Search Results (10,294)

The transition toward low-carbon and resource-efficient construction materials has intensified interest in geopolymer binders incorporating industrial and post-consumer wastes. Waste glass powder (WGP), a silica-rich component of the global glass waste stream, has emerged as a promising circular-economy precursor in alkali-activated systems; however, reported durability trends remain inconsistent and are often interpreted without mechanistic integration. This review synthesises current knowledge of WGP reactivity, gel chemistry, and long-term performance through an explicit reaction–transport–ageing (R–T–A) framework that links dissolution behaviour and phase assemblage development to pore connectivity, ion ingress, and time-dependent degradation. Under alkaline activation, the amorphous structure of WGP promotes silica release, modifying Si/Al ratios and governing the formation of N-A-S-H or hybrid N-A-S-H/C-(A)-S-H gels. These reaction products determine transport characteristics and ageing evolution, which collectively control chemical resistance, chloride ingress, alkali–silica reaction-type instability, and dimensional stability. Variability across studies is shown to arise from imbalances in particle fineness, replacement level, precursor chemistry, and activator design rather than intrinsic inconsistency in WGP behaviour. The R–T–A framework clarifies how reaction completeness, pore network architecture, and long-term phase stability interact to produce system-dependent durability outcomes. WGP demonstrates strong potential as a circular-economy precursor in alkali-activated binders; however, reliable structural application requires durability-informed mix design grounded in coupled reaction–transport–ageing mechanisms and supported by extended exposure testing under realistic service conditions. Full article

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Remaining Useful Life (RUL) prediction is a key component of prognostics and health management for modern industrial systems. While deep learning methods have significantly improved prediction accuracy, many existing approaches rely on large neural networks that are difficult to deploy on resource-constrained edge devices. This study presents a deployment-oriented evaluation of compact neural networks for RUL prediction using the NASA C-MAPSS turbofan engine benchmark. Two lightweight hybrid architectures, CNN–GRU and CNN–TCN, were developed with approximately 28k–32k parameters to represent realistic models for CPU-based edge inference. A systematic experimental analysis was conducted across all four C-MAPSS subsets (FD001–FD004), which represent increasing levels of operational and fault complexity. In addition to baseline performance, two post-training compression techniques (i.e., global unstructured magnitude pruning and dynamic INT8 quantization) were evaluated. To assess real deployment behavior, inference latency was measured on both a high-performance Intel x86 workstation and a resource-constrained ARM platform. Results show that CNN–GRU generally achieves higher predictive accuracy, whereas CNN–TCN provides more consistent and lower inference latency due to its convolution-only temporal modeling. Unstructured pruning can yield modest improvements in prediction accuracy, suggesting a regularization effect, but it does not reliably reduce model size or latency on standard CPUs due to the overhead associated with pruning masks. Dynamic quantization substantially reduces model size (particularly for CNN–GRU) while preserving predictive accuracy; however, it increases runtime latency because of additional quantization and dequantization operations. These findings demonstrate that compression techniques commonly used for large models do not necessarily translate into deployment benefits for already compact RUL architectures and highlight the importance of hardware-aware evaluation when designing edge prognostics systems. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Background/Objectives: The valorization of bioactive compounds from food industry by-products aligns with sustainable development goals and represents a strategy for obtaining functional ingredients. Hazelnut (Corylus avellana L.) skins are a phenolic-rich residue with high antioxidant potential, but their extraction conditions and cellular mechanisms of action remain insufficiently explored. Methods: Ultrasound-assisted extraction was optimized using a 3³ Full Factorial Design (FFD) by investigating temperature (30–50–70 °C), extraction time (1–2–3 h), and solvent composition (water/ethanol). Antioxidant activity was evaluated using multiple in vitro assays, including Total Phenolic Content (TPC), DPPH, ABTS, FRAP, and β-carotene bleaching (BCB) assays. The optimized extract (OE) was chemically characterized by UHPLC–MS/MS and its activity was evaluated in HepG2 cells for biocompatibility, modulation of intracellular ROS levels, and antioxidant pathway activation. Results: Optimal extraction conditions were identified as 30 °C, 70.86 min (1.181 h), and 21.13% ethanol (v/v), yielding an extract with enhanced antioxidant capacity. UHPLC–MS/MS analysis revealed 25 bioactive compounds, mainly flavonoids and phenolic acids, relevant for oxidative stress modulation. The extract significantly reduced tert-butyl hydroperoxide (TBH)-induced intracellular ROS levels, restoring antioxidant proteins involved in the Nuclear Factor erythroid 2-related factor 2 (NRF-2)-mediated defense pathway. Conclusions: The optimized hazelnut skin extract combines strong antioxidant efficacy with cellular compatibility, supporting its potential application as a functional ingredient for nutraceutical and pharmaceutical strategies targeting oxidative stress-related conditions. Full article

