Search Results (334)

Mountain settlements represent culturally rich but environmentally fragile landscapes, shaped by enduring processes of ecological adaptation and human resilience. In western Sichuan, Jiarong Tibetan villages, with their distinctive integration of defensive stone towers and settlements, embody this coupling of culture and the environment. We hypothesize that settlement cores in these villages were shaped by natural environmental factors, with subsequent expansion reinforced by the cultural significance of towers. To test this, we applied a micro-scale spatial–environmental framework to four sample villages in Suopo Township, Danba County. High-resolution World Imagery (Esri, 0.5–1 m, 2022–2023) was classified via a Random Forest algorithm to generate detailed land-use maps, and a 100 × 100 m fishnet grid extracted topographic metrics (elevation, slope, aspect) and accessibility measures (distances to streams, roads, towers). Geographically weighted regression (GWR) was then used to examine how slope, elevation, aspect, proximity to water and roads, and tower distribution affect settlement patterns. The results show built-up density peaks on southeast-facing slopes of 15–30°, at altitudes of 2600–2800 m, and within 50–500 m of streams, co-locating with historic watchtower sites. Based on these findings, we propose four zoning strategies—a Core Protected Zone, a Construction And Development Zone, an Ecological Conservation Zone, and an Industry Development Zone—to balance preservation with growth. The resulting policy recommendations offer actionable guidance for sustaining traditional settlements in complex mountain environments. Full article

The increasing intersection of psychotherapy, cognitive science, neuroscience, and systems engineering beckons us to rethink what it means to talk the language of the human mind in the clinical setting. This position paper proposes the idea of entangled autopoiesis, a metatheoretical paradigm that addresses the mind and therapy not as linear processes but as self-organizing, adaptive processes enfolded across neural, cognitive, relational, and cultural domains. Psychotherapy, from this viewpoint, is less a corrective technique and more a zone of systemic integration, wherein resilience and meaning are co-created in the interaction of embodied brains, lived stories, and relational fields. Neuroscience informs us about plasticity and regulation; cognitive science emphasizes the embodied and extended nature of cognition; and systems engineering sheds light on feedback, emergence, and adaptive dynamics. Artificial intelligence appears as a double presence: as a metaphor for complexity and as a practical tool able to chart patterns below human sensibility. By adopting a complexity-aware epistemology, we advocate a relocation in clinical thinking—one recognizing the psyche as an autopoietic network, entangled with culture and technology and able to renew itself in therapeutic encounters. The implications for clinical methodology, therapist training, and future interdisciplinary research are discussed. Full article

Buildings account for a major share of global energy demand and emissions, prioritizing lighting for efficiency improvements. This study evaluates a daylight-assisted lighting system’s energy and environmental performance through a fully measurement-based approach. Monitored illuminance data were processed within a transparent workflow linking lighting demand to power use, electricity consumption, and life-c ycle greenhouse gas emissions. Energy demand was derived from luminaire efficacy and an illuminated area, while environmental impacts were quantified using an attributional life cycle assessment (LCA) framework consistent with ISO 14040/14044 standards. Use-phase carbon footprints were calculated with regional grid emission factors, and manufacturing, transport, and end-of-life stages were included as background conditions. The results demonstrate that the daylight-aware control strategy achieved an average electricity reduction of 17% (95% CI: 15.7–18.3%) compared to the constant baseline, with the greatest savings occurring in daylight-rich months. When translated into environmental terms, these operational reductions yielded a corresponding ~17% decrease in use-phase CO₂ emissions under a regional grid factor of 0.40 kg CO₂/kWh. Importantly, the system’s embodied impacts were outweighed within an operational payback period of approximately 18–20 months, underscoring both environmental and economic viability. Sensitivity analyses across illuminance thresholds, luminaire efficacy, and grid emission factors confirmed the robustness of these outcomes. Overall, the study provides a reproducible methodology that directly integrates empirical daylight measurements with life-cycle assessment, clarifying the contribution of smart lighting control to sustainable building design. Full article

