Search Results (24,020)

In recent years, global climate change, combined with increased human activities, has led to habitat degradation and range shifts in rare medicinal plants, potentially affecting the quality of medicinal herbs. In this study, we assessed how key environmental variables shape the potential distribution of Apocynum venetum L. based on 281 wild occurrence records and nine environmental variables using the MaxEnt model. The results revealed that the mean temperature of the coldest quarter, solar radiation in June, and elevation are the most significant factors affecting the distribution of A. venetum, with optimal values ranging from −10 to 5 °C, 21,000 to 23,000 kJ m⁻² day⁻¹, and 200 to 1500 m, respectively. Ecological niche modeling indicated that highly suitable habitats are primarily located in Xinjiang, Gansu, Shaanxi, Shanxi, Henan, Hebei, Jiangsu, Shandong, and Inner Mongolia. However, future projections under climate change suggest an expansion of these suitable areas, shifting towards higher latitudes in the northwestern regions and high-altitude mountains. These findings provide a scientific basis for guiding the production and sustainable utilization of A. venetum resources. Full article

Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R²_train = 0.9182, R²_test = 0.9306, and R²_global = 0.9208 with low prediction errors RMSE_train = 4.7888%, RMSE_test = 4.5485%, and RMSE_global = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R²_train/R²_test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

The transition toward Circular Economy (CE) models has become a central challenge for organizations seeking to enhance sustainability performance and long-term value creation. While existing research has extensively examined technological, regulatory, and operational drivers of CE adoption, limited attention has been paid to the internal organizational orientations that enable firms to implement circular practices in a coherent and sustained manner. Addressing this gap, this study examines the role of Sustainable Marketing Orientation (SMO) as an organizational driver of CE adoption. Drawing on survey data collected from 368 micro-, small-, and medium-sized enterprises operating in environmentally relevant sectors in an emerging economy context, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test a hierarchical model in which SMO is conceptualized as a multidimensional organizational orientation composed of ethical capabilities, social commitment, and strategic integration. The results demonstrate that SMO significantly and positively influences CE adoption, explaining 41.5% of the variance. Among the dimensions of SMO, social commitment and strategic integration emerge as particularly influential in supporting circular economy practices. The findings contribute to the literature by empirically validating SMO as a higher-order organizational orientation and by identifying it as a key antecedent of CE adoption. Beyond theoretical contributions, the study offers practical insights for managers and policymakers, highlighting the importance of integrating sustainability into organizational strategy, stakeholder relationships, and performance measurement systems to facilitate circular economy transitions. Overall, the results position sustainable marketing orientation as a critical organizational mechanism supporting systemic sustainability and socio-economic transitions. Full article

Generative Artificial Intelligence (GenAI), particularly Foundation Models (FMs), has recently become a key component of Industry 5.0. Despite growing interest in integrating these technologies into industrial environments, comprehensive analyses of the socio-technical opportunities and challenges of deploying these emerging AI systems in real-world settings remain limited. This article proposes a socio-technical conceptual perspective, termed Responsible Agentic Robotics (RAR), which structures the lifecycle deployment of agentic AI-enabled robotic systems around three core layers: context, design, and value. Additionally, this article presents a brief review of 21 peer-reviewed studies published between 2023 and 2025 (post-ChatGPT era) on FMs and agentic AI-enabled Human–Robot Collaboration (HRC) in industrial assembly/disassembly environments. The results indicate that existing research remains predominantly technology-centric, with a strong emphasis on enhancing robot autonomy, while comparatively limited attention is devoted to human-centered and responsible practices. Moreover, empirical evaluations of human, social, and sustainability dimensions, such as worker empowerment, human factors, well-being, inclusivity, resource utilization, and environmental impact, are rarely conducted and poorly discussed. This article concludes by identifying key socio-technical gaps, outlining future research directions. Full article

