Search Results (49)

To address the problems of complex process knowledge sources, heterogeneous representations, dispersed semantic associations, and limited reusability in the domain of machining distortion of thin-walled parts, this study proposes a knowledge graph construction method for the workpiece machining distortion domain, together with an intelligent decision-making framework driven by the collaboration of knowledge graphs and large language models. First, a domain ontology model is established around core concepts, including workpiece objects, deformation-driving factors, analytical resources, analytical methods, and optimization knowledge, thereby providing a unified semantic foundation for domain knowledge organization. Second, considering the characteristics of domain texts, such as dense technical terminology, ambiguous entity boundaries, and complex relation expressions, a dual-channel knowledge extraction method integrating BERT-BiLSTM-CRF and Universal Information Extraction (UIE) is developed to achieve high-precision extraction of entities and relations from unstructured texts. Knowledge fusion is further carried out through cross-validation, entity disambiguation, coreference resolution, and semantic alignment, and the extracted knowledge is ultimately stored and organized in Neo4j. Furthermore, an intelligent decision-making framework based on the collaboration of knowledge graphs and large language models is constructed. In this framework, a LoRA-tuned Qwen model is employed for user intent recognition and key information extraction, RapidFuzz WRatio is adopted for similar-node retrieval, and local subgraph construction, Label Propagation-based community detection, Betweenness Centrality-based key-node analysis, and evidence fusion are integrated to support process recommendation and intelligent question answering. Based on the proposed framework, an intelligent decision-making system is further developed for process recommendation and intelligent question answering in machining distortion scenarios. Experimental results show that the proposed dual-channel knowledge extraction model achieves an F1-score of 0.88, demonstrating its effectiveness in knowledge acquisition for the machining distortion domain. The constructed knowledge graph contains 4639 entities and 5822 relations, enabling a systematic representation of machining distortion knowledge. Case studies further demonstrate that the proposed method can generate interpretable recommendation results under complex process constraints in real industrial query scenarios. Overall, the proposed approach provides a feasible pathway for the structured organization, intelligent retrieval, and decision support of workpiece machining distortion knowledge. Full article

Autonomous driving technology is regarded as an effective approach to improving traffic safety and reducing accidents. However, its performance in complex urban scenarios, particularly at intersections, still requires improvement. To enhance trajectory prediction and tracking control, a planning and control framework based on interacting-vehicle trajectory prediction is proposed. First, a deep learning trajectory prediction model integrating an attention mechanism and driving intention recognition is developed to predict the future trajectories of interacting vehicles with high accuracy. Based on the prediction results, collision-avoidance trajectory planning is performed. Then, a trajectory tracking controller combining compensated explicit model predictive control (C-EMPC) and PID control is designed to achieve both high tracking accuracy and real-time performance. The proposed framework is evaluated in a typical intersection scenario through simulation. Results show that the proposed framework can improve driving safety and efficiency in the tested scenario. Furthermore, hardware-in-the-loop experiments indicate the real-time execution feasibility and effectiveness of the proposed method. Full article

To address the limitations of existing vehicle trajectory prediction methods, including insufficient modeling of dynamic inter-vehicle interactions, weak temporal continuity of complex driving intentions such as lane-changing, and high uncertainty in future trajectory prediction, this paper proposes a vehicle trajectory prediction method that integrates Dynamic Graph Neural Networks (DyGNN) with Transformer. Specifically, a time-varying interaction graph is constructed to model the dynamically evolving topological interaction relationships among vehicles, while a Transformer encoder is employed to extract temporal dependency features from historical trajectory sequences. In this way, the joint representation of spatial interaction information and temporal evolution information is achieved, thereby improving the accuracy and continuity of driving intention recognition in complex traffic scenarios. On this basis, driving intention is further introduced into the trajectory prediction process as a prior constraint, which effectively reduces the uncertainty of future trajectory prediction. Comparative experiments on real-world traffic datasets demonstrate that the proposed method maintains low prediction errors across different prediction horizons, showing good effectiveness and robustness. Full article

