Search Results (1,420)

As the region with the earliest housing stock market and the most advanced development in China, the Pearl River Delta has experienced extensive housing demolition and construction, leading to buildings having short lifespans. The environmental pollution generated during this process has brought attention to the concept of green buildings. However, whether due to previous patterns of demolition and construction or the significant impacts of social and economic changes in the current and future housing stock contexts, the comprehensive adaptability of human-centered living spaces remains a crucial issue. This focus is strongly related to the residents’ psychological responses, such as sense of belonging, safety, and atmosphere, across different scales of physical environment. However, most housing evaluation systems regarding sustainable issues are green building evaluation systems. And their concept and practice are often accompanied by a neglect of the interrelationship between people and the built environment, as well as a lack of an appropriate methodological framework to integrate these elements in the temporal dimension. This paper primarily tries to provide new answers to old questions about housing durability by reconceptualizing evaluation systems beyond ecological metrics, while simultaneously challenging accepted answers that privilege material and energy indicators over sociocultural embeddedness. Moreover, an effective housing evaluation framework must transcend purely technical or ecological indicators to systematically integrate the temporal and sociocultural factors that sustain long-term residential quality, particularly in rapidly transforming urban contexts. Therefore, theories closely related to building longevity, such as open building and the “level” strategy, were introduced. Based on this combined methodological framework, selected cases of local traditional housing and green building evaluation systems were studied, aiming to identify valuable longevity factors and improved evaluation methods. Furthermore, two rounds of expert consultation and a data analysis were conducted. The first round helped determine the local indexes and preliminary evaluation methods, while the second round helped confirm the weighting value of each index through a questionnaire study and data analysis. This systematic study ultimately established a preliminary long-term housing evaluation system for the Pearl River Delta. Full article

In Italy, several changes to academic and professional standards and rules in kinesiology and sport have recently occurred. On the university side, no data collection has started regarding these changes and effects on specific scholars. The aim of this study was to evaluate the opinions of Italian university scholars in Exercise and Sport Sciences regarding recent disciplinary reclassifications, the emergence of the kinesiologist as a formal profession, and related curricular updates. Specifically, this study aimed to measure scholars’ views on the usefulness of unification, hybridization with other fields of knowledge, interdisciplinarity with pedagogy, the distinctiveness of undergraduate education in light of the new kinesiologist profile, and the inclusion of Technical and Laboratory Activities (TLA) credited through the European Credit Transfer System (ECTS). These aspects were explored through an eight-question survey offering three multiple-choice answers. An exploratory survey was distributed to a defined population of 261 Italian scholars (48 full professors, 137 associate professors, and 76 researchers). A total of 83 responses were collected: 14 full professors, 45 associate professors, and 24 researchers (response rate: 31.8%). Descriptive statistics and inferential analyses (Chi-Square tests, Cramér’s V, and Pearson/Spearman correlations) were conducted. Results indicated that 72.3% perceived overlap between pedagogical and medical disciplinary groups, and 85.5% considered practical/laboratory activities essential to the kinesiologist’s role. Significant differences in keyword-sharing perceptions across academic ranks emerged (p = 0.012; V = 0.3), and a near-significant trend was found regarding the importance of discipline-aligned research (p = 0.058; V = 0.3). Full agreement was found on the use of updated scientific evidence in lectures (100%), and 81.9% supported standardized education for the kinesiologist profession (Q6). Positive correlations were observed between support for keyword sharing and belief in its usefulness for promoting interdisciplinarity among full professors (r = 0.58, p = 0.02), associate professors (r = 0.68, p < 0.01), and researchers (r = 0.83, p < 0.01). Conversely, negative correlations emerged between the importance placed on practical activities and support for interdisciplinarity among associate professors and researchers, with values ranging from r = −0.31 to −0.46. The results are significant and tended toward autonomy from pedagogy, training aligned with the bachelor’s and master’s degree kinesiologist, and interdisciplinarity inherent in typical Exercise and Sport Sciences (ESS) keywords. This study should be replicated to increase the sample and to expand the ad hoc questionnaire to other issues. These findings highlight the need for greater alignment between academic training, disciplinary definitions, and professional practice through shared epistemological frameworks and updated descriptors that reflect scientific and labor market developments. Full article

