Search Results (6,557)

This study investigates the structural evolution and projected trajectory of greenhouse gas (GHG) emissions across the EU27 from 1990 to 2030, with a particular focus on their implications for the effectiveness of European climate policy. Drawing on official sectoral data and employing a multi-method framework combining time series modelling (ARIMA), machine learning (Random Forest), regime-switching analysis, and segmented linear regression, we assess past dynamics, detect structural shifts, and forecast future trends. Empirical findings, based on Markov-switching models and segmented regression analysis, indicate a statistically significant regime change around 2014, marking a transition to a new emissions pattern characterised by a deceleration in reduction rates. While the energy sector experienced the most significant decline, agriculture and industry have gained relative prominence, underscoring their growing strategic importance as targets for policy interventions. Hybrid ARIMA–ML forecasts indicate that, under current trajectories, the EU is unlikely to meet its 2030 Fit for 55 targets without adaptive and sector-specific interventions, with a projected shortfall of 12–15 percentage points relative to 1990 levels, excluding LULUCF. The results underscore critical weaknesses in the EU’s climate policy architecture and reveal a clear need for transformative recalibration. Without accelerated action and strengthened governance mechanisms, the post-2014 regime risks entrenching a plateau in emissions reductions, jeopardising long-term climate objectives. Full article

Urban sustainability has become a defining governance challenge as smart cities increasingly integrate artificial intelligence (AI) into public service delivery and decision-making. While AI-enabled systems promise efficiency and responsiveness, growing concerns regarding trust, legitimacy, and citizen engagement suggest that technological adoption alone does not guarantee sustainable urban outcomes. Existing studies have largely emphasized technological performance or individual adoption, paying limited attention to the governance mechanisms through which AI acceptance translates into sustainability co-creation. To address this gap, this study develops and empirically examines the AI–Urban Citizen Sustainability Co-Creation Framework (AI–CSCF) within the context of smart cities in Thailand. A quantitative survey was conducted with 1002 citizens across three smart city settings, and structural equation modeling (SEM) was employed to examine the relationships among AI acceptance, trust in AI, citizen adaptability, and sustainability co-creation. The results indicate that AI acceptance functions as a foundational condition shaping trust in AI and citizen adaptability, through which its influence on sustainability co-creation is indirectly transmitted. Trust in AI emerges as a key mediating mechanism linking AI-enabled governance to participatory sustainability outcomes. These findings underscore the importance of human-centered and trustworthy AI governance that strengthens citizen trust, enhances adaptive capacities, and positions citizens as active co-creators of sustainable urban development aligned with SDG 11. Full article

This study examines how AI development and the labor force’s gender structure jointly influence female employment and female’s economic contributions from a dual-sector firm–household perspective. Using panel data from 58 countries spanning 2000–2022, we construct a theoretical model and conduct empirical tests. Results indicate that the labor force’s gender imbalance significantly suppresses the scale of female employment and female economic contributions; at the current stage, AI generally exerts a negative impact on female employment and economic contributions, but exhibits a significant interaction with the labor force gender structure. In scenarios of severe gender imbalance, AI’s skill-restructuring effect partially mitigates these adverse impacts; AI also generates a limited “time-release effect” by reducing women’s time spent on household labor, indirectly promoting female employment. The gendered effects of AI exhibit pronounced institutional variations across different developmental stages and gender structure conditions. This study emphasizes that AI is not a gender-neutral technology; its fairness depends on institutional and structural environments. Accordingly, it proposes policy recommendations, including improving multi-tiered systems for female talent development, guiding gender-inclusive AI applications, and strengthening global gender–governance cooperation. Full article

