Search Results (3,606)

This study aimed to examine reading interest profiles among preservice Chinese language teachers and related factors making them begin to like or dislike reading. In total, 321 college students majoring in Chinese language education in elementary and secondary schools participated in this study and completed a reading interest questionnaire. The questionnaire contains one close-ended question asking about their reading interest levels across seven periods (from preschool to college) and three open-ended questions asking about the reasons influencing their reading interest levels. Latent profile analysis (LPA) was used to identify reading interest profiles, and qualitative analysis was used to examine factors influencing their reading interests. The LPA results revealed three profiles, namely, mountain (up-down), valley (up-down-up), and upslope (up). The qualitative analysis revealed that motivators encouraging students to read included literacy sponsors, improved reading ability, reading time, extrinsic motivators, curiosity and desire for knowledge, access to reading, discovery of preferred texts, and relief from academic stress and relaxation. By contrast, barriers associated with the decline in reading interest included academic burdens and pressure, the availability of alternatives, a lack of reading ability, a loss of reading autonomy, a lack of literacy sponsors, limited access to reading, and inappropriate texts. Literacy researchers and educators should listen to students’ voices, understand their reading experiences, and consider developing appropriate intervention programs for literacy at different periods. Full article

Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, existing models typically extract non-linear representations from explicit features in a relatively simplistic manner and fail to fully exploit semantic heterogeneity of relation types and entity co-occurrence frequencies. Consequently, these models struggle to capture critical predictive cues embedded in various entities and relations. To address these limitations, this paper proposes a relation aware spectral decoupling attention network for KGR (RASD). First, a spectral decoupling attention network module projects joint embeddings of entities and relations into the frequency domain, extracting features across different frequency bands and adaptively allocating attention at the global level to model frequency specific information. Next, a relation-aware learning module employs relation aware filters and an augmentation mechanism to preserve distinct relational properties and suppress redundant features, thereby enhancing representation of heterogeneous relations. Experimental results demonstrate that RASD achieves significant and consistent improvements over multiple leading baseline models on link prediction tasks across five public benchmark datasets. Full article

The rapid retrieval and precise acquisition of land use approval information are crucial for enhancing the efficiency and quality of land use approval, as well as for promoting the intelligent transformation of land use approval processes. As an advanced retrieval method, question-answering (Q&A) technology has become a core technical support for addressing current issues such as low approval efficiency and difficulty in obtaining information. However, existing Q&A technologies suffer from significant hallucination problems and limitations in considering spatiotemporal factors in the land use approval domain. To effectively address these issues, this study proposes a spatiotemporal–semantic coupling intelligent Q&A method for land use approval based on knowledge graphs (KGs) and intelligent agent technology, aiming to enhance the efficiency and quality of land use approval. Firstly, a land use approval knowledge graph (LUAKG) is constructed, systematically integrating domain knowledge such as policy clauses, legal regulations, and approval procedures. Then, by combining large language models (LLMs) and intelligent agent technology, a spatiotemporal–semantic coupling Q&A framework is designed. Through the use of spatiotemporal analysis tools, this framework can comprehensively consider spatial, temporal, and semantic factors when handling land approval tasks, enabling dynamic decision-making and precise reasoning. The research results show that, compared to traditional Q&A based on LLMs and Q&A based on retrieval-enhanced generation (RAG), the proposed method improves accuracy by 16% and 9% in general knowledge Q&A tasks. In the project review Q&A task, F1 scores and accuracy increase by 2% and 9%, respectively, compared to RAG-QA. Particularly, under the spatiotemporal–semantic multidimensional analysis, the improvement in F1 score and accuracy ranges from 2 to 6% and 7 to 10%, respectively. Full article