This study compares the environmental impacts of building reconstruction and renovation in aging building improvement projects and quantitatively assesses their carbon reduction potential from a life cycle perspective. A life cycle assessment (LCA) methodology was used to estimate greenhouse gas emissions across all stages—production, transportation, construction, operation, and disposal. A reinforced concrete (RC) structure in Seoul served as the case study, with three scenarios modeled: maintaining the existing structure, reconstruction, and renovation. Results show that renovation produced a carbon emission intensity of approximately 1.37 × 10³ kg–CO₂eq/m²—46.21% lower than the existing building and 22.34% lower than reconstruction. Renovation offered significant embodied carbon savings during the production and demolition phases. In the operational phase, emissions were reduced by 47.50% through upgrades such as high-performance insulation, better windows, and renewable energy systems. While reconstruction showed some emission reductions, its environmental burden remained higher due to the need for new materials and additional demolition waste. Overall, renovation demonstrates greater carbon reduction potential across the building’s life cycle. These findings underscore its value as a key strategy for achieving carbon neutrality in the building sector by 2050 and provide scientific evidence to inform design and policy decisions. Full article

Rural residential structures account for a substantial share of carbon emissions within the construction industry. Enhancing building envelopes can diminish structural carbon emissions, thereby facilitating the attainment of “dual carbon” objectives. Current algorithm-driven research on the low-carbon retrofitting of residential building envelopes generally neglects temperate regions in low-latitude plateaus, often misses embodied carbon, and utilizes rather limited methodologies for issue identification. This study focuses on rural dwellings in Lijiang, utilizing a cross-validation method that incorporates sensitivity analysis, infrared thermal imaging, and energy efficiency criteria to systematically identify vulnerable regions in the building envelope. Consequently, critical issues are converted into optimization variables for the NSGA-II method, aiming to minimize both embodied carbon and operational energy usage. BAPV is concurrently implemented to partially mitigate renovation expenses. A weighted summation approach delineates stakeholder preferences, resulting in three optimum options. The findings reveal that all three methods correspond to their unique preferences, illustrating distinct trade-offs among energy efficiency, carbon reduction, and economic feasibility. The government-oriented approach attained an energy saving rate (ESR) of 45.11%, a life cycle carbon reduction (LCCR) of 1215.76 kgCO₂/m², and a dynamic payback period (DPP) of 3.65 years. The architect-oriented approach realized the highest energy savings and carbon reduction (45.41%, 1218.96 kgCO₂/m²), with a payback period of 3.99 years. The villager-oriented approach emphasized economic viability, achieving an energy savings rate of 41.55%, a carbon reduction of 1149.46 kgCO₂/m², and the shortest payback period of 2.87 years. This study provides an optimization process and reference parameters for building envelopes in a low-carbon design for residential buildings in temperate regions of low-latitude plateaus. Full article

Recycled coarse aggregates (RCA) offer an alternative to natural coarse aggregates in concrete production, reducing natural aggregate extraction and landfill burdens and potentially lowering embodied energy and CO₂ emissions. This study leverages machine learning algorithms to predict the dynamic yield stress (DYS) and plastic viscosity (PV) of RCA concrete (RCAC). A database of 380 RCAC mixtures, incorporating 11 input features, was analyzed using six machine learning models: Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Support Vector Machine (SVM). The model performance was compared, followed by sensitivity analyses to identify critical factors influencing DYS and PV. For DYS, the DT model demonstrated the highest predictive performance (testing R²/RMSE/MAE = 0.95/18.25/13.99; others: 0.90–0.93/12.14–26.10/15.40–19.50) due to its robustness on smaller datasets. The XGBoost model led for PV (testing R²/RMSE/MAE = 0.93/7.06/4.58; others: 0.82–0.89/8.69–11.20/6.06–7.51) owing to its sequential residual minimization that captures nonlinear interactions. Sensitivity analyses revealed that polycarboxylate superplasticizer content and water-to-binder ratio significantly influence DYS, while cement content and saturated-surface-dried water absorption of RCA (i.e., measured with open pores filled and the aggregate surface dry) dominate PV. The time-dependent role in affecting PV was also highlighted. By optimizing and comparing different machine learning algorithms, this study advances predictive methodologies for the rheological properties of RCAC, addressing the underexplored use of machine learning for RCAC rheology (DYS and PV) and the limitations of traditional empirical rheology methods, thereby promoting the efficient use of recycled materials in sustainable concrete design. Full article