In response to the urban heat island challenge, various mitigation measures have been explored, with water spray systems emerging as a cost-effective and efficient solution for urban outdoor cooling. However, the influential factors of a water spray system on cooling efficiency have not been fully understood, thus hindering the application of the water spray system. This study delves into the following two questions: (1) what is the cooling performance of a water mist spray in a hot and humid urban climate? (2) What are the effects of different influencing factors? To answer these two questions, the computational fluid dynamics (CFD) simulations are used to modelthe cooling process of water mist spray inside an ideal two-dimensional street canyon with an aspect ratio of 1. A sound validation for the water spray cooling was conducted prior to the following CFD simulations. Results show that for given values of the water flow rate (i.e., 9.0 L/min) and the spray nozzle height (i.e., 3 m), a maximum temperature reduction of about 4.6 °C can be achieved at pedestrian height. Raising the installation height is more effective in maintaining the cooling zone proportion than decreasing the water flow rate. The clockwise recirculation inside the street canyon disappears with the upward airflow weakened when the spray nozzle is installed in the middle of the street canyon. Full article

The development of medical question-answering systems (QASs) encounters substantial challenges due to the complexities of medical terminologies and the lack of reliable datasets. The shortcomings of traditional artificial intelligence (AI) driven QAS lead to generating outcomes with a higher rate of hallucinations. In order to overcome these limitations, there is a demand for a reliable QAS to understand and process complex medical queries and validate the quality and relevance of its outcomes. In this study, we develop a medical QAS by integrating SPARQL, retrieval-augmented generation (RAG), and generative pre-trained transformer (GPT)-Neo models. Using this strategy, we generate a synthetic dataset to train and validate the proposed model, addressing the limitations of the existing QASs. The proposed QAS was generalized on the MEDQA dataset. The findings revealed that the model achieves a generalization accuracy of 87.26% with a minimal hallucination rate of 0.16. The model outperformed the existing models by leveraging deep learning techniques to handle complex medical queries. The dynamic responsive capability of the proposed model enables it to maintain the accuracy of medical information in a rapidly evolving healthcare environment. Employing advanced hallucination reduction and query refinement techniques can fine-tune the model’s performance. Full article

In this paper, we report how digital resources support engineering students in the early stages of mathematical modelling within a Challenge-Based Education (CBE) course. The study was conducted in a second-year engineering course involving mathematics, physics, and ethics. Through a case study of two student teams, we analyze how a digital curriculum resource—specifically, a dashboard designed for feedback and progress monitoring—helped students identify, define, and begin modelling a real-world problem related to crowd flow on train platforms. Using the instrumental approach, we examined the dual processes of instrumentation (integration of resources) and instrumentalization (adaptation and repurposing of tools). Results show that the Dashboard played a central role in fostering self-regulated learning, interdisciplinary collaboration, and the iterative refinement of guiding questions. Students used data analysis, simulations, and modelling techniques to build and validate mathematical representations in answer to the guiding questions. Our findings contribute to ongoing discussions on how mathematics education in engineering can be enhanced through activity-based learning and targeted use of digital tools. We argue that digital feedback systems like dashboards can bridge the gap between abstract mathematical content and its meaningful application in engineering contexts, thus fostering engagement, autonomy, and authentic learning. Full article

Recent advances in large language models and retrieval-augmented generation, a method that enhances language models by integrating retrieved external documents, have created opportunities to deploy AI in secure, offline environments. This study explores the feasibility of using locally hosted, open-weight large language models with integrated retrieval-augmented generation capabilities on CPU-only hardware for tasks such as question answering and summarization. The evaluation reflects typical constraints in environments like government offices, where internet access and GPU acceleration may be restricted. Four models were tested using LocalGPT, a privacy-focused retrieval-augmented generation framework, on two consumer-grade systems: a laptop and a workstation. A technical project management textbook served as the source material. Performance was assessed using BERTScore and METEOR metrics, along with latency and response timing. All models demonstrated strong performance in direct question answering, providing accurate responses despite limited computational resources. However, summarization tasks showed greater variability, with models sometimes producing vague or incomplete outputs. The analysis also showed that quantization and hardware differences affected response time more than output quality; this is a tradeoff that should be considered in potential use cases. This study does not aim to rank models but instead highlights practical considerations in deploying large language models locally. The findings suggest that secure, CPU-only deployments are viable for structured tasks like factual retrieval, although limitations remain for more generative applications such as summarization. This feasibility-focused evaluation provides guidance for organizations seeking to use local large language models under privacy and resource constraints and lays the groundwork for future research in secure, offline AI systems. Full article