Background/Objectives: Nurse retention remains a major global challenge for healthcare systems, intensified by workforce aging, rising care complexity, and the long-term impact of the COVID-19 pandemic. Despite extensive research, the evidence on nurse retention remains fragmented and frequently focuses on isolated determinants. This review aimed to synthesize the multifactorial determinants of nurse retention by integrating organizational, relational, and individual perspectives. Methods: An integrative review was conducted following Whittemore and Knafl’s approach and reported according to PRISMA 2020 guidelines where applicable. A systematic search of six databases identified studies published between 2016 and 2026 addressing nurse retention in hospital settings. Included studies underwent methodological quality appraisal using validated tools, and findings were synthesized narratively. Results: Twenty-five articles were included. The analysis revealed differences in perspective between nurse managers and nurses regarding the factors that influence retention. Transformational and participative leadership among nurse managers enhanced staff retention through supportive organizational climates and higher professional commitment. For staff nurses, positive work environments, collegial support, and psychological resources such as self-efficacy and resilience were key predictors of intention to stay. These findings can be interpreted through Herzberg’s Two-Factor Theory, Self-Determination Theory and Theory of Planned Behavior, which collectively highlight how recognition, autonomy, and competence satisfaction drive nurses’ intention to remain in their roles. Conclusions: Nurse retention reflects dynamic, multilevel processes rather than the influence of single determinants. Integrated, theory-informed approaches targeting organizational structures, relational climates, and individual psychological resources are required to strengthen workforce sustainability and support high-quality care delivery. Full article

As universities increasingly work towards the adoption of their third mission—fostering entrepreneurship and innovation—the concept of the Entrepreneurial University (EntUni) emphasizes the need to cultivate a defined set of entrepreneurial competencies in students, such as opportunity recognition, risk-taking, perseverance, self-efficacy, and adaptability. The purpose of this study is to identify which entrepreneurial competencies are most critical for student readiness within the context of an Entrepreneurial University. However, limited consensus remains on which competencies are most essential. This study identifies the entrepreneurial competencies most critical for students within an Entrepreneurial University context through a mixed-methods approach. A student survey assesses self-perceived competencies; a stakeholder survey captures the perspectives of faculty, industry experts, and entrepreneurs; and qualitative interviews with industry professionals explore best practices for competency development. Findings reveal six core competencies that EntUnis should help students cultivate: proactiveness, perseverance, grit, risk propensity, self-efficacy, and entrepreneurial intention. Industry experts further highlight the importance of teamwork, ethical and sustainable thinking, and ambiguity tolerance—competencies often underdeveloped in academic environments. The study also identifies a disconnect between entrepreneurial education and practical application, with many students demonstrating high entrepreneurial intention but limited participation in start-up activities. These insights offer actionable implications for educators, policymakers, and university administrators. Overall, the study highlights the importance of experiential learning, academia-industry collaboration, and structured competency-building to enhance entrepreneurial readiness. By addressing these gaps, EntUnis can better equip students to drive innovation, economic growth, and societal impact. Full article

With the rapid advancement of artificial intelligence technology, multi-agent systems are being widely applied in fields such as autonomous driving and robotic collaboration. However, existing methods often suffer from the disconnection between intention recognition and strategy optimization, leading to inefficiencies in group collaboration. This paper proposes a multi-agent cooperative group game model based on Intention-Strategy Optimization (ISO-MAGCG). The model establishes a two-layer optimization framework encompassing intention and strategy, enabling dynamic adaptation through the co-evolution of upper-layer intention recognition and lower-layer strategy optimization. A Group Attention-based Intention Recognition Network (GAIN) is designed to efficiently capture complex interactions among agents. Furthermore, an Adaptive Group Evolution Algorithm (AGEA) is proposed to ensure the stability of large-scale cooperative endeavors. Experiments conducted in navigation, resource collection, and defense collaboration scenarios validate the effectiveness of the proposed method. Compared with mainstream algorithms such as QMIX, MADDPG, and MAPPO, ISO-MAGCG demonstrates significant superiority in metrics including task success rate and cooperative efficiency, achieving an average improvement of 8.4% in task success rate, a 12% enhancement in cooperative efficiency, and an intention recognition accuracy of 94.3%. The results indicate notable performance advantages and favorable scalability. Full article

The development of autonomous tunneling is crucial for building the intelligent underground infrastructure that smart cities require. However, in complex urban environments, the need for frequent manual intervention during Tunnel Boring Machine (TBM) operation remains a challenge, hindering overall efficiency and safety. To address the human–machine collaboration gap, this study analyzes practical experiences from six tunnel projects that use autonomous driving systems. Building on this foundation, we develop an intelligent assistant powered by a large language model (LLM). The assistant constructs a complete service architecture and intervention mechanism, proposes a phased intention recognition framework, and uses conversational interaction to achieve efficient human–machine communication. Experimental results demonstrate the strong classification performance of our intention recognition model. Furthermore, engineering case studies validate the assistant’s effectiveness in enhancing operational transparency, increasing user trust, bridging the human–machine information gap, and ultimately ensuring safer and more reliable tunneling. This research provides a feasible and innovative technological path for human–machine collaboration in the construction of critical urban infrastructure. Full article

