Search Results (1,025)

The construction industry consumes a substantial amount of resources. The associated environmental degradation and accelerating biodiversity loss highlight the urgent need for sustainable building materials that can match the performance of conventional alternatives. The objective of this experimental study was to investigate a fully reused, binder-free earth-based material that remains recyclable after its useful life. The material consists of smectite-rich excavation earth and processed demolition waste in a 2:1 ratio, which was compacted under high pressures and subsequently tested to evaluate its mechanical properties. Cylindrical specimens were fabricated via double-ended uniaxial compaction at pressures ranging from 12.5 to 100 MPa and consolidation times between 1 s and 30 min. They were then tested for their compressive strength and water durability. The findings indicate a strong positive correlation between compaction pressure, density, and compressive strength. A compressive strength of 19.2 MPa was reached by specimens that were compacted at 100 MPa for 30 min, achieving values comparable to standard C20/25 concrete. Despite an increase in strength, water durability decreased with increasing compaction pressure but improved with higher molding water content, possibly due to changes in the microstructure. The findings confirm that compressed earth can reach similar compressive strength to conventional materials with a significantly smaller ecological footprint. Full article

This study aims to evaluate the potential of circular economy principles in earth-based construction using an experimental rammed earth building located in Pasłęk, Poland as a case study. The research focuses on end-of-life scenarios for earth materials, with particular emphasis on rammed earth, adobe, and compressed earth blocks stabilized with Portland cement. A scenario-based life-cycle assessment (LCA) was conducted to compare alternative demolition and reuse strategies, including manual and mechanical deconstruction, as well as on-site and off-site material reuse. Greenhouse gas emissions associated with demolition (Module C1) and transport (Module C2) were estimated for each scenario. The results indicate that manual deconstruction combined with local, on-site reuse leads to the lowest carbon footprint, whereas off-site reuse involving long-distance transport significantly increases greenhouse gas emissions. In addition, qualitative reuse pathways were identified for wood, glass, ceramics, and insulation materials. The study reveals a lack of standardized technical procedures for the recovery and reuse of stabilized earthen materials after demolition and highlights the importance of integrating end-of-life planning into the early design phase using digital tools such as material passports and BIM. The findings demonstrate that properly designed rammed earth systems can provide a viable low-tech solution for reducing construction waste and supporting circular material flows in the built environment. Full article

The accounting and analysis of agricultural grey water footprint (AGWF) are crucial for building a low-water-consumption agricultural production model and improving water resource efficiency in Fujian Province. This study innovatively integrated green development indicators into an extended STIRPAT model, quantitatively analyzed the drivers of AGWF from six dimensions (population, economy, technology, dietary structure, meteorology, and green development) based on data from 2009 to 2023. The results indicated that the AGWF in Fujian Province exhibited an overall upward trend, increasing from 114.61 billion m³ to 221.30 billion m³. Population expansion (elasticity: 0.49853) and economic growth (elasticity: 0.46329) were identified as the primary positive drivers, while technological progress exerted a mitigating effect (elasticity: −0.07253). The impacts of dietary structure, precipitation, and green development measures, though statistically significant, were quantitatively limited within the study period (elasticities of 0.0312, 0.0273, and 0.004, respectively). These findings provide quantitative support for formulating targeted policies for agricultural water resource management and non-point source pollution control in regions with similar characteristics. Full article

The fusion of Artificial Intelligence and the Internet of Things (AI-IoT, also widely referred to as AIoT) offers transformative potential for smart cities, yet presents a critical challenge: how to process heterogeneous data streams from intelligent sensing—particularly crowd sensing data derived from citizen interactions like text, voice, and system logs—into reliable intelligence for sustainable urban governance. To address this challenge, we introduce the Intelligent Multimodal Ticket Processing System (IMTPS), a novel AI-IoT smart system. Unlike ad hoc solutions, the novelty of IMTPS resides in its theoretically grounded architecture, which orchestrates Information Theory and Game Theory for efficient, verifiable extraction, and employs Causal Inference and Meta-Learning for robust reasoning, thereby synergistically converting noisy, heterogeneous data streams into reliable governance intelligence. This principled design endows IMTPS with four foundational capabilities essential for modern smart city applications: Sustainable and Efficient AI-IoT Operations: Guided by Information Theory, the IMTPS compression module achieves provably efficient semantic-preserving compression, drastically reducing data storage and energy costs. Trustworthy Data Extraction: A Game Theory-based adversarial verification network ensures high reliability in extracting critical information, mitigating the risk of model hallucination in high-stakes citizen services. Robust Multimodal Fusion: The fusion engine leverages Causal Inference to distinguish true causality from spurious correlations, enabling trustworthy integration of complex, multi-source urban data. Adaptive Intelligent System: A Meta-Learning-based retrieval mechanism allows the system to rapidly adapt to new and evolving query patterns, ensuring long-term effectiveness in dynamic urban environments. We validate IMTPS on a large-scale, publicly released benchmark dataset of 14,230 multimodal records. IMTPS demonstrates state-of-the-art performance, achieving a 96.9% reduction in storage footprint and a 47% decrease in critical data extraction errors. By open-sourcing our implementation, we aim to provide a replicable blueprint for building the next generation of trustworthy and sustainable AI-IoT systems for citizen-centric smart cities. Full article

