Search Results (10,918)

Informal food trading is a cornerstone of urban livelihoods and food security in Namibia, yet traders operate under fragile conditions marked by limited capital, policy exclusion, and exposure to shocks such as COVID-19. Despite this vulnerability, traders exhibit resilience through everyday forms of social innovation. This study investigates how adaptive pricing, customer credit, and digital communication and e-payment practices function as pathways of resilience among 470 informal food traders in Windhoek, using Structural Equation Modelling to assess gender-differentiated determinants and outcomes. The analysis reveals that women’s adoption of adaptive pricing and digital tools is driven primarily by education and startup capital, while men’s innovation practices are shaped by vendor type and access to financing. Social innovations mediate the effects of these structural factors on enterprise growth, demonstrating that innovation acts as a critical mechanism linking resources and resilience. The study concludes that enhancing informal traders’ resilience requires policies that strengthen human and financial capital, improve digital inclusion, and recognize gendered differences in access to opportunity. It recommends targeted support for women’s entrepreneurial training, affordable credit, and digital infrastructure to transform the informal food sector into a more equitable and sustainable component of Namibia’s urban economy. Full article

Measuring national progress toward green transformation remains challenging due to fragmented assessment frameworks. This study develops and validates a Green Transformation Index that captures the capacity for sustainability transitions by integrating resource efficiency, innovation systems, and policy instruments. Using OECD Green Growth Indicators covering 58 economies from 2017 to 2025, we construct a composite index from 47 standardized indicators organized into three theoretically grounded dimensions. The GTI measures transformation capacity through innovation investment and policy frameworks rather than environmental outcomes. Results reveal substantial heterogeneity in transformation capacity with a Gini coefficient of 0.283, indicating persistent global inequality. Temporal analysis identifies a three-phase trajectory: consolidation from 2017 to 2019, acceleration during 2021 to 2023 driven by green recovery investments, and marked reversal in 2024 to 2025, highlighting vulnerability to economic shocks. Cluster analysis identifies four distinct pathways: innovation-driven, balanced integration, resource-first, and policy-led approaches. Critical findings show only 19 percent of countries demonstrate strong coordination between innovation investments and policy instruments, revealing significant governance fragmentation. Validation tests confirm the index effectively measures innovation capacity but shows weak correlation with emissions outcomes, underscoring the distinction between transformation inputs and environmental performance. Full article

During on-orbit service, spacecraft are subjected to hypervelocity impacts (HVIs) from micrometeoroids and space debris, causing diverse damage types that challenge structural health assessment. Unimodal approaches often struggle with similar damage patterns due to mechanical noise and imaging distance variations. To overcome these physical limitations, this study proposes a physics-informed multimodal fusion framework. Innovatively, we integrate a distance-aware infrared enhancement strategy with vibration spectral subtraction to align heterogeneous data qualities while employing a dual-stream ResNet coupled with Dempster–Shafer (D-S) evidence theory to rigorously resolve inter-modal conflicts at the decision level. Experimental results demonstrate that the proposed strategy achieves a mean accuracy of 99.01%, significantly outperforming unimodal baselines (92.96% and 97.11%). Notably, the fusion mechanism corrects specific misclassifications in micro-cracks and perforation, ensuring a precision exceeding 96.9% across all categories with high stability (standard deviation 0.74%). These findings validate the efficacy of multimodal fusion for precise on-orbit damage assessment, offering a robust solution for spacecraft structural health monitoring. Full article

Municipalities aiming for climate neutrality and resilience must take a systemic approach to planning, implementing, and monitoring climate actions, to be able to mobilise the resources needed to achieve this ambitious goal. This involves complementing conventional top-down and technological measures with bottom-up and inclusive strategies that include not only citizen engagement but also the innovation of social practices. This study presents a comparative analysis of social innovation actions for climate neutrality planned by 53 cities from 21 countries participating in the Pilot Programme of the EU-funded project NetZeroCities. By identifying 445 actions across all cities’ pilot programmes and classifying them into 10 categories and 38 sub-categories, it is found that 53.71% of actions are linked with social innovation, offering timely insights into how social innovations are being designed in cities’ urban plans. The results reveal emerging patterns and geographical variations across Europe. With more than half of all social innovation interventions focused on stationary-energy and Scope-3-related emissions reduction, the analysis reveals that cities are increasingly relying on social innovation to foster the behavioural and socio-technical changes needed to shape sustainable energy use, consumption, and mobility patterns. These actions are based on co-creation, co-design, cross-sectoral partnerships, and public-sector capacity building, with regional differences. The comparative approach and analysis contribute to the transdisciplinary discourse on social innovation assessment in systemic innovation for transitions. Full article

