Search Results (1,847)

The management of waste bentonite slurry (WBS) produced during slurry shield TBM excavation involves environmental and operational challenges from the perspective of developing a more sustainable tunnelling construction process. In this study, the potential reuse of WBS as a complete replacement for bentonite in two-component grout formulations used for TBM backfilling is explored. A comprehensive laboratory testing program is conducted, in which the effects of WBS on the properties of two-component grout (unit weight, viscosity, bleeding, gel time, and mechanical strength) are assessed after various curing times, and the outcomes are compared with standard values commonly given in technical specifications. WBS produced from two different commercial bentonites is investigated. The results show that while the first formulation exhibits rapid setting and irregular gelation, the mix derived from the second bentonite demonstrates superior mechanical performance, increasing compressive strength by up to 40%. This enhancement is primarily governed by a physical filler effect, where fine soil particles optimize packing density and refine the microstructure. Consequently, the incorporation of selected types of WBS into a two-component grout could be a practicable approach, since it offers benefits in terms of mechanical performance, although careful mix design would be required to manage workability. This study shows how tunnelling can become more sustainable by reusing excavation waste and transforming it into a useful by-product. Full article

Pyrolysis of refuse-derived fuel (RDF) is a promising waste-to-energy route, but its use in higher-value applications remains limited by tar carryover, benzene, toluene, ethylbenzene, and xylenes (BTEX), heteroatom-containing compounds, and pollutant accumulation in recirculated scrubber water. This study evaluated operating windows for RDF pyrolysis coupled with direct wet scrubbing and closed-loop water reuse, with the aim of identifying regimes suitable for different end-use tiers. A Taguchi L27 design of experiments (DOE), i.e., an orthogonal array comprising 27 experimental runs, was applied to evaluate the effects of pyrolysis temperature, residence time, scrubber liquid-to-gas ratio, and scrubber-water temperature, while sequential reuse of the same scrubber-water inventory was evaluated at 5, 10, and 15 cycles. Cleaned-gas pollutants were quantified by compound-resolved gas chromatography–mass spectrometry (GC–MS) after solid-phase adsorption (SPA) sampling, while phenolics and polycyclic aromatic hydrocarbons (PAHs) in scrubber water were determined by extraction followed by GC–MS. Feasibility within each end-use tier was defined as simultaneous satisfaction of tier-specific cleaned-gas thresholds (C_tar, C_BTEX, I_N, and I_S) and the corresponding water-loop hazard limit (I_tox), using literature-informed engineering screening criteria. The results showed that stronger scrubbing reduced gas-phase tar and BTEX burdens, whereas extended water reuse caused systematic accumulation of phenolics and PAHs and increased the composite water-loop hazard index. Boiler-grade operation remained feasible across a broad operating range, with 23 of the 27 tested conditions remaining robust, whereas internal combustion engine combined heat and power (ICE-CHP) feasibility was restricted to a narrow robust regime, and no robust microturbine-grade condition was identified. These findings show that operating windows for RDF pyrolysis must be defined jointly by gas cleanliness and water-loop management constraints. Full article

The aim of this study is to determine teachers’ views on integrating sustainable artificial intelligence use into classroom teaching processes. The study was conducted using a qualitative research approach and adopted a case study design. The study group consisted of 38 teachers who were selected using maximum diversity sampling, who currently use AI, and who participated in a 4-week structured “Sustainable AI Training Program.” To ensure methodological triangulation, data were collected through semi-structured interviews, researcher diaries, and participant diaries and analyzed using inductive thematic content analysis. According to the analysis results, some findings reveal that teachers considered filtering AI tools through a pedagogical filter centered around the question “Is it really necessary?” rather than using them directly and intensively. Furthermore, digital minimalism was adopted in classroom practices, along with the use of a single, optimized prompt instead of trial-and-error queries, the practice of archiving and reusing generated content, and a shift toward low-tech alternatives. It was determined that teachers would adopt digital minimalism in classroom practices, aiming to serve as role models for sustainable use by bringing the hidden environmental costs of technology into the learning process and fostering eco-digital citizenship awareness among students. Consequently, AI integration has evolved from a technical decision into a pedagogical redesign process encompassing ethical and ecological dimensions. Full article