Background/Objective: To assess vulnerable Romanian children’s knowledge, attitudes, and beliefs regarding the HPV vaccination. Methods: Vulnerable children (ethnic minorities, high social vulnerability index, or low socioeconomic status) from three schools in Cluj County, Romania, were enrolled in a short educational presentation regarding HPV and were delivered a physical questionnaire consisting of 26 items. Results: 199 vulnerable school students concluded the questionnaire with a mean age of 14.62. Most participants were unaware of the HPV infection or the HPV vaccine. Following the educational program, most participants exhibited a reasonably elevated level of knowledge, which positively correlated with the intention to vaccinate. Fifty-three per cent of respondents would vaccinate in school if the vaccine were available, fifty-four per cent would vaccinate if the product were free of charge or at minimal cost, and over sixty-four per cent would vaccinate at their doctor’s recommendation. Several knowledge items, beliefs, and attitudes towards vaccination were disclosed to influence children’s preference to participate in vaccination campaigns. Conclusions: This analysis unveiled the pivotal role of knowledge about HPV in the immunization uptake within underserved, vulnerable populations of Romanian children. An intricate interplay between vulnerability, knowledge, accessibility, and the willingness to vaccinate was impacted by several beliefs and attitudes towards HPV vaccination. Most children were willing to participate in HPV immunization campaigns, whether school-based, reimbursed, or at the doctor’s recommendation. These findings act as pillars for assembling future educational campaigns in vulnerable Romanian communities of children, aiming to enhance awareness and coverage of HPV vaccination and ensure inclusive health policies. Full article

Predictive maintenance (PdM) represents a significant evolution in maintenance strategies. However, challenges such as system integration complexity, data quality, and data availability are intricately intertwined, collectively impacting the successful deployment of PdM systems. Recently, large model-based agents, or agentic artificial intelligence (AI), have evolved from simple task automation to active problem-solving and strategic decision-making. As such, we propose an AI agent-enabled PdM method that leverages an agentic AI development platform to streamline the development of a multimodal data-based fault detection agent, a RAG (retrieval-augmented generation)-based fault classification agent, a large model-based fault diagnosis agent, and a digital twin-based fault handling simulation agent. This approach breaks through the limitations of traditional PdM, which relies heavily on single models. This combination of “AI workflow + large reasoning models + operational knowledge base + digital twin” integrates the concepts of BaaS (backend as a service) and LLMOps (large language model operations), constructing an end-to-end intelligent closed loop from data perception to decision execution. Furthermore, a tentative prototype is demonstrated to show the technology stack and the system integration methods of the agentic AI-based PdM. Full article

Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework that integrates Bayesian inference with symmetric deep neural architectures to enable adaptive, efficient knowledge assessment. Our method models student knowledge as latent representations within a graph-structured concept dependency network, where probabilistic mastery states, updated through variational inference, are encoded by symmetric graph properties and symmetric concept representations that preserve structural equivalences across similar knowledge configurations. The system employs a symmetric dual-network architecture: a concept embedding network that learns scale-invariant hierarchical knowledge representations from assessment data and a question selection network that optimizes symmetric information gain through deep reinforcement learning with symmetric reward structures. We introduce a novel uncertainty-aware objective function that leverages symmetric uncertainty measures to balance exploration of uncertain knowledge regions with exploitation of informative question patterns. The hierarchical structure captures both fine-grained concept mastery and broader domain understanding through multi-scale graph convolutions that preserve local graph symmetries and global structural invariances. Our symmetric information-theoretic method ensures balanced assessment strategies that maintain diagnostic equivalence across isomorphic concept subgraphs. Experimental validation on large-scale educational datasets demonstrates that our method achieves 76.3% diagnostic accuracy while reducing the question count by 35.1% compared to traditional assessments. The learned concept embeddings reveal interpretable knowledge structures with symmetric dependency patterns that align with pedagogical theory. Our work generalizes across domains and student populations through symmetric transfer learning mechanisms, providing a principled framework for intelligent tutoring systems and adaptive testing platforms. The integration of probabilistic reasoning with symmetric neural pattern recognition offers a robust solution to the fundamental trade-off between assessment efficiency and diagnostic precision in educational technology. Full article

Treating silk with metal salts was a common practice starting in the second half of the 19th century until the early 20th century. It aimed to increase the weight and thickness of the fibres. However, the presence of metal salts is believed to accelerate and aggravate the deterioration of historical silk textiles, and weighted silks are nowadays considered one of the most pressing issues in textile conservation. This paper explores the history of the practice of metal salt weighting of silk: the materials and methods used, the reasons behind weighting, and how this practice developed as the product of a specific historical and economic context. A total of 147 primary textual sources (patents, dyers’ manuals, and books) were investigated and from these 136 weighting methods were collected and reviewed. The results highlighted tin salts as the most commonly mentioned metal salts for weighting silks of any colour. Iron compounds combined with tannins were the method of choice for dark silks, although also in combination with tin in half of the cases. The knowledge gained from this research will help further the study of the degradation pathways of historical silk fabrics, as representative reproductions of weighted silks will be produced based on the findings. Full article

