Search Results (929)

With the rapid expansion of urban subway networks, vibrations induced by subway operations have become an increasingly significant concern for nearby structures. To assess the influence of subway-induced vibrations on nearby structures, it is essential to predict the vibration effects accurately prior to the construction of the subway system. By combining an improved Long Short-Term Memory (LSTM) model with a spectral analysis, this paper proposes a hybrid method to enhance the accuracy and efficiency of predicting structural vibrations induced by subway operations. The improved LSTM model is composed of BiLSTM, an attention mechanism, and the DBO algorithm. The symmetry inherent in the vibration propagation paths and the structural layouts of subway systems is leveraged to improve the feature extraction and modeling accuracy. Additionally, the hybrid method utilizes the symmetric properties of vibration signals in the spectral domain to enhance prediction robustness and efficiency. Then, the hybrid method is utilized to rapidly achieve highly accurate vibration responses induced by subway operations. The verification results demonstrate the following: (1) The improved LSTM model enhances the ability to recognize patterns in time-series vibration data, leading to improved model convergence and generalization. The improved LSTM mode has a significant improvement in prediction accuracy compared to the standard LSTM network. For numerical simulation and real-world measured signals, values of R² increased by 3% and 49.37%. (2) The proposed hybrid method significantly reduces computational time while ensuring results consistent with those obtained from the time-history analysis method. Applying the proposed hybrid method for data augmentation enhances the accuracy of the spectral analysis. The hybrid method achieves an improvement of 7% for the prediction accuracy. Full article

Canine Parvovirus (CPV) is a highly contagious virus that causes severe hemorrhagic enteritis and myocarditis, posing a major threat to the life and health of dogs. The molecular mechanism by which VP2, the major capsid protein of CPV, infects host cells and utilizes host cell proteins for self-replication remains poorly understood. In this study, 140 host proteins specifically binding to CPV VP2 protein were identified by immunoprecipitation combined with liquid chromatography–mass spectrometry (LC-MS/MS). Subsequently, the protein Interaction Network (PPI), the annotation of gene ontology (GO) and the database of Kyoto Encyclopedia of Genes and Genomes (KEGG) were constructed for in-depth analysis. The results showed that CPV VP2 protein participated mainly in cell metabolism, cell biosynthesis, protein folding and various signal transduction processes. According to the results of proteomics analysis, we randomly selected seven proteins for co-immunoprecipitation verification, and the experimental results were consistent with the LC-MS/MS data. In addition, our study found that the expression level of the VP2-interacting protein FHL2 mediated CPV replication. Preliminary studies have shown that knockdown of FHL2 promotes CPV replication by decreasing the expression of interferon β (IFN-β) and interferon-stimulated genes (ISGs), while overexpression of FHL2 can inhibit the replication of CPV by up-regulating the expression of IFN-β and related ISGs. This study lays the foundation for elucidating the potential function of CPV VP2 protein in the process of viral infection and proliferation which provides a theoretical basis for the design of antiviral agents and vaccines. Full article

In-hand manipulation with Cartesian-force-control-based pushing primitives is introduced to achieve the precise placement of an object in a desired position at a manufacturing site. In the bin picking process, achieving the desired grasping posture is challenging due to limitations in the sensing and control of the robotic arm, interference from clustered objects, and unintended collisions, which hinder achieving the planned pose. Even under such conditions, in cases that require precise operations, such as manufacturing processes, maintaining a desired placement posture is crucial for the precise placement of objects into the machine slot. In this paper, a pushing primitive incorporating force feedback control is applied to ensure that the gripper is consistently positioned at the edge of the grasped object regardless of the initial grasping position by utilizing the surrounding environment of the processing machine. Modeling the exact contact friction between the gripper and the grasped object is challenging; therefore, instead of relying on a motion planning approach, we addressed the problem using a control method that leverages feedback from the external force information of the robot manipulator. Additional sensors such as external cameras or tactile sensors in the gripper are not required. The pushing primitive is executed by applying a force greater than the frictional force between the gripper and the grasped object, leveraging the surrounding environment. Experimental verification confirmed that the proposed method achieves precise placement into the machine slot, regardless of initial grasping positions. It also proved to be effective on an actual testbed. Full article

This paper presents a Regeneration filter for reducing near Salt-and-Pepper (nS&P) noise in images, designed for selective noise removal while simultaneously preserving structural details. Unlike conventional methods, the proposed filter eliminates the need for median or other filters, focusing exclusively on restoring noise-affected pixels through localized contextual analysis in the immediate surroundings. Our approach employs an iterative processing method, where additional iterations do not degrade the image quality achieved after the first filtration, even with high noise densities up to 97% spatial distribution. To ensure the results are measurable and comparable with other methods, the filter’s performance was evaluated using standard image quality assessment metrics. Experimental evaluations across various image databases confirm that our filter consistently provides high-quality results. The code is implemented in the R programming language, and both data and code used for the experiments are available in a public repository, allowing for replication and verification of the findings. Full article

