Search Results (16,646)

In Luoyuan Bay, China, Sporobolus alterniflorus invasion has hindered mangrove restoration and disrupted faunal communities within mangrove habitats. This study investigated its impact on mollusk, crab, and fish assemblages across mangrove, mudflat, and invaded habitats from 2019 to 2020. Results showed that species diversity of three assemblages did not differ significantly between invaded and non-invaded mangrove habitats; however, assemblage structure was altered and functional traits declined markedly in invaded areas. Compared with non-invaded mangroves, invaded habitats showed decreases of 81.6% in mollusk density, 50.7% in mollusk biomass, 66.6% in crab density and 84.2% in crab biomass. Dominant fish species (Acanthogobius ommaturus, Liza carinata, Stolephorus chinensis) also exhibited lower body size, total size and biomass in invaded habitats. Given the close dependence of coastal residents on these faunal resources, a socioeconomic analysis of livelihood strategies was conducted, revealing Sinonovacula constricta aquaculture achieved the highest net income-to-investment ratio, 122.7% higher than nearshore fishery and 308.3% higher than shallow-sea oyster cultivation, while professional shellfish farming yielded the highest net income per hectare, 23.6% higher than oyster cultivation. Thus, both forms of shellfish aquaculture provide greater economic returns than other livelihood options. Based on these findings and niche theory, we propose a management framework: after removing S. alterniflorus, plant native mangroves (Kandelia obovata) in mid-to-high intertidal zones and lease lower flats for shellfish farming. This framework has the potential to integrate ecological restoration with local livelihoods and may inform similar efforts in other regions facing biological invasions and restoration challenges. Full article

The COVID-19 pandemic prompted governments to implement lockdowns and social distancing measures to curb transmission, which, in South Africa, disrupted economic activity, reduced household income, and challenged the sustainability of household food access. This study assessed food accessibility, availability, dietary diversity, food security status, and coping strategies among households in the Tygerberg region during lockdowns. A cross-sectional design was employed using a researcher-administered questionnaire to collect sociodemographic and household data. Food security was assessed using the Household Food Insecurity Access Scale and the Household Food Security Survey Module, dietary diversity using a 24 h recall, and coping strategies through a standardized tool. Among the 432 households surveyed, 62% reported reduced income during lockdowns, while approximately 80% experienced food insecurity in the preceding 30 days and 72% over the past year. Dietary diversity was low in 47.3% of households, consuming fewer than seven food groups, and medium in 46.4%, consuming eight to eleven food groups. Common coping strategies included purchasing cheaper, less preferred foods, skipping meals, and reliance on social relief measures such as food parcels and the Social Relief of Distress grant. Overall, while food availability remained relatively stable, economic access emerged as the principal constraint, undermining dietary quality and household resilience and highlighting the need for income-responsive and socially sustainable food security interventions to strengthen urban food system resilience during prolonged socio-economic shocks. Full article

The sharing economy has become a significant phenomenon of the modern economy in recent years, enabling more efficient use of resources through digital platforms. At the same time, the development of generative artificial intelligence (AI) has begun to reshape the functioning of these platforms. This article explores the intersection of sustainability and AI within sharing economy platforms, focusing on environmental, economic and social dimensions. The study adopts a conceptual and exploratory research design combining a literature review with a comparative case study analysis of selected sharing economy platforms, namely Airbnb and BlaBlaCar, complemented by an industrial platform example (Xometry). The analysis examines how generative AI can support sustainable consumption, operational efficiency, and user engagement while raising important ethical concerns related to data usage, trust, bias, and algorithmic governance. The findings suggest that AI integration can improve resource utilization, accessibility, and platform efficiency, but simultaneously introduces new ethical challenges related to transparency, data governance, and algorithmic decision-making. These results highlight the dual role of AI as both a driver of sustainability and a source of emerging ethical risks in digital platform ecosystems. Full article

