Search Results (961)

This essay proposes a transdisciplinary framework that positions cooperation as a foundational principle for ecosystem stewardship in the Anthropocene. Drawing from microbial ecology, evolutionary theory, and sustainability science, we argue that cooperation, rather than competition, is a robust and scalable strategy for resilience across biological and institutional systems. In particular, microbial behaviors such as biofilm formation, quorum sensing, and horizontal gene transfer are especially pronounced in extreme environments, where cooperation becomes essential for survival. These strategies serve as functional analogues that illuminate the structural logics of resilience: interdependence, redundancy, distributed coordination, and adaptation. As the Anthropocene progresses toward increasingly extreme conditions, including potential “Hothouse Earth” scenarios driven by climate disruption, such ecological heuristics offer concrete insights into how human institutions can adapt to stress and uncertainty. Rather than reiterating familiar calls for hybrid governance, we use microbial cooperation as a heuristic to reveal the functional architecture already present in many resilient governance practices. These microbial strategies emerging from life in extreme environments demonstrate how interdependence, redundancy, and distributed coordination can create system resilience and sustainability in the long run. By translating microbial survival strategies into institutional design principles, this framework reframes ecosystem stewardship not as a normative ideal, but as an ecological imperative grounded in the evolutionary logic of cooperation. Full article

This study examines the mural paintings of the Chapter House of the Monastery of Santa Catalina in Cusco within the context of Andean colonial architecture, aiming to analyze their iconography as a symbolic and theological resource. A qualitative methodology was employed, based on iconographic analysis according to Erwin Panofsky’s theory, complemented by documentary review, photographic recording, and thematic categorization of the pictorial elements. The results reveal that the paintings not only decorate but also structure a visual theological discourse representing the spiritual transition of the soul from the mundane to the divine through scenes such as penance, ascetic life, redemption, and glorification. This mural narrative, primarily directed at the female religious community of the convent, integrates European and indigenous motifs, hagiographical figures, Trinitarian allegories, and ornamental symbolism that reinforces the spirituality of the monastic space. It is concluded that these representations do not solely serve catechetical purposes but configure a symbolic architecture of contemplation and spiritual formation that visually articulates the doctrinal principles of the Christian tradition through a pictorial language coherent with Andean Baroque. Full article

Accurate observations of Ocean Surface Wind Speed (OSWS) are vital for predicting extreme weather and understanding ocean–atmosphere interactions. However, spaceborne sensors (e.g., ASCAT, SMAP) often experience data loss due to harsh weather and instrument malfunctions. Existing inpainting methods often rely on reanalysis data that is released with delays, which restricts their real-time capability. Additionally, deep-learning-based methods, such as Transformers, face challenges due to their high computational complexity. To address these challenges, we present the Multimodal Wind Speed Inpainting Dataset (MWSID), which integrates 12 auxiliary forecasting variables to support real-time OSWS inpainting. Based on MWSID, we propose the MMamba framework, combining the Multimodal Feature Extraction module, which uses mutual information (MI) theory to optimize feature selection, and the OSWS Reconstruction module, which employs Attention-Mamba-2 within a Residual-in-Residual-Dense architecture for efficient OSWS inpainting. Experiments show that MMamba outperforms MambaIR (state-of-the-art) with an RMSE of 0.5481 m/s and an SSIM of 0.9820, significantly reducing RMSE by 21.10% over Kriging and 8.22% over MambaIR in high-winds (>15 m/s). We further introduce MMamba-L, a lightweight 0.22M-parameter variant suitable for resource-limited devices. These contributions make MMamba and MWSID powerful tools for OSWS inpainting, benefiting extreme weather prediction and oceanographic research. Full article

Architectural education programs are rapidly expanding the use of immersive technologies worldwide. An increasing number of architecture schools have incorporated 360-degree videos as one of the accessible and cost-effective immersive tools. Despite their availability and ease of use, research on their effectiveness as a learning tool in architectural pedagogy remains limited and mostly focused on architectural design education. Few studies have discussed their application in theoretical courses and their potential to support cognitive understanding of architecture. Learning cultural heritage is considered a foundation of architectural theory. This study examines how the utilization of 360-degree videos, compared to conventional 2D videos, supports spatial cognition, learning performance and experience in cultural heritage education among undergraduate architecture students. An educational experiment was conducted with 89 students in their second year of the architecture degree at the Applied Science Private University, Jordan. Both 360-degree videos and conventional 2D videos were inserted as learning tools within the curriculum of History of Architecture 1 and 2 courses. A mixed-research-method framework, including observation and a post-test survey, was carried out. Using SPSS and Excel programs, the data were analyzed through a set of statistical analyses such as paired-sample t-tests, AHP, and basic descriptive analysis. The findings demonstrate that students were highly immersed and motivated when using 360-degree videos. Compared to conventional 2D videos, 360-degree videos enhanced students’ spatial cognition, performance, engagement, and participation levels in both face-to-face and online courses. These results suggest that 360-degree videos can serve as a sufficient, low-cost, and equipment-free learning tool, responding to the urgent need to utilize technologies in both theoretical and practical architectural pedagogy. Full article