To investigate the spatiotemporal dynamics and wall shear stress patterns of a PVC (precessing vortex core) within a bounded swirling flow for agricultural irrigation, LES (Large Eddy Simulation) simulations based on a guide-vane hydro-cyclone were conducted and validated by physical experiments. Coherent structures were extracted through flow modal decomposition, and a reduced-order model was established. The modal analysis of the flow reveals the following: A modal pairing phenomenon exists in the swirling flow, starting from the swirling section downstream of the guide-vane. The flow converts from a basic pipe flow to swirling flow. Compared to the vane section, the composite PVC in the swirling section exhibits mutual momentum exchange, leading to increasingly fragmented evolution of the vortex core over time and space. The application of vortex identification criteria to the reconstructed reduced-order model reveal that the precessing vortex core exhibits a tendency to spiral downstream along the guide-vane twist direction, with its rotation direction perfectly aligned with the guide-vane twist. As the Reynolds number of the bounded swirling flow increases, the circumferential precession of the PVC exhibits a linear weakening trend. As the relative length l/d of the guide-vane to the pipe increases, the circumferential precession of the PVC shows a linear strengthening trend. The wall shear stress analysis results indicate that the stress coefficient magnitude near the downstream location of the guide-vane is approximately zero, representing the lowest value across the entire flow. The region exhibits a rotational precession trend downstream. The stress coefficient magnitude between guide-vanes is relatively high, about 0.1 times dynamic pressure of approaching flow, and this trend also develops downstream with a rotational precession tendency. Full article

Five prevention is an important technical means to prevent maloperations in substations, and knowledge extraction from wiring diagrams is the key to intelligent “five prevention logic verification”. To address the error accumulation caused by multimodal object matching in traditional methods, this paper proposes a hierarchical recognition-based approach for topological knowledge extraction. This method establishes a multi-level recognition framework utilizing image tiling, decomposing the wiring diagram recognition task into three hierarchical levels from top to bottom: connection modes, bay types, and switching devices. A depth-first strategy is employed to establish parent–child node relationships, forming an initial topological structure. Based on the recognition results, the proposed approach performs regularized parsing and leverages a bay topology knowledge base to achieve automated matching of inter-device topological relationships. To enhance recognition accuracy, the model incorporates a Swin Transformer block to strengthen global feature perception and adds an ultra-small target detection layer to improve small-object recognition. The experimental results demonstrate that all recognition layers achieve mAP@0.5 exceeding 90%, with an overall precision of 93.9% and a recall rate of 91.7%, outperforming traditional matching algorithms and meeting the requirements for wiring diagram topology knowledge extraction. Full article

Climate change constitutes a growing challenge for high-Andean communities worldwide, whose livelihoods depend directly on agriculture, livestock farming, and the stability of local ecosystems. In this context, the study seeks to understand the construction of social imaginaries among agricultural producers regarding the dynamics of climate variability, with the aim of analyzing both the vulnerabilities and adaptive capacities that emerge in their everyday practices. Based on a qualitative approach, supported by 32 interviews with key informants from 16 communities, 04 focus groups, and documentary analysis, field data were collected and processed using Atlas.ti software. The testimonies of community members from Cojata, Puno–Peru, revealed social imaginaries and collective responses linked to this phenomenon. The findings show feelings of concern and uncertainty, diverse interpretations of climate change dynamics, reconfiguration of cultural meanings, and the deployment of hybrid adaptation strategies that combine ancestral knowledge with contemporary resources. Overall, these findings show that social imaginaries play a central role in how communities face the climate crisis, revealing both the persistence of structural inequalities and the need to strengthen intercultural territorial policies that recognize local knowledge, promote communal cooperation, and foster a horizon of resilience and climate justice. Full article

High-quality code representations are fundamental to code intelligence. Achieving such representations with parameter-efficient fine-tuning (PEFT) remains a key challenge. While code pre-trained models (CodePTMs) offer a robust foundation for general-purpose embeddings, current PEFT approaches face two main obstacles when adapting them: (i) they fail to adequately capture the deep structural characteristics of programs, and (ii) they are limited by the model’s finite internal parameters, restricting their ability to overcome inherent knowledge bottlenecks. To address these challenges, we introduce a parameter-efficient code representation learning framework that combines retrieval augmentation with structure-aware priors. Our framework features three complementary, lightweight modules: first, a structure–semantic dual-channel retrieval mechanism that infuses high-quality external code knowledge as non-parametric memory to alleviate the knowledge bottleneck; second, a graph relative bias module that strengthens the attention mechanism’s capacity to model structural relationships within programs; and third, a span-discriminative contrastive objective that sharpens the distinctiveness and boundary clarity of span-level representations. Extensive experiments on three benchmarks spanning six programming languages show that our method consistently outperforms state-of-the-art parameter-efficient baselines. Notably, on structure-sensitive tasks using the PLBART backbone, RS-Rep surpasses full fine-tuning, delivering a 22.1% improvement in Exact Match for code generation and a 4.4% increase in BLEU scores for code refinement, all while utilizing only about 5% of the trainable parameters. Full article