This study examines the design and pedagogical evaluation of Extended Reality (XR) applications, with a primary focus on location-based Augmented Reality (AR). The XR applications were implemented within an environmental education program delivered by the Education Center for the Environment and Sustainability (E.S.E.C.) of Kastoria, aiming to enhance students’ understanding of lake ecosystems and environmental awareness through immersive, situated learning experiences. The development followed the ADDIE instructional design framework and was grounded in principles of experiential and situated learning. The educational intervention was conducted in an authentic field setting along the shoreline of Lake Kastoria and combined location-based AR activities with complementary immersive VR experiences. Evaluation data were collected through a structured questionnaire administered to 271 primary and secondary school students, employing XR-relevant constructs including Challenge/Satisfaction/Enjoyment, Ease of Use, Usefulness/Knowledge, Experiential and Situated Learning, Interaction/Collaboration, and Intention to Reuse. In addition, accompanying teachers provided supplementary qualitative feedback to support the interpretation of the findings under authentic field conditions. Descriptive statistical analysis indicated consistently high scores across all constructs (M = 3.27–4.40, SD = 0.41–0.64). Pearson correlation analysis revealed strong associations between Experiential/Situated Learning and Usefulness/Knowledge (r = 0.737), Experiential/Situated Learning and Challenge/Satisfaction/Enjoyment (r = 0.642), Intention to Reuse and Challenge/Satisfaction/Enjoyment (r = 0.635), as well as Usefulness/Knowledge and Challenge/Satisfaction/Enjoyment (r = 0.619). Multiple regression analyses further supported key relationships, including Usefulness/Knowledge as a predictor of Experiential/Situated Learning (β = 0.57, p < 0.001), Experiential/Situated Learning as a predictor of Challenge/Satisfaction/Enjoyment (β = 0.47, p < 0.001), and Interaction/Collaboration as a predictor of Intention to Reuse (β = 0.31, p < 0.001). Intention to reuse was mainly associated with interaction and collaboration, enjoyment and motivation, perceived usefulness/knowledge, and ease of use. Overall, the findings indicate that XR-supported outdoor learning is positively associated with key experiential, emotional, social, and perceived learning dimensions when embedded within a coherent pedagogical framework. Full article

During the adaptive reuse of industrial heritage buildings, existing opening systems and envelope performance often pose major constraints. These restrictions make it difficult for the building to meet the requirements of the updated indoor environment, resulting in insufficient daylight and increased energy consumption. Therefore, optimizing lighting and energy performance has become the primary goal of the retrofit design. However, with limited interventions, the retrofit of heritage buildings to achieve significant overall performance improvement is still a challenge. From a sustainability perspective, improving daylight utilization and reducing energy demand are essential strategies for achieving low-carbon and resource-efficient building retrofit. This study proposes a grid-based parametric multi-objective optimization approach to optimize the window openings of the building envelope. The approach defines the position, size and material properties of the roof and facade openings as design variables. Implemented via the Honeybee and Octopus platforms, it integrates a genetic algorithm with EnergyPlus and Radiance simulations to co-optimize daylight performance, circadian frequency, and energy use intensity. Taking a single-story typical industrial heritage building in China’s cold climate zone as a case study, it is shown that coordinated multi-objective constraints significantly improve the overall performance across various evaluation metrics. The optimization results also provide interpretable window configuration strategies and recommended parameter ranges, which fully consider the climate adaptability of the surrounding environment. These findings offer useful guidance for sustainable retrofit design decision-making in similar single-story industrial heritage buildings. Full article

