Search Results (1,917)

Apple pomace represents a sustainable source of phenolic compounds with significant antioxidant potential. This study investigated the recovery of water-extractable bioactive constituents from apple pomace using aqueous ultrasound-assisted extraction (UAE) combined with biochemical profiling and machine learning-based modelling. Extraction conditions were varied according to time (10–30 min), temperature (25–75 °C), and solvent-to-solid ratio (10–20 mL/g). The obtained extracts were evaluated for total phenolic content (TP), total flavonoid content (TF), antioxidant capacity (DPPH, ABTS, CUPRAC, FRAP, metal chelating, and phosphomolybdenum assays), and enzyme inhibitory activities against acetylcholinesterase, butyrylcholinesterase, tyrosinase, α-amylase, and α-glucosidase. TP and TF ranged from 4.70 to 9.97 mg GAE/g and 0.21–0.79 mg RE/g, respectively, while antioxidant assays demonstrated substantial variation depending on extraction conditions. Strong correlations (r = 0.827–0.945, p < 0.001) were observed between phenolic content and antioxidant activity. Artificial neural networks, random forests, support vector machines, and a hybrid ensemble model were applied to predict extraction outcomes, with predictive performance varying substantially among biochemical responses and modelling approaches. Multi-objective optimization using the NSGA-II formulation identified a representative Pareto compromise at approximately 15.1 min, 25.7 °C, and a solvent-to-solid ratio of 13.7 mL/g, balancing desirable biochemical responses with processing requirements. Full article

A sustainable water-resistant coating based on a chitosan/sacred lotus leaf wax composite was developed and deposited onto cotton fabrics using a simple dip–dry–cure process. The effect of the chitosan-to-sacred lotus leaf wax weight ratio on the surface wettability, air permeability, water vapor permeability, mechanical properties, and laundering durability of the coated fabrics was systematically investigated. The incorporation of sacred lotus leaf wax significantly enhanced the hydrophobicity of the cotton fabrics, with the highest water contact angle (WCA) of 167.12° achieved at a chitosan-to-wax weight ratio of 7:3. Although increasing the wax content improved water repellency, it also resulted in slight reductions in tensile strength, air permeability, and water vapor permeability. After 20 home-machine washings, the coated fabric retained a WCA of 137.11°, demonstrating good laundering durability. These results demonstrate that the chitosan/sacred lotus leaf wax composite is a biodegradable, non-toxic, and environmentally friendly alternative to conventional water-repellent finishes, offering considerable potential for the development of sustainable functional textile coatings. Full article

The South Asian monsoon sustains nearly one-quarter of the global population, yet the combined effects of water supply and atmospheric evaporative demand on regional aridity remain poorly integrated in long-term assessments. This study characterizes spatial and temporal aridity dynamics across the South Asian Monsoon region from 1901 to 2024 using the UNEP Aridity Index, offering a comprehensive view of hydroclimatic stress beyond precipitation alone. We integrate gridded climate observations with sea surface temperature records through Empirical Orthogonal Function decomposition and an interpretable machine learning framework, comparing linear regression against Random Forest and Hybrid models trained on historical data and validated independently. Our analysis reveals pronounced warming, spatially heterogeneous drying concentrated in northwestern regions, and a robust ENSO-aridity teleconnection modulated by the Indian Ocean Dipole. Machine learning models demonstrate superior skill in capturing nonlinear, threshold-dependent responses, yet their performance varies substantially across aridity zones, with linear approaches failing entirely in humid regions while hybrid frameworks excel in drylands. Critically, after removing long-term trends, interannual predictability persists most strongly in hyper-arid and semi-arid zones, where ENSO and IOD signals remain detectable, but declines elsewhere. SHapley Additive exPlanations identify Niño3.4 as the dominant oceanic predictor, with extreme El Niño events disproportionately intensifying aridity. These findings demonstrate that tropical ocean signals alone explain only modest year-to-year variability in the regional mean, with predictability concentrated in specific dryland zones. This work provides a diagnostic foundation for drought early-warning systems while emphasizing the need to incorporate local terrestrial processes and long-term trends for operational forecasting in one of the world’s most climate-vulnerable regions. Full article