Food fraud represents a growing global challenge with significant implications for public health, market integrity, sustainability, and consumer trust. Beyond economic losses, fraudulent practices undermine the environmental and social sustainability of food systems by distorting markets, misusing natural resources, and weakening incentives for authentic and responsible production. Despite the establishment of harmonized frameworks of the European Union for official controls, the increasing complexity of food supply chains has exposed persistent gaps in fraud detection, particularly for high-value products such as those with PDO (Protected Designation of Origin) and PGI (Protected Geographical Ιndication) Certification. This study investigates the perceptions, attitudes, and experiences of frontline inspectors in Greece to assess current challenges and opportunities for strengthening official food fraud controls. Data were collected through a structured questionnaire, validated by experts and administered nationwide, involving 122 participants representing all major national food inspection authorities. Statistical analysis revealed significant institutional differences in perceptions of fraud prevalence, with mislabeling of origin, misleading organic claims, ingredient substitution, and documentation irregularities identified as the most common fraudulent practices. Olive oil, honey, meat, and dairy emerged as the most vulnerable product categories. Inspectors reported relying primarily on consumer complaints and institutional databases as key tools for identifying fraud risks. Food fraud was perceived to contribute strongly to losses in consumer trust in food safety and product authenticity, as well as to the erosion of sustainable production models that depend on transparency, fair competition, and responsible resource use. Overall, the findings highlight detection gaps, uneven resources across authorities, and the need for improved coordination and capacity-building to support more efficient, transparent, and sustainability-oriented food fraud control in Greece. Full article

The industrial heritage of the Third Front construction (hereafter referred to as Third Front industrial heritage) serves as a significant physical manifestation of China’s urban society, economy, and culture during a unique historical period. Its widespread abandonment not only constitutes a waste of social resources but also accelerates the erosion of collective memory surrounding the Third Front initiative. As one of Sichuan Province’s (including present-day Chongqing) key Third Front construction regions during that era, Guangyuan City possesses a substantial legacy of Third Front industrial heritage sites. These sites are predominantly idle and face ongoing risks of deterioration, necessitating comprehensive and systematic research into their classification, protection, and regeneration. This paper focuses on 39 Third Front industrial heritage sites in Guangyuan City, employing architectural typology to construct a ‘type-medium-value’ research framework integrating field research with strategic distribution analysis at the urban level, spatial form analysis at the settlement level, and spatial combination analysis at the building level to quantitatively identify and qualitatively deconstruct the spatial logic of these sites. This enables the analysis of the functional characteristics, structural logic, and spatial intent embodied by different types, thereby exploring the multidimensional value implications of Third Front industrial heritage through this value medium. Ultimately, this research proposes targeted adaptive mechanisms and revitalisation pathways for Third Front industrial heritage. It aims to promote the cultural legacy of this heritage and perpetuate the Third Front spirit within the context of strengthening the Chinese national community consciousness in the new era, while aligning with the Party and state’s development strategies. This approach aims to provide a reference for revitalising and utilising Third Front industrial heritage in other underdeveloped regions. Full article

This study introduces an Impact Thinking Approach (ITA) as a strategic framework to strengthen traceability implementation in the fashion and textile industry. The research examines how ESG impact dimensions shape sustainable strategy definition and how traceability can act as a strategic enabler rather than a mere compliance tool. A mixed-method design combining a narrative literature review, content analysis of 69 sustainability sources, and a two-round Delphi study with 19 experts was employed to identify, evaluate, and prioritize impact drivers related to traceability adoption. The resulting ITA framework connects regulatory requirements, impact materiality, and traceability demands into a unified structure that clarifies the strategic relevance of environmental, social, and governance dimensions. Findings reveal that governance-related factors—particularly data transparency, stakeholder engagement, innovation capacity, and cross-sector partnerships—are the strongest enablers for activating effective traceability schemes. The framework provides practitioners with structured guidance for integrating traceability into sustainable business strategies and for developing impact-aligned KPIs and decision-making mechanisms. The study contributes theoretical insights into the ESG–traceability nexus and offers a practical model to support regulatory alignment, organizational readiness, and long-term strategic planning. Full article