The research focuses on the development and evaluation of a legal question–answer system for the Kazakh language, a low-resource and morphologically complex language. Four datasets were compiled from open legal sources—Adilet, Zqai, Gov, and a manually created synthetic set—containing question–аnswer pairs extracted from official legislative documents and government portals. Seven large language models (GPT-4o mini, GEMMA, KazLLM, LLaMA, Phi, Qwen, and Mistral) were fine-tuned using structured prompt templates, quantization methods, and domain-specific training to enhance contextual understanding and efficiency. The evaluation employed both automatic metrics (ROUGE and METEOR) and expert-based manual assessment. GPT-4o mini achieved the highest overall performance, with ROUGE-1: 0.309, ROUGE-2: 0.175, ROUGE-L: 0.263, and METEOR: 0.320, and received an expert score of 3.96, indicating strong legal reasoning capabilities and adaptability to Kazakh legal contexts. The results highlight GPT-4o mini’s superiority over other tested models in both quantitative and qualitative evaluations. This work demonstrates the feasibility and importance of developing localized legal AI solutions for low-resource languages, contributing to improved legal accessibility, transparency, and digital governance in Kazakhstan. Full article

Despite the remarkable developments in advanced materials, silicon and gallium arsenide remain among the leading semiconductors of our time. Nanomechanical studies of these semiconductor crystals, including nanoindentation-induced structural phase transformations and dislocation generation, remain important for science and technology. Of particular interest are studies on the onset of plasticity. What phenomenon initiates plastic deformation in Si and GaAs during nanoindentation? Through complex experiments and computer simulations, significant progress has been made in answering this question over the past twenty years. Indeed, equipping nanoindentation systems with the ability to record Raman spectra and exploring new interatomic interaction models for classical molecular dynamics have opened up new avenues for studying the non-trivial interplay between structural phase transformations and dislocation activity in semiconductor crystals. The diversity of high-pressure phases, especially silicon, and the largely unexplored sequences of transformations between them continue to inspire new scientific challenges. This article reviews selected works introducing the reader to the fascinating and still open topic of nanoindentation-induced incipient plasticity in silicon and gallium arsenide. Full article

This study designed and empirically evaluated a method to enhance information accessibility for museum and art gallery visitors using a small Large Language Model (sLLM) based on the Retrieval-Augmented Generation (RAG) framework. Over 199,000 exhibition descriptions were collected and refined, and a question-answering dataset consisting of 102,000 pairs reflecting user personas was constructed to develop DocentGemma, a domain-optimized language model. This model was fine-tuned through Low-Rank Adaptation (LoRA) based on Google’s Gemma2-9B and integrated with FAISS and OpenSearch-based document retrieval systems within the LangChain framework. Performance evaluation was conducted using a dedicated Q&A benchmark for the docent domain, comparing the model against five commercial and open-source LLMs (including GPT-3.5 Turbo, LLaMA3.3-70B, and Gemma2-9B). DocentGemma achieved an accuracy of 85.55% and a perplexity of 3.78, demonstrating competitive performance in language generation and response accuracy within the domain-specific context. To enhance retrieval relevance, a Spatio-Contextual Retriever (SC-Retriever) was introduced, which combines semantic similarity and spatial proximity based on the user’s query and location. An ablation study confirmed that integrating both modalities improved retrieval quality, with the SC-Retriever achieving a recall@1 of 53.45% and a Mean Reciprocal Rank (MRR) of 68.12, representing a 17.5 20% gain in search accuracy compared to baseline models such as GTE and SpatialNN. System performance was further validated through field deployment at three major exhibition venues in Seoul (the Seoul History Museum, the Hwan-ki Museum, and the Hanseong Baekje Museum). A user test involving 110 participants indicated high response credibility and an average satisfaction score of 4.24. To ensure accessibility, the system supports various output formats, including multilingual speech and subtitles. This work illustrates a practical application of integrating LLM-based conversational capabilities into traditional docent services and suggests potential for further development toward location-aware interactive systems and AI-driven cultural content services. Full article

