Search Results (829)

Geopolymers are an environmentally sustainable class of low-calcium alkali-activated materials (AAMs), distinct from high-calcium C–A–S–H gel systems. Synthesized from aluminosilicate-rich precursors such as fly ash, metakaolin, slag, waste glass, and coal gasification fly ash (CGFA), geopolymers offer a significantly lower carbon footprint, valorize industrial by-products, and demonstrate superior durability in aggressive environments compared to Ordinary Portland Cement (OPC). Recent advances in thermodynamic modeling and phase chemistry, particularly in CaO–SiO₂–Al₂O₃ systems, are improving precursor selection and mix design optimization, while Artificial Neural Network (ANN) and hybrid ML-thermodynamic approaches show promise for predictive performance assessment. This review critically evaluates geopolymer chemistry and composition, emphasizing precursor reactivity, Si/Al and other molar ratios, activator chemistry, curing regimes, and reaction mechanisms in relation to microstructure and performance. Comparative insights into alkali aluminosilicate (AAS) and aluminosilicate phosphate (ASP) systems, supported by SEM and XRD evidence, are discussed alongside durability challenges, including alkali–silica reaction (ASR) and shrinkage. Emerging applications ranging from advanced pavements and offshore scour protection to slow-release fertilizers and biomedical implants are reviewed within the framework of the United Nations Sustainable Development Goals (SDGs). Identified knowledge gaps include standardization of mix design, LCA-based evaluation of novel precursors, and variability management. Aligning geopolymer technology with circular economy principles, this review consolidates recent progress to guide sustainable construction, waste valorization, and infrastructure resilience. Full article

As a renewable and carbon-storing raw material, wood is playing an increasingly important role in the transformation of the construction sector towards a circular economy (CE). However, extant scientific studies have largely analyzed its technical, environmental, and social aspects in isolation from one another. The present article provides a problem-oriented and conceptual narrative overview, integrating these three dimensions from a design perspective. The objective of this study is not to provide a systematic review of the extant literature, but rather to structure existing knowledge by categorizing topics as follows: well-recognized, moderately developed, and niche. This approach enables the identification of gaps and links relevant to architectural practice. A qualitative thematic approach was adopted, underpinned by a comprehensive analysis of peer-reviewed articles sourced from the Scopus and Web of Science databases. This approach was further enriched by the incorporation of a select array of highly cited sources, serving to substantiate the study’s findings and provide a comprehensive overview of the pertinent literature. The review identified four research areas with high potential but low recognition: digital tracking of the life cycle of wooden elements, upcycling of low-quality wood, development of innovative wood-based materials, and socio-cultural acceptance of CE-based architecture. These subjects are currently marginal in the field of research, despite their significant implications for design strategies, adaptive resource use, and the development of interdisciplinary tools. The article posits the necessity of integrating materials science, digital technologies and architectural theory as a prerequisite for the scalable development of circular wood construction. The proposed classification provides a conceptual framework to support further research and guide innovation in the built environment. Full article

Underground hydrogen storage (UHS) in shale and tight reservoirs offers a promising solution for large-scale energy storage, playing a critical role in the transition to a hydrogen-based economy. However, the successful deployment of UHS in these low-permeability formations depends on a thorough understanding of the geomechanical and geochemical factors that affect storage integrity, injectivity, and long-term stability. This review critically examines the geomechanical aspects, including stress distribution, rock deformation, fracture propagation, and caprock integrity, which govern hydrogen containment under subsurface conditions. Additionally, it explores key geochemical challenges such as hydrogen-induced mineral alterations, adsorption effects, microbial activity, and potential reactivity with formation fluids, to evaluate their impact on storage feasibility. A comprehensive analysis of experimental studies, numerical modeling approaches, and field applications is presented to identify knowledge gaps and future research directions. Full article