This study presents an integrated micro-system solution to address the challenges of gait instability in patients with impaired hip motor function. We developed a novel wearable hip exoskeleton, where a flexible support unit and a parallel drive mechanism achieve self-alignment with the biological hip joint to minimize parasitic forces. The system is driven by an active brain–computer interface (BCI) that synergizes an augmented reality visual stimulation (AR-VS) paradigm for enhanced motor intent recognition with a high-performance decoding algorithm, all implemented on a real-time embedded processor. This integration of micro-sensors, control algorithms, and actuation enables the establishment of a gait phase-dependent hybrid controller that optimizes assistance. Online experiments demonstrated that the system assisted subjects in completing 10 gait cycles with an average task time of 37.94 s, a correlated instantaneous rate of 0.0428, and an effective output ratio of 82.17%. Compared to traditional models, the system achieved an 18.64% reduction in task time, a 28.31% decrease in instantaneous rate, and a 7.36% improvement in output ratio. This work demonstrates a significant advancement in intelligent micro-system platforms for human-centric rehabilitation robotics. Full article

Driver intention recognition is essential for optimizing driving decisions by dynamically adjusting speed and trajectory to enhance system performance. However, in the underground coal mine environment, traditional vision-based methods face significant limitations in accuracy and adaptability. To effectively improve the accuracy of vision-based driver intention recognition, this study introduces a novel approach leveraging cross-modal knowledge distillation (CMKD) to integrate electroencephalography (EEG) signals with video data to identify driver intentions in coal mining operations. By combining these modalities, the method capitalizes on their complementary strengths to achieve a more comprehensive understanding of driver intent. Experimental analysis across various models evaluates the performance of the proposed CMKD method, which integrates EEG signals with video data. Results reveal a substantial improvement in recognition accuracy over traditional machine vision-based approaches, with a maximum accuracy of 84.38%. This advancement enhances the reliability of driver intention detection and offers more robust support for decision making in automated mine transport systems. Full article

In mobility service environments, recognizing the user condition and driving status is critical in driving safety and experiences. While speech emotion recognition is one of the possible features to predict the driver status, current emotion recognition models have a fundamental limitation: they target to classify only single emotion classes, not multi-classes. It prevents the comprehensive understanding of the driver’s condition and intention during driving. In addition, mobility devices inherently generate noises that might affect speech emotion recognition performances in the mobility service. Considering mobility service environments, we investigate possible models that detect multiple emotions while mitigating noise issues. In this paper, we propose a speech-emotion recognition model based on the autoencoder for multi-emotion detection. First, we analyze the Mel Frequency Cepstral Coefficients (MFCCs) to design the specific features. We also develop a multi-emotion detection scheme based on an autoencoder to detect multiple emotions with substantial flexibility compared to existing models. With our proposed scheme, we investigate and analyze mobility noise impacts and mitigation approaches to evaluate performance results. Full article

Human Activity Recognition (HAR) has recently attracted the attention of researchers. Human behavior and human intention are driving the intensification of HAR research rapidly. This paper proposes a novel Motion History Mapping (MHI) and Orientation-based Convolutional Neural Network (CNN) framework for action recognition and classification using Machine Learning. The proposed method extracts oriented rectangular patches over the entire human body to represent the human pose in an action sequence. This distribution is represented by a spatially oriented histogram. The frames were trained with a 3D Convolution Neural Network model, thus saving time and increasing the Classification Correction Rate (CCR). The K-Nearest Neighbor (KNN) algorithm is used for the classification of human actions. The uniqueness of our model lies in the combination of Motion History Mapping approach with an Orientation-based 3D CNN, thereby enhancing precision. The proposed method is demonstrated to be effective using four widely used and challenging datasets. A comparison of the proposed method’s performance with current state-of-the-art methods finds that its Classification Correction Rate is higher than that of the existing methods. Our model’s CCRs are 92.91%, 98.88%, 87.97.% and 87.77% which are remarkably higher than the existing techniques for KTH, Weizmann, UT-Tower and YouTube datasets, respectively. Thus, our model significantly outperforms the existing models in the literature. Full article