Scene Knowledge-guided Visual Grounding (SK-VG) is a multi-modal detection task built upon conventional visual grounding (VG) for human–computer interaction scenarios. It utilizes an additional passage of scene knowledge apart from the image and context-dependent textual query for referred object localization. Due to the inherent difficulty in directly establishing correlations between the given query and the image without leveraging scene knowledge, this task imposes significant demands on a multi-step knowledge reasoning process to achieve accurate grounding. Off-the-shelf VG models underperform under such a setting due to the requirement of detailed description in the query and a lack of knowledge inference based on implicit narratives of the visual scene. Recent Vision–Language Models (VLMs) exhibit improved cross-modal reasoning capabilities. However, their monolithic architectures, particularly in lightweight implementations, struggle to maintain coherent reasoning chains across sequential logical deductions, leading to error accumulation in knowledge integration and object localization. To address the above-mentioned challenges, we propose SplitGround—a collaborative framework that strategically decomposes complex reasoning processes by fusing the input query and image with knowledge through two auxiliary modules. Specifically, it implements an Agentic Annotation Workflow (AAW) for explicit image annotation and a Synonymous Conversion Mechanism (SCM) for semantic query transformation. This hierarchical decomposition enables VLMs to focus on essential reasoning steps while offloading auxiliary cognitive tasks to specialized modules, effectively splitting long reasoning chains into manageable subtasks with reduced complexity. Comprehensive evaluations on the SK-VG benchmark demonstrate the significant advancements of our method. Remarkably, SplitGround attains an accuracy improvement of 15.71% on the hard split of the test set over the previous training-required SOTA, using only a compact VLM backbone without fine-tuning, which provides new insights for knowledge-intensive visual grounding tasks. Full article

Under the framework of the “dual-carbon” goals of China (“carbon peak” by 2030 and “carbon neutrality” by 2060), the escalating complexity of emerging power systems presents significant challenges to safety governance. Traditional management models are now confronting bottlenecks, notably in knowledge inheritance breakdown and lagging risk prevention and control. This paper explores the application of large AI models in safety and emergency management in the power industry. Through core capabilities—such as natural language processing (NLP), knowledge reasoning, multimodal interaction, and auxiliary decision making—it achieves full-process optimization from data fusion to intelligent decision making. The study, anchored by 18 cases across five core scenarios, identifies three-dimensional challenges (including “soft”—dimension computing power, algorithm, and data bottlenecks; “hard”—dimension inspection equipment and wearable device constraints; and “risk”—dimension responsibility ambiguity, data bias accumulation, and model “hallucination” risks). It further outlines future directions for large-AI-model application innovation in power industry safety and management from a four-pronged outlook, covering technology, computing power, management, and macro-level perspectives. This work aims to provide theoretical and practical guidance for the industry’s shift from “passive response” to “intelligent proactive prevention”, leveraging quantified scenario-case analysis. Full article

Background: Vitamin D deficiency is a worldwide health problem associated with various health complications. This study aimed to determine the prevalence of vitamin D supplementation in the Czech Republic, understand reasons for supplementation, and assess participants’ knowledge of vitamin D’s physiological effects. Methods: The study included 1812 participants representing the Czech population aged 15+, stratified by gender, age, and regional distribution. Data analysis was performed using SASD 1.5.8, using chi² independence tests and regression analysis. Results: The results revealed that only 13.5% of the participants maintained regular year-round vitamin D supplementation, while 51.5% never supplemented. A slight increase in supplementation was observed during the pandemic (2020–2021). Supplementation patterns were influenced by socioeconomic factors including age, gender, marital status, income, employment, and education (p > 0.001). Regarding vitamin D knowledge, 67.5% and 65.6% of participants recognized its role in immune system modulation and bone health, respectively. There were minor misconceptions, with 1.4% believing that it aggravates allergies and 1.8% linking it to cancer. Knowledge levels varied with education and residence size. Conclusions: Despite the high prevalence of vitamin D deficiency in the Czech population, regular supplementation remains low, indicating the need for enhanced prevention programs and awareness campaigns. Full article