Carotenoids are increasingly studied for their robust antioxidant capacity, anti-inflammatory potential, protective vision and validated contribution to human health. Carotenoids are mainly obtained through chemical synthesis and plant extraction, which results in relatively high costs for producing carotenoids. However, microalgae represent a sustainable and high-yield platform for natural carotenoid production, with advantages including rapid growth, high pigment accumulation, and broad environmental adaptability. This review summarizes recent biotechnological advances in enhancing carotenoid production, with a focus on metabolic engineering, environmental regulation, and cultivation strategies. CRISPR/Cas9 enables precision metabolic pathway engineering, while environmental factors like light, nutrients, and stress significantly influence yield. Different cultivation strategies allow carotenoids to fulfill commercial or research needs. The two-stage strategy achieves rapid biomass increase during the growth stage, then shifts to accumulate carotenoids. This regulatory mode significantly reduces cell death by continuous stress, providing high productivity and stability in large-scale production. Carotenoids participate in many innovative applications across various fields, including treatments in medicine, skin protection in cosmetics, protein stabilization in foods, enhancing animals’ survival and so on. Future research will integrate bioprocess optimization, precision strain engineering, and adaptive environmental strategies to scale high-value microalgal carotenoid production as a commercially and environmentally viable solution. Full article

Civil engineering faces the dual challenge of addressing climate change and managing construction and demolition waste (CDW). While existing analyses often focus solely on the mechanical characteristics of recycled materials, there is a significant gap in research on integrating these technical advancements with climate-resilient infrastructure and public policies that encourage circularity. This article offers a detailed review of the technical possibilities for materials derived from CDW, shifting the focus from “low-value recycling” to higher value-added uses. We analyze progress in this area over the past decade (2015–2025), specifically exploring the role of Building Information Modeling (BIM), Artificial Intelligence (AI), and advanced pretreatment processes (such as carbonation and alkaline activation) in improving material properties. A unique contribution of this work is the creation of a conceptual framework connecting materials science to global sustainability indicators and urban resilience strategies. Our findings show that, while technical feasibility is well established, the transition to a circular economy is hampered by the absence of standardized environmental metrics and effective public policies. This review summarizes these interdisciplinary trajectories and presents a plan for engineers and policymakers to transform construction and demolition waste (CDW) from a problem into a strategic resource for climate-adaptable urban development. Full article

The Brazilian Amazon has been endangered by agro-extractivism, a development model characterized by the expansion of the agricultural frontier to produce raw commodities embedded in power-asymmetrical commodity chains. Recently, the socio-biodiversity economy has emerged as an alternative development model, aimed at reconciling local development with nature conservation. While the environmental and social contrasts between the two models are well documented, the commercial dimension of the socio-biodiversity economy remains underexplored. These two models are typically approached as separate systems, yet their coexistence and interaction within the same actors and across interconnected value chains has not been empirically examined. In this paper, we provide a qualitative analysis of dynamics and upgrading mechanisms in two cocoa value chains in the Brazilian Amazon: raw (bulk) and fine-flavor (fino) cocoa. Through this comparison, we examine how each chain differs in terms of commercial relations and how socio-biodiversity economy and agro-extractivism interact within the commercial sphere. The research took place in three municipalities along the Transamazon highway between March and September 2024. Data were gathered through semi-structured interviews with cocoa producers, buyers, and supporting actors such as NGOs, companies, and public agencies, complemented by participant observation and participation in cocoa-related events. Findings suggest that the bulk and fino cocoa chains present distinct commercial configurations, the former displaying agro-extractivist patterns, the latter consistent with the socio-biodiversity economy. Cocoa production in the region is part of an emergent socio-biodiversity economy that remains commercially embedded in agro-extractivism. Notably, farmers engage in both chains as part of their livelihood strategies, while relying predominantly on the bulk trade. We argue that the fino cocoa chain may represent a pathway for transforming commercial relations in the region, provided that the structural conditions sustaining agro-extractivist patterns in the bulk chain are addressed. More broadly, we show that production-level transitions toward sustainable farming do not automatically translate into the transformation of commercial relations, and call for greater analytical attention to the commercial dimension of socio-biodiversity economies. Full article