Functionally graded materials (FGMs) show continuous variations in properties and offer unique multifunctional capabilities. This study presents a simulation of the powder bed fusion (PBF) process for FGM fabrication using a combination of Unity-based deposition and lattice Boltzmann method (LBM)-based process models. The study introduces a diffusion model that allows for the simulation of material mixtures, in particular AISI 316L austenitic steel and 18Ni maraging 300 martensitic steel. The Unity-based model simulates particle deposition with controlled distribution, incorporating variations in particle size, friction coefficient, and chamber wall rotation angles. The LBM model that simulated free-surface fluid flow, heat flow, melting, and solidification during the PBF process was extended with diffusion models for mixture fraction and concentration-dependent properties. Comparison of the results obtained in simulation with the experimental data shows that they are consistent. Future research may be connected with further verification and validation of the model, by modeling different materials. The presented model can be used for the simulation, study, modeling, and optimization of the production of functionally graded materials in PBF processes. Full article

Remote sensing change detection (RSCD), which utilizes dual-temporal images to predict change locations, plays an essential role in long-term Earth observation missions. Although many deep learning based RSCD models perform well, challenges remain in effectively extracting change information between dual-temporal images and fully leveraging interactions between their feature maps. To address these challenges, a constraint- and interaction-based network (CINet) for RSCD is proposed. Firstly, a constraint mechanism is introduced that uses labels to control the backbone of the network during training to enhance the consistency of the unchanged regions and the differences between the changed regions in the extracted dual-temporal images. Secondly, a Cross-Spatial-Channel Attention (CSCA) module is proposed, which realizes the interaction of valid information between dual-temporal feature maps through channels and spatial attention and uses multi-level information for more accurate detection. The verification results show that compared with advanced parallel methods, CINet achieved the highest F1 scores on all six widely used remote sensing benchmark datasets, reaching a maximum of 92.00 (on LEVIR-CD dataset). These results highlight the excellent ability of CINet to detect changes in various practical scenarios, demonstrating the effectiveness and feasibility of the proposed constraint enhancement and CSCA module. Full article

The hemispherical resonator gyroscope is a gyroscope based on the principle of Coriolis vibration, widely used in inertial measurement systems of spacecraft. This article decomposes the gyroscope into two parts: the resonator shell and the gyroscope head, establishes the energy dissipation mechanism of the gyroscope, and conducts experimental verification. Firstly, based on the working principle of the gyroscope, a mechanical analysis model of the hemispherical resonator gyroscope head with a resonator spherical shell containing quality defects under second-order vibration state was established. The unbalanced force applied by the resonator spherical shell to the hemispherical resonator gyroscope head was analyzed, and the energy transfer path and dissipation mechanism from the spherical shell to the hemispherical resonator gyroscope head were explained. Finally, through the constructed testing platform, the circumferential quality factor test of the hemispherical resonator gyroscope before and after assembly was completed according to the designed experimental plan, and the consistency between theory and experimental phenomena was verified experimentally. Full article

The Retrieval-Augmented Generation (RAG) framework enhances Large Language Models (LLMs) by retrieving relevant knowledge to broaden their knowledge boundaries and mitigate factual hallucinations stemming from knowledge gaps. However, the RAG Framework faces challenges in effective knowledge retrieval and utilization; invalid or misused knowledge will interfere with LLM generation, reducing reasoning efficiency and answer quality. Existing RAG methods address these issues by decomposing and expanding queries, introducing special knowledge structures, and using reasoning process evaluation and feedback. However, the linear reasoning structures limit complex thought transformations and reasoning based on intricate queries. Additionally, knowledge retrieval and utilization are decoupled from reasoning and answer generation, hindering effective knowledge support during answer generation. To address these limitations, we propose the CRP-RAG framework, which employs reasoning graphs to model complex query reasoning processes more comprehensively and accurately. CRP-RAG guides knowledge retrieval, aggregation, and evaluation through reasoning graphs, dynamically adjusting the reasoning path based on evaluation results and selecting knowledge-sufficiency paths for answer generation. CRP-RAG outperforms the best LLM and RAG baselines by 2.46 in open-domain QA, 7.43 in multi-hop reasoning, and 4.2 in factual verification. Experiments also show the superior factual consistency and robustness of CRP-RAG over existing RAG methods. Extensive analyses confirm its accurate and fact-faithful reasoning and answer generation for complex queries. Full article

