Search Results (38)

Urban infrastructure construction poses significant risks to surrounding the infrastructure due to ground settlement, structural disturbances, and underground utility disruptions. Traditional risk assessment methods often rely on static models and experience-based decision-making, limiting their ability to adapt to dynamic construction conditions. This study proposes an integrated framework combining digital twin and knowledge graph technologies to enhance real-time risk assessment and emergency response in tunnel construction. The digital twin continuously integrates real-time monitoring data, including settlement measurements, TBM operational parameters, and structural responses, creating a virtual representation of the tunneling environment. Meanwhile, the knowledge graph structures domain knowledge and applies rule-based reasoning to infer potential hazards, detect abnormal conditions, and suggest mitigation strategies. The proposed approach has been successfully applied to a practical tunnel project in China, where it played a crucial role in emergency response and risk mitigation. By integrating real-time monitoring data with the knowledge-driven reasoning system, the developed framework enabled the early identification of anomalies, rapid risk assessment, and the formulation of effective mitigation strategies, preventing further structural impact. This bidirectional interaction between the digital twin and the knowledge graph ensured that the real-world data informed the automated reasoning, while the inference results were visualized within the digital twin for intuitive decision support. The proposed framework not only enhances current risk management practices but also serves as a foundation for future innovations in smart infrastructure and automated emergency response systems. Full article

A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models. Full article

With the rapid development of quantum communication technologies, controlled double-direction cyclic (CDDC) quantum communication has become an important research direction. However, how to choose an appropriate quantum state as a channel to achieve double-direction cyclic (DDC) quantum communication for multi-particle entangled states remains an unresolved challenge. This study aims to address this issue by constructing a suitable quantum channel and investigating the DDC quantum communication of two-particle states. Initially, we create a 25-particle entangled state using Hadamard and controlled-NOT (CNOT) gates, and provide its corresponding quantum circuit implementation. Based on this entangled state as a quantum channel, we propose two new four-party CDDC schemes, applied to quantum teleportation (QT) and remote state preparation (RSP), respectively. In both schemes, each communicating party can synchronously transmit two different arbitrary two-particle states to the other parties under supervisory control, achieving controlled quantum cyclic communication in both clockwise and counterclockwise directions. Additionally, the presented two schemes of four-party CDDC quantum communication are extended to situations where $n>3$ communicating parties. In each proposed scheme, we provide universal analytical formulas for the local operations of the sender, supervisor, and receiver, demonstrating that the success probability of each scheme can reach 100%. These schemes only require specific two-particle projective measurements, single-particle von Neumann measurements, and Pauli gate operations, all of which can be implemented with current technologies. We have also evaluated the inherent efficiency, security, and control capabilities of the proposed schemes. In comparison to earlier methods, the results demonstrate that our schemes perform exceptionally well. This study provides a theoretical foundation for bidirectional controlled quantum communication of multi-particle states, aiming to enhance security and capacity while meeting the diverse needs of future network scenarios. Full article

A visual deformation prediction method was proposed to improve the accuracy and visualization of the surrounding rock deformation prediction in tunnel construction, combining the Variational Modal Decomposition (VMD) and Bidirectional Long- and Short-Term Memory (BiLSTM) network. Based on the VMD method to decompose the measured data of tunnel surrounding rock deformation, the BiLSTM model was used to predict the final deformation value. The prediction results were also embedded into the tunnel’s Building Information Modeling (BIM) as plug-ins, and the data were visualized through graphs and color warnings. Taking the measured data of the arch settlement of the Loushan tunnel as an example, the results showed that the prediction results were more consistent with the measured situation, and the visualization expression could effectively warn of the risk of vault settlement in the construction stage. This study realized the combined use of surrounding rock deformation prediction and BIM technology, which could be used as a reference for similar projects. Full article

