Search Results (946)

In this paper, we present an extremely compact eleven-state microwave filter with four concentric split-ring resonators (SRRs). Reconfigurability is achieved by switching off either single or multiple SRRs, thereby obtaining different triple-band, dual-band, and single-band configurations from the initial quad-band topology. Switches are placed on the vertical branches of SRRs in order to minimize the additional insertion loss. As switching elements, we first use traditional RF switches—PIN diodes—and then examine the integration of non-volatile RF switches—memristors—into filter design. Memristors’ ability to remember previous electrical states makes them a main building block for designing circuits that are both energy-efficient and adaptive, opening a new era in electronics and artificial intelligence. As RF memristors are not commercially available, PIN diodes are used for experimental filter verification. Afterwards, we compare the filter characteristics realized with PIN diodes and memristors to present capabilities of memristor technology. Memristors require no bias, and their parasitic effects are modeled with low resistance for the ON state and low capacitance for the OFF state. Measured performances of all obtained configurations are in good agreement with the simulations. The filter footprint area is 26 mm × 29 mm on DiClad substrate. Full article

This study proposes an integrated computational framework that combines deep learning-based visual perception analysis with multi-criteria decision making to optimize indoor architectural layouts in terms of both visual coherence and sustainability. The framework initially employs a deep learning method leveraging edge pixel feature recombination to extract critical spatial layout features and determine key visual focal points. A fusion model is then constructed to preprocess visual representations of interior layouts. Subsequently, an evolutionary deep learning algorithm is adopted to optimize parameter convergence and enhance feature extraction accuracy. To support comprehensive evaluation and decision making, an improved Analytic Hierarchy Process (AHP) is integrated with the entropy weight method, enabling the fusion of objective, data-driven weights with subjective expert judgments. This dual-focus framework addresses two pressing challenges in architectural optimization: sensitivity to building-specific spatial features and the traditional disconnect between perceptual analysis and sustainability metrics. Experimental results on a dataset of 25,400 building images demonstrate that the proposed method achieves a feature detection accuracy of 92.3%, surpassing CNN (73.6%), RNN (68.2%), and LSTM (75.1%) baselines, while reducing the processing time to under 0.95 s and lowering the carbon footprint to 17.8% of conventional methods. These findings underscore the effectiveness and practicality of the proposed model in facilitating intelligent, sustainable architectural design. Full article

This review is focused on glass fibers and natural fibers, exploring their applications in vehicles and buildings and emphasizing their significance in promoting sustainability and enhancing performance across various industries. Glass fibers, or fiberglass, are lightweight, have high-strength (3000–4500 MPa) and a Young’s modulus range of 70–85 GPa, and are widely used in automotive, aerospace, construction, and marine applications due to their excellent mechanical properties, thermal conductivity of ~0.045 W/m·K, and resistance to fire and corrosion. On the other hand, natural fibers, derived from plants and animals, are increasingly recognized for their environmental benefits and potential in sustainable construction, offering advantages such as biodegradability, lower carbon footprints, and reduced energy consumption, with a sound absorption coefficient (SAC) range of 0.7–0.8 at frequencies above 2000 Hz and thermal conductivity range of 0.07–0.09 W/m·K. Notably, the integration of these materials in construction and automotive sectors reflects a growing trend towards sustainable practices, driven by the need to mitigate carbon emissions associated with traditional building materials and enhance fuel efficiency, as seen in hybrid composites achieving 44.9 dB acoustic insulation at 10,000 Hz and a thermal conductivity range of 0.05–0.06 W/m·K in applications such as the BMW i3 door panels. Natural fibers contribute to reducing reliance on fossil fuels, supporting a circular economy through the recycling of agricultural waste, while glass fibers are instrumental in creating lightweight composites for improved vehicle performance and structural integrity. However, both materials face distinct challenges. Glass fibers, while offering superior strength, are vulnerable to chemical degradation and can pose recycling difficulties due to the complex processes involved. On the other hand, natural fibers may experience moisture absorption, affecting their durability and mechanical properties, necessitating innovations to enhance their application in demanding environments. The ongoing research into optimizing the performance of both materials highlights their relevance in future sustainable engineering practices. In summary, this review underscores the growing importance of glass and natural fibers in addressing modern environmental challenges while also improving product performance. As industries increasingly prioritize sustainability, these materials are poised to play crucial roles in shaping the future of construction and transportation, driving innovations that align with ecological goals and consumer expectations. Full article