Background: Street food vending provides vital employment and nutrition in low- and middle-income countries (LMICs), but poor health and safety compliance pose significant public health and business risks. Despite growing policy recognition, the link between hygiene practices and vendor performance remains underexplored. Objective: This integrative review examines the influence of health and safety practices on the business performance of informal street food vendors, with a particular focus on both global and South African contexts. Methods: A total of 76 studies published between 2015 and 2025 were retrieved between June 2024 and May 2025 and analyzed using an integrative review methodology. Sources were identified through five major academic databases and grey literature repositories. Thematic synthesis followed PRISMA logic and was guided by the Health Belief Model (HBM) and Balanced Scorecard (BSC) frameworks. Results: There was a marked increase in publications post-2019, peaking in 2023. Sub-Saharan Africa accounted for the majority of studies, with South Africa (28%) and Ghana (14%) most represented. Among the 76 included studies, the most common designs were quantitative (38%), followed by qualitative (20%), case studies (14%), and mixed-methods (11%), reflecting a predominantly empirical and field-based evidence base. Thematic analysis showed that 26% of studies focused on food safety knowledge and practices, 14% focused on infrastructure gaps, and 13% focused on policy and regulatory challenges. Of the 76 studies included, 73% reported a positive relationship between hygiene compliance and improved business performance (such as customer trust, revenue, and operational resilience), based on vote-counting across qualitatively synthesized results and business outcomes. The review identifies a conceptual synergy between the HBM’s cues to action and the BSC’s customer dimension, highlighting how hygiene compliance simultaneously influences vendor behaviour and consumer trust. Conceptual saturation was observed in themes related to hygiene protocols, consumer trust indicators, and regulatory barriers. Conclusions: Health and safety practices function not only as compliance imperatives but also as strategic assets in the informal food economy. However, widespread adoption is impeded by structural barriers including limited infrastructure, education gaps, and uneven regulatory enforcement. The findings call for context-sensitive policy interventions and public health models that align with vendor realities and support sustainable, safe, and competitive informal food systems. Full article

To address the new challenges of sustainable international trade under the digital transformation, this study aims to explore the relevance and mechanism of the relationship between technological specialization and the sustainable development of digital service trade (focusing on economic sustainability). Based on panel data from 50 economies from 2006 to 2022, the core hypothesis of “whether technological specialization can enhance the sustainable competitiveness of digital service trade by optimizing the global value chain and industrial structure” is verified. An improved index of technological specialization is proposed, breaking through the limitations of traditional indicators, and for the first time introducing the dimension of “knowledge breadth,” reinterpreting the “Leontief Paradox” in the context of digital trade. The study finds that technological specialization significantly enhances the export of digital services, and the effect is more significant in countries with strict intellectual property protection, latecomers in technology, and the European region. Mechanically, this is achieved through improving the position in the global value chain and upgrading the industrial structure. This provides a theoretical breakthrough to solve the technology–trade paradox in the digital age and offers a path for latecomer economies to reconstruct competitive advantages and achieve sustainable development through technological specialization. Full article

The growing need to improve the management of end-of-life vehicle (ELV) waste and mitigate its environmental impact is a global concern. One promising approach to enhancing the recyclability of these vehicles is leveraging synergies between the automotive and construction industries as part of a circular economy strategy. In this context, ELV waste emerges as a valuable source of secondary raw materials, enabling the development of sustainable innovations that capitalize on its physical and mechanical properties. This paper aims to develop and evaluate construction industry composites incorporating waste from ELV carpets, with a focus on maintaining or enhancing performance compared to conventional materials. To achieve this, an experimental program was designed to assess cementitious composites, specifically subfloor mortars, incorporating automotive carpet waste (ACW). The results demonstrate that, beyond the physical and mechanical properties of the developed composites, the dynamic stiffness significantly improved across all tested waste incorporation levels. This finding highlights the potential of these composites as an alternative material for impact noise insulation in flooring systems. From an academic perspective, this research advances knowledge on the application of ACW in cement-based composites for construction. In terms of managerial contributions, two key market opportunities emerge: (1) the commercial exploitation of composites produced with ELV carpet waste and (2) the development of a network of environmental service providers to ensure a stable waste supply chain for innovative and sustainable products. Both strategies contribute to reducing landfill disposal and mitigating the environmental impact of ELV waste, reinforcing the principles of the circular economy. Full article

This study examines how the newly established United States pursued economic development through diplomatic and commercial initiatives with the Ottoman Empire, navigating regional powers and the era’s political-economic conditions. It analyzes using American archival sources how America endeavored to establish commercial and diplomatic relations with the Ottoman Empire in the Mediterranean and Black Sea regions, which it viewed as critical markets in the late 18th and early 19th centuries, before signing any formal agreement. The research tracks how these early efforts laid foundations for what would become one of the world’s largest economies. The study analyzes America’s diplomatic efforts to secure an agreement with the Ottoman Empire prior to the 7 May 1830 trade agreement—which laid the foundation for bilateral relations—alongside the reactions of regional powers, the prevailing conditions of the period, and the Ottoman administration’s reluctance due to various factors, based on U.S. archival sources that, to the best of our knowledge, have not previously been utilized in existing studies. Full article