Excessive use of fossil fuel-powered vehicles is a major problem for the entire world today, because of which greenhouse gases are increasing day by day. As a result, climate change and global warming have grown to be serious problems that affect both the environment and life on Earth. However, the effective way of reducing greenhouse gases is to use electric vehicles for commuting. The assessment and selection of the best possible way of charging an electric vehicle is a convoluted decision-making challenge due to the presence of assorted contradictory criteria. Additionally, individual decision makers’ minds and insufficient data are obstacles to doing this. In this regard, interval-valued picture fuzzy sets have been considered as a compatible tool to handle vagueness. In this paper, a multi-attribute group decision-making problem with the bidirectional projection-based VlseKriterijumska Optimizacija I Kompromisno Resenje $\left(VIKOR\right)$ method is considered where the weights are partially known. The objective weights of the attributes in this model are determined using the deviation-based approach. The compromised solution is also assessed using the $VIKOR$ approach. Both the interval-valued image fuzzy Schweizer–Sklar power weighted geometric operator and the interval-valued picture fuzzy Schweizer–Sklar power weighted averaging operator are used in this process. Lastly, a numerical example showing the most suitable way to charge an electric vehicle is given to demonstrate the suggested methodology. To evaluate the robustness and efficacy of the suggested strategy, a comparative analysis with current techniques and a sensitivity analysis of the parameters are also carried out. Full article

To investigate the effect of combined end-and-shaft post-grouting on the vertical load-bearing performance of bridge-bored piles in the Dongting Lake area of Hunan, two post-grouted piles were subjected to bi-directional O-cell and top-down load tests before and after combined end-and-shaft grouting, based on the Wushi to Yiyang Expressway project. A comparative analysis was conducted on the bearing capacity, deformation characteristics, and load transfer behavior of the piles before and after grouting. This study also examined the conversion coefficient γ values of different soil layers obtained from the bi-directional O-cell test for bearing capacity calculations. Additionally, the characteristic values of the end bearing capacity, obtained from the bi-directional O-cell and top-down load tests, were compared with the values calculated using the relevant formulas in the current standards, which validated the accuracy of existing regulations and traditional loading methods. The results indicate that the stress distribution along the pile shaft differed between the two test methods. In the bi-directional O-cell test, the side resistance developed from the end to the head, while in the top-down load test, it developed from the head to the end. After combined post-grouting, the ultimate bearing capacity of the piles significantly increased, with side resistance increasing by up to 81.03% and end resistance by up to 105.66%. The conversion coefficients for the side resistance in silty sand and gravel before and after grouting are 0.86 and 0.80 and 0.81 and 0.69, respectively. The characteristic values of the end bearing capacity, as measured by the bi-directional O-cell and top-down load tests, were substantially higher than those calculated using the current highway bridge and culvert standards, showing increases of 133.63% and 86.15%, respectively. These findings suggest that the current standard formulas are overly conservative. Additionally, the measured values from the top-down load test may underestimate the actual bearing capacity of piles in engineering projects. Therefore, it is recommended that future pile foundation designs incorporate both bi-directional O-cell testing and combined post-grouting techniques to optimize design solutions. Full article

The widely recognized measure for resources called resource strength (RS) does not fully capture the resources complexity of a project. Therefore, it cannot be used as a standalone measure to distinguish the complexity of various instances of project scheduling problems. Consequently, additional resource measures such as total amount of overflow (TAO) have been introduced, which should be used in conjunction with the RS. Extensive experimental studies have shown that as the value of TAO increases in a project, scheduling schemes with higher dimensional scheduling schemes such as bi-directional and tri-directional result in schedules with shorter makespans. In this study, an effective approach is proposed for integrating projects in multi-project environments, called the integrated project approach (IPA), taking into account the influence of TAO and building upon the relation between the TAO and the scheduling generation schemes. To assess the performance of IPA, we develop a new random multi-project generator based on the well-known benchmark sets, which utilizes TAO as a control tool to generate instances. The findings indicate that prioritizing the projects and frequency of the projects integration, facilitated by the proposed IPA, have a positive impact on the quality of multi-project schedules. Full article