Global warming, anthropogenic pressure, and urban expansion at the expense of green spaces are leading to an increase in the incidence of urban heat islands, creating discomfort and health issue for citizens. This present research aimed at quantifying the impact of nature-based solutions to support decision-making processes in sustainable energy action plans. A simple method is provided, linking applied thermodynamics to physics-informed modeling of urban built-up and green areas, high-resolution climate models at urban scale, greenery modeling, spatial georeferencing techniques for energy, and entropy exchanges evaluation in urban built-up areas, with and without vegetation. This allows the outdoor climate conditions and thermo-hygrometric well-being to improve, reducing the workload of cooling plant-systems in buildings and entropy flux to the environment. The finalization and post-processing of obtained results allows the definition of entropy footprints. The main findings show a decrease in greenery’s contribution for different scenarios, referring to a different climatological dataset, but an increase in entropy that becomes higher for the scenario with higher emissions. The comparison between the entropy footprint values for different urban zones can be a useful support to public administrations, stakeholders, and local governments for planning proactive resilient cities and anthropogenic impact reduction and climate change mitigation. Full article

Contemporary construction faces the need to reduce its negative impact on the environment, prompting designers, investors, and contractors to seek more sustainable materials and technologies. One area of dynamic development is the use of natural fibers as an alternative to conventional, often synthetic, building components. Plant- and animal-based fibers, such as hemp, flax, jute, straw, bamboo, and sheep’s wool, are characterized by low energy consumption in production, renewability, and biodegradability. Their use is in line with the concept of a circular economy and reduces the carbon footprint of buildings. Natural fibers offer a number of beneficial physical and functional properties, including good thermal and acoustic insulation parameters, as well as hygroscopicity, which allows for the regulation of indoor humidity, improving air quality and comfort of use. In recent years, there has also been a renaissance of traditional building techniques, such as straw construction, often combined with modern engineering standards. Their potential is particularly recognized in green and energy-efficient construction. The article provides an overview of the types of natural fibers available for use in construction and analyzes their technical, environmental, and economic properties. It also draws attention to current regulations, standards, and certifications (e.g., LEED, BREEAM) that promote the popularization of these solutions. In light of the analyzed data, the role of natural fibers as a viable alternative supporting the transformation of the construction sector towards sustainable development is considered. Full article

Against the background of carbon emission reduction, this paper explores the driving factors of carbon footprint knowledge system toughness for building construction enterprises through the theory of constraints (TOC) and optimises the carbon footprint knowledge system toughness under static and dynamic perspectives, respectively. Under the static perspective, the fuzzy set qualitative comparative analysis method (fsQCA) is used to explore the development path of the carbon footprint knowledge system toughness for building construction enterprises, and the study finds three kinds of grouping paths. Under the dynamic perspective, system dynamics is used to analyse the causality of the driving factors of the carbon footprint knowledge system toughness and draw the causality diagram. The stock flow diagram is drawn according to the relationship between the factors, and G1 method is combined with the expert distribution to determine the weight of each factor, and then, the model equation is established to complete the construction of the system dynamics of the carbon footprint knowledge system toughness based on the control variable method of the four capabilities under the influence of the factors to simulate the comparison and to explore the extent of the influence of different factors on the carbon footprint knowledge system toughness. Through the two-dimensional analysis framework, we provide an integrated solution for path selection and dynamic regulation for building construction enterprises to help them achieve the adaptive optimisation of the carbon footprint knowledge system and promote the low-carbon transformation and sustainable development of the construction industry. Qualitative results show that three configuration paths affect resilience, with core factors including management, emission, predictive, and construction capabilities. Quantitative results indicate fsQCA overall consistency (0.861) and coverage (0.808); system dynamics simulation shows that management capability has the highest impact weight (0.355). Full article

Sustainability in the construction and building sector with the use of greener and more eco-friendly building materials can minimize carbon footprint, which is one of the prime goals of the twenty-first century. The use of natural fibers in ancient and traditional buildings and structures is not new, but in the last fifty years, only man-made fibers have predominantly occupied the market for structural retrofitting or upgrading. This research investigated the potential of utilizing natural fibers, particularly jute fiber products, to enhance masonry’s thermal and structural characteristics. The study meticulously investigated the utilization of materials such as jute net (with a mesh size of 2.5 cm × 1.25 cm), jute fiber diatons, and jute fiber composite mortar (with 1% jute fiber with respect to the dry mortar mass) in the context of masonry upgrading. The research evaluated the structural and thermal performance of these upgraded walls. Notably, the implementation of natural fiber textile-reinforced mortar (NFTRM) resulted in an astounding increase of over 500% in the load-bearing capacity of the walls, while simultaneously enhancing insulation by more than 36%. Furthermore, the study involved a meticulous analysis of crack patterns during in-plane cyclic testing utilizing the advanced Digital Image Correlation (DIC) tool. The upgraded/retrofitted wall exhibited a maximum crack width of approximately 7.84 mm, primarily along the diagonal region. 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