The thermal influence of Urban Heat Islands (UHIs) is not limited to periods of high temperature but persists throughout the year. The present study utilizes hourly data collected over a period of one year from a network of hygrothermal monitoring stations with a high density, which were deployed across the city of Cáceres (Spain). The network was designed in accordance with the World Meteorological Organization’s guidelines for urban measurements (employing radiation footprints and surface roughness) and ensures representation of each Local Climate Zone (LCZ), characterized by those factors (such as building typology and density, urban fabric, vegetation, and anthropogenic activity, among others) that influence potential solar radiation absorption. The magnitude of the heat island effect in this city has been determined to be approximately 7 °C in summer and winter at the first hours of the morning. In order to assess the energy impact of UHIs, Cooling and Heating Degree Days (CDD and HDD) were calculated for both summer and winter periods across the different LCZs. Following the implementation of rigorous quality control procedures and the utilization of gap-filling techniques, the analysis yielded discrepancies in energy demand of up to 10% between LCZs within the city. The significance of incorporating UHIs into the design of building envelopes and climate control systems is underscored by these findings, with the potential to enhance both energy efficiency and occupant thermal comfort. This methodology is particularly relevant for extrapolation to larger and denser urban environments, where the intensification of UHI effects exerts a direct impact on energy consumption and costs. The following essay will provide a comprehensive overview of the relevant literature on the subject. Full article

In modern construction, natural materials with a low carbon footprint and full recyclability are becoming increasingly important. A typical group here is products made from solid wood, including glued wood, plywood, and wood-based composites. With their many advantages, however, they all burden the environment with the costs of production processes, as well as the need to use harmful chemicals (adhesives and impregnants). Solid wood is devoid of these disadvantages; however, it is often treated as a rather archaic material. One of the arguments here is its low durability compared to, e.g., glued wood. The article discusses the durability of solid wood using the example of a group of wooden churches preserved in Poland, in Upper Silesia. Some of these buildings are over five hundred years old, making them a reliable source of information about the durability of the material from which they were built. A total of 85 churches, at least 200 years old, were analyzed, evaluating the technical state of the main load-bearing elements of their structures. In view of the number of facilities and the inability to conduct tests in most of them, the assessment was limited to a visual inspection of the technical condition, carried out by an experienced building expert. The assessment estimated the area of corrosion damage, probed its depth, and measured the depth of cracks. The relationship between their technical condition and the environmental conditions in which they were used was described and discussed. In this way, both the threats to the durability of solid wood and the ways to keep it in good condition for hundreds of years were identified, refuting the thesis that solid wood is a material with low durability. Its use in structural elements therefore supports efficient resource management and contributes to sustainable construction, especially in small and medium-sized buildings. Full article

The building sector is significantly responsible for the world’s energy consumption and carbon emissions. Net-zero energy buildings (NZEBs) have become an effective solution to move towards sustainability, maximizing energy efficiency, and minimizing carbon footprint. However, achieving net-zero energy targets requires a comprehensive understanding of building performance from the perspectives of technologies, materials, and strategies, for which existing studies have a knowledge gap. This study aims to bridge the knowledge gap within existing studies through an empirical investigation. Based on a review of the literature, this study employs semi-structured interviews in the United Kingdom (UK) with industrial professionals experienced in NZEBs. The qualitative data collected from interview participants are analyzed minutely using NVivo to identify key themes and patterns, including 14 technologies, 12 materials, and seven strategies for NZEBs. Based on the literature review and, more importantly, the interview analysis, a conceptual framework is well established to describe an NZEB as a complex system that must incorporate appropriate technology adoption, careful material selection, and successful strategy implementation into consideration. This study provides a comprehensive understanding of NZEBs from a systematic point of view. It also contributes to the full fulfillment of Sustainable Development Goals (SDGs) established by the United Nations (UN). Full article