Spherical shells exhibit superior strength-to-geometry efficiency, making them ideal for industrial applications such as fluid storage tanks, architectural domes, naval vehicles, nuclear containment systems, and aeronautical and aerospace components. Given their critical role, careful attention to the design parameters and engineering constraints is essential. The present paper investigates the buckling responses of bio-inspired helicoidal laminated composite spherical shells under normal and torsional loading, including the effects of a Winkler elastic medium. The pre-buckling equilibrium equations are derived using linear three-dimensional (3D) elasticity theory and the principle of virtual work, solved via the classical finite element method (FEM). The buckling load is computed using a nonlinear Green strain formulation and a generalized geometric stiffness approach. The shell material employed in this study is a T300/5208 graphite/epoxy carbon fiber-reinforced polymer (CFRP) composite. Multiple helicoidal stacking sequences—linear, Fibonacci, recursive, exponential, and semicircular—are analyzed and benchmarked against traditional unidirectional, cross-ply, and quasi-isotropic layups. Parametric studies assess the effects of the normal/torsional loads, lamination schemes, ply counts, polar angles, shell thickness, elastic support, and boundary constraints on the buckling performance. The results indicate that quasi-isotropic (QI) laminate configurations exhibit superior buckling resistance compared to all the other layup arrangements, whereas unidirectional (UD) and cross-ply (CP) laminates show the least structural efficiency under normal- and torsional-loading conditions, respectively. Furthermore, this study underscores the efficacy of bio-inspired helicoidal stacking sequences in improving the mechanical performance of thin-walled composite spherical shells, exhibiting significant advantages over conventional laminate configurations. These benefits make helicoidal architectures particularly well-suited for weight-critical, high-performance applications in aerospace, marine, and biomedical engineering, where structural efficiency, damage tolerance, and reliability are paramount. Full article

Topophilia, understood as a form of relationship between humans and their environment, can manifest in diverse ways—not only across various domains of art and life but also within the realm of music. This article seeks to expand the thesis of topophilia as a category defining the musical space of creation, performance, and perception of a musical work. Topophilia is seen here in the context of human activity in the artistic dimensions—philosophical, creative, architectural, and environmental. The methodological background is derived from the philosophy of place, phenomenology of perception, and musical analysis. This provides the opportunity to apply hermeneutic–philosophical analysis with elements of the theory of place. The thesis of this study is probably one of the first approaches to the category of topophilia in musical analysis, examining the style of composers, such as J.S. Bach, F. Chopin, K. Szymanowski, W. Lutosławski, A. Webern, and I. Xenakis, enriched with elements of musical performance. Full article

This paper proposes a formal framework to model the propagation of software antipatterns across architectural layers, quantifying their impact using principles from complex systems theory, technical debt economics, and cognitive load theory. By extending the 4+1 architectural view model with a propagation graph and economic simulation, the proposed framework enables software teams to predict, visualize, and mitigate the systemic effects of structural faults. We support our proposal with a mathematical model, a conceptual propagation engine, and simulation results Full article