The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs. Full article

In the era of the digital economy, how digital transformation (DT) contributes to economic development has become a topic of growing interest. This study focuses on business model innovation (BMI) driven by DT in the manufacturing sector. From this perspective, we aim to explore how DT can reshape the fundamental connotation of economic development. To this end, we construct a mathematical model grounded in a Multi-Structural Economic System framework and employ econometric models focusing on fixed effects, mediation effects, and moderation effects. We also compile a panel dataset using data from China spanning from 2008 to 2024. The empirical findings reveal that BMI serves as a mediation mechanism between the DT and competitive advantage (CA) of manufacturing enterprises. However, competitive imitation of BMI by peer enterprises partially offsets this effect, weakening the relationship between DT and enhanced CA. These findings offer evidence-based insights into the role of BMI in the digital era. For policymakers and industry regulators, this study provides practical implications for promoting knowledge spillovers from BMI, thereby fostering market dynamism and enabling structural transformation in the manufacturing industry. Full article

Autoclaved Aerated Concrete (AAC) is a lightweight, thermally insulating, and fire-resistant building material that has become prominent in sustainable construction due to its reduced production energy demands and minimal environmental impact. As an increasing number of AAC-based structures reach end-of-life, the effective recycling and reuse of AAC waste present both challenges and opportunities within the context of sustainable building practices and circular economy frameworks. This study presents a scientometric review of AAC recycling research published between 2014 and 2024, using the Web of Science database and bibliometric tools such as CiteSpace. Key trends, techniques, and knowledge gaps in AAC recycling are identified, highlighting issues such as high energy consumption, limited practical implementation, and the absence of standardized recovery protocols. The study also outlines emerging research pathways, including detailed material characterization, development of recycling standards, innovative reuse techniques, hybrid material systems, and the integration of recycled AAC in new construction. These insights provide a foundation for advancing sustainable building material strategies and inform policy and practice in construction waste management. Full article

Accurate identification of non-food crops underpins food security by clarifying land-use dynamics, promoting sustainable farming, and guiding efficient resource allocation. Proper identification and management maintain the balance between food and non-food cropping, a prerequisite for ecological sustainability and a healthy agricultural economy. Distinguishing large-scale non-food crops—such as oilseed rape, tea, and cotton—remains challenging because their canopy reflectance spectra are similar. This study proposes a novel phenology-aware Vision Transformer Model (PVM) for accurate, large-scale non-food crop classification. PVM incorporates a Phenology-Aware Module (PAM) that fuses multi-source remote-sensing time series with crop-growth calendars. The study area is Hunan Province, China. We collected Sentinel-1 SAR and Sentinel-2 optical imagery (2021–2022) and corresponding ground-truth samples of non-food crops. The model uses a Vision Transformer (ViT) backbone integrated with PAM. PAM dynamically adjusts temporal attention using encoded phenological cues, enabling the network to focus on key growth stages. A parallel Multi-Task Attention Fusion (MTAF) mechanism adaptively combines Sentinel-1 and Sentinel-2 time-series data. The fusion exploits sensor complementarity and mitigates cloud-induced data gaps. The fused spatiotemporal features feed a Transformer-based decoder that performs multi-class semantic segmentation. On the Hunan dataset, PVM achieved an F1-score of 74.84% and an IoU of 61.38%, outperforming MTAF-TST and 2D-U-Net + CLSTM baselines. Cross-regional validation on the Canadian Cropland Dataset confirmed the model’s generalizability, with an F1-score of 71.93% and an IoU of 55.94%. Ablation experiments verified the contribution of each module. Adding PAM raised IoU by 8.3%, whereas including MTAF improved recall by 8.91%. Overall, PVM effectively integrates phenological knowledge with multi-source imagery, delivering accurate and scalable non-food crop classification. Full article

Natural colorants, with their sustainable origins, offer a promising alternative for various applications. Advanced studies have unveiled the remarkable properties, resilience, and durability of these ancient dyes, which our ancestors developed through sustainable material processing. This serves as a testament to the potential of sustainable solutions in our field. As part of our research, we prepared three medieval temperas using gum arabic, parchment glue, and casein glue. These tempera were explicitly designed to protect the purples obtained from Chrozophora tinctoria extracts. A comprehensive multi-analytical approach guides our research on natural colorants. Central to this approach is the use of molecular fluorescence by microspectrofluorimetry, a key tool in our study. By analyzing the emission and excitation spectra in the visible range, we can identify specific formulations. This method is further supported by fingerprinting techniques, including Fourier Transform Infrared Spectroscopy (FTIR) and High-Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD). These are further complemented by Fiber Optics Reflectance Spectroscopy (FORS) and colorimetry. Building on our understanding of orcein purples, we have extended our research to purples derived from Chrozophora tinctoria extracts. Our findings reveal the unique properties of Chrozophora tinctoria, which can be accurately distinguished from orcein purples, highlighting the distinctiveness of each. Full article