This study investigates the spatiotemporal dynamics and driving mechanisms of Water Supply Ecosystem Service Value (ESV) in the North China Plain from 2002 to 2022. Addressing the critical challenges of water scarcity and ecological degradation in this densely populated and agriculturally intensive region, the research develops an integrated framework to quantify the relative contributions of multi-dimensional drivers to the water supply service (quantified by biophysical supply, W). A Particle Swarm Optimization (PSO) algorithm was employed to automate hyperparameter tuning for XGBoost and Random Forest models, with model interpretability enhanced via SHAP (SHapley Additive exPlanations) to elucidate non-linear feature importance and directional impacts. Results demonstrate that the PSO-XGBoost model outperforms PSO-Random Forest in predictive performance (R² = 0.8013 vs. 0.7443). The total water supply exhibited a significant annual decline of 1.98 billion m³ (p < 0.05), with 53.4% of the study area showing significant pixel-level temporal trends. The supply structure is dominated by soil moisture (80–90%), while externally transferred water, despite increasing rapidly, exhibits high interannual variability. SHAP analysis identifies vegetation cover (NDVI), clay content, GDP, and population density as the predominant drivers. Notably, GDP shows a strong negative correlation with water supply, reflecting a trade-off where intensive socio-economic expansion increases water consumption at the expense of ecosystem supply capacity. Methodologically, the PSO-XGBoost-SHAP framework enables both high predictive accuracy and detailed attribution of driving factors. These findings highlight the strategic importance of soil water (“Green Water”) conservation and offer actionable insights for adaptive water resource management, providing a replicable analytical approach for other regions facing similar hydrological challenges. Full article

Maize (Zea mays L.) is a significant staple food crop in the developing world. Despite its significance, diseases and pests are limiting its supply. Farmers have primarily relied on synthetic chemicals as control measures; however, these chemicals are harmful to humans, animals, and the environment and exacerbate pest recurrence. Medicinal plants have shown promising potential as alternative pest- and disease-controlling agents, offering an economical, sustainable, biodegradable, and cost-effective approach. This review article synthesises phytochemical, ethnobotanical, and experimental data from relevant peer-reviewed papers published across various years to identify medicinal plants. Thirty-one unique plant families have been identified and have been used to control pests and diseases of maize. Some families represented both antifungal and insecticidal applications. Medicinal plants such as Senna obtusifolia, Euphorbia balsamifera, Aristolochia ringens, Allium sativum, Azadirachta indica, Carica papaya, Moringa oleifera, and Ficus exasperata have shown antifungal and insecticidal properties, primarily under laboratory conditions. Most of the evidence is derived from laboratory studies, with only limited validation in real field conditions and with limited evaluation of safety for non-target organisms. Furthermore, this review highlighted the extraction methods, solvents used, plant parts, major active ingredients, and mode of action. Future prospects for integrating ethnobotanical knowledge with contemporary scientific methods to optimise biopesticide production are also discussed, along with the challenges of standardisation, formulation, and commercialisation. Full article

Sustainable (ESG) investments have gained significant interest, prompting renewed attention to retail investors’ decision-making processes. ESG investing is motivated by both financial concerns and psychological factors. However, despite growing interest, the motivational underpinnings of sustainable asset allocation remain underexplored. This study bridges economic psychology and sustainable finance to examine drivers of ESG investment intentions and choices in the Italian market. Drawing on the Theory of Planned Behavior, it explores how attitudes, subjective norms, perceived behavioral control, and trust shape ESG investing intentions and choices. Results show that each factor significantly influences investing intentions when considered independently. In particular, the affective dimension of attitudes emerges as especially relevant. These findings challenge traditional views of financial rationality in ESG contexts, suggesting that the motivations of sustainability-oriented investors may differ meaningfully from those of traditional investors. Practical implications are that ESG communication should appeal to emotional and ethical dimensions of decisions, while educational initiatives should enhance investors’ ability to critically assess ESG-related information. Full article