Geological CO₂ storage must operate within pressure and stress limits that preserve caprock, fault, and well integrity while sustaining climate-relevant injection. Existing reviews often treat multiphysics simulation, machine learning, and physics-informed learning separately, which obscures the different evidence required for stability screening, first slip, aseismic deformation, dynamic rupture, monitoring analytics, and containment consequences. This structured critical review integrates direct CO₂ storage observations, laboratory studies, injection analogues, multiphysics numerical methods, data-driven machine learning, and scientific machine learning within a target-specific evidence framework. We compare continuum, discontinuum, interface, and diffuse fracture formulations; one-way, staggered, and monolithic coupling; field and laboratory validation; seismic, deformation, pressure, and fiber optic monitoring; and physics-informed neural networks, neural operators, and reduced-order models. The synthesis herein identifies the strongest evidence base for pressure and deformation modeling and seismic signal processing, for which repeated field or operational applications and task-specific evaluation are available. By contrast, prospective fault slip and seismicity forecasting remain at a limited evidence level because of uncertain in situ stress, fault connectivity, CO₂-conditioned friction, monitoring detection limits, model discrepancy, and scarce cross-site validation. We propose task-appropriate metrics, explicit method maturity criteria, a validation ladder, and a staged, human-supervised digital twin roadmap. Machine learning and physics-informed methods are most credible as bounded complements to verified simulators and monitoring systems, and operational readiness should be judged by uncertainty-calibrated prospective evidence rather than algorithm novelty. Full article

The microbiome, as a vast and dynamic community of microbes, is now recognized as a key regulator of host physiology, profoundly influencing health and susceptibility to disease. Accordingly, probiotics are a mainstay of prevention and treatment. But the individual complexity and dynamic specificity of an individual’s microbiome make the previous “one-size-fits-all” research model completely invalid. This review systematically analyzes the applications of artificial intelligence (AI) and machine learning (ML) as transformative computational tools necessary to surmount these challenges. In this article, we detail how these computational methods have been applied throughout the entire research and development pathway of probiotics, including novel strain identification (through multi-omics analysis), formulation and production optimization, and elucidation of complex mechanisms of action in the host. Furthermore, we highlight the emerging frontier of personalized probiotic therapy, demonstrating how AI/ML can be utilized to predict treatment efficacy based on individual host data. The objective of this article is to provide a detailed discourse on the actual and prospective applications of AI and ML in this process, ultimately delineating their revolutionary potential to inform the design of the next generation of probiotics with unprecedented precision, efficacy, and sustainability. Full article

Epigenetic regulation has transformed our understanding of how fish adapt to changing environments by modulating gene expression without altering the underlying DNA sequence. This review explores the “dark mastery” of fish epigenetics by providing mechanistic insights into the principal epigenetic processes, including DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs, that govern development, immunity, stress responses, and disease susceptibility. These regulatory mechanisms enable fish to respond dynamically to environmental stressors such as temperature fluctuations, salinity shifts, hypoxia, pollutants, ultraviolet radiation, and nutritional changes, thereby influencing physiological resilience, reproductive performance, and survival. Recent advances in next-generation sequencing and multi-omics technologies have substantially expanded our understanding of the fish epigenome, while bioinformatics has become indispensable for integrating and interpreting complex genomic, transcriptomic, and epigenomic datasets. Furthermore, artificial intelligence (AI) and machine learning (ML) are emerging as powerful approaches for biomarker discovery, predictive modeling of disease susceptibility, environmental risk assessment, and precision aquaculture. The integration of epigenetics with bioinformatics and AI provides unprecedented opportunities to decipher complex regulatory networks, identify adaptive epigenetic signatures, and develop data-driven strategies for improving fish health and aquaculture sustainability. Despite these advances, important challenges remain, including limited species-specific epigenomic resources, difficulties in multi-omics integration, model interpretability, and the need for standardized analytical frameworks. This review highlights current knowledge, emerging computational approaches, and future perspectives for translating epigenetic discoveries into sustainable aquaculture practices and aquatic ecosystem conservation under accelerating environmental change. Full article