The development of cementitious materials with multifunctional performance is increasingly important to address environmental demands and durability requirements in modern infrastructure. This study investigates calcium sulfoaluminate (CSA) cement partially substituted with blast furnace slag (BFS), fly ash (FA), and TiO₂ nanoparticles, aiming to combine sustainability with photocatalytic self-cleaning functionality. Phase analysis by X-ray diffraction confirmed the formation of characteristic CSA hydration products, including ettringite, ye’elimite, anhydrite, and calcite, indicating that partial substitution did not disrupt the primary hydration mechanisms. Microstructural observations revealed that the incorporation of BFS, FA, and TiO₂ induced noticeable morphological changes, with increased porosity and microstructural heterogeneity at higher replacement levels. Mechanical testing showed that moderate BFS contents of 5 to 10 wt% enhanced compressive strength in reference mixtures, while systems containing TiO₂ exhibited slightly lower strength values and increased dispersion, particularly at elevated slag contents. The photocatalytic performance, evaluated through Rhodamine B degradation under solar irradiation, demonstrated a marked improvement for TiO₂-containing samples, reaching degradation efficiencies of up to 80%, in contrast to negligible activity in unmodified systems. These results confirm that the combined use of industrial by-products and photocatalytic nanoparticles in CSA-based matrices represents a viable strategy for producing sustainable cementitious materials with added environmental functionality, without compromising fundamental structural performance. Full article

In the digital economy era, how to effectively leverage data elements to promote green productivity has become a critical issue. The Big Data Comprehensive Pilot Zone (BDCPZ) serves as an institutional arrangement to promote data circulation, governance, and efficient allocation. Utilizing panel data from Chinese A-share listed firms spanning 2012–2023, this study treats the 2016 establishment of BDCPZ as a quasi-natural experiment and employs a difference-in-differences (DID) model to investigate how improvements in the data institutional environment induced by BDCPZ affect enterprise green total factor productivity (GTFP). Empirical results indicate that the establishment of BDCPZ significantly enhances GTFP, with results remaining robust across specification tests. Heterogeneity analyses demonstrate that these positive effects are more pronounced among non-heavily polluting enterprises, high-technology enterprises, and enterprises in less competitive markets. Mechanism analyses suggest that data-oriented institutional reforms primarily enhance GTFP through innovation incentives, human capital accumulation, and industrial structure upgrading. Furthermore, superior managerial efficiency and stronger managerial equity ownership amplify these positive effects. This study provides firm-level empirical evidence on the relationship between data-oriented institutional reforms and GTFP enhancement, contributing to the literature on data-driven institutional reforms and green productivity, and policy implications for optimizing data element utilization and promoting sustainable development. Full article

With the continuous advancement of industrialization in China, industrial wastewater discharge has become a critical factor influencing water environmental quality, public health, and the long-term sustainability of regional development. This study systematically examines both the direct and spatial spillover effects of industrial wastewater on disease incidence. Based on panel data from 30 provincial-level regions in China over the period 2011–2020, a composite incidence index of four waterborne infectious diseases is constructed using the entropy weight method, and the Spatial Durbin Model (SDM) is employed to capture both local and cross-regional effects. The results show that industrial wastewater discharge significantly increases disease incidence and exhibits clear spatial spillover effects, suggesting that the associated health risks may extend beyond local boundaries. Moreover, the analysis suggests that the “Water Ten Plan” reduced both local effects and regional spillovers, highlighting the value of stricter discharge control and coordinated basin-level governance for sustainable regional development. Overall, this study uncovers the spatial health externalities of industrial pollution and provides empirical support for integrated policy approaches linking environmental governance with public health protection. Full article