This article proposes a comparative framework for interpreting architectural and armorial artefacts through morphological and symbolic analysis. Focusing on the Romanesque, Gothic, and Renaissance periods, the study explores how buildings and body armour—though differing in scale and function—encode similar cultural values related to protection, identity, and representation. Rather than seeking direct historical transmission, the research reveals convergent design logics shaped by shared symbolic imperatives. Methodologically, the article combines typological comparison with embodied heritage practices. These include experimental reconstruction, traditional stone carving, and field-based conservation conducted through the Ambulance for Monuments (Ambulanța pentru Monumente) programme. Such experiences support a situated understanding of proportion, articulation, and material behaviour in both architecture and armour. By repositioning historical armour as a culturally meaningful artefact rooted in craft knowledge and symbolic logic, the study contributes to current debates in heritage science. It argues for the inclusion of martial objects within broader frameworks of heritage interpretation. The findings highlight how architecture and armour function as co-expressive elements of a shared design culture, offering new insights for research, conservation, and the communication of historical meaning. Full article

This article reconceptualises London as a learnt city, a dynamic learning ecosystem co-produced through digital mediation, affective experience, and embodied practice. Focusing on international university students in London, a transient, hyper-digital city, we employ a participatory reflective-mapping methodology to examine how urban learning unfolds across mediated, affective, and experiential layers of city life. The mediated city describes students’ imaginaries shaped by digital media and mapping apps. The affective city captures emotional registers, such as nostalgia, autonomy, and (dis)orientation, that emerge during urban adaptation. The experiential city foregrounds embodied engagements: movement, infrastructure use, routine navigation, and elective belonging. These three dimensions interweave to form an “urban collage,” revealing how students continuously remake both their identities and the city itself through integrated online and offline practices. The article advances critical urban and communication studies by contesting technocratic and neoliberal framings of urban learning. It positions learning as inherently spatial, affective, and relational—a sense-making process enacted in everyday urban experiences. By framing the city as a contested site of knowledge production and identity formation, this article contributes to debates in digital urbanism and critical digital pedagogy. The learnt city concept offers a novel lens for understanding how global cities—characterised by frictions of belonging and mobility—are lived, known, and shaped by those negotiating their multiple mediated, affective, and material dimensions. Full article

The construction sector is a major contributor to environmental degradation due to high energy consumption and CO₂ emissions. This study presents a low-tech, sustainable construction system based on the manual fabrication of curved laminated bamboo beams, assembled with screws and steel straps, without adhesives or heavy machinery. The case study is part of a bamboo roof structure built within Jericoacoara National Park, Brazil, using Dendrocalamus asper for its mechanical strength and carbon storage capacity. The construction process of three vertical lower laminated curved beams (Vig.CLIV-1, CLIV-2, and CLIV-3) was systematized into two main phases—preparation and construction. Due to the level of detail involved, only Vig.CLIV-1 is fully presented, broken down into work items, processes, and sub-processes to identify critical points for quality control and time efficiency. Comparative analysis of the three beams complements the findings, highlighting differences in logistics, labor performance, and learning outcomes. The results demonstrate the potential of this handcrafted system to achieve high geometric accuracy in complex site conditions, with low embodied energy and strong replicability. Developed by bamboo specialists from Colombia and Peru with support from local assistants, this experience illustrates the viability of low-impact, appropriate construction solutions for ecologically sensitive contexts and advances the integration of sustainable, replicable practices in architectural design. Full article

This paper introduces and defines ‘Living Cultural Infrastructure’ as dynamic social-ecological systems where plant heritage and community knowledge are co-produced to reclaim degraded urban landscapes. Addressing the dual challenges of ecological degradation and cultural erosion, we demonstrate this concept through a case study on the Maekha Canal in Chiang Mai, Thailand, employing Participatory Landscape Architecture integrated with urban ethnobotany. Through co-design workshops, biocultural spatial analysis, and ethnobotanical surveys involving 20 key community members, the project engaged residents to reclaim the canal as a functional biocultural corridor. The research documented 149 culturally significant plant species and resulted in a co-created trail system that embodies the principles of a living infrastructure, fostering intergenerational knowledge exchange and strengthening community stewardship. This study demonstrates how a participatory, ethnobotany-informed process can regenerate degraded urban waterways into Living Cultural Infrastructure. The research advances a new paradigm for landscape architecture by providing replicable governance and design tools. Full article