The primary challenge for European society today is to strike a balance between maximizing energy efficiency and environmental care, while also ensuring an accessible and safe living environment. The research presented in this Special Issue addressed various aspects of energy storage methods and covered advances in the energy efficiency of buildings and cities in light of the climate change awareness and the need to reduce energy consumption and the carbon footprint from the built environment. Results of empirical and modelling research were compared to advanced simulations and measurements rooted in real-world case studies performed with the purpose of extending the knowledge on holistic sustainable design towards efficient energy use. Key aspects enabling improvements in the energy performance of buildings and contributing to the achievement of climate goals cover thermal comfort and overheating in buildings and cities, including district heating, hydrogen energy storage, renewable energy source integration, carbon emissions, and the economic benefits of building deep renovation. The research findings help us to understand the critical importance of transforming the built environment into renewable energy sources while supporting the energy efficiency of buildings, cities, and neighbourhoods. Full article

Embedded systems in IoT are expected to be run by reliable, resource-efficient software. Devices on the edge are typically required to communicate with central nodes, and in some setups with each other, constituting a distributed system. The Erlang language, favored for its constructs that support building fault-tolerant, distributed systems, offers solutions to these challenges. Its dynamic type system and higher-level abstractions enable fast development, while also featuring tools for building highly available and fault-tolerant applications. To study the viability of using Erlang in embedded systems, we analyze the solutions the language offers, contrasting them with the challenges of developing embedded systems, with a particular focus on resource use. We measure the footprint of the language’s constructs in executing tasks characteristic of end devices, such as gathering, processing and transmitting sensor data. We conduct our experiments with constructs and data of varying sizes to account for the diversity in software complexity of real-world applications. Our measured data can serve as a basis for future research, supporting the design of the software stack for embedded systems. Our results demonstrate that Erlang is an ideal technology for implementing software on embedded systems and a suitable candidate for developing a prototype for a real-world use case. Full article

Evaluating the renewable energy potential, such as that of solar photovoltaics (PV), is important for developing renewable energy policies. This study investigated rooftop solar PV potential in Thailand based on open-source geographic information system (GIS) building footprints, solar PV power output, and the most recent land use data (2022). GIS-based overlay analysis, buffering, fishnet modeling, and spatial join operations were applied to assess rooftop availability across various building types, taking into account PV module installation parameters and optimal panel orientation. Economic feasibility and sensitivity analyses were conducted using standard economic metrics, including net present value (NPV), internal rate of return (IRR), payback period, and benefit–cost ratio (BCR). The findings showed a total rooftop solar PV power generation potential of 50.32 TWh/year, equivalent to 25.5% of Thailand’s total electricity demand in 2022. The Central region contributed the highest potential (19.59 TWh/year, 38.94%), followed by the Northeastern (10.49 TWh/year, 20.84%), Eastern (8.16 TWh/year, 16.22%), Northern (8.09 TWh/year, 16.09%), and Southern regions (3.99 TWh/year, 7.92%). Both commercial and industrial sectors reflect the financial viability of rooftop PV installations and significantly contribute to the overall energy output. These results demonstrate the importance of incorporating rooftop solar PV in renewable energy policy development in regions with similar data infrastructure, particularly the availability of detailed and standardized land use data for building type classification. Full article

Nanofluids are an innovative working medium in solar hot water installations (DHWs), thanks to their increased thermal conductivity and heat transfer coefficient. The aim of this work was to assess the effect of Al₂O₃ nanofluids in a water–ethylene glycol base (40:60%) and with the addition of Tween 80 surfactant (0.2 wt%) on thermal efficiency (ε) and exergy (η_ex) in a plate heat exchanger at DHW flows of 3 and 12 L/min. The numerical NTU–ε model was used with dynamic updating of thermophysical properties of nanofluids and the solution of the ODE system using the ode45 method, and the validation was carried out against the literature data. The results showed that the nanofluids achieved ε ≈ 0.85 (vs. ε ≈ 0.87 for the base fluid) and η_ex ≈ 0.72 (vs. η_ex ≈ 0.74), with higher entropy generation. The addition of Tween 80 reduced the viscosity by about 10–15%, resulting in a slight increase of Re and h-factor; however, the impact on ε and η_ex was marginal. The environmental analysis with an annual demand of Q = 3000 kWh/year and an emission factor of 0.2 kg CO₂/kWh showed that for ε < 0.87 the nanofluids increased the emissions by ≈16 kg CO₂/year, while at ε ≈ 0.92, a reduction of ≈5% was possible. This paper highlights the need to optimize nanofluid viscosity and exchanger geometry to maximize energy and environmental benefits. Nowadays, due to the growing problems of global warming, the analysis of energy efficiency and carbon footprint related to the functioning of a building seems to be crucial. Full article

