Search Results (264)

High-altitude environments characterized by low air pressure, hypoxia, and strong solar radiation have a significant impact on human thermal comfort; however, existing thermal comfort theories and evaluation models are primarily developed under low-altitude climatic conditions, and their applicability in plateau regions remains limited. With the acceleration of urbanization and the increase in residential, tourism, and occupational activities in high-altitude areas, systematically reviewing the research progress on thermal comfort in such environments is of great practical significance. This study combines systematic literature retrieval and bibliometric analysis, based on the Web of Science Core Collection and China National Knowledge Infrastructure (CNKI) databases, to analyze relevant studies published since 2001. Using CiteSpace, research hotspots, collaboration networks, and evolutionary trends are visualized. The results indicate that current research hotspots mainly focus on physiological responses and thermal adaptation mechanisms under low-pressure and hypoxic conditions, thermal comfort regulation strategies for high-altitude buildings and environments, and the applicability and modification of conventional thermal comfort models. Emerging trends include multi-environmental factor coupling analysis, adaptive model development, region-specific building design approaches, and health-oriented comprehensive evaluation frameworks. The findings provide valuable references for building thermal environment design, regional revision of thermal comfort evaluation standards, and policy-making in high-altitude regions. Full article

Introduction: Large language models (LLMs) are increasingly employed as cognitive aids in research and professional inquiry, yet their fluent outputs are frequently regarded as authoritative knowledge. We contend that this practice signifies a fundamental epistemic misalignment. Methods/Approach: Building on Peirce’s theory of inquiry, Sellars’ concept of the space of reasons, Stanovich’s tripartite model of cognition, and knowledge-building theory, we develop a conceptual framework for analyzing epistemic agency in human–LLM collaboration. Results/Argument: We demonstrate that LLM outputs fail to satisfy the conditions for knowledge because they lack reflective regulation, resistance to revision, and normative commitment. While LLMs display strong autonomous and algorithmic abilities (e.g., pattern recognition and hypothesis development), reflective control remains a distinctly human function. This asymmetry supports a principled division of epistemic labour and motivates the concept of the Knowledge-Building Partner (KBP): an AI system designed to support inquiry without claiming epistemic authority. Discussion/Implications: We identify prompt-, system-, and model-level design requirements and introduce a triangulated framework for operationalizing epistemic agency through explainable AI, discourse analysis, and rational-thinking measures. These contributions collectively reposition LLM limitations as epistemic design challenges rather than technical issues. Full article

The forestry extractivist model has systematically transgressed and violated Mapuche territories, thereby generating tensions, crises, and socioenvironmental injustices. The following objectives were proposed: (a) Unveil the implications of forestry extractivism on bodies of water in rural Mapuche territories. (b) Investigate Mapuche knowledge, sociocultural activities, and their relationship with preservation and sustainability of water in Mapuche and non-Mapuche territories. A qualitative methodology was employed, framed within constructivist grounded theory. To collect the information, in-depth interviews and participant observation were used. The study subjects corresponded to 51 kimeltuchefes (People with knowledge, experience and ancestral wisdom). Regarding objective (a), the findings reveal that pine and eucalyptus forestry extractivism has considerably deteriorated natural (sacred) spaces and the soil. Along with this, it has caused water scarcity, which in turn has reduced medicinal plant and food production and, in general, has deteriorated the ixofil mogen (a concept similar to biodiversity). It was concluded that the forestry extractivist model threatens the existence of all forms of life that cohabit in nature (material and immaterial); it deteriorates Mapuche culture; likewise, it poses a considerable risk to the health and survival of the Mapuche population. Regarding objective (b), the findings reveal that the knowledge of az mapu, ngülam, pepilkantün, rakizuam, llellipun and kümelkawün, and the sociocultural activities, trawün and kelluwün, constitute essential contributions for the preservation and sustainability of water. These forms of knowledge and activities are founded on ethical and moral principles that underlie the normative, legal, social, and educational frameworks of the Mapuche people. It was concluded that sociocultural knowledge and activities are essential for conserving and ensuring water in a sustainable, equitable, and efficient manner for both the Mapuche and non-Mapuche populations and for all life; likewise, they safeguard and promote Mapuche culture. Indeed, these forms of knowledge and sociocultural activities must be incorporated into environmental public policies. Full article

Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements. Full article

Modeling forced nonlinear multivariable dynamical systems remains challenging, particularly when first-principles models are unavailable or strong nonlinear couplings are present. In recent years, data-driven approaches grounded in the Koopman operator theory have gained attention for their ability to represent nonlinear dynamics via linear evolution in appropriately lifted spaces. This work presents a data-driven modeling framework for forced nonlinear multiple-input multiple-output (MIMO) systems based on Hankel Dynamic Mode Decomposition with control and lifting functions (HDMDc+Lift). The proposed methodology exploits Hankel matrices to encode temporal correlations and employs lifting functions to approximate the Koopman operator’s action on observable functions. As a result, an augmented-order linear state-space model is identified exclusively from input–output data, without relying on explicit knowledge of the system’s governing equations. The effectiveness of the proposed approach is demonstrated using operational data from a real multivariable tank system that was not used during the identification stage. The identified model achieves a coefficient of determination exceeding 0.87 in multi-step prediction tasks. Furthermore, spectral analysis of the resulting linear operator reveals that the dominant dynamical modes of the physical system are accurately captured. At the same time, additional modes associated with nonlinear interactions are also identified. These results highlight the HDMDc+Lift framework’s ability to provide accurate and interpretable linear representations of forced nonlinear MIMO dynamics. Full article

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet-of-Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model-based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we call the Massive Open Online Laboratory. Full article

Based on extensive ethnographic fieldwork conducted in Mianning County, Liangshan Yi Autonomous Prefecture between 2023 and 2024, this paper analyzes the “xiō bū” (ꑭꁮ) ritual of the Liangshan Yi people. Framed within contemporary approaches to religious anthropology and social memory theory, the study explores how this ritual constructs Yi ecological ethics, social integration, and cultural identity through nature worship, ancestral spirit beliefs, and ritual practices. The ethnographic evidence reveals that the “xiō bū” ritual, by designating wooded mountains as sacred space and performing sacrifices to nature deities and ancestral spirits, integrates “humans—nature—ancestors” into a symbiotic system of the “community of life.” This reflects the Yi people’s relational ontology and embedded ecological knowledge. The sacrificial offerings, shared meals, and purification practices in the ritual not only reinforce reverence for nature through symbolic acts but also unify the community through Durkheimian “collective effervescence,” thereby restoring the community’s spiritual order. As a carrier of social memory, the “xiō bū” ritual, through epic chanting, symbolic performances (such as clothing, ritual implements), and bodily practices (like the ritual specialist’s movements), embeds individual memories into the collective historical narrative of the group, dynamically constructing the cultural boundaries of the “Yi” people. The ritual specialists (Bimo or Suni), as intermediaries of knowledge and power, maintain religious authority through bricolage-like symbolic reorganization and foster the creative transformation of tradition in response to the challenges of modernity. The study further reveals that while the ritual faces challenges in the contemporary context, such as secularization and population mobility, it continues to activate ethnic identity by simplifying rituals, preserving core symbols, and coupling with ecological discourses, offering a model for the modern adaptation of traditional religions. This paper argues that ritual studies should engage with contemporary theoretical approaches like the ontological turn, focus on the agency of individuals, and reflect on the insights traditional knowledge systems offer in the face of globalization and ecological crises. Full article

Programmable mechanical metamaterials enable precise regulation of mechanical responses through geometric design, ushering in transformative paradigms for transformable structures. To systematically map the knowledge landscape and development trends in this field, this study employs knowledge mapping methods to analyze the current research status, core hotspots, and future directions of programmable mechanical metamaterials. During the research process, we expanded keywords using the litsearchr tool to optimize the retrieval strategy. Bibliometric tools, including CiteSpace 6.3.R3 and bibliometrix, were utilized to conduct multidimensional analyses on 2017 original papers related to mechanical metamaterials in transformable architecture from 2015 to 2025. These analyses encompass co-word analysis, co-citation clustering, and structural variation analysis. Key aspects include (1) identifying core journals and their attributes to clarify interdisciplinary dynamics, (2) mapping research themes and evolutionary trends through keyword analysis and clustering, and (3) pinpointing research hotspots and future directions based on citation networks and clustering results. The results reveal significant interdisciplinary characteristics, with core knowledge emerging from the intersection of materials science, mechanics, and civil engineering. Mathematical system theory provides a cross-scale modeling foundation for metamaterial microstructure design. The field is evolving from static structural design toward environment-adaptive intelligent systems. Future efforts should prioritize multi-physics collaborative regulation, engineering integration, and technical chain refinement. These findings offer a theoretical reference for the innovative development of transformable architecture. Full article