In the context of rapid technological evolution and increasing market uncertainty, technoparks have emerged as critical ecosystems for bridging scientific research and high-tech industrial production; however, their effectiveness is often constrained by limited flexibility, fragmented control mechanisms, and delayed decision-making processes. Motivated by these challenges, this article investigates the automation of control processes in research-driven and flexible manufacturing environments within technopark infrastructures, positioning automation as a strategic lever for enhancing operational adaptability and innovation throughput. The study conceptualizes control process automation as a multi-stage framework encompassing data acquisition, processing, intelligent analysis, and real-time decision execution and examines the role of enabling technologies such as artificial intelligence, the Internet of Things (IoT), and cyber-physical systems in supporting this paradigm. The analysis demonstrates that the integration of these technologies significantly improves production flexibility, resource optimization, and responsiveness to dynamic conditions, while simultaneously accelerating the transformation of scientific and research outputs into measurable economic value. By combining theoretical foundations with illustrative practical applications, the article substantiates the effectiveness of automated control systems and highlights their strategic relevance for increasing the competitiveness of technoparks, fostering sustainable technological innovation, and shaping resilient long-term development strategies. Full article

Artificial intelligence is increasingly embedded in cyber–physical systems that coordinate sensing, computation, communication, and control across critical and semi-critical physical environments. Within the European Union, however, its adoption is shaped not only by technological maturity and economic value, but also by an increasingly dense regulatory landscape governing data processing, cybersecurity, product security, accountability, traceability, interoperability, and safety-relevant deployment. A PRISMA ScR-informed scoping review is used to examine how European Union regulation influences artificial intelligence adoption across four representative domains: energy and smart grids, smart buildings, mobility and transport systems, and industrial and manufacturing environments. The analysis draws on primary legal sources, the peer-reviewed literature, and policy and standards-related materials, and is structured around three dimensions: regulatory barriers, technological and architectural challenges, and cross-sector implications for governance, innovation, and competitiveness. The results show that regulation functions simultaneously as a constraint and an enabling condition. It increases compliance burden, raises integration complexity, and slows deployment in higher risk settings, while promoting trustworthy artificial intelligence, stronger cybersecurity, lifecycle governance, clearer accountability, and more interoperable digital infrastructures. The central finding is that regulation is not external to artificial intelligence adoption in cyber–physical systems, but actively shapes the design space within which such systems can be developed, integrated, validated, and scaled. Future progress therefore depends on regulation-aware systems engineering, stronger implementation guidance, and cross-sector reference architectures capable of aligning legal compliance with technical architecture and operational value creation. Full article

Sweet cherry cracking severely impairs its commercial value and causes huge economic losses, and the accurate real-time detection of fine cracking defects remains a challenging small-target detection task. Traditional manual sorting and conventional machine vision methods suffer from low efficiency and poor robustness, while existing YOLO-based models have limitations in multi-scale feature fusion, local feature discrimination and spatial information retention for cherry cracking detection, and their effectiveness in natural production environments has not been statistically validated. To address these issues, this study proposes YOLO-CY for cherry cracking defect detection. Three key modules were optimized: the C3k2_AdditiveBlock was designed to enhance multi-scale feature extraction, the C2PSA_CGLU module improved the discriminability of local crack features via refined channel attention, and the Efficient Up-Convolution Block replaced traditional upsampling to reduce spatial information loss. Experiments were conducted on a self-constructed dataset of 3662 cherry images acquired on a real sorting line under natural ambient light. The results showed that YOLO-CY achieved an mAP50 of 94.88% and an mAP50-95 of 64.92%, with precision and recall reaching 93.90% and 90.81%, respectively, significantly outperforming mainstream lightweight YOLO models and two-stage detectors. Ablation experiments verified the synergistic effect of the three improved modules, and the model only had a marginal increase in parameters (2.62 M) and GFLOPs (6.60), maintaining lightweight characteristics. YOLO-CY can accurately detect fine, low-contrast and pedicel-overlapping cracks and is suitable for real-time detection on automated cherry-sorting lines, providing a technical solution for intelligent cherry quality inspection. Full article