Crowdsourcing Software Development (CSD) platforms, i.e., TopCoder, function as intermediaries connecting clients with developers. Despite employing systematic methodologies, these platforms frequently encounter high task abandonment rates, with approximately 19% of projects failing to meet satisfactory outcomes. Although existing research has focused on task scheduling, developer recommendations, and reward mechanisms, there has been insufficient attention to the support of platform moderators, or copilots, who are essential to project success. A critical responsibility of copilots is estimating project duration; however, manual predictions often lead to inconsistencies and delays. This paper introduces an innovative machine learning approach designed to automate the prediction of project duration on CSD platforms. Utilizing historical data from TopCoder, the proposed method extracts pertinent project attributes and preprocesses textual data through Natural Language Processing (NLP). Bidirectional Encoder Representations from Transformers (BERT) are employed to convert textual information into vectors, which are then analyzed using various machine learning algorithms. Zero-shot learning algorithms exhibit superior performance, with an average accuracy of 92.76%, precision of 92.76%, recall of 99.33%, and an f-measure of 95.93%. The implementation of the proposed automated duration prediction model is crucial for enhancing the success rate of crowdsourcing projects, optimizing resource allocation, managing budgets effectively, and improving stakeholder satisfaction. Full article

To solve complex multi-attribute decision-making (MADM) problems within a triangular dual hesitant fuzzy (TDHF) environment where the attribute weights (Aws) are either fully or partially known, a novel bidirectional projection method is proposed, named multi-attribute decision-making and based on the consistent bidirectional projection measures of triangular dual hesitant fuzzy sets (TDHFSs). First, some notions are developed, such as the operation laws, score and accuracy functions, negative ideal points (NIPs), and positive ideal points (PIPs) of TDHFSs. The correlation coefficients and the cosine of the angle between the vectors of each alternative and the triangular dual hesitant fuzzy (TDHF) points are introduced. Then, the consistent bidirectional projection decision-making method based on the TDHFSs’ correlation coefficients is proposed. Additionally, an optimization model is established via maximizing the consistent coefficient to determine the Aws. Furthermore, some approaches are investigated based on the proposed approaches concerning the MADM issues with attribute values represented by triangular dual hesitant fuzzy elements (TDHFEs). Finally, a supply chain management (SCM) problem is illustrated, and comparative analyses are implemented to demonstrate the presented approach’s feasibility and efficiency. Full article

Cu pillars serve as interconnecting structures for 3D chip stacking in heterogeneous integration, whose height uniformity directly impacts chip yield. Compared to typical methods such as white-light interferometry and confocal microscopy for measuring Cu pillars, microscopic fringe projection profilometry (MFPP) offers obvious advantages in throughput, which has great application value in on-line bump height measurement in wafer-level packages. However, Cu pillars with large curvature and smooth surfaces pose challenges for signal detection. To enable the MFPP system to measure both the top region of the Cu pillar and the substrate, which are necessary for bump height measurement, we utilized rigorous surface scattering theory to solve the bidirectional reflective distribution function of the Cu pillar surface. Subsequently, leveraging the scattering distribution properties, we propose a hybrid bright-dark-field MFPP system concept capable of detecting weakly scattered signals from the top of the Cu pillar and reflected signals from the substrate. Experimental results demonstrate that the proposed MFPP system can measure the height of Cu pillars with an effective field of view of 15.2 mm × 8.9 mm and a maximum measurement error of less than 0.65 μm. Full article

Diabetes mellitus (DM) poses a significant challenge to global health, with its prevalence projected to rise dramatically by 2045. This narrative review explores the bidirectional relationship between periodontitis (PD) and type 1 diabetes mellitus (T1DM), focusing on cellular and molecular mechanisms derived from the interplay between oral microbiota and the host immune response. A comprehensive search of studies published between 2008 and 2023 was conducted to elucidate the association between these two diseases. Preclinical and clinical evidence suggests a bidirectional relationship, with individuals with T1DM exhibiting heightened susceptibility to periodontitis, and vice versa. The review includes recent findings from human clinical studies, revealing variations in oral microbiota composition in T1DM patients, including increases in certain pathogenic species such as Porphyromonas gingivalis, Prevotella intermedia, and Aggregatibacter actinomycetemcomitans, along with shifts in microbial diversity and abundance. Molecular mechanisms underlying this association involve oxidative stress and dysregulated host immune responses, mediated by inflammatory cytokines such as IL-6, IL-8, and MMPs. Furthermore, disruptions in bone turnover markers, such as RANKL and OPG, contribute to periodontal complications in T1DM patients. While preventive measures to manage periodontal complications in T1DM patients may improve overall health outcomes, further research is needed to understand the intricate interactions between oral microbiota, host response, periodontal disease, and systemic health in this population. Full article