Addressing the prevalent issue of “physical preservation but spiritual silence” in the revitalisation of China’s national industrial heritage, this study proposes and empirically validates a “dual-track narrative” design framework that systematically translates cultural values into spatial experiences. The framework integrates a “figure–history” narrative, which crystallises historical lineage and symbolic spirit through spatial sequences, commemorative landmarks, and authentic remains, with a “scene–activity” narrative, which transforms former production spaces into dynamic, culturally vibrant stages through ecological restoration displays, industrial landscape transformation, and flexible activity implantation. Using Nantong Guangsheng Oil Mill as a single-case study, the research employs qualitative methods including archival analysis, field observation, and semi-structured interviews to examine how the dual-track framework operates in practice. The findings reveal that the “figure–history” narrative manifests in a walkable “time corridor” along the north–south axis, where architectural remnants from different eras are organised to materialise Zhang Jian’s industrial salvation ethos and the collective memory of generations of workers. Meanwhile, the “scene–activity” narrative activates underutilised spaces—such as the repurposing of acid treatment ponds into constructed wetlands and paved grounds into public stages—enabling ongoing cultural production, community interaction, and ecological healing. The study demonstrates that the dual-track framework bridges the historical and contemporary dimensions often treated separately in heritage practice, establishing a systematic “translation mechanism” from cultural decoding to design intervention. Theoretically, it contributes to industrial heritage research by integrating narratology, memory studies, heritage interpretation, and situationism into a coherent design methodology. Practically, it offers decision-makers evaluation criteria beyond the preservation-versus-development binary, provides designers with a mode of creative transformation grounded in material authenticity, and suggests to operators a content-driven, event-based model for sustaining heritage spaces. By spatialising and eventising narratives, the dual-track approach enables industrial heritage to function as a catalyst for cultural identity, social vitality, and economic sustainability, offering a transferable paradigm for the adaptive reuse of industrial heritage in contemporary urban contexts. Full article

Proteomics represents a fundamental layer for understanding the molecular complexity of solid tumors by quantifying protein abundance and capturing proteoforms and post-translational modifications undetected in genomics or transcriptomics analyses. As mass spectrometry-based technologies and public proteomics repositories have expanded, opportunities for large-scale data reuse have grown accordingly. Nevertheless, data availability has not been translated into straightforward reuse: differences in experimental design, acquisition strategies, quantification workflows and metadata quality still limit the reproducibility and cross-study comparability. In this review, proteomics data reuse is defined as the systematic reanalysis and integration of publicly available datasets to support precision oncology applications such as biomarker assessment and antibody–drug conjugate target prioritization. We discuss reuse as an end-to-end analytical process, focusing on data analysis workflows, harmonization strategies, and the impact of heterogeneous experimental and analytical choices on interoperability. The increased application of artificial intelligence in proteomics data integration and reuse is also addressed, highlighting its analytical potential while underscoring the risks of overinterpretation when biological context and data structure are not adequately considered. Using colorectal and prostate cancer as representative examples, we illustrate how proteomics data reuse can support biological discovery and translational research, while critically examining the factors that limit robustness and clinical relevance. Full article

Repurposing decommissioned wind turbine blades provides a vital pathway to mitigate carbon emissions, yet the escalating volume of large-scale waste poses a severe environmental challenge. Recognizing the limitation that existing research focuses predominantly on small-scale legacy blades, this study addresses this gap by assessing the mechanical properties and microstructure of a 54-m (2.0 MW) blade decommissioned due to repowering after 10 years of service. GFRP samples extracted from the root, mid-span, and tip were investigated using X-ray computed tomography and a comprehensive suite of mechanical tests. The investigation confirmed a low internal porosity (~1.2%) without service-induced macroscopic interfacial cracking, alongside superior residual performance, exemplified by a tensile strength of 849.5 MPa at the root. Statistical analysis employing ANOVA revealed significant spatial variations, supporting a graded reuse strategy: roots with superior tensile strengths for critical members, mid-spans for axial compression, and tips as a reliable property baseline for general reuse, while Weibull analysis verified the statistical reliability required for structural design. Based on these superior residual properties, a raft-type wave energy converter utilizing repurposed blade segments was proposed. A comparative carbon footprint assessment revealed that this blade-repurposed WEC achieved a 71.5% reduction in carbon emissions and a 37.4% reduction in structural mass compared to conventional steel counterparts. These findings substantiate the viability of large-scale DWTBs as high-value resources for decarbonizing marine infrastructure within a circular economy. Full article