Building extraction underpins land-use assessment, urban planning, and disaster mitigation, yet dense urban scenes still cause missed small objects, target adhesion, and ragged contours. We present High-Resolution-Mamba (HR-Mamba), a high-resolution semantic segmentation network that augments a High-Resolution Network (HRNet) parallel backbone with edge-aware and sequence-state modeling. A Canny-enhanced, median-filtered stem stabilizes boundaries under noise; Involution-based residual blocks capture position-specific local geometry; and a Mamba-based State Space Models (Mamba-SSM) global branch captures cross-scale long-range dependencies with linear complexity. Training uses a composite loss of binary cross entropy (BCE), Dice loss, and Boundary loss, with weights selected by joint grid search. We further design a feature-driven adaptive post-processing pipeline that includes geometric feature analysis, multi-strategy simplification, multi-directional regularization, and topological consistency verification to produce regular, smooth, engineering-ready building outlines. On dense urban imagery, HR-Mamba improves F1-score from 80.95% to 83.93%, an absolute increase of 2.98% relative to HRNet. We conclude that HR-Mamba jointly enhances detail fidelity and global consistency and offers a generalizable route for high-resolution building extraction in remote sensing. Full article

High-speed silicon (Si) photonic transmitters operating in the O-band require higher on-chip optical power to support advanced modulation formats and ever-increasing line rates. A straightforward approach is to operate laser diodes at higher output power or employ more specialized sources, but this raises cost and exacerbates nonlinear effects such as self-phase modulation, two-photon absorption, and free-carrier generation in high-index-contrast Si waveguides. This paper proposes a low-cost 4 × 1 tree-cascade multimode interference (MMI) power combiner on a Si-on-insulator platform at 1310 nm wavelength that enables coherent power scaling while remaining fully compatible with standard commercial O-band lasers. The device employs adiabatic tapers and low-loss S-bends to ensure uniform field evolution, suppress local field enhancement, and mitigate nonlinear phase accumulation. The optimized layout occupies a compact footprint of 12 µm × 772 µm and achieves a simulated normalized power transmission of 0.975 with an insertion loss of 0.1 dB. Spectral analysis shows a 3 dB bandwidth of 15.8 nm around 1310 nm, across the O-band operating window. Thermal analysis shows that wavelength drift associated with ±50 °C temperature variation remains within the device bandwidth, ensuring stable operation under realistic laser self-heating and environmental changes. Owing to its broadband response, fabrication tolerance, and compatibility with off-the-shelf laser diodes, the proposed combiner is a promising building block for O-band transmitters and photonic neural-network architectures based on cascaded splitter and combiner meshes, while preserving linear transmission and enabling dense, large-scale photonic integration. Full article

Rapid urbanization in developing megacities has exacerbated hydrological imbalances, positioning urban flooding as a major environmental and socio-economic challenge of the twenty-first century. This study investigates the spatial relationship between building density, topography, and flood susceptibility in Jakarta, Indonesia—one of the most flood-prone urban regions globally. Employing geospatial analysis and spatial autocorrelation techniques, the research assesses how variations in land-use concentration and elevation influence the spatial clustering of flood vulnerability. The analytical framework integrates multiple spatial datasets, including Digital Elevation Models (DEMs), building footprint densities, and flood hazard maps, within a Geographic Information System (GIS) environment. Spatial statistical measures, specifically Moran’s I and Local Indicators of Spatial Association (LISA), are utilized to quantify and visualize patterns of flood susceptibility. The findings reveal that zones characterized by high building density and low elevation form statistically significant clusters of heightened flood risk, particularly within the southern and eastern subdistricts of Jakarta. The study concludes that incorporating spatially explicit and statistically rigorous methodologies enhances the accuracy of flood-risk assessments and supports evidence-based strategies for sustainable urban development and resilience planning. Full article

Background: Nearshoring has risen after shocks and policy shifts. We synthesize evidence in a compact loop linking triggers (trade frictions, supply-chain risk, new agreements) to location choices mediated by multidimensional proximity (geographic, institutional, organizational, social, cognitive, functional) to components (manufacturing footprint, Foreign Direct Investment (FDI), employment) and outcomes (spillovers, productivity, innovation) conditioned by absorptive capacity and institutions. Methods: We conducted a literature review using major bibliographic databases. A staged screening pipeline (deduplication, pre-eligibility, and title–abstract screening) preceded full-text coding aligned with the review framework (triggers, proximity, components, outcomes, mediators). Studies were appraised with a five-criterion checklist, and themes were consolidated with basic bibliometric checks. Results: Evidence is North Atlantic and manufacturing-centric. Supply-chain disruptions dominate triggers; non-geographic proximity strongly moderates relocation. FDI anchors ecosystems, while employment effects are lagged and compositional. Strong capability and policy mixes yield broader spillovers; otherwise, benefits remain enclave-like. Sustainability and transformative outcomes are rarely assessed. Conclusions: The loop clarifies feedback from outcomes to future siting. Firms should build proximity beyond geography and pair early FDI with supplier and skills upgrading; policymakers should align instruments to governance, capability formation, and logistics. Research should expand Global South coverage and integrate environmental and inclusion metrics. Full article