Lane changing is a crucial scenario in traffic environments, and accurately recognizing and predicting lane-changing behavior is essential for ensuring the safety of both autonomous vehicles and drivers. Through considering the multi-vehicle information interaction characteristics in lane-changing behavior for vehicles and the impact of driver experience needs on lane-changing decisions, this paper proposes a lane-changing model for vehicles to achieve safe and comfortable driving. Firstly, a lane-changing intention recognition model incorporating interaction effects was established to obtain the initial lane-changing intention probability of the vehicles. Secondly, by accounting for individual driving styles, a lane-changing behavior decision model was constructed based on a Gaussian mixture hidden Markov model (GMM-HMM) along with a parameter estimation method. The initial lane-changing intention probability serves as the input for the decision model, and the final lane-changing decision is made by comparing the probabilities of lane-changing and non-lane-changing scenarios. Finally, the model was validated using real-world data from the Next Generation Simulation (NGSIM) dataset, with empirical results demonstrating its high accuracy in recognizing and predicting lane-changing behavior. This study provides a robust framework for enhancing lane-changing decision making in complex traffic environments. Full article

In order to quickly and accurately identify the lane changing intention of vehicles, and to deeply consider the time series characteristics of vehicle driving processes and the interactive effects between vehicles, a lane changing intention recognition model, namely, Model_TA, was constructed by combining the time series feature extraction ability of the encoder in the Transformer model, the bidirectional gating mechanism of the bidirectional gated recurrent unit, and the additive attention mechanism. The performance of the Model_TA model was trained and validated on the I-80 dataset in NGSIM. The experimental results showed that the accuracy of model intent recognition was 97.01%, which was 20.3%, 4.73%, and 1.73% higher than that of SVM, LSTM, and Transformer models, respectively; the prediction accuracy at 2.0 s, 2.5 s, and 3.0 s is 90.15%, 84.58%, and 83.13%, respectively, which is better than similar models. It is proved that the model can better predict the lane changing intention of vehicles. Full article

Existing autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting in decisions misaligned with drivers’ intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers’ electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers’ spatial cognition across two dimensions: relative distance and relative orientation. It consists of two components: EEG signal preprocessing and spatial cognition decoding, enabling the autonomous driving system to make more contextually aligned decisions regarding the targets drivers focus on. To enhance the detection accuracy of drivers’ spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. The DTFNet outperforms existing methods, achieving 65.67% and 50.65% accuracy in three-class tasks and 84.46% and 70.50% in binary tasks. Furthermore, we investigated the temporal dynamics of drivers’ spatial cognition and observed that drivers’ perception of relative distance occurs slightly later than their perception of relative orientation, providing valuable insights into the temporal aspects of cognitive processing. Full article

In recent years, sustainable entrepreneurship has emerged as a dynamic field, driving innovative solutions to environmental, social, and financial issues, as evidenced by the improvement of income systems. The purpose of this study is to explore the impact of the forward-looking strategy on enterprise performance so as to ensure that enterprises can maintain the ability of sustainable development. Foresight can promote the enhancement of sustainable development. Therefore, the current research mainly determines that forward-looking strategies will ultimately affect the performance of enterprises through the impact on their own dynamic capabilities. Through the empirical investigation of 125 enterprises, the corresponding research data are obtained. The results show that the forward-looking strategy has a positive impact on enterprise performance, while enterprise dynamic capability, as an intermediary variable, has a positive impact between the forward-looking strategy and enterprise performance. This research introduces market dynamic capability as a moderating variable to explore whether forward-looking strategies can adapt to changes in the external market environment. Structural equation modeling (SEM) is used to examine the complex relationships between multiple independent and dependent variables of forward-looking strategies and dynamic capabilities, including the impact of latent variables. Under the background of digital economy, digital technology gradually infiltrates the operation of enterprises and plays a vital role in enterprise performance. Digital transformation has become a realistic need for enterprises to respond to changes in the market environment and seek development. The forward-looking strategy has brought new opportunities to solve this problem. The core of the forward-looking strategy lies in “forward thinking” and “pioneering intention”. It not only emphasizes the ability to predict and identify potential opportunity windows in uncertain environments but also pays attention to the cultivation of enterprise resilience and openness to maintaining sustainable competitive advantage. Its foresight can build dynamic capabilities and ensure the sustainable development ability of enterprises through continuous insight into market information and technology resources, such as opportunity recognition perception, knowledge absorption and transformation, resource replacement and innovation, organizational change, and reconstruction. Full article