The deployment of large language models (LLMs) in closed-network industrial environments remains constrained by privacy and connectivity limitations. This study presents a retrieval-augmented question-answering system designed to operate entirely offline, integrating local vector embeddings, ontology-based semantic enrichment, and quantized LLMs, while ensuring compliance with industrial security standards like IEC 62351. The system was implemented using OpenChat-3.5 models with two quantization variants (Q5 and Q8), and evaluated through comparative experiments focused on response accuracy, generation speed, and secure document handling. Empirical results show that both quantized models delivered comparable answer quality, with the Q5 variant achieving approximately 1.5 times faster token generation under limited hardware. The ontology-enhanced retriever further improved semantic relevance by incorporating structured domain knowledge into the retrieval stage. Throughout the experiments, the system demonstrated effective performance across speed, accuracy, and information containment—core requirements for AI deployment in security-sensitive domains. These findings underscore the practical viability of offline LLM systems for privacy-compliant document search, while also highlighting architectural considerations essential for extending their utility to environments such as smart grids or defense-critical infrastructures. Full article

This study proposes a novel, scalable framework for the automated classification and synthesis of survey literature by integrating state-of-the-art Large Language Models (LLMs) with robust ensemble voting techniques. The framework consolidates predictions from three independent models—GPT-4, LLaMA 3.3, and Claude 3—to generate consensus-based classifications, thereby enhancing reliability and mitigating individual model biases. We demonstrate the generalizability of our approach through comprehensive evaluation on two distinct domains: Question Answering (QA) systems and Computer Vision (CV) survey literature, using a dataset of 1154 real papers extracted from arXiv. Comprehensive visual evaluation tools, including distribution charts, heatmaps, confusion matrices, and statistical validation metrics, are employed to rigorously assess model performance and inter-model agreement. The framework incorporates advanced statistical measures, including k-fold cross-validation, Fleiss’ kappa for inter-rater reliability, and chi-square tests for independence to validate classification robustness. Extensive experimental evaluations demonstrate that this ensemble approach achieves superior performance compared to individual models, with accuracy improvements of 10.0% over the best single model on QA literature and 10.9% on CV literature. Furthermore, comprehensive cost–benefit analysis reveals that our automated approach reduces manual literature synthesis time by 95% while maintaining high classification accuracy (F1-score: 0.89 for QA, 0.87 for CV), making it a practical solution for large-scale literature analysis. The methodology effectively uncovers emerging research trends and persistent challenges across domains, providing researchers with powerful tools for continuous literature monitoring and informed decision-making in rapidly evolving scientific fields. Full article

The rapid advancement of AI technologies and the emergence of large language models (LLMs) such as ChatGPT have facilitated the integration of intelligent question-answering systems into education. However, students often hesitate to ask questions, which negatively affects learning outcomes. To address this issue, this study proposes a closed, locally deployed generative AI teaching assistant system that enables instructors to upload course PDFs to generate customized Q&A platforms. The system is based on a Retrieval-Augmented Generation (RAG) architecture and was developed through a comparative evaluation of components, including open-source large language models, embedding models, and vector databases to determine the optimal setup. The implementation integrates RAG with responsive web technologies and is evaluated using a standardized test question bank. Experimental results demonstrate that the system achieves an average answer accuracy of up to 86%, indicating a strong performance in an educational context. These findings suggest the feasibility of the system as an effective, privacy-preserving AI teaching aid, offering a scalable technical solution to improve digital learning in on-premise environments. Full article