Over the past 50 years, researchers have produced a considerable body of work substantiating that gross domestic product (GDP) is not a measure of social welfare. In response, numerous measures, collectively known as Beyond GDP (BGDP) measures, have been developed to provide a more balanced assessment of the social, environmental, and economic impacts of economic activity on current and future generations. BGDP measures have gained the attention not only of academics, but also of government practitioners concerned with prevailing measures of national and regional progress that overrepresent narrow economic objectives and underrepresent sustainability objectives. Despite this widespread support for alternatives, few governments have made significant progress in implementing BGDP measures to inform public policy. Viewed through an operational lens, this study examines strategies used by two governments that have progressed in implementing BGDP measures. We analyze their strategies against five practical considerations: (1) alignment with mission, (2) fiscal and resources constraints, (3) communication and public messaging challenges, (4) challenges with political and public commitment, and (5) gaps in internal agency knowledge and training. These five considerations were identified as the five most prominent barriers to implementing BGDP measure based on a systematic review of the BGDP literature published over the past 50 years. We conclude that these two governments implemented actions that addressed key elements of these five barriers and succeeded in adopting BGDP measures. We conclude that others could emulate these successes to advance the broader adoption of BGDP measures. Full article

This study critically examines residential tourism in Isla Cortés within the context of the real estate boom and the growing sustainability challenges facing coastal regions. Driven by global mobility, investment flows, and lifestyle migration, residential tourism is reshaping coastlines through intensive urban expansion. The analysis highlights the socio-environmental consequences of this model, including habitat fragmentation, mangrove loss, increasing pressure on water resources, and the gradual privatization of coastal areas. Using a qualitative research design that combines literature review, comparative case analysis, and territorial assessment, the study identifies structural similarities between Isla Cortés and other coastal tourism enclaves while emphasizing locally specific processes shaped by Mexico’s political economy and regulatory context. Findings suggest the structurally unsustainable character of this development pathway. Although residential tourism has stimulated short-term economic growth, it has also intensified socio-spatial segregation, commodified coastal commons, and generated long-term ecological and social vulnerabilities. The study challenges dominant narratives that portray residential tourism as inherently sustainable and instead draws on ecological reflexivity and socio-ecological systems perspectives to outline alternative planning pathways. It underscores the need for stronger regulatory frameworks, nature-based solutions, participatory governance, and regenerative planning strategies capable of aligning economic activity with ecological integrity and social inclusion in coastal territories. Full article

Agriculture faces significant challenges from crop diseases, which threaten global food security and cause substantial economic losses annually. While deep learning has advanced plant disease detection, existing models often struggle with generalization across heterogeneous environments and real-time deployment constraints, hindering their practical application in diverse agricultural settings. This paper proposes AD-DETR, an enhanced real-time detection transformer framework specifically designed for agricultural scenarios. The model incorporates three key innovations to address these issues. First, the Multi-Scale Align Network (MSANet) achieves adaptive feature alignment through an Adapt Fusion Align (AFA) block, effectively preserving disease detail information across varying scales. Second, the Spatial–Spectral Attentive Feature Fusion (SSAFF) module integrates frequency-domain processing with attention mechanisms, enhancing feature representation quality by combining spatial and spectral information. Third, the IPIoUv2 loss function improves bounding-box regression accuracy through an internal perception mechanism and scale-adaptive weighting. Comprehensive experiments demonstrate that AD-DETR achieves strong performance, with 90.2% mean average precision at $IoU=0.5$ on the Crop Disease dataset and 97.4% on the PlantDoc dataset. It maintains high efficiency with 16.4 million parameters, 47.2 GFLOPs computational complexity, and inference speeds of 230–242 frames per second. These results indicate that AD-DETR is robust to domain shift and suitable for resource-constrained applications, such as real-time monitoring on mobile and edge platforms. Full article