Heavy metal contamination of surface waters poses serious environmental and public health concerns, particularly in industrialized river basins. This study presents an integrated assessment of heavy metal pollution and associated human health risks in the Nilüfer Stream (Bursa, Türkiye) based on a five-year monitoring dataset (2020–2024). Seasonal water samples collected from 15 stations along the main stream and its tributaries were analyzed for total concentrations of As, Al, B, Cr, Cu, Fe, Mn, Ni, Pb, and Zn. Pollution levels were evaluated using the Heavy Metal Pollution Index (HPI), Heavy Metal Evaluation Index (HEI), and Degree of Contamination (Cd), while non-carcinogenic and carcinogenic health risks for adults and children were assessed via ingestion exposure following USEPA guidelines. Mean concentrations of Al, Fe, Mn, As, and Ni exceeded international drinking water guideline values, indicating significant contamination within the basin. All indices classified the Nilüfer Stream as severely polluted (HPI = 274.32; HEI = 49.59; Cd = 49.59), with higher values during summer and autumn due to reduced dilution. Principal component analysis revealed strong associations among Al, Fe, Mn, Ni, Cr, and Cu, suggesting a common origin likely related to cumulative anthropogenic inputs, while arsenic exhibited a distinct pattern linked to toxicological risk. Health risk assessment showed that the hazard index exceeded safe thresholds for both age groups, with children being more vulnerable. Arsenic and nickel were the main contributors to both non-carcinogenic and carcinogenic risks, with arsenic posing an unacceptable lifetime cancer risk. Overall, the results indicate severe cumulative heavy metal pollution and associated health risks, highlighting the need for continuous monitoring, effective pollution control, and integrated river basin management. Full article

Agrivoltaic systems integrate solar electricity generation with agricultural production on the same land and have emerged as a promising strategy to address land-use conflicts between food and energy systems. This PRISMA-based systematic review synthesizes evidence from 249 peer-reviewed studies published between 2010 and 2025, applying an integrated three-dimensional framework that simultaneously examines technical efficiency, environmental sustainability, and institutional governance. The results show that agrivoltaic systems consistently achieve superior land-use performance, with Land Equivalent Ratio values typically ranging between 1.2 and 1.8, indicating 20–80% greater territorial efficiency than separate agricultural and photovoltaic systems. In water-stressed regions, reported improvements in water-use efficiency commonly reach 15–30%, while life-cycle assessments indicate substantial reductions in greenhouse gas emissions and other environmental impacts. The integrated analysis also reveals important design-dependent trade-offs related to panel density, crop selection, and local agroclimatic conditions. Despite their demonstrated technical and environmental maturity, the large-scale deployment of agrivoltaic systems remains constrained by institutional barriers, including the lack of dedicated regulatory frameworks, fragmented agricultural and energy policies, and the strong geographical concentration of research in the Global North, with limited evidence from Latin America and other regions of the Global South. Overall, the findings indicate that agrivoltaic systems represent a credible component of integrated land-use and energy transition strategies, but their responsible scaling will depend primarily on advances in governance, policy alignment, and context-specific system design. Full article

This study explores the interconnection between the variables of ChatGPT usage, energy consumption, water consumption, and carbon dioxide CO₂ emissions. A new integer and fractional order model using the Caputo derivative is proposed to comprehend the long-term dependencies of these variables. Boundedness, and global and local stability are examined for the fractional order model. The equilibrium points of these variables are shown to determine the stability of the model. The Runge–Kutta 7 numerical method is employed for the integer order model, whereas the semi-implicit linear interpolation (L1) method is used for the fractional order model. The parameter sensitivity is conducted on the system’s parameters to understand the variables’ impact by varying the relevant parameters for the system. To increase the efficacy of our analysis, we used machine learning approaches to model and predict the dynamics of CO₂ emissions, energy and water consumption, and ChatGPT usage. The Prophet ML model stood out among the other methods because it is adept at identifying long-term growth trends, seasonal changes, and the impact of outside variables in intricate time-series data. It is extremely beneficial for research centered on sustainability, where accurate projections are essential for wellinformed decision-making, because it can produce robust, interpretable forecasts against missing values and outliers. Using the Prophet ML model, our research guarantees precise and expandable predictions and provides valuable information that can direct tactics to balance environmental sustainability and technological progress. Full article