Schools situated in indigenous territories have historically replicated Western Eurocentric spatial models, often excluding local cultural knowledge and practices. This exclusion has impacted students’ emotional well-being, learning quality, and the contextual relevance of pedagogical approaches. This study aims to explores the socio-ecological context of school spaces in Mapuche territories in La Araucanía, Chile, and examines how teachers perceive these spaces and their influence on emotional regulation and learning. Using a qualitative multi-case study design, data were collected from three schools in Cholchol, Nueva Imperial, and Toltén through land cover/use mapping within a 3 km radius and semi-structured interviews with 15 teachers. Analysis was conducted using constructivist grounded theory. The findings reveal that schools are embedded in landscapes comprising agricultural zones, water bodies, monoculture plantations, and nearby Mapuche communities. Teachers conceptualize school spaces beyond physical infrastructure, recognizing socio-ecological and cultural dimensions. However, school design remains predominantly Western and monocultural, with limited integration of Mapuche spiritual and territorial elements. The main contribution of this research is to provide empirical evidence that strengthening the connection between school spaces and their biocultural context can enhance students’ sense of belonging, emotional regulation, and learning. This study advances the topic by highlighting the critical role of teachers’ perceptions and the socio-ecological context in the design of intercultural and sustainable schools, offering a concrete framework for overcoming regulatory and architectural limitations that continue to impede the implementation of inclusive public policies in indigenous educational settings. Full article

If S and T are two non-self-mappings, then a solution of equation $S{a}^{*}=T{a}^{*}=a^{*}$ does not necessarily exist. The common best proximity point problem is to find the approximate optimal solution of such type of equation and have a key role in theory of approximation and optimization. The primary goal of this paper is to introduce an inertial-type self-adaptive algorithm for solving the common best proximity point, generalized equilibrium and split variational inclusion problems in Hilbert spaces. The strong convergence of the proposed algorithm is given under some mild conditions. It is worth mentioning that the step size in many existing algorithms requires the prior knowledge of operator norms which is difficult to compute, whereas our proposed algorithm does not require this condition. Numerical examples are given to illustrate the efficiency and applicability of the proposed approach. We further apply the proposed algorithm to an image restoration problem and show that it achieves a higher signal-to-noise ratio compared with the existing algorithms considered in this study. Full article

As a foundational analytical tool, the discernibility matrix plays a pivotal role in the systematic reduction of knowledge in rough set-based systems. Recent advancements in rough set theory have witnessed the proliferation of discernibility matrix-based knowledge reduction algorithms, with notable applications in classical, neighborhood, covering, and fuzzy rough set models. However, the quadratic growth of the discernibility matrix’s complexity (relative to domain size) imposes fundamental scalability limits, rendering it inefficient for real-world applications with massive datasets. To address this issue, we introduced a discernibility hashing strategy to limit the growth scale of the discernibility attributes and proposed a feature selection algorithm via discernibility hash based on rough set theory. First, on the premise of keeping the information of the original discernibility matrix unchanged, the method maps the discernibility attribute set of all objects to the storage unit through a hash function and records the number of collisions to construct a discernibility hash. By using this mapping, the two-dimensional matrix space can be reduced to a one-dimensional hash space, which greatly removes invalid and redundant elements. Secondly, based on the discernibility hash, an efficient knowledge reduction algorithm is proposed. The algorithm avoids invalid and redundant element attribute sets to participate in the knowledge reduction process and improves the efficiency of the algorithm. Finally, the experimental results show that the method is superior to the discernibility matrix method in terms of storage space and running time. Full article