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

Wildfires increasingly threaten Mediterranean landscapes, particularly in regions like Attica, Greece, where urban sprawl, agricultural abandonment, and climatic conditions heighten the risk at the Wildland–Urban Interface (WUI). The Mediterranean basin, recognized as one of the global wildfire “hotspots”, has witnessed a steady increase in both fire severity, frequency, and burned area during the last four decades, a trend amplified by urban sprawl and agricultural land abandonment. This study represents the first integrated, region-wide mapping of the WUI and associated wildfire risk in Attica, the most densely urbanized area in Greece and one of the most fire-exposed metropolitan regions in Southern Europe, utilizing advanced techniques such as Earth Observation and GIS analysis. For this purpose, various geospatial datasets were coupled, including Copernicus High Resolution Layers, multi-decadal Landsat fire history archive, UCR-STAR building footprints, and CORINE Land Cover, among others. The research delineated WUI zones into 40 interface and intermix categories, revealing that WUI encompasses 26.29% of Attica, predominantly in shrub-dominated areas. An analysis of fire frequency history from 1983 to 2023 indicated that approximately 102,366 hectares have been affected by wildfires. Risk assessments indicate that moderate hazard zones are most prevalent, covering 36.85% of the region, while approximately 25% of Attica is classified as moderate, high, or very high susceptibility zones. The integrated risk map indicates that 37.74% of Attica is situated in high- and very high-risk zones, principally concentrated in peri-urban areas. These findings underscore Attica’s designation as one of the most fire-prone metropolitan regions in Southern Europe and offer a viable methodology for enhancing land-use planning, fuel management, and civil protection efforts. Full article

The rising presence of artificial intelligence (AI) across European industries is gradually reshaping how societies manage resources, reduce waste, and pursue long-term sustainability. While researchers widely acknowledge the economic and social implications of AI, they have not yet sufficiently explored its contribution to advancing a circular economy. This study examines how varying levels of AI adoption across EU Member States relate to material footprint, resource productivity, waste generation, and recycling performance. The analysis draws on harmonized Eurostat data from 2023, the most recent year for which complete and comparable indicators are available, enabling a coherent cross-sectional perspective that reflects the period when AI began to exert a more visible influence on economic and environmental practices. By combining measures of AI uptake with key circular economy indicators and applying factor analysis, neural network modelling, and cluster analysis, the study identifies underlying patterns and country-specific profiles. The results suggest that higher AI adoption is often associated with greater resource productivity and more efficient material use. However, its effects on waste generation and recycling remain uneven across Member States. These findings indicate that AI can support circular economy objectives when embedded in coordinated national strategies and supported by robust institutional frameworks. Strengthening the alignment between digital innovation and sustainability goals may help build more resilient, resource-efficient economies across Europe. Full article

An accurate and reliable extraction of building structures from high-resolution (HR) remote sensing images is an important research topic in 3D cartography and smart city construction. However, despite the strong overall performance of recent deep learning models, limitations remain in handling significant variations in building scales and complex architectural forms, which may lead to inaccurate boundaries or difficulties in extracting small or irregular structures. Therefore, the present study proposes MSA-UNet, a reliable semantic segmentation framework that leverages multiscale feature aggregation and attentive skip connections for an accurate extraction of building footprints. This framework is constructed based on the U-Net architecture, incorporating VGG16 as a replacement for the original encoder structure, which enhances its ability to capture low-discriminative features. To further improve the representation of image buildings with different scales and shapes, a serial coarse-to-fine feature aggregation mechanism was used. Additionally, a novel skip connection was built between the encoder and decoder layers to enable adaptive weights. Furthermore, a dual-attention mechanism, implemented through the convolutional block attention module, was integrated to enhance the focus of the network on building regions. Extensive experiments conducted on the WHU and Inria building datasets validated the effectiveness of MSA-UNet. On the WHU dataset, the model demonstrated a state-of-the-art performance with a mean Intersection over Union (mIoU) of 94.26%, accuracy of 98.32%, F1-score of 96.57%, and mean Pixel accuracy (mPA) of 96.85%, corresponding to gains of 1.41% in mIoU over the baseline U-Net. On the more challenging Inria dataset, MSA-UNet achieved an mIoU of 85.92%, indicating a consistent improvement of up to 1.9% over the baseline U-Net. These results confirmed that MSA-UNet markedly improved the accuracy and boundary integrity of building extraction from HR data, outperforming existing classic models in terms of segmentation quality and robustness. Full article