The evolution toward prospective sixth-generation (6G) wireless networks is expected to significantly increase user density, bandwidth demand, and architectural complexity, reinforcing the need for scalable multiple-input multiple-output (MIMO) deployments. In this context, two fundamentally different design strategies have emerged: scaling up centralized antenna arrays and scaling out distributed cooperative infrastructures. This paper presents a system-level comparative benchmark of scale-up and scale-out MIMO architectures under identical operating conditions of three representative downlink deployments: centralized Massive MIMO, centralized XL-Massive MIMO, and distributed Cell-Free MIMO. All architectures are assessed under identical urban channel conditions, transmit power, bandwidth, and traffic assumptions, considering sub-6 GHz (3.5 GHz) and millimeter-wave (28 GHz) frequency bands as proxies for 5G and prospective 6G operation. A unified Monte Carlo simulation framework is employed to jointly evaluate aggregate throughput, spectral efficiency, coverage performance, interference behavior, and energy efficiency over a wide range of user densities and service radii. The results highlight the distinct architectural trade-offs between centralized and distributed deployments: XL-Massive MIMO maximizes aggregate throughput and spatial reuse in dense hotspot scenarios, whereas Cell-Free MIMO provides superior coverage uniformity and improved energy efficiency in wide-area deployments. By isolating the impact of architectural scaling under consistent assumptions, the presented benchmark offers quantitative guidance for 6G network design and deployment planning. Full article

This paper presents the performance analysis of a 100 Gbps long-reach passive optical network (LR-PON) based on intensity modulation and direct detection (IM-DD). The LR-PON is designed for low-complexity environments that reuse previously deployed infrastructure and extend coverage to rural areas. It features a point-to-multipoint PON topology with a 1:64 split and links up to 100 km long. The paper analyzes the impact of the booster amplifier, preamplifier, and chromatic-dispersion-compensating module on the bit error rate (BER) using OptSim simulations. The results demonstrate that the LR-PON, operating at 100 Gbps over a 100 km link and with losses over 3 dB over a legacy network, maintains acceptable BER levels in the order of ${10}^{-6}$, validating its viability as a scalable, efficient, and economical solution for optical access networks in suburban or rural areas in locations such as Quito city (Ecuador). Full article

Traditional architectural decorative patterns are increasingly reused in contemporary design, yet the link between object selection and design generation often remains experience-driven: public perceptual differences are rarely formalized, and evaluation outcomes seldom constrain generative decisions. This study proposes a perceptual demand-driven layered filtering and design response model (PD–LFDR) that treats traditional architectural decorative patterns as comparable and traceable design resources. Perceptual inputs from multiple stakeholders are converged via Kansei-based semantic aggregation into four core dimensions—symbolism, heritage authenticity, recognition and regionality—and are organized as a perceptual evaluation matrix. Grey relational analysis (GRA) is then applied using an expected perceptual level as the reference sequence to identify representative pattern samples suitable for design intervention. An empirical study on decorative patterns from Shaanxi vernacular dwellings demonstrates a closed-loop workflow: (i) first-round GRA filters representative theme samples, (ii) a second-round GRA selects operable minimal gene units, and, under a unified parametric rule set and a traceable two-layer parameter basis (parameter domain definition and parameter selection), (iii) multiple alternatives are generated and re-evaluated through a third-round GRA to support scheme selection. Robustness checks indicate stable rankings under moderate parameter and weight variation, improving interpretability, reproducibility, and decision efficiency for the computational translation of regional cultural visual resources. Full article

Energy recovery from sewage sludge represents a sustainable and technically feasible alternative to promote integration between environmental sanitation and renewable energy generation. This study presents a case analysis of the municipality of Ribeirão Preto, São Paulo, focusing on comparisons between two wastewater treatment systems: an Upflow Anaerobic Sludge Blanket (UASB) reactor and a continuous-flow activated sludge system. Using the UASB configuration, we prepared a preliminary design of a treatment plant based on population and effluent generation projections over a 20-year horizon. The estimated sludge and biogas production allowed us to simulate electricity generation then. The comparative economic assessment, which employed Net Present Value (NPV) and Internal Rate of Return (IRR) indicators in accordance with ANEEL Resolution No. 482/2012, showed that the UASB system yields hard superior methane (up to 3235.6 m³/day) and higher electricity generation potential (1839.7 MWh/year) than the activated sludge system (1990 m³/day and 1654.3 MWh/year, respectively). Both systems were economically viable, with a positive NPV, an IRR of up to 16.83%, and payback periods starting in the first cycle. Furthermore, we estimated the cost per cubic meter of generated biomethane, conducted a sensitivity analysis, and assessed the impact on the most important economic indicators, all to identify the advantages and disadvantages of the proposed project and the best use of the generated biogas. This analysis showed that it is possible to recover energy from sewage treatment systems while also reusing sewage sludge for agricultural applications, thereby highlighting additional environmental and economic benefits, particularly in regions with a strong presence of agribusiness, e.g., Ribeirão Preto. Full article