Industrial heritage in resource-depleted mining towns faces the dual challenge of physical decay and social severance. To achieve sustainable urban revitalization, adaptive reuse strategies must align with local collective memory and emerging experiential consumption trends. Adopting a Scene Theory perspective, this study constructs a multi-level analytical framework using Meitanba Town (Hunan, China) and its power plant as a case study. A mixed-methods approach was employed, combining semantic network analysis of 1582 online user comments with 61 offline questionnaires distributed to local residents to quantitatively diagnose current scene elements, functions, and features. The quantitative results reveal a significant imbalance: while “Functional Media” achieved the highest comprehensive score (10.0) due to strong historical recognition, “Diverse Groups” scored the lowest (3.4), indicating a lack of social inclusivity. Specifically, residents expressed the highest demand for sports facilities (31.2%) and cultural spaces (23.7%), identifying the main workshop (26.4%) and chimney as core carriers of industrial identity. Responding to these findings, the paper proposes three targeted strategies: (1) Activate: creating open-access recreation scenes to satisfy urgent sports demands; (2) Link: constructing immersive cultural scenes to narrate the “coal–electricity–life” history; and (3) Enhance: developing industry-powered commercial scenes to avoid homogenization. This study enriches the localized application of Scene Theory and provides a data-driven, context-adjustable analytical and strategic model that can inform the sustainable renewal of mining towns globally, with its specific implementation requiring adaptation to local social, economic, and cultural characteristics. Full article

Sustainable materials are increasingly being incorporated into high-strength concrete (HSC) to reduce environmental impact while maintaining structural performance. This study experimentally investigates the combined use of steel scale waste (SSW) as a replacement for natural fine aggregates and graphite nano/micro platelets (GNMPs) as a nano-modifying additive in HSC. Natural sand was replaced with SSW at levels of 0%, 50%, and 100%, while GNMPs were incorporated at dosages of 0%, 0.1%, 0.3%, and 0.5% by weight of cement. The results indicate that partial replacement of sand with SSW significantly improves concrete density and mechanical performance due to enhanced particle packing and the high specific gravity of steel scale particles. At the nanoscale, GNMPs contribute to pore refinement, improved nucleation of hydration products, and crack-bridging within the cement matrix, thereby strengthening the interfacial transition zone and delaying crack propagation. The combined effect of these mechanisms produces a synergistic enhancement in concrete performance. The optimum mixture containing 50% SSW and 0.3% GNMPs achieved a compressive strength of 68.2 MPa and splitting tensile strength of 7.6 MPa, representing improvements of approximately 54% and 52%, respectively, compared with the control mix. Durability-related properties such as water absorption and sorptivity were also significantly improved due to matrix densification and pore structure refinement. Although the incorporation of SSW and GNMPs reduced workability, all mixtures remained within a practical range for casting. The developed concrete is particularly suitable for structural applications requiring high strength and durability, such as high-rise building components, bridge elements, and precast structural members. The findings demonstrate that the combined use of industrial steel waste and nano-reinforcement offers a promising pathway toward sustainable and high-performance concrete. Full article

Edible coatings (ECs) derived from natural biopolymers represent an effective preservation strategy for fruits and vegetables and a promising postharvest approach aligned with the increasing demand for sustainable agricultural practices. These Generally Recognized As Safe (GRAS)-based coatings, which are mainly polysaccharide-, protein-, and lipid-based, can extend shelf-life with minimal impact on texture, flavor, and nutritional value, reducing reliance on synthetic packaging and helping mitigate food loss and waste. Beyond acting as a physical barrier, ECs can significantly influence fruit and vegetable metabolism by modulating biochemical and molecular processes. This review focuses on these effects by summarizing evidence from conventional analytical methods, including targeted metabolite analyses, as well as omics-based approaches, primarily transcriptomics and metabolomics, which remain poorly explored in the current EC research literature. Furthermore, integrated metabolomic and transcriptomic analyses are examined, as they offer a more comprehensive understanding of the molecular mechanisms underlying quality attributes, stress responses, and preservation outcomes. Collectively, this work offers detailed insights into coating-induced changes in metabolite profiles and gene expression in coated fruits and vegetables, including formulations derived from agri-food by-products and coatings enriched with bioactive compounds with antioxidant, antimicrobial, and antifungal properties. Overall, by addressing a current gap in the literature, it provides an integrative and innovative framework for interpreting coating performance at both applied and molecular levels, with potential relevance for the agri-food industry and for future research aimed at developing more sustainable, effective, and commodity-tailored postharvest technologies. Full article