This study investigates the determinants of green energy penetration in the Next Eleven (N-11) economies over the period 2000–2022, with a particular focus on the roles of foreign direct investment (FDI), green transition, governance quality, industrial growth, and urbanization. The primary objective of the study is to assess how investment flows, structural transformation, and institutional capacity jointly shape the adoption of renewable energy in fast-growing emerging economies. To achieve this goal, the study employs a second-generation panel econometric and machine-learning framework that accounts for cross-sectional dependence, slope heterogeneity, and long-run equilibrium relationships. Specifically, cross-sectional dependence and slope homogeneity tests are conducted, followed by CADF and CIPS unit root tests and the Westerlund cointegration approach. Long-run effects are then estimated using Partialing-Out LASSO and Cross-Fit machine-learning estimators, complemented by SHAP analysis to interpret nonlinear and heterogeneous effects. The results indicate that green transition, governance quality, and urbanization significantly promote green energy penetration. In contrast, FDI and industrial growth exert adverse effects, reflecting carbon-intensive investment and production structures. The findings highlight the importance of coordinated investment strategies, institutional strengthening, and urban planning in accelerating renewable energy transitions in emerging economies. These results provide policy-relevant insights for achieving sustainable energy development while supporting long-term economic growth in the N-11 countries. Full article

Weld quality plays a critical role in ensuring the structural integrity and long-term performance of critical piping systems used across petrochemical, oil and gas, marine, and healthcare sectors. Although gas tungsten arc welding, shielded metal arc welding, and gas metal arc welding are widely applied in pipe fabrication, existing studies often examine these processes independently and provide limited insight into the comparative influence of process parameters on weld morphology, microstructure, and mechanical performance. This review consolidates findings from recent research to evaluate how welding current, arc voltage, heat input, travel speed, shielding gas composition, and joint preparation interact to affect weld bead geometry, heat-affected zone evolution, tensile properties, hardness, and overall weld integrity in piping systems. The primary objective of this review is to critically compare fusion welding process parameter optimization strategies and to identify unresolved challenges in achieving controlled weld root geometry for high-integrity piping applications. Recent industrial failure investigations, particularly in ethylene oxide service piping, further underscore the importance of weld root control. Several documented leak events were traced to excessive root protrusion and inadequate interpretation of non-destructive testing data, where elevated reinforcement disrupted internal flow and promoted turbulence-induced degradation. These recurring issues highlight a broader industry challenge and strengthen the need for improved root-height optimization in critical piping applications. A significant research gap is identified in the limited optimization of weld root reinforcement, particularly in gas tungsten arc welding processes, where most reported studies document root heights exceeding 3 mm. Achieving a root height below 2 mm, which is an important requirement for reducing flow-induced turbulence and meeting industry acceptance criteria, remains insufficiently addressed. This review highlights this gap and outlines future research opportunities involving advanced parameter optimization and improved process monitoring techniques. The synthesis presented here provides a comprehensive reference for enhancing weld quality in critical piping systems and establishes a pathway for next-generation welding strategies aimed at producing high-integrity weld joints compliant with the American Society of Mechanical Engineers B31.3 requirements. Full article