Mine water hazards represent one of the principal threats to safe coal mine operations; therefore, accurately predicting mine water inflow is critical for drainage system design and water hazard mitigation. Because mine water inflow is governed by the combined influence of multiple hydrogeological factors and thus exhibits pronounced non-linear characteristics, conventional approaches are inadequate in terms of forecasting accuracy and medium- to long-term predictive capability. To address this issue, this study proposes a Chaos-Autoformer-based method for predicting mine water inflow. First, the univariate inflow series is mapped into an m-dimensional phase space by means of phase-space reconstruction from chaos theory, thereby fully preserving its non-linear features; the reconstructed vectors are then used to train and forecast inflow with an improved Chaos-Autoformer model. On top of the original Autoformer architecture, the proposed model incorporates a Chaos-Attention mechanism and a Lyap-Dropout scheme, which enhance sensitivity to small perturbations in initial conditions and complex non-linear propagation paths while improving stability in long-horizon forecasting. In addition, the loss function integrates the maximum Lyapunov exponent error and earth mode decomposition (EMD) indices so as to jointly evaluate dynamical consistency and predictive performance. An empirical analysis based on monitoring data from the Liangshuijing Coal Mine for 2022–2025 demonstrates that the trained model delivers high accuracy and stable performance. Ablation experiments further confirm the significant contribution of the chaos-aware components: when these modules are removed, forecasting accuracy declines to only 76.5%. Using the trained model to predict mine water inflow for the period from June 2024 to June 2025 yields a root mean square error (RMSE) of 30.73 m³/h and a coefficient of determination (R²) of 0.895 against observed data, indicating excellent fitting and predictive capability for medium- to long-term tasks. Extending the forecast to July 2025–November 2027 reveals a pronounced annual cyclical pattern in future mine water inflow, with markedly higher inflow in summer than in winter and an overall slowly declining trend. These findings show that the Chaos-Autoformer can achieve high-precision medium- and long-term predictions of mine water inflow, thereby providing technical support for proactive deployment and refined management of mine water hazard prevention. Full article

Concealed water bodies within surrounding rock formations pose a serious threat to tunnel construction. To address this risk, this study integrates physics-based heat conduction theory with deep learning, unlike existing methods that treat temperature as isolated data points or rely solely on empirical models. The approach introduces three key innovations: (a) analytical temperature–location relationships for water body characterization; (b) pseudo-temporal modeling of spatial sequences and (c) physics-guided neural architecture design. First, a steady-state heat conduction model is established to characterize axial temperature distribution patterns caused by concealed water bodies during excavation. From this, quantitative relationships between temperature anomalies and the location and size of the water bodies are derived. Next, a deep learning model, ST-HydraNet, is proposed to treat tunnel axial temperature data as a pseudo-time series for hazard prediction. Experimental results demonstrate that the model achieves high accuracy (91%) and perfect precision (1.0), significantly outperforming existing methods. These findings show that the proposed framework provides a non-invasive, interpretable, and robust solution for real-time hazard detection, with strong potential for integration into intelligent tunnel safety systems. By enabling earlier and more reliable detection, the model directly enhances construction safety, economic efficiency, and environmental sustainability. Full article

This study investigates the integration of Large Language Models (LLMs) into supply chain workflow automation, with a focus on their technical, operational, financial, and socio-technical implications. Building on Dynamic Capabilities Theory and Socio-Technical Systems Theory, the research explores how LLMs can enhance logistics operations, increase workflow efficiency, and support strategic agility within supply chain systems. Using two developed prototypes, the Q inventory management assistant and the nodeStream© workflow editor, the paper demonstrates the practical potential of GenAI-driven automation in streamlining complex supply chain activities. A detailed analysis of system architecture and data governance highlights critical implementation considerations, including model reliability, data preparation, and infrastructure integration. The financial feasibility of LLM-based solutions is assessed through cost analyses related to training, deployment, and maintenance. Furthermore, the study evaluates the human and organisational impacts of AI integration, identifying key challenges around workforce adaptation and responsible AI use. The paper culminates in a practical roadmap for deploying LLM technologies in logistics settings and offers strategic recommendations for future research and industry adoption. Full article

Recent developments in machine learning (ML), deep learning (DL), and statistical signal processing have led to substantial improvements in the accuracy of time series forecasting, particularly for environmental parameters such as temperature. The accuracy of air temperature prediction is not only vital for meteorological forecasting but also critically impacts agriculture, energy management, and environmental monitoring. In this study, a comprehensive modeling approach is proposed by incorporating both data-driven learning methods and classical signal processing techniques. Specifically, statistical models such as Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX) are evaluated alongside modern neural network architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based attention mechanisms. The implemented models utilize key atmospheric variables—humidity, pressure, and past temperature values—to predict ambient temperature for future time horizons, such as one week and six months ahead. The SARIMAX model, which is grounded in digital signal processing theory, is particularly examined for its ability to capture seasonality and trend components in structured data. Meanwhile, deep learning models excel in learning complex, nonlinear temporal dependencies. Experimental results show that while LSTM performs well in short-term predictions (mean absolute error (MAE): 2.27, mean squared error (MSE): 6.63), the attention-based Transformer model is superior in capturing the predictions in the long term (MAE: 2.99, MSE: 14.92). SARIMAX, on the other hand, demonstrates a reliable performance in the short term compared to LSTM. These findings provide valuable insights into the strengths and limitations of each modeling approach, guiding future efforts in temperature forecasting and time series analysis. Full article