The aim of this study was to evaluate the potential of minimally processed beetroot pomace (BP), obtained from an industrial juice producer selected as a case study, converted into a stable beetroot pomace flour (BPF) at an industrial scale level, for flour fortification, functional confectionery development, and dietary supplementation. It was characterized by a high dietary fiber content (~27 g/100 g) and a very low carbohydrate-to-fiber ratio (1.9). High level of total phenolics and flavonoids (14.1 ± 0.1 mg GAE/g and 1.43 ± 0.1 mg QE/g), betacyanins and betaxanthins (898 ± 54 and 960 ± 65 µg/g), as well as pronounced antioxidant (FRAP 31.5 ± 1.1 and DPPH 25.8 ± 2.9 µmol TE/g), anti-hyperglycemic and anti-inflammatory activity (27.3 ± 1.3% and 41.0 ± 3.4%) remained upon in vitro digestion. Replacing 14–28% of cereal and pseudo-cereal flour with BPF reduced the carbohydrate-to-fiber ratio to the recommended 10:1, while incorporation of 20% BPF into cookies reduced this ratio by 2.5-fold and the glycemic index from ~56 to ~30. Furthermore, long-term supplementation of standard and high-fat/high-sucrose diet with BPF (0.5% w/w) reduced feed efficiency by 1.7 and 2.6-fold respectively, and improved glucose tolerance in C57BL/6J mice. Findings show the effectiveness of the by-product in bridging the fiber intake gap and body weight regulation. Full article

The agglomeration of the Unmanned Aerial Vehicle (UAV) industry is a key driver of the low-altitude economy. To understand how UAV industrial agglomeration emerges across cities with different socioeconomic foundations, this study investigates its dynamic configurational pathways. It develops an analytical framework that integrates the institutional environment, market conditions, and knowledge-based capabilities. Using panel data for 280 Chinese cities from 2017 to 2023, we apply panel data qualitative comparative analysis (QCA) to identify configurational pathways toward UAV industrial agglomeration. Seven socioeconomic conditions are considered: science and technology expenditure, policy support, infrastructure, social consumption level, financial development, urban innovation capacity, and human capital. The results show that UAV industrial agglomeration arises from the joint effects of multiple conditions, not from any single factor. We identify six pathways that are grouped into three archetypes: institution–knowledge-driven, institution–market-driven, and multidimensional synergistic configurations. The dominant pathways shift over time and differ across city sizes. These findings provide macro-level evidence on the mechanisms underpinning UAV industrial agglomeration. They also offer implications for strengthening the UAV industrial ecosystem. Full article

Energy storage plays a key role in the energy transition by enabling the effective integration of variable renewable energy sources such as solar and wind power and by supporting the stability and flexibility of modern energy systems. The rapid development of energy storage technologies has become one of the pillars of sustainable energy management; however, it simultaneously raises environmental, material, and systemic challenges. This review analyses the environmental implications of energy storage development using an integrative perspective that combines technological, environmental, and system-level analysis. The paper examines major classes of energy storage technologies, including electrochemical, mechanical and physical, thermal energy storage, and chemical pathways within Power-to-X, with particular emphasis on their technical characteristics, maturity, and life cycle environmental performance. Lithium-ion battery systems typically achieve round-trip efficiencies of 85–92% and cycle lifetimes exceeding 5000 cycles, while flow batteries may exceed 10,000 cycles under stationary operating conditions. Mechanical storage technologies such as pumped hydro provide efficiencies of approximately 70–85% with operational lifetimes exceeding several decades. Key challenges related to critical raw material availability, recycling, end-of-life management, and ecosystem impacts are discussed, highlighting the importance of sustainable production and recovery strategies in supporting the circular economy. In addition, the review addresses the consequences of insufficient reuse of secondary materials and the growing relevance of digitisation and cyber resilience of energy storage systems as indirect contributors to environmental risk. The review also considers geopolitical aspects related to critical material supply chains and the cyber security of energy storage infrastructure, emphasising their growing importance for the resilience and environmental sustainability of future energy systems. The analysis indicates that further development of energy storage technologies will significantly influence not only power systems but also transport, industry, and heat sectors. The results emphasise that sustainable deployment of energy storage requires hybrid system architectures and policy frameworks that account for environmental performance, system flexibility, and long-term resilience in line with the principles of sustainable development. Full article