The determination of principal dimensions in the early ship design stage requires iterative calculations based on the basis ship particulars and ship owner’s requirements, demanding considerable time and engineering effort. In modern shipbuilding practice, errors introduced at the early design stage carry a high risk of necessitating a complete redesign, particularly under the mandatory EEDI Phase 3 requirements. To address these challenges, this study presents an automated optimization system for the determination of principal dimensions, adopting $L_{BP}$ (Length Between Perpendiculars), B (Breadth), D (Depth), and $C_B$ (Block Coefficient) as design variables. The NSGA-II (Non-Dominated Sorting Genetic Algorithm) is employed to minimize total resistance ($R_T$), specific fuel oil consumption (SFOC), and lightweight (LWT) as objective functions, with EEDI Phase 3 compliance and minimum freeboard requirements imposed as design constraints. The developed program was applied to a 114K Aframax Tanker with VLSFO/LNG dual-fuel capability, yielding a reduction in total resistance of approximately 65 kN relative to the basis ship with improved propulsive efficiency and economic feasibility. The proposed methodology is expected to enhance the efficiency of the early ship design process and provide a systematic framework for meeting stringent environmental regulations. Full article

Water scarcity is a major constraint to agricultural productivity in arid and semi-arid regions, yet its implications for communal goat production systems remain insufficiently documented. This study assessed communal goat farmers’ perceptions of water scarcity and identified factors influencing this challenge in the Eastern Cape, South Africa. A structured questionnaire was administered to 218 smallholder goat farmers, and data were analysed using SPSS (v29). A ranking index was employed to prioritise production constraints, goat functions, and water sources. Additionally, water samples from dams, streams, and rainwater were analysed for key physicochemical parameters. Results showed that theft (index = 0.233) was the most important production constraint, followed by parasites/diseases (0.219), predators (0.211), and water scarcity (0.187), which consistently ranked fourth across seasons. Despite this ranking, farmers perceived water scarcity to have substantial impacts on production, including increased disease prevalence (46.3% severe), mortality (45.0% severe), reduced weight at maturity (61.9% severe), increased trekking distance to water sources (59.2% severe), and reduced feed quality (54.6% severe). Farmers generally perceived water as clean and non-saline; however, laboratory analysis revealed poor quality, with pH values ranging from 9.14 to 10.72 and turbidity exceeding recommended thresholds (<5 NTU) in most dam and stream samples. Water accessibility was limited, with goats travelling an average of 5.85 km to dams and 7.71 km to streams. Key drivers of water scarcity included reduced rainfall (50.9%), lack of government intervention (49.1%), and drying of dams (40.4%). The study highlights a critical mismatch between perceived and actual water quality and demonstrates the multidimensional impacts of water scarcity on goat health, productivity, and welfare. Future research requires longitudinal studies linking water quality to goat health outcomes, intervention research on farmer education, low-cost water-treatment technologies, governance studies of water infrastructure, and economic analyses quantifying productivity losses. Full article

Fatty alcohols and aliphatic hydrocarbons occur abundantly in nature and serve as critical feedstocks for the surfactant and fuel industries, respectively. However, their industrial-scale separation and purification are significantly hampered by high boiling points and the formation of complex azeotropes. To address these challenges, this study explores a five-column high-vacuum pressure-swing distillation (HVPSD-5C) strategy. Vapor–liquid equilibrium (VLE) analysis of the key components (n-hexanol, n-octanol, n-dodecane, and n-tridecane) validated the thermodynamic viability of the process and established optimal operating conditions. To further enhance efficiency, a heat-pump-integrated configuration (HPI-HVPSD-5C) featuring vapor recompression and heat integration was designed, optimized, and evaluated. Comparison with the baseline HVPSD-5C process demonstrates that the HPI-HVPSD-5C configuration significantly improves sustainability and economics, reducing the total annual cost (TAC) by 17.48%, CO₂ emissions by 16.09%, and energy consumption cost by 12.79%. These findings provide a robust framework for the efficient separation of fatty alcohols from aliphatic hydrocarbons, offering a valuable reference for the purification of other pressure-sensitive azeotropic mixtures. Full article