Sustainable shell structures are thin, curved systems such as domes, vaults, and cylindrical shells that achieve strength and stability primarily through membrane action, allowing significant material savings. Their sustainability lies in minimizing embodied energy and CO₂ emissions by using less material, integrating recycled or bio-based components, and applying optimization strategies to extend service life and enable reuse or recycling, all while maintaining structural performance and architectural quality. This review critically examines the state-of-the-art in sustainable shell structures, focusing on their buckling behavior and material-efficient design strategies. Integrating bibliometric analysis with thematic synthesis, the study identifies key research trends, theoretical advancements, and optimization tools that support structural efficiency. Emphasis is placed on recent developments in composite and bio-based materials, imperfection-sensitive buckling models, and performance-based design approaches. Advanced computational methods, including finite element analysis, machine learning, and digital twins, are highlighted as critical in enhancing predictive accuracy and sustainability outcomes. The findings underscore the dual challenge of achieving both structural stability and environmental responsibility, while outlining research gaps and future directions toward resilient, low-impact shell construction. Full article

This study presents a comprehensive study integrating machine learning, life cycle assessment (LCA) and heuristic optimization to achieve a low-carbon medical waste (MW)-to fuel process. A detailed process simulation coupled with cradle to gate LCA is employed to generate a dataset covering diverse process operation conditions, embodied carbon of supplying H₂ and the associated carbon emission factor of MW treatment (CEF). Four machine learning techniques, including support vector machine, artificial neural network, Gaussian process regression, and XGBoost, are trained, each achieving test R² close to 0.90 and RMSE of ~0.26. These models are integrated with heuristic algorithms to optimize operating parameters under various green hydrogen mixes (20–80%). Our results show that machine learning models outperform the detailed process model (DPM), achieving a minimum CEF of ~1.3 to ~1.1 kg CO₂-eq/kg MW with higher computational stabilities. Importantly, the optimization times dropped from hours (DPM) to seconds (machine learning models) and the combination of Gaussian process regression and particle swarm optimization is highlighted, with an optimization time under one second. The optimized process holds promise in carbon reduction compared to traditional MW disposal methods. These findings show machine learning can achieve high predictive accuracy while dramatically enhancing optimization speed and stability, providing a scalable framework for extensive scenario analysis during waste-to-energy process design and further real-time optimization application. Full article

The growing demand for sustainable and energy-efficient construction has driven significant interest in the development of advanced insulation materials that reduce energy usage while minimizing environmental impact. Although conventional insulation materials such as polyurethane, polystyrene, and mineral wools offer excellent thermal and acoustic performance, they are derived from non-renewable sources, have high embodied carbon (EC) (up to 7.3 kg CO₂-eq/kg), and pose end-of-life disposal challenges. Thus, this review critically examines the emergence of insulation materials derived from natural and recycled sources, which align with circular economy principles by minimizing waste, promoting material reuse, and extending product life cycles. Sustainable alternatives such as sheep wool, hemp, flax, and jute not only exhibit competitive thermal conductivity (as low as 0.031–0.046 W/m·K) and very good sound absorption but also offer low EC, biodegradability, and regional availability. Despite some limitations, including variable fire resistance and thickness requirements, these bio-based insulators present a viable path toward greener building solutions. The review highlights that waste-based insulation materials are essential for sustainable construction due to their low EC, renewability, and contribution to waste reduction, making them a necessary alternative even when conventional materials demonstrate superior short-term performance. Full article

The building sector is a pivotal driver of global resource depletion and environmental deterioration, being responsible for 40% of raw material consumption, 16% of water usage, 25% of timber utilization, and 40% of total energy demand. It also accounts for 30% of worldwide greenhouse gas (GHG) emissions, predominantly CO₂. The operational phase of buildings is the most energy-intensive and emission-heavy stage, accounting for 85–95% of their total life-cycle energy consumption. This energy is primarily expended on heating, cooling, ventilation, and hot water systems, which are largely dependent on fossil fuels. Furthermore, embodied energy, the cumulative energy expended from the extraction of materials through construction, operation, and eventual demolition, plays a substantial role in a building’s overall environmental footprint. To address these pressing challenges, this study discusses sustainable innovations within green architecture and biomimicry. Our topic supports the 2030 vision Sustainable Development Goals (SDGs), both directly and indirectly (SDGs 7, 9, 11, 12, and 13). This study also explores cutting-edge applications, such as algae- and slime mold-inspired decentralized urban planning, which offer innovative pathways toward energy efficiency and sustainability. Considering the integration of renewable energy sources, passive design methodologies, and eco-friendly materials, this research emphasizes the transformative potential of biomimicry and green architecture in fostering a sustainable built environment, mitigating climate change, and cultivating a regenerative coexistence between human habitats and the natural world. Full article

This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO₂/m³ reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies. Full article