Given the importance of plants for ecosystem functioning, sustainability, and human well-being, children should be introduced to local species as early as possible. This study investigated whether kindergarten children (n = 24) can acquire knowledge of trees through a motion-based educational game and a subsequent half-day excursion. During the game, illustrations of trees were shown, their names were called out, and the children were asked to perform certain movements relating to features/names of the trees they had practiced. In semi-structured interviews directly after the activities and three months later, the children were asked to identify the trees by their leaves and to provide reasons why they had remembered their names. Already, after playing the game for four weeks, species with large and iconic leaves such as Norway maple (Acer platanoides) were correctly identified in nature by about 80% of the children. The interviews showed that even after three months, children correctly identified more than half of the species presented. They recognized the trees by their shape and the texture of their leaves but also by remembering the corresponding movements. The combination of motion-based play and hands-on, sensory investigations can be recommended to promote plant knowledge right from kindergarten age. Full article

Loess landslide disasters are influenced by a multitude of factors, including slope conditions, triggering mechanisms, and spatial attributes. Extracting these factors from unstructured geological texts is challenging due to nested entities, semantic ambiguity, and rare domain-specific terms. This study proposes a joint extraction framework guided by a domain ontology that categorizes six types of loess landslide influencing factors, including spatial relationships. The ontology facilitates conceptual classification and semi-automatic nested entity annotation, enabling the construction of a high-quality corpus with eight tag types. The model integrates a Soft-Lexicon mechanism that enhances character-level GloVe embeddings with explicit lexical features, including domain terms, part-of-speech tags, and word boundary indicators derived from a domain-specific lexicon. The resulting hybrid character-level representations are then fed into a BiLSTM-CRF architecture to jointly extract entities, attributes, and multi-level spatial and causal relationships. Extracted results are structured using a content-knowledge model to build a spatially enriched knowledge graph, supporting semantic queries and intelligent reasoning. Experimental results demonstrate improved performance over baseline methods, showcasing the framework’s effectiveness in geohazard information extraction and disaster risk analysis. Full article

On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model’s ability to uncover nuanced, implicit contextual cues—such as family roles and holiday-specific behaviors—making it particularly effective for personalized, dynamic recommendations in high-frequency scenes. Full article

Agentic AI and Large Language Models (LLMs) are transforming how language is understood and generated while reshaping decision-making, automation, and research practices. LLMs provide underlying reasoning capabilities, and Agentic AI systems use them to perform tasks through interactions with external tools, services, and Application Programming Interfaces (APIs). Based on a structured scoping review and thematic analysis, this study identifies that core challenges of LLMs, relating to security, privacy and trust, misinformation, misuse and bias, energy consumption, transparency and explainability, and value alignment, can propagate into Agentic AI. Beyond these inherited concerns, Agentic AI introduces new challenges, including context management, security, privacy and trust, goal misalignment, opaque decision-making, limited human oversight, multi-agent coordination, ethical and legal accountability, and long-term safety. We analyse the applications of Agentic AI powered by LLMs across six domains: education, healthcare, cybersecurity, autonomous vehicles, e-commerce, and customer service, to reveal their real-world impact. Furthermore, we demonstrate some LLM limitations using DeepSeek-R1 and GPT-4o. To the best of our knowledge, this is the first comprehensive study to integrate the challenges and applications of LLMs and Agentic AI within a single forward-looking research landscape that promotes interdisciplinary research and responsible advancement of this emerging field. Full article

This paper refers to the discussion on how to implement socially responsible measures in agribusiness, a complex and often heterogeneous system. It indicates the similarities between Corporate Social Responsibility and Agribusiness Social Responsibility as well as the unique characteristics that distinguish agribusiness. The focus was on the analysis of the processes taking place in the supply chain of the pig market operating in Poland, due to the author’s detailed knowledge of the phenomena taking place there. As part of these considerations, the following three key questions were asked: (1) What are the differences between the definitions of CSR and ASR, and is there any reason to define the two concepts separately? (2) Which links in the food supply chain require particular attention when implementing social responsibility? (3) To what extent should social responsibility principles be adhered to on a voluntary basis? The analyses were based on a critical review of the literature on the subject, inspired by Denyer and Tranfield’s literature review structure. The following two repositories were used: Google Scholar, which is publicly available, and Web of Science, which is a licensed network. The study found that, despite significant similarities between ASR and CSR, fundamental differences exist. Understanding the specific nature of agribusiness social responsibility requires not only accepting its differences but, above all, taking a holistic view of the processes accompanying food production, processing, and distribution. Furthermore, it requires considering the economic, organizational, and social diversity of entities comprising the food supply chain. Full article