Urban rooftop photovoltaics (URPV) are crucial for decarbonizing China’s building sector, yet large-scale assessment remains limited by difficulties in rooftop extraction, cross-city generalization, and linkage to forward-looking decarbonization strategies. This study developed an AI-integrated framework combining semantic segmentation, interpretable machine learning, and scenario simulation to support spatially explicit URPV planning across 690 Chinese cities. A Transformer-based model (Mask2Former) extracted rooftop areas from high-resolution imagery in 149 representative cities (mIoU = 86.6%), and a Random Forest model trained on nine socioeconomic indicators extrapolated rooftop availability to the remaining 541 cities. SHAP analysis identified total nighttime light and permanent population as the dominant predictors. The estimated national rooftop area reaches 113,264.86 km². Under a baseline scenario (conversion factor = 0.35; PV efficiency = 0.20), the URPV carbon mitigation potential reaches 6155.80 MtCO₂, equivalent to 48.9% of China’s energy-related CO₂ emissions in 2023 and 1.21 times the 2021 whole-process carbon emissions of China’s building sector. Clustering identified four urban typologies—resource-rich, balanced-development, high-potential, and low-potential—supporting differentiated deployment. Accounting for urban expansion and power-mix transition, projections suggest sustained mitigation of 4700–4910 MtCO₂ by 2030 under the Announced Pledges Scenario. This interpretable, data-driven framework offers scalable decision support for urban renewable energy planning and the low-carbon transformation of the built environment. Full article

Transportation infrastructure construction generates substantial greenhouse gas emissions, yet construction-phase emissions from small- and medium-span concrete bridges remain difficult to quantify at the design stage. This study develops a data-driven framework for carbon accounting and prediction using 58 urban concrete bridges in Guangdong Province, China, comprising 23 hollow slab girder bridges and 35 concrete box girder bridges. Construction-phase emissions were quantified using process-based life cycle assessment and inventory analysis. Three prediction approaches—Kriging, Kriging–support vector machine, and sequential sampling–ISC–Kriging—were evaluated across low-, medium-, and high-dimensional variable spaces. Material production dominated the carbon footprint, accounting for 84.7% of emissions from hollow slab bridges and 82.0% from box girder bridges. Average unit-volume emissions were 499.81 and 686.78 kg CO₂e/m³, respectively. The sequential sampling–ISC–Kriging model achieved the best performance in the high-dimensional space (R² = 0.920), and its error for the independent case bridge was 5.65%. These findings show that integrating structural, construction, and transportation variables can support early-stage carbon estimation and comparison of low-carbon bridge alternatives. Full article

Evapotranspiration (ET) is a major component of the water and energy cycle, influencing hydrologic processes, agricultural management, groundwater recharge, and land surface–atmosphere interactions. Water balance methods for estimating ET are widely used because of their direct connection to the conservation of mass and their applicability across scales. This review examines the theoretical basis and recent developments in water balance approaches for estimating ET across different hydroclimatic regimes. It summarizes classic soil water balance methods, physically based hydrologic models, remote-sensing approaches, and integrated machine learning techniques. Major themes include uncertainty in precipitation, runoff, and storage estimates; groundwater flow; water balance closure; spatial heterogeneity; and the integration of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and ground-based observations. More recently, hybrid physics-based and machine learning approaches have advanced ET estimation by combining process-based understanding with data-driven methods. Advances in computational hydrology, data assimilation, and Earth observation datasets are improving applications related to irrigation management, drought assessment, climate adaptation, and water-resource planning. Challenges remain in quantifying uncertainty, assessing model transferability, and representing groundwater and storage dynamics under changing hydroclimatic conditions. Overall, the review highlights the continued importance of water balance approaches for understanding ET dynamics and supporting sustainable water-resource management. Full article

Amid growing divergence and confusion in Environmental, Social, and Governance (ESG) rating methodologies and results for assessing corporate sustainability, this study proposes a dual-dimensional framework that conceptualizes ESG rating quality through two distinct yet complementary lenses—validity (the rigor, transparency, and reproducibility of rating methodologies) and utility (the practical value and relevance of rating outputs for user-specific objectives). While the framework provides a structured approach for evaluating existing ratings, it also serves as prescriptive guidance for constructing user-oriented ESG assessment models. To demonstrate its operational value, the framework is implemented in the Chinese A-share market with two explicit utility targets—local adaptability (addressing the poor cross-regional transferability of international ESG standards) and entrepreneurial enablement (counteracting the systematic size-based ESG discrimination). The findings demonstrate that the dual-dimensional framework not only provides a coherent basis for assessing ESG rating quality from the bottom (an overall quality score of 71.67 assessed for the implemented model) but also yields meaningful empirical patterns that support the top objectives (corresponding ESG trends following China’s major policy events reflected in both rating distributions and market reactions, and significantly flattened ESG–size correlation from 0.27 to 0.18 and the reduced missing indicator ratio for smaller firms from 81% to 74%). Methodologically, the target alignment is benefited by four streams of data science techniques—event-based and location-based data, machine learning for carbon footprint estimation, generative AI for extracting and summarizing structured ESG information, and a hybrid analytic hierarchy process–entropy-weighting approach. This study contributes a replicable and user-interactive approach to ESG assessment, bridging macro-level policy influence and micro-level data validity, with practical implications for investors, regulators, rating agencies, and small enterprises navigating the “long tail” of sustainable development. Full article