In recent years, research into the optimization of teaching reform for computer-aided mapping has continuously advanced in China. It plays a vital role in the development of urban forestry-related curricula and the cultivation of urban forestry professionals in higher education institutions. However, current research into the teaching reform of computer-aided mapping within the urban forestry domain remains insufficient. To address this, the study employs CiteSpace to conduct a visualization analysis of existing literature samples from journals, systematically integrating research trends and cutting-edge knowledge. The results indicate that relevant research perspectives primarily focus on teaching methods and theories, teaching reform and improvement, and teaching application and practice of computer-aided mapping. Furthermore, the study proposes future prospects for existing computer-aided mapping courses within urban forestry disciplines in China. In future higher education teaching, the urban forestry discipline can draw upon existing computer-aided mapping teaching methods, theories, innovative reforms, and practical applications. Emphasis should be placed on conducting more research in areas such as building multi-party academic collaboration, comprehensively utilizing diverse teaching methods, and expanding theories from multiple perspectives. This will facilitate the systematic and scientific development of computer-aided mapping curricula within the urban forestry discipline. Full article

As AI systems increasingly structure educational processes, they shape not only what is learned, but also how epistemic authority is distributed and whose knowledge is recognized. This article explores the normative and technopolitical implications of this development by examining two prominent paradigms in AI ethics: Ethical AI and Responsible AI. Although often treated as synonymous, these frameworks reflect distinct tensions between formal universalism and contextual responsiveness, between rule-based evaluation and governance-oriented design. Drawing on deontology, utilitarianism, responsibility ethics, contract theory, and the capability approach, the article analyzes the frictions that emerge when these frameworks are applied to algorithmically mediated education. The argument situates these tensions within broader philosophical debates on technological mediation, normative infrastructures, and the ethics of sociotechnical design. Through empirical examples such as algorithmic grading and AI-mediated admissions, the article shows how predictive systems embed values into optimization routines, thereby reshaping educational space and interpretive agency. In response, it develops the concept of situated ethics, emphasizing epistemic justice, learner autonomy, and democratic judgment as central criteria for evaluating educational AI. To clarify what is at stake, the article distinguishes adaptive learning optimization from education as a process of subject formation and democratic teaching objectives. Rather than viewing AI as an external tool, the article conceptualizes it as a co-constitutive actor within pedagogical practice. Ethical reflection must therefore be integrated into design, implementation, and institutional contexts from the outset. Accordingly, the article offers (1) a conceptual map of ethical paradigms, (2) a criteria-based evaluative lens, and (3) a practice-oriented diagnostic framework to guide situated ethics in educational AI. The paper ultimately argues for an approach that attends to the relational, political, and epistemic dimensions of AI systems in education. Full article

Generative artificial intelligence (AI) is reshaping higher education (HE) by reconfiguring how knowledge becomes visible, how judgment is exercised, and how recognition is distributed. These systems intervene in the pedagogical and democratic conditions under which plurality, critique, and participation can be sustained. This study examines how students and lecturers interpret and navigate these transformations and what they reveal about the possibilities of democratic education under algorithmic mediation. Drawing on n = 151 written articulations (122 students, 29 lecturers) to open-ended questions collected via LimeSurvey, analyzed through Grounded Theory in combination with biographical interpretation and oriented by education theory (Bildung) and democracy pedagogy, the research reconstructs five orientations that range from pragmatic coping to struggles over recognition. These orientations illuminate how systemic dynamics of acceleration, opacity, and infrastructural authority are refracted into everyday academic practice. They are further synthesized into three broader axes of temporal sovereignty, epistemic opacity and accountability, and recognition ecologies. The findings highlight how fragile orientations emerge as both risks and resources. The study contributes to HE didactics by outlining strategies to transform fragility into pedagogical occasions, emphasizing reflective delay, dialogical engagement with opacity, and diversification of recognition practices. It concludes that democratic education depends on cultivating spaces where algorithmic pressures become educable and fragile orientations can develop into dispositions of reflexivity, critique, and participation. Full article