With the rapid development of artificial intelligence technology, DRL has shown great potential in solving complex real-time optimal power flow problems of modern power systems. Nevertheless, traditional DRL methodologies confront dual bottlenecks: (a) suboptimal coordination between exploratory behavior policies and experience-based data exploitation in practical applications, compounded by (b) users’ distrust from the opacity of model decision mechanics. To address these, a model–data hybrid-driven physics-informed reinforcement learning (PIRL) algorithm is proposed in this paper. Specifically, the proposed methodology uses the proximal policy optimization (PPO) algorithm as the agent’s foundational framework and constructs a PI-actor network embedded with prior model knowledge derived from power flow sensitivity into the agent’s actor network via the PINN method, which achieves dual optimization objectives: (a) enhanced environmental perceptibility to improve experience utilization efficiency via gradient-awareness from model knowledge during actor network updates, and (b) improved user trustworthiness through mathematically constrained action gradient information derived from explicit model knowledge, ensuring actor updates adhere to safety boundaries. The simulation and validation results show that the PIRL algorithm outperforms the baseline PPO algorithm in terms of training stability, exploration efficiency, economy, and security. Full article

The review provides the results of the analysis of research publications in the field of energy economy on a global scale. The review aims to test three hypotheses and build and analyze the main trend lines and clusters to determine the direction of movement of new knowledge in the energy economy research. This review delves into the multifaceted nature of energy transitions, highlighting the pivotal role of policy frameworks, financial instruments, and technological innovation. By examining the socio-economic implications of renewable energy deployment and addressing the challenges associated with energy storage and grid integration, this study contributes to the ongoing discourse on sustainable energy development. The review used scientometric, correlation, and bibliometric methods of analysis. Artificial Intelligence was used to process 411,396 units of information (a special prompt was created). There is a new scientific result: (a) research hypotheses 1 and 3 were accepted, research Hypothesis 2 was rejected; (b) new trend lines showed the directions where the global energy economy is heading; (c) new clusters showed the top five leading countries and top 10 keywords in the field of energy economy; (d) leading journals in the studied area were found. The review indicated an underrepresentation of specialized journals in this field among the top journals. The review also showed that several keywords characterizing the sixth technological paradigm are missing from the top 10 keywords. This result suggests that these research areas were underrepresented in citation-based bibliometric data in energy in 2021–2024. The review is useful to identify promising and problematic areas for future research in the global energy economy. Full article

Water, the lifeblood of our planet, sustains ecosystems, economies, and communities. However, climate change and increasing hydrological variability have exacerbated global water scarcity, threatening livelihoods and economic stability. According to the United Nations, over 2 billion people currently live in water-stressed regions, a figure expected to rise significantly by 2030. To address this urgent challenge, this study proposes an AI-driven anomaly detection framework for smart water metering networks (SWMNs) using machine learning (ML) techniques and data resampling methods to enhance water conservation efforts. This research utilizes 6 years of monthly water consumption data from 1375 households from Location A, Windhoek, Namibia, and applies support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (kNN) models within ensemble learning strategies. A significant challenge in real-world datasets is class imbalance, which can reduce model reliability in detecting abnormal patterns. To address this, we employed data resampling techniques including random undersampling (RUS), SMOTE, and SMOTEENN. Among these, SMOTEENN achieved the best overall performance for individual models, with the RF classifier reaching an accuracy of 99.5% and an AUC score of 0.998. Ensemble learning approaches also yielded strong results, with the stacking ensemble achieving 99.6% accuracy, followed by soft voting at 99.2% and hard voting at 98.1%. These results highlight the effectiveness of ensemble methods and advanced sampling techniques in improving anomaly detection under class-imbalanced conditions. To the best of our knowledge, this is the first study to explore and evaluate the combined use of ensemble learning and resampling techniques for ML-based anomaly detection in SWMNs. By integrating artificial intelligence into water systems, this work lays the foundation for scalable, secure, and efficient smart water management solutions, contributing to global efforts in sustainable water governance. Full article