Bio-based alternatives to conventional rigid foams have proven to be good substitutes owing to their enhanced sustainability and competitive performance. However, because their manufacturing processes are complex and destructive testing is often impractical, this study investigates whether microstructural features can be correlated with mechanical properties in lignin-containing rigid polyurethane (PU) foams using machine learning approaches. Various types and percentages of lignin-based polyols were investigated as partial replacements for polyol, including LHO, DCA, DCA-D, LHO-O, Kraft lignin (KL), and LHO-MD, at polyol replacement levels ranging from 12.5% to 50%, together with a control formulation. Scanning electron microscopy (SEM) images and corresponding mechanical compression data were used to train a custom state-of-the-art dual-head convolutional neural network (CNN) targeting the specific prediction of density, specific compression modulus, specific yield stress, and specific compression strength. The CNN was optimized with a weighted multi-output loss function, achieving strong predictive performance with R² values ranging from 0.850 to 0.91 and correlation coefficients above 0.92, while maintaining mean absolute error percentages below ≈9%. This proves the trained network’s capability to predict and capture morphological features governing load-bearing responses. On the other hand, Grad-CAM visualization revealed that the network focused its predictions on physically meaningful microstructural regions such as cell walls and strut junctions, which confirms that the proposed network can be classified as an interpretable, non-destructive, and data-driven framework for predicting and understanding bio-based PU foams’ mechanical behavior, hence reducing the inconvenience caused by time-consuming manufacturing and destructive testing. Full article

Cellulose ionogels have emerged as promising functional soft materials for flexible electronics, energy storage, and biosensing owing to their inherent biocompatibility and unique ionic conductivity. However, establishing precise structure–property relationships remains a fundamental challenge due to the complex, non-equilibrium dynamic processes—such as transient solvation, competing hydrogen-bonding networks, and mesoscopic phase separation—that occur during dissolution and gelation. Traditional static and post-mortem characterizations fail to capture these spatiotemporally dynamic behaviors, creating a critical knowledge gap. To overcome this bottleneck, the integration of real-time in situ/operando characterization techniques with multiscale computational simulations has established a novel, synergistic paradigm. This review comprehensively synthesizes recent advances in decoding the multiscale architectures of cellulose ionogels. We systematically analyze how molecular-scale calculations and time-resolved vibrational/electronic spectroscopies reveal interfacial solvation mechanisms and dynamic bond cleavage/reconstruction. We further evaluate how mesoscopic scattering, nanomechanical mapping, and rheological tools resolve network topology and structural heterogeneity. By bridging these multiscale diagnostics with macroscopic transport and mechanics, the dynamic coupling/decoupling mechanisms governing ionic conductivity, mechanical toughness, and thermal stability are critically decoded. Finally, key technical bottlenecks and future trajectories—including physics-informed machine learning, operando multi-field coupling probes, and AI-driven inverse material design—are outlined, providing theoretical guidelines and technical blueprints for next-generation sustainable ionogels. Full article