Sustainability in the agri-food sector has become a cornerstone of global efforts to combat climate change, ensure food security through climate-smart agriculture, and strengthen economic resilience. Sustainability reporting within agri-food systems has gained increasing regulatory significance with the introduction of mandatory frameworks such as the Turkish Sustainability Reporting Standards (TSRSs). This article searches for the sustainability reports of agri-business firms listed in BIST in Turkey. Although TSRS reporting is not yet mandatory for the agribusiness sector, this study examines the first TSRS-aligned sustainability reports published by eight agri-food companies, excluding the retail sector. The analysis assesses how effectively these reports address sector-specific environmental and social challenges defined in the GRI 13 Agriculture, Aquaculture and Fishing Sector Standard and their alignment with the United Nations Sustainable Development Goals (SDGs). Using a structured content analysis approach, disclosure patterns were examined at both thematic and company levels. The findings indicate that TSRS-aligned reports place strong emphasis on environmental and climate-related disclosures, particularly emissions, climate adaptation and resilience, water management, and waste. In contrast, agro-ecological and land-based impacts—such as soil health, pesticide use, and ecosystem conversion—are weakly addressed. Economic disclosures are predominantly framed around climate-related financial risks and supply chain traceability, while social reporting focuses mainly on occupational health and safety, employment practices, and food safety, with limited attention to labor and equity issues across the broader value chain. Company-level results reveal marked heterogeneity, with internationally active firms demonstrating deeper alignment with GRI 13 requirements. From an SDG alignment perspective, high levels of coverage are observed across all companies for SDG 13 (Climate Action), SDG 12 (Responsible Consumption and Production), and SDG 6 (Clean Water and Sanitation). By contrast, SDGs critical to agro-ecological integrity and social equity—namely SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 10 (Reduced Inequalities), and SDG 15 (Life on Land)—are weakly represented or entirely absent. Overall, the results suggest that while TSRS-aligned reporting enhances transparency in climate-related domains, it achieves only selective alignment with the SDG agenda. This underscores the need for a stronger integration of sector-specific sustainability priorities into mandatory sustainability reporting frameworks. Full article

Cutaneous viral infections result from the complex interaction between viruses and skin structures, influenced by viral tropism and the host immune response. They can generate lesions ranging from transient rashes to chronic or potentially tumorous formations. Cutaneous manifestations are often the first sign of infection and allow for early recognition. The aim of this review is to analyze the role of viruses in skin pathology, the mechanisms of infection, and the clinical impact. A narrative review of the recent literature was performed, including original articles, systematic reviews, and clinical guidelines on cutaneous viral infections. Data on pathogenic mechanisms, types of lesions, evolution, and therapeutic options were evaluated, covering the main viruses involved in dermatology: herpesviruses, papillomaviruses, poxviruses, and viruses associated with acute rashes. Cutaneous viral infections can be self-limited, recurrent, or chronic, and some can promote malignant transformation of skin cells. The variability of clinical manifestations reflects the virus–host interaction and influences diagnosis and management. Recent advances highlight the development of vaccines and targeted antiviral therapies, which improve prognosis and infection control. Viruses play a major role in the etiology of skin diseases, and their early recognition is essential for preventing complications. Understanding the mechanisms of infection and the cutaneous response contributes to the optimization of therapeutic and preventive strategies, strengthening the modern management of viral cutaneous pathology. Full article

This study explores the factors that influence Gen Z consumers’ green purchasing behavior, addressing a gap in current research by combining the Theory of Planned Behavior, the Theory of Consumption Values, and Generational Theory, and applying them to the touristic accommodation context. Based on a quantitative survey of Gen Z tourists from Spain, Norway, and Lithuania, the study examined traditional constructs of the Theory of Planned Behavior alongside subjective knowledge, environmental consciousness, perceived value, and green trust. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results indicate a significant role for price-based functional value and emotional value in shaping Gen Z’s attitudes toward sustainable accommodation. Furthermore, subjective knowledge and environmental consciousness strengthen green trust, which in turn contributes to a more positive attitude toward sustainable touristic options. Attitude, subjective norms, and perceived behavioral control significantly predict behavioral intention, while both perceived behavioral control and behavioral intention directly influence actual purchasing behavior. The findings suggest that tourism marketers should focus on enhancing price-related and emotionally meaningful value propositions, while also fostering trust through clear communication of environmental performance. Overall, the study offers a comprehensive understanding of the drivers behind Gen Z’s sustainable accommodation choices and provides practical implications for promoting environmentally responsible tourism. Full article