Automatic static analysis tools (ASATs), also known as static analyzers, have demonstrated their significance and practicability in detecting software defects. ASATs assist developers to identify potential vulnerabilities, errors, and security hazards in source code without executing the software. As software systems grow in scale and complexity, ASATs are replacing manual security audits and becoming crucial for detecting issues in code. However, ASATs often generate numerous warnings with high false positive rates, while developers typically only take measures on a small portion of actionable alarms. To cope with this problem, we propose an innovative method that combines the pre-trained CodeBERT model and neural networks to reduce false positives detected by ASATs. Our approach was evaluated on the Defects4J dataset, which comprises 835 real-world software defects extracted from 17 open-source Java projects. The experimental results explicitly manifest the effectiveness in processing static analysis alarms. By employing a bidirectional recurrent neural network for context embeddings, our approach achieved an accuracy of 95.77% and an AUC score of 98.3%. This research enables developers to minimize false positive alarms and ensure a reasonable number of actionable warnings while guaranteeing software quality and security. Full article

Simultaneous localization and mapping (SLAM) is a hot research area that is widely required in many robotics applications. In SLAM technology, it is essential to explore an accurate and efficient map model to represent the environment and develop the corresponding data association methods needed to achieve reliable matching from measurements to maps. These two key elements impact the working stability of the SLAM system, especially in complex scenarios. However, previous literature has not fully addressed the problems of efficient mapping and accurate data association. In this article, we propose a novel hash multi-scale (H-MS) map to ensure query efficiency with accurate modeling. In the proposed map, the inserted map point will simultaneously participate in modeling voxels of different scales in a voxel group, enabling the map to represent objects of different scales in the environment effectively. Meanwhile, the root node of the voxel group is saved to a hash table for efficient access. Secondly, considering the one-to-many (1 $\times {10}^3$ order of magnitude) high computational data association problem caused by maintaining multi-scale voxel landmarks simultaneously in the H-MS map, we further propose a bidirectional matching algorithm (MSBM). This algorithm utilizes forward–reverse–forward projection to balance the efficiency and accuracy problem. The proposed H-MS map and MSBM algorithm are integrated into a completed LiDAR SLAM (HMS-SLAM) system. Finally, we validated the proposed map model, matching algorithm, and integrated system on the public KITTI dataset. The experimental results show that, compared with the ikd tree map, the H-MS map model has higher insertion and deletion efficiency, both having $O\left(1\right)$ time complexity. The computational efficiency and accuracy of the MSBM algorithm are better than that of the small-scale priority matching algorithm, and the computing speed of the MSBM achieves 49 ms/time under a single CPU thread. In addition, the HMS-SLAM system built in this article has also reached excellent performance in terms of mapping accuracy and memory usage. Full article

The coupling between modern electric power physical and cyber systems is deepening. An increasing number of users are gradually participating in power operation and control, engaging in bidirectional interactions with the grid. The evolving new power system is transforming into a highly intelligent socio–cyber–physical system, featuring increasingly intricate and expansive architectures. Demands for stable system operation are becoming more specific and rigorous. The new power system confronts significant challenges in areas like planning, dispatching, and operational maintenance. Hence, this paper aims to comprehensively explore potential synergies among various power system components from multiple viewpoints. It analyzes numerous core elements and key technologies to fully unlock the efficiency of this coupling. Our objective is to establish a solid theoretical foundation and practical strategies for the precise implementation of integrated planning and operation dispatching of source–grid–load–storage systems. Based on this, the paper first delves into the theoretical concepts of source, grid, load, and storage, comprehensively exploring new developments and emerging changes in each domain within the new power system context. Secondly, it summarizes pivotal technologies such as data acquisition, collaborative planning, and security measures, while presenting reasonable prospects for their future advancement. Finally, the paper extensively discusses the immense value and potential applications of the integrated planning and operation dispatching concept in source–grid–load–storage systems. This includes its assistance in regards to large-scale engineering projects such as extreme disaster management, facilitating green energy development in desertification regions, and promoting the construction of zero-carbon parks. Full article

Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1-measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1-measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance. Full article