Sustainable consumption is central to sustainable development goals (SDGs), yet research on consumer behavior (CB) remains scattered. Studies on green consumption, digital retail, fashion, food systems, digital finance, energy, and tourism have developed largely in isolation, and few studies have investigated what these settings share or how sustainability-relevant behavior unfolds after the moment of purchase. This study takes a wider view, applying the SPAR-4-SLR protocol to a decade of CB and sustainability scholarship and combining machine-learning-based BERTopic thematic modeling with SDG mapping, organized through a Theory–Context–Method and Antecedents–Decision–Outcome (TCM–ADO) lens. Seven themes emerged, concentrated on SDGs 8 (decent work), 9 (industry innovation), and 12 (sustainable consumption). Together, they point to a shift away from individual purchase choices toward consumption that is multistage, digitally mediated, and institutionally embedded. Across the seven theme-level summaries, trust, engagement, risk perception, ethics, literacy, and experience appear repeatedly. Because several of these constructs were included in the retrieval query, this pattern is interpreted as cross-theme recurrence within the query-defined corpus rather than as independent evidence of universal prevalence across sustainable consumer-behavior research. Digital and platform mediation appears as one cross-cutting condition, and is most visible where consumption is heavily platform-based. On this basis, the review proposes a process framework that treats antecedents as capability, vulnerability, and layered trust; decisions as multistage and mediated; and outcomes as interdependent SDG bundles. Twelve propositions and a research agenda follow, testable across food, energy, finance, fashion, tourism, and digital retail. Full article

Against a backdrop of global climate fluctuations and intensifying human activities, wetlands are undergoing severe degradation. Understanding the mechanisms that govern wetland evolution is essential for the sustainable development of wetland ecosystems. However, wetland evolution is highly heterogeneous across space and time, and existing studies have generally paid insufficient attention to nonlinear effects and interactions among driving mechanisms, limiting a comprehensive understanding of its intrinsic processes. We integrated the Light Gradient Boosting Machine model with SHapley Additive exPlanations to explain the nonlinear driving mechanisms of spatiotemporal wetland evolution across the Sanjiang Plain using multitemporal remote sensing data from 1990 to 2023. Wetland dynamics exhibited a stage-dependent pattern characterized by substantial natural-wetland loss in the early period, followed by partial marsh-wetland recovery and continued artificial-wetland expansion. Artificial wetlands expanded continuously, whereas marsh wetlands declined markedly before 2005 and showed partial recovery thereafter. From 1990 to 2005, wetland evolution was mainly controlled by topographic and climatic factors. From 2005 to 2023, the influence of socioeconomic development and proximity to the road network on wetland change intensified. Nonlinear interactions among driving factors shifted from synergistic promotion by natural factors in the early stage to inhibitory effects among natural, socioeconomic, and locational factors in the later stage. This stage-specific change in driving mechanisms was crucial to the shift in dominant drivers of wetland evolution. By characterizing single-factor nonlinear effects and multifactor interactions, this study reveals the stage-specific driving mechanisms of wetland evolution in the Sanjiang Plain and provides scientific support for regional wetland management and remote sensing monitoring. Full article

Bacterial self-healing cement-based materials (BSHCMs) couple microbial mineralization with cement-based material design to autonomously seal cracks and potentially restore durability. However, their performance depends on a complex interaction among bacterial viability and physiological state, mineralization pathway, carrier and nutrient systems, calcium availability, matrix chemistry, crack characteristics, moisture, and exposure history. This review, supported by bibliometric mapping of 805 Scopus-indexed records, examines these interdependencies through the specific lens of artificial intelligence (AI) readiness. The mapping revealed six interconnected research themes spanning bacterial mineralization, sustainable cement-based systems, matrix chemistry and transport, encapsulation, durability, and machine learning (ML). AI evidence was classified as directly demonstrated in bacterial self-healing systems, transferable from adjacent concrete and structural health monitoring applications, or prospective. Although existing ML studies report high internal predictive performance, their engineering generalizability remains limited by heterogeneous and frequently literature-derived datasets, random train–test partitioning, potential feature leakage, synthetic data dependence, insufficient uncertainty reporting, and scarce independent laboratory or field validation. The analysis further shows that sustainability and economic benefits cannot be assumed from the biological nature of the technology but must be demonstrated through service life extension relative to the additional burdens of cultivation, nutrients, carriers, and processing. Advancing BSHCMs toward trustworthy AI-supported engineering, therefore, requires harmonized and machine-readable datasets, matched attribution controls, delayed cracking and realistic exposure experiments, explicit uncertainty and negative result reporting, study-grouped and external validation, and pilot- to field-scale testing. AI should consequently be regarded not as a substitute for biological healing, but as a decision support layer whose value depends fundamentally on the quality, traceability, and transferability of the underlying experimental evidence. Full article