This work is devoted to the development of dialogical hermeneutics. As a special field of research, hermeneutics was formed as a result of the efforts of Schleiermacher, Dilthey, Heidegger, and Gadamer. The first source of hermeneutics is Aristotle’s treatise “On Interpretation”, which formulates the special type of speech—‘logos apophantikos’—that aligns speech with the identification of thinking and being. However, this approach is challenged by the hermeneutics of the sophists, for whom speech is a command, a prayer, a question, an answer, or a narrative. The second source of hermeneutics is the predominantly Protestant tradition of interpreting biblical texts. This paper examines the hermeneutic strategies of Jewish classical texts, which differ significantly from the Christian tradition of understanding text. Jewish classical texts, from Tanakh and Talmud to Jewish mysticism and philosophy, are more focused not on propositions, but on commands, prayers, questions, answers, dialogue, and narrative. Thus, the hermeneutic strategy of Jewish texts converges with investigations of the Greek sophists. Particular emphasis is placed on the medieval Jewish philosophy. The paper examines three works: “Emunot ve-deot” by Saadia Gaon, “Kuzari” by Halevi, and “Guide of the Perplexed” by Maimonides. In this regard, we discuss the system of dual argumentation, the relation between halakha and aggadah, and the strategy of concealment and revelation in language—approaches that in many ways present an alternative to the hermeneutics of understanding. The Study of rabbinic tradition leads us to the development of dialogical hermeneutics that forms the methodological foundation of humanistic culture. Full article

General large language models (LLMs) often suffer from semantic misinterpretation, information redundancy, and hallucinated content when applied to educational question-answering tasks. These issues hinder their effectiveness in supporting students’ specialized course learning and self-directed study. To address these challenges, this study proposes an intelligent tutoring model that integrates a knowledge graph with a large language model (KG-CQ). Focusing on the Data Structures (C Language) course, the model constructs a course-specific knowledge graph stored in a Neo4j graph database. It incorporates modules for knowledge retrieval, domain-specific question answering, and knowledge extraction, forming a closed-loop system designed to enhance semantic comprehension and domain adaptability. A total of 30 students majoring in Educational Technology at H University were randomly assigned to either an experimental group or a control group, with 15 students in each. The experimental group utilized the KG-CQ model during the answering process, while the control group relied on traditional learning methods. A total of 1515 data points were collected. Experimental results show that the KG-CQ model performs well in both answer accuracy and domain relevance, accompanied by high levels of student satisfaction. The model effectively promotes self-directed learning and provides a valuable reference for the development of knowledge-enhanced question-answering systems in educational settings. Full article

A multimodal interactive system, HortiVQA-PP, is proposed for horticultural scenarios, with the aim of achieving precise identification of pests and their natural predators, modeling ecological co-occurrence relationships, and providing intelligent question-answering services tailored to agricultural users. The system integrates three core modules: semantic segmentation, pest–predator co-occurrence detection, and knowledge-enhanced visual question answering. A multimodal dataset comprising 30 pest categories and 10 predator categories has been constructed, encompassing annotated images and corresponding question–answer pairs. In the semantic segmentation task, HortiVQA-PP outperformed existing models across all five evaluation metrics, achieving a precision of 89.6%, recall of 85.2%, F1-score of 87.3%, $mAP@50$ of 82.4%, and IoU of 75.1%, representing an average improvement of approximately 4.1% over the Segment Anything model. For the pest–predator co-occurrence matching task, the model attained a multi-label accuracy of 83.5%, a reduced Hamming Loss of 0.063, and a macro-F1 score of 79.4%, significantly surpassing methods such as ASL and ML-GCN, thereby demonstrating robust structural modeling capability. In the visual question answering task, the incorporation of a horticulture-specific knowledge graph enhanced the model’s reasoning ability. The system achieved 48.7% in BLEU-4, 54.8% in ROUGE-L, 43.3% in METEOR, 36.9% in exact match (EM), and a GPT expert score of 4.5, outperforming mainstream models including BLIP-2, Flamingo, and MiniGPT-4 across all metrics. Experimental results indicate that HortiVQA-PP exhibits strong recognition and interaction capabilities in complex pest scenarios, offering a high-precision, interpretable, and widely applicable artificial intelligence solution for digital horticulture. Full article