Machine learning has demonstrated significant potential as a valuable tool for aerodynamic design. However, collecting an abundant training set is usually computationally expensive and time-consuming. To address this data scarcity, meta-learning and transfer learning offer viable strategies. Meta-learning enables models to learn efficiently from limited data by leveraging experience across related tasks, while transfer learning reduces data requirements by reusing knowledge from pre-trained models. In addition, integrating physics knowledge into the models provides a complementary path to enhance the reliability and generalizability under data-scarce conditions. This paper studies meta-learning and transfer learning strategies to realize the prediction of supercritical airfoil pressure distribution under multiple free stream conditions with a small-scale dataset. All the models are tested both in the source domain and the target domain. Then, a systematic comparative analysis of different models across different target domain training sample scales is studied. Results show that meta-learning and transfer learning both improve target-domain performance compared to the baseline model. Yet, meta-learning still achieves limited accuracy in the target domain, and data-driven transfer learning exhibits poor generalization. Compared with data-driven models, the Mach number weighted transfer learning model provides more generalized results and higher accuracy. Full article

Rural architectural heritage sites in Korea, such as rice mills, breweries, and granaries, face increasing risks of neglect, deterioration, and demolition. Most of these structures are not recognized within formal heritage designation systems, and no established evaluation framework exists. Consequently, their conservation and management remain challenging. This study proposes a comprehensive evaluation framework for the preservation and utilization of rural architectural heritage. Based on a literature review and expert consultation, 18 evaluation indicators were derived and grouped into six value criteria: historical, architectural/artistic, social/cultural, landscape, economic, and utilitarian values. The Analytic Hierarchy Process (AHP) was employed to determine the relative importance and priority of these indicators. Historical value received the highest weight, followed by architectural/artistic and social/cultural values. Among the 18 indicators, “representativeness of the period” ranked highest, followed by “rarity,” “historicity,” “local identity,” and “architectural excellence.” However, the indicators associated with economic and utilitarian values had relatively low weights. The framework was validated by applying it to 17 rural architectural heritage sites in Buyeo, South Korea. This study presents a systematic and value-based evaluation framework that reflects the regional and industrial characteristics of rural architectural heritage and provides both policy and practical implications for sustainable conservation and adaptive reuse. Full article

The built environment continues to encounter significant challenges related to waste generation and resource depletion, driving increased interest in circular economy strategies that extend material lifecycles and mitigate environmental impacts. This systematic review synthesises findings from 60 studies on waste-to-resource innovations across construction and household contexts. Although the existing literature predominantly addresses construction and demolition waste, this review foregrounds household operational waste, an area that remains insufficiently explored despite its importance for everyday resource recovery. The analysis examines how materials generated through routine use, maintenance, and minor renovation activities can be captured and redirected into productive resource streams, with particular attention to governance mechanisms such as Extended Producer Responsibility (EPR). The findings indicate that effective waste-to-resource systems depend on coherent regulatory frameworks and enforcement, economic incentives, enabling technologies, community engagement, and product design that facilitates reuse and disassembly. Key barriers include low public awareness, fragmented supply chains, high recovery costs, weak compliance mechanisms, and materials that are difficult to separate. The review concludes that improving waste-to-resource outcomes in the built environment requires coordinated action among producers, households, local authorities, and technology providers, and it articulates policy-relevant and community-oriented pathways to support more effective resource recovery systems. Full article