As artificial intelligence advances toward unprecedented capabilities, society faces a choice between two trajectories. One continues scaling transformer-based architectures, such as state-of-the-art large language models (LLMs) like GPT-4, Claude, and Gemini, aiming for broad generalization and emergent capabilities. This approach has produced powerful tools but remains largely statistical, with unclear potential to achieve hypothetical “superintelligence”—a term used here as a conceptual reference to systems that might outperform humans across most cognitive domains, though no consensus on its definition or framework currently exists. The alternative explored here is the Mindful Machines paradigm—AI systems that could, in future, integrate intelligence with semantic grounding, embedded ethical constraints, and goal-directed self-regulation. This paper outlines the Mindful Machine architecture, grounded in Mark Burgin’s General Theory of Information (GTI), and proposes a post-Turing model of cognition that directly encodes memory, meaning, and teleological goals into the computational substrate. Two implementations are cited as proofs of concept. Full article

This study explores opportunities for improving the learning process of design students using multimedia and microlearning, with a focus on architectural visualization. It analyzes the learning habits of students and faculty in higher education, reflects on the need for digitalization and adaptation to the cognitive characteristics of Generations Z and Alpha, and emphasizes the importance of visual perception in design thinking. The research includes a survey of 130 respondents from eight Bulgarian universities and an experiment with three groups of students using different learning methods—live demonstration, video demonstration, and a combined approach. The results indicate that the combined method leads to the highest levels of understanding, confidence, and task performance. The research is grounded in pedagogical theories related to visual learning and cognitive engagement, particularly relevant for Generations Z and Alpha. Students expressed a preference for short, practice-oriented formats, such as project-based learning and video tutorials, aligning with their digital fluency and attention patterns. The results underline the importance of incorporating multimedia elements and flexible instructional strategies to support motivation, engagement, and effective skill development in design education. Full article

Building Information Modeling (BIM) is an innovative and effective solution to transform the Architecture, Engineering, and Construction (AEC) sector, offering advantages that extend across the entire lifecycle of project management. Nonetheless, several obstacles hinder the comprehensive implementation of BIM. As a result of these obstacles, construction enterprises opt to delegate the development and utilization of BIM models to specialized outsourcing providers that focus on BIM services. Since limited research focused on examining the formation mechanisms behind BIM outsourcing process, this paper elucidates the mechanisms that underpin BIM outsourcing behavior by applying Ajzen’s theory of planned behavior (TPB) and integrating transition costs along with institutional pressures theory. The behavioral model underwent empirical validation through the application of partial least squares structural equation modeling (PLS-SEM) on survey data collected from construction engineers working for construction enterprises in China. The results indicated that (1) BIM outsourcing degree is motivated by an organization’s BIM outsourcing intention and BIM application capability; (2) behavioral attitudes, especially external production cost advantage, contributes the most toward achieving a high BIM outsourcing intention, followed by normative pressures; (3) transition cost contributes the most toward achieving a low BIM outsourcing intention. This research expands the theoretical framework of the TPB and provides insight into BIM outsourcing behavior in construction enterprises. Full article

This entry is based on the premise that pressing issues of climate change, social injustice, and post-COVID practices appear to have superseded some essential values of architectural and design pedagogy, leading to improvements in content that may be offset by a loss of focus on the core curriculum. The entry reimagines architectural pedagogy by arguing for a transformative shift from traditional product-based education to a process-oriented, inquiry-driven approach that cultivates critical thinking and empirical making, predicated upon experiential learning. It aims to integrate rigorous critical inquiry into both studio-based and lecture-based settings, thus critiquing assumed limitations of conventional approaches that prioritise final outcomes over iterative design processes, dialogue, and active engagement. Employing a comprehensive qualitative approach that incorporates diverse case studies and critical reviews, the analysis is divided into two main threads: one that places emphasis on the studio environment and another that focuses on lecture-based courses. Within these threads, the analysis is structured around a series of key themes central to experiential learning, each of which concludes with a key message that synthesises the core insights derived from case studies. The two threads instigate the identification of aligned areas of emphasis which articulate the need for active engagements and reflection, for bridging theory and practice, and for adopting interdisciplinary and experiential approaches. Conclusions are drawn to establish guidance for a future direction of a strengthened and pedagogically enriched architectural education. Full article