Vector road networks are vital components of intelligent transportation systems and electronic navigation maps. There is a pressing need for efficient and rapid dynamic updates for road network data. In this paper, we propose a series of methods designed specifically for geometric change detection and the topological consistency updating of multi-source vector road networks without relying on complicated road network matching. For geometric change detection, we employ buffer analysis to compare various sources of vector road networks, differentiating between newly added, deleted, and unchanged road features. Furthermore, we utilize road shape similarity analysis to detect and recognize partial matching relationships between different road network sources. For incremental updates, we define topology consistency and propose three distinct methods for merging road nodes, aiming to preserve the topological integrity of the road network to the greatest extent possible. To address geometric conflicts and topological inconsistencies, we present a fusion and update method specifically tailored for partially matched road features. In order to verify the proposed methods, a road central line network with a scale of 1:10000 from the official institution is employed to geometrically update the commercial navigation road network of a similar scale in the remote area. The experiment results indicate that our method achieves an impressive 91.7% automation rate in detecting geometric changes for road features. For the remaining 8.3% of road features, our method provides suggestions on potential geometric changes, albeit necessitating manual verification and assessment. In terms of the incremental updating of the road network, approximately 89.2% of the data can be seamlessly updated automatically using our methods, while a minor 10.8% requires manual intervention for road updates. Collectively, our methods expedite the updating cycle of vector road network data and facilitate the seamless sharing and integrated utilization of multi-source road network data. Full article

This study presents the production of FAMEs from non-edible Silybum marianum oil using a catalyst consisting of an MXene/SrTiO₃ composite. The primary aim of this study was to reduce our reliance on petroleum-based fuels by harnessing non-edible oil sources. The catalyst, once prepared, achieved an impressive conversion rate of 98.8%. The optimal parameters for this catalytic conversion included a 7 wt% catalyst concentration, a 1:12 molar ratio of oil to methanol, a 100 min reaction time, and a reaction temperature of 60 °C. These parameters ensured the successful completion of the FAME conversion process. The physicochemical properties of Silybum marianum oil confirmed its suitability as a biodiesel source on an industrial scale. The verification of the synthesized MXene/SrTiO₃ catalyst was conducted via XRD, SEM, EDX, and BET, and synthesized biodiesel was confirmed via ¹H and ¹³C-NMR, FTIR, and GC-MS. These results indicate that the catalyst described in this study exhibits significant potential for cost-effective biodiesel production under the appropriate reaction conditions. Full article

Geothermal resources are abundant in the Southern North China Basin, which is one of the prospective areas hosting low–medium-temperature geothermal resources in sedimentary basins in China. The purpose of this work is to reveal the formation and storage conditions of the geothermal resources in the western margin of the Taikang Uplift and delineate the range of potential geothermal reservoirs. This paper uses five magnetotelluric sounding profiles for data processing and analysis, including the calculation of 2D skewness and electric strike. Data processing, analysis, and NLCG 2D inversion were performed on MT data, which consisted of 111 measurement points, and reliable two-dimensional resistivity models and resistivity planes were obtained. In combination with drilling verification and the analysis of geophysical logging data, the stratigraphic lithology and the range of potential geothermal reservoirs were largely clarified. The results show that using the magnetotelluric sounding method can well delineate the range of deep geothermal reservoirs in sedimentary basins and that the MT method is suitable for exploring buried geothermal resources in deep plains. The analytical results showed that the XZR-1 well yielded 1480 cubic meters of water per day, with the water temperature of the wellhead being approximately 78 °C, and combined with the results of this electromagnetic and drilling exploration, a geothermal geological model and genesis process of the west of the Taikang Uplift area was constructed. The water yield and temperature were higher than those of previous exploration results, which has important guiding significance for the future development and utilization of karst fissure heat reservoirs in the western Taikang Uplift. Full article

Machine learning (ML) is increasingly used to support or automate decision processes in critical decision systems such as self driving cars or systems for medical diagnosis. These systems require decisions in which human lives are at stake and the decisions should therefore be well founded and very reliable. This need for reliability contrasts with the black-box nature of many ML models, making it difficult to ensure that they always behave as intended. In face of the high stakes involved, the resulting uncertainty is a significant challenge. Explainable artificial intelligence (XAI) addresses the issue by making black-box models more interpretable, often to increase user trust. However, many current XAI applications focus more on transparency and usability than on enhancing safety of ML applications. In this work, we therefore conduct a systematic literature review to examine how XAI can be leveraged to increase safety of ML applications in critical decision systems. We strive to find out for what purposes XAI is currently used in critical decision systems, what are the most common XAI techniques in critical decision systems and how XAI can be harnessed to increase safety of ML applications in critical decision systems. Using the SPAR-4-SLR protocol, we are able to answer these questions and provide a foundational resource for researchers and practitioners seeking to mitigate risks of ML applications. Essentially, we identify promising approaches of XAI which go beyond increasing trust to actively ensure correctness of decisions. Our findings propose a three-layered framework to enhance safety of ML in critical decision systems by means of XAI. The approach consists of Reliability, Validation and Verification. Furthermore, we point out gaps in research and propose future directions of XAI research for enhancing safety of ML applications in critical decision systems. Full article