Aircraft icing prediction is crucial for aerodynamic design and airworthiness assessment. Traditional physics-based models struggle with complex multi-physical processes, while existing AI methods (function-based characterization or direct image learning) face issues like multi-valued mapping, high data dependency, or lack of physical interpretability. This study proposes a deep learning framework based on point set displacement description, transforming the icing process into airfoil boundary point movements. PCA dimensionality reduction mitigates the curse of dimensionality while retaining physical meaning. A neural network is used to map environmental parameters to low-dimensional principal components. Comparative analysis shows the 64 × 64 network achieves optimal fitting; 2000 samples reproduce complex ice shapes, and 800 low samples characterize simple ones. Balancing efficiency, accuracy, and interpretability with reduced data dependency, this method provides a new approach for rapid engineering icing prediction. Full article

Mushrooms offer a promising solution for sustainable food production due to their nutritional value, low resource requirements, and ability to grow in diverse environments. As interest in mushrooms grows, it is important to understand where current research is focused and where key gaps remain. A bibliometric analysis of 776 research articles indexed in Web of Science revealed a strong emphasis on yield, substrate reuse, and enzymatic degradation, but limited attention to molecular approaches, climate adaptation, and studies from arid regions such as the Middle East. Building on these findings, this review explores the ecological diversity of mushrooms and their adaptations across tropical, temperate, boreal, and arid ecosystems. It discusses the role of mycorrhizal and microbial interactions in nutrient cycling and environmental resilience, including desert truffle symbioses. Key pathways and genetic regulation involved in lignin degradation are outlined, along with recent advancements in transcriptomics, proteomics, genomics, metabolomics, and metagenomics that support improved cultivation and bioactive compound production. The review also addresses sustainable practices, such as microbiome integration and resource recycling, to enhance mushroom farming. The aim is to bring together ecological insights and molecular strategies to support sustainable mushroom production, particularly in regions facing resource and climate challenges. Full article

The transition to a circular economy is a strategic direction of the European Union, and the agri-food sector is essential in this transformation through resource consumption, climate impact and an economic role in the food chain. This study analyses the relationship between the circularity of the economy and the sustainable performance of agri-food systems in the EU-27, using Eurostat data for the period of 2014–2023. Circularity is operationalised through a composite index built from the circularity of materials and resource productivity, aggregated through principal component analysis and complemented by an alternative measure based on the average of the standardised components. Sustainable performance is assessed through economic indicators (value added and output in agriculture, value added in the food industry), environmental indicators (greenhouse gas emissions from agriculture) and, complementary, energy indicators (energy intensity in the food industry), the latter being analysed separately on the available observations. The results do not indicate clear aggregate differences in sustainable performance associated with circularity measured at the macro level over the analysed period, underlining the importance of connecting circularity objectives with interventions and indications specific to the agri-food chain for monitoring and policy design at the EU level. Full article

Retaining the green workforce, employees driving sustainability and environmental innovation, is essential for organizational resilience and long-term environmental goals. While prior Green HRM research has primarily relied on survey-based methodologies and theoretical frameworks to examine retention factors, these approaches lack predictive capability and fail to provide actionable, employee-specific insights. This study advances beyond descriptive and correlational analyses by employing explainable artificial intelligence (XAI) to develop a transparent, data-driven framework for identifying attrition drivers and quantitatively evaluating retention strategies. Unlike existing studies that rely on self-reported perceptions, our approach leverages objective HR data and machine learning to predict individual-level attrition risk with calibrated probabilities. Leveraging the IBM HR Analytics dataset as a proxy for sustainability-focused roles, we construct an interpretable logistic regression model with strong predictive performance and isotonic regression calibration. Global and local interpretability techniques, including SHAP, LIME, and permutation importance, show that non-monetary factors, such as excessive overtime, frequent business travel, and limited promotion opportunities, have a greater impact on turnover risk than salary levels. These findings align with Green Human Management (Green HRM) principles, which emphasize work–life balance and employee well-being. Crucially, our policy simulation framework, absent from prior Green HRM studies, demonstrates that eliminating overtime could reduce predicted attrition probability by 17.35% for affected employees, potentially retaining 31 staff members, substantially outperforming modest salary adjustments. This work expands the value of predictive AI into HR analytics by consolidating HR analytics with Green HRM through a novel methodology that bridges the gap between prediction and actionable intervention. It represents the first systematic integration of XAI-based predictive modeling with counterfactual policy simulation in environmentally conscious sustainable organizations. Full article