Because artificially cemented granular (ACG) materials employ diverse combinations of aggregates and binders—including cemented soil, low-cement-content cemented sand and gravel (LCSG), and concrete—their stress–strain responses vary widely. In LCSG, the binder dosage is typically limited to 40–80 kg/m³ and the sand–gravel skeleton is often obtained directly from on-site or nearby excavation spoil, endowing the material with a markedly lower embodied carbon footprint and strong alignment with current low-carbon, green-construction objectives. Yet, such heterogeneity makes a single material-specific constitutive model inadequate for predicting the mechanical behavior of other ACG variants, thereby constraining broader applications in dam construction and foundation reinforcement. This study systematically summarizes and analyzes the stress–strain and volumetric strain–axial strain characteristics of ACG materials under conventional triaxial conditions. Generalized hyperbolic and parabolic equations are employed to describe these two families of curves, and closed-form expressions are proposed for key mechanical indices—peak strength, elastic modulus, and shear dilation behavior. Building on generalized plasticity theory, we derive the plastic flow direction vector, loading direction vector, and plastic modulus, and develop a concise, transferable elastoplastic model suitable for the full spectrum of ACG materials. Validation against triaxial data for rock-fill materials, LCSG, and cemented coal–gangue backfill shows that the model reproduces the stress and deformation paths of each material class with high accuracy. Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. The corresponding coefficients of determination R² between the predicted and measured values are 0.997 for peak stress and 0.987 for peak volumetric strain, demonstrating the excellent engineering accuracy of the proposed model. The results provide a unified theoretical basis for deploying ACG—particularly its low-cement, locally sourced variants—in low-carbon dam construction, foundation rehabilitation, and other sustainable civil engineering projects. Full article

Quarry dust (QD) landfill causes environmental issues that cannot be ignored. In this study, we systematically explore its potential application as a supplementary cementitious material (SCM) in cemented paste backfill (CPB), revealing the activated mechanism of modified QD (MQD) and exploring the hydration process and workability of CPB containing QD/MQD. The experimental results show that quartz, clinochlore and amphibole components react with CaO to form reactive dicalcium silicate (C₂S) and amorphous glass phases, promoting pozzolanic reactivity in MQD. QD promotes early aluminocarbonate (M_c) formation through CaCO₃-derived CO₃²⁻ release but shifts to hemicarboaluminate (H_c) dominance at 28 d. MQD releases active Al³⁺/Si⁴⁺ due to calcination and deconstruction, significantly increasing the amount of ettringite (AFt) in the later stage. With the synergistic effect of coarse–fine particle gradation, MQD-type fresh backfill can achieve a 161 mm flow spread at 20% replacement. Even if this replacement rate reaches 50%, a strength of 19.87 MPa can still be maintained for 28 days. The good workability and low carbon footprint of MQD-type backfill provide theoretical support for—and technical paths toward—QD recycling and the development of low-carbon building materials. Full article

Building height information plays an important role in many urban-related applications, such as urban planning, disaster management, and environmental studies. With the rapid development of real scene maps, street view images are becoming a new data source for building height estimation, considering their easy collection and low cost. However, existing studies on building height estimation primarily utilize remote sensing images, with little exploration of height estimation from street-view images. In this study, we proposed a deep learning-based method for estimating the height of a single building in Baidu panoramic street view imagery. Firstly, the Segment Anything Model was used to extract the region of interest image and location features of individual buildings from the panorama. Subsequently, a cross-view matching algorithm was proposed by combining Baidu panorama and building footprint data with height information to generate building height samples. Finally, a Two-Branch feature fusion model (TBFF) was constructed to combine building location features and visual features, enabling accurate height estimation for individual buildings. The experimental results showed that the TBFF model had the best performance, with an RMSE of 5.69 m, MAE of 3.97 m, and MAPE of 0.11. Compared with two state-of-the-art methods, the TBFF model exhibited robustness and higher accuracy. The Random Forest model had an RMSE of 11.83 m, MAE of 4.76 m, and MAPE of 0.32, and the Pano2Geo model had an RMSE of 10.51 m, MAE of 6.52 m, and MAPE of 0.22. The ablation analysis demonstrated that fusing building location and visual features can improve the accuracy of height estimation by 14.98% to 69.99%. Moreover, the accuracy of the proposed method meets the LOD1 level 3D modeling requirements defined by the OGC (height error ≤ 5 m), which can provide data support for urban research. Full article