The goji fruit fly, Neoceratitis asiatica, is a major pest on the well-known medicinal plant Lycium barbarum. Dissecting the molecular mechanisms of the oviposition selection of N. asiatica regarding the host plant will help to identify new strategies for pest fly control. However, the molecular mechanism of chemical communication between the goji fruit fly and the host goji remains unclear. Hence, our study found that young goji fruit volatiles induced the oviposition response of gravid female N. asiatica. After N. asiatica was exposed to young goji fruit volatiles, the expression of six chemosensory genes (NasiOBP56h3 and OBP99a1 in the antennae; OBP99a2, OBP99a3 and CSP2 in the legs; and OBP56a in the ovipositor) was significantly upregulated in different organs of female N. asiatica compared with the group without odor treatment according to transcriptome data. Further results of qPCR verification show that the expression levels of the six selected upregulated genes after the flies were exposed to host plant volatiles were mostly consistent with the results of transcriptome data. We concluded that six upregulated genes may be involved in the recognition of young goji fruit volatiles by gravid female N. asiatica. Our study preliminarily identifies young goji fruit volatiles as a key factor in the oviposition behavior of N. asiatica, which will facilitate further studies on the mechanisms of host oviposition selection in N. asiatica. Full article

The main aim of the study was the determination of the strength parameters of composite bonded joints consisting of galvanised steel elements, an adhesive layer, and Carbon-Fiber-Reinforced Plastic (CFRP) fabric. For this purpose, shear laboratory tests were carried out on 60 lapped specimens composed of 2 mm thick hot-dip galvanised steel plates of S350 GD. The specimens were overlapped on one side with SikaWrap 230 C carbon fibre textile (CFT) using SikaDur 330 adhesive. The tests were carried out in three series that differed in overlap length (15 mm, 25 mm, and 35 mm). A discussion on the failure mechanism in the context of the bonding capacity of the composite joint was carried out. We observed three forms of joint damage, namely, at the steel-adhesive interface, fibre rupture, and mixed damage behaviour. Moreover, an advanced numerical model using the commercial finite element (FE) program ABAQUS/Standard and the coupled cohesive zone model was developed. The material behaviour of the textile was defined as elastic-lamina and the mixed-mode Hashin damage model was implemented with bi-linear behaviour. Special attention was focused on the formulation of reliable methodologies to determine the load-bearing capacity, failure mechanisms, stress distribution, and the strength characteristics of a composite adhesive joint. In order to develop a reliable model, validation and verification were carried out and self-correlation parameters, which brought the model closer to the laboratory test, were proposed by the authors. Based on the conducted analysis, the strength characteristics including the load-bearing capacity, failure mechanisms, and stress distribution were established. The three forms of joint damage were observed as steel-adhesive interface failure, fibre rupture, and mixed-damage behaviour. Complex interactions between the materials were observed. The most dangerous adhesive failure was detected at the steel and adhesive interface. It was also found that an increase in adhesive thickness caused a decrease in joint strength. In the numerical analysis, two mechanical models were employed, namely, a sophisticated model of adhesive and fabric components. It was found that the fabric model was very sensitive to the density of the finite element mesh. It was also noticed that the numerical model referring to the adhesive layer was nonsensitive to the mesh size; thus, it was regarded as appropriate. Nevertheless, in order to increase the reliability of the numerical model, the authors proposed their own correlation coefficients α and β, which allowed for the correct mapping of adhesive damage. Full article

The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. This study proposes an automatic on-machine measuring apparatus for assessing inner diameter, straightness, and roundness errors. Based on the axial-section measurement principle, an integrated measuring module was designed, including a self-centering mechanism, a diameter measuring sensor, and a positioning reference sensor, all embedded within a control system. On this basis, calculations of the inner diameter, and evaluations of the straightness and roundness errors are presented. Experimental verification is conducted on a blind deep hole with a nominal 100 mm inner diameter and 700 mm depth. Compared with measurements performed on a coordinate measuring machine (CMM), which is limited to a maximum hole depth of 300 mm, the proposed apparatus achieved full-depth on-machine measurements. Meanwhile, the measurement results were consistent with the data obtained by the CMM. The straightness error is considered less than 0.05 mm, and the roundness error is considered less than 0.015 mm. Ultimately, without requiring any additional reference platform, the proposed apparatus shows promise for measuring deep-hole parts on various machine tools, with diameters of no less than 80 mm and theoretically unlimited hole depth. Full article