Collaboration Protocol (CVCP), a multi-agent framework that integrates symmetry detection, symmetry-guided adversarial testing, Round-Trip Review Protocol (RTRP), and Asynchronous Voting Resolution (AVR) to address these shortcomings. We evaluate our method on the CodeELO dataset, showing significant improvements in performance, with Elo Ratings increasing by up to 7.1% and Pass Rates for hard problems improving by as much as 1.8 times compared to the SOP baseline. Full article

Infections caused by antibiotic resistant bacteria pose a serious threat to global health, leading to higher medical costs, longer hospital stays, and increased morbidity and mortality. An increasing number of bacteria have been implicated in untreatable infections due to multiple resistance mechanisms. In 2017, the World Health Organization published a list of the most important antibiotic resistant bacteria worldwide, for which there is an urgent need to develop new therapeutic options. In recent years, fortunately, new antibiotics have been approved for the treatment of infections caused by multidrug-resistant bacteria. The purpose of this review is to present the most impactful new antibiotics that are currently available for the treatment of these infections. The discovery of new therapeutic strategies will help to limit the spread of multidrug-resistant bacteria, but careful prescribing, appropriate use and monitoring of resistant strains will be crucial to ensure that they remain effective in the future. Full article

This study, motivated by the pronounced fluid loss characteristics of water-based fracturing fluids, developed a fluid–solid coupling model to investigate water-based fracturing in geological reservoirs. The model was further employed to analyse the effects of multiple factors on fracture propagation and the seepage capacity of water-based fracturing fluids. Moreover, the underlying mechanisms of fracture propagation and seepage enhancement were elucidated from a microscopic molecular perspective. The results obtained that the high apparent viscosity of water-based fracturing fluids not only enhances the fracturing efficiency of reservoir rocks but also results in a reduced seepage volume (−17 mL) in low-permeability reservoirs. Furthermore, the reservoir porosity (+2.5%) exhibits a clear inverse proportional relationship with fracturing efficiency (−0.9 m), while the seepage volume (+7 mL) of water-based fracturing fluids continues to increase. The strength and quantity of hydrogen bonds between molecules in water-based fracturing fluid, influenced by external factors, directly affect fluid seepage. The seepage behaviour of water-based fracturing fluids in geological reservoirs, together with the influence of reservoir conditions on fracture propagation, provides valuable reference data for rock fracturing and reservoir stimulation. However, the absence of data analysis and microscopic images of microscopic molecular dynamics constitutes a challenging problem that demands attention. Full article

To address the ill-posedness of the Hessian matrix in monocular visual-inertial SLAM (Simultaneous Localization and Mapping) caused by unobservable depth of feature points, which leads to convergence difficulties and reduced robustness, this paper proposes a Pre-Dog-Leg feature optimization method based on an adaptive preconditioner. First, we propose a multi-candidate initialization method with robust characteristics. This method effectively circumvents erroneous depth initialization by introducing multiple depth assumptions and geometric consistency constraints. Second, we address the pathology of the Hessian matrix of the feature points by constructing a hybrid SPAI-Jacobi adaptive preconditioner. This preconditioner is capable of identifying matrix pathology and dynamically enabling preconditioning as a strategy. Finally, we construct a hybrid adaptive preconditioner for the traditional Dog-Leg numerical optimization method. To address the issue of degraded convergence performance when solving pathological problems, we map the pathological optimization problem from the original parameter space to a well-conditioned preconditioned space. The optimization equivalence is maintained by variable recovery. The experiments on the EuRoC dataset show that the method reduces the number of Hessian matrix conditionals by a factor of 7.9, effectively suppresses outliers, and significantly improves the overall convergence time. From the analysis of trajectory error, the absolute trajectory error is reduced by up to 16.48% relative to RVIO2 on the MH_01 sequence, 20.83% relative to VINS-mono on the MH_02 sequence, and up to 14.73% relative to VINS-mono and 34.0% relative to OpenVINS on the highly dynamic MH_05 sequence, indicating that the algorithm achieves higher localization accuracy and stronger system robustness. Full article

In massive multiple-input and multiple-output (mMIMO) systems, superimposed channel state information (CSI) feedback is developed to improve the occupation of uplink bandwidth resources. Nevertheless, the interference from this superimposed mode degrades the recovery performance of both downlink CSI and uplink data sequences. Although machine learning (ML)-based methods effectively mitigate superimposed interference by leveraging the multi-domain features of downlink CSI, the complex interactions among network model parameters cause a significant burden on system resources. To address these issues, inspired by sensing-assisted communication, we propose a novel superimposed CSI feedback method assisted by inactive sensing information that previously existed but was not utilized at the base station (BS). To the best of our knowledge, this is the first time that inactive sensing information is utilized to enhance superimposed CSI feedback. In this method, a new type of modal data, different from communication data, is developed to aid in interference suppression without requiring additional hardware at the BS. Specifically, the proposed method utilizes location, speed, and path information extracted from sensing devices to derive prior information. Then, based on the derived prior information, denoising processing is applied to both the delay and Doppler dimensions of downlink CSI in the delay—Doppler (DD) domain, significantly enhancing the recovery accuracy. Simulation results demonstrate the performance improvement of downlink CSI and uplink data sequences when compared to both classic and novel superimposed CSI feedback methods. Moreover, against parameter variations, simulation results also validate the robustness of the proposed method. Full article