Sustainability challenges increasingly require higher education institutions (HEIs) to demonstrate measurable social, institutional, and environmental impacts through their extension and social outreach activities. Within this context, social innovation has emerged as a key mechanism for advancing sustainable development, particularly when framed through collaboration models involving multiple actors, such as the Quintuple Helix (QH) framework. Although the QH model emphasizes the strategic role of academia in innovation oriented toward sustainability, the contribution of HEIs to generating sustainable social impacts remains insufficiently measured. This study proposes and validates a statistical model to assess social innovation impacts oriented toward sustainability and generated by university extension projects, based on the articulation among academia, industry, government, civil society, and the natural environment. Using data from 77 Colombian universities collected between 2019 and 2024, the research applies a methodological approach implemented in two sequential phases, combining Principal Component Analysis and Structural Equation Modeling. The results confirm the robustness, reliability, and empirical validity of the proposed model, allowing HEIs to be classified according to their levels of global and governmental sustainability impact. This article is a revised and extended version of the conference paper titled “Incidence of the Quintuple Helix Model on Social Innovation Generated in University Extension Projects from the ‘Impact’ Variable”, presented at the International Conference on Project Management (ICPM2024), held in Bogotá, Colombia, on 6 September 2024. Full article

The rapid digital transformation of higher education institutions (HEIs) has created new opportunities to promote sustainability-focused teaching, learning, and assessment. At the same time, traditional assessment methods often fail to accurately measure complex skills needed for sustainability, such as systems thinking, critical reflection, and real-world problem-solving. This study examines the integration of adaptive learning platforms with performance-based assessment (PBA) as an innovative way to support inclusive, sustainability-oriented learning in higher education. Based on principles of Education for Sustainable Development (ESD), Universal Design for Learning (UDL), and constructivist learning theory, the study investigates how adaptive learning technologies tailor instruction for diverse learners while PBAs offer genuine measures of sustainability skills. Using a mixed-methods approach, data were gathered from forty-eight undergraduate students enrolled in an inclusive education course that used an adaptive learning module and PBA tasks. Learning analytics, rubric-based performance scores, and student perception surveys were analyzed to explore effects on engagement, accessibility, and skill development. The results show that this combined method enhances student inclusion, supports differentiated learning pathways, boosts engagement in sustainability tasks, and yields more complete evidence of sustainability competencies than traditional assessments. The study provides a framework for HEIs aiming to align digital transformation initiatives with sustainability objectives. It emphasizes the potential of integrating adaptive learning and PBA to promote innovative, inclusive, and sustainability-focused assessment practices. Implications for policy, curriculum design, and future digital sustainability efforts are also discussed. Full article

During use, the surface of textile fabrics is prone to wear, which can cause changes such as pilling. Pilling (entanglement of fibers) is primarily assessed using the standard visual method EN ISO 12945-4:2020, but it can also be quantitatively measured by instrumental methods with image analysis software. Due to non-uniform digital imaging conditions, such as variations in magnification and analyzed surface area, the assessed area is often inconsistent. As a result, the total percentage of the fabric specimen surface area covered with pills is often omitted. To ensure uniform digital imaging, an innovative apparatus was designed and constructed in this research and applied to woven fabrics made from 100% cotton, wool, viscose, polyamide 6.6, polyester, and acrylic fiber. Pilling in the fabric specimens was induced by rubbing with the Martindale pilling tester (EN ISO 12945-2:2020) using two different abradant materials, through predefined pilling rubs ranging from 125 to 30,000. Pilling assessment was conducted using both the visual method and the improved instrumental method, following established grading classes based on the total percentage of the fabric specimen surface area covered with pills. The research results highlight the importance of uniform digital imaging and digital grading, as these demonstrate the high comparability of pilling grades assigned by the standard visual method while providing better distinction between consecutive grades. Full article

Municipal smart city programs remain hampered by conceptual fragmentation and the absence of validated, context-specific maturity assessments. We develop the Smart Municipality Maturity Model (SMMM) via a design-science process, synthesizing 183 publications and adapting a practitioner-oriented self-assessment with 99 binary items across ten dimensions. Validation proceeded in four stages: expert review, industry validation, a pilot with 24 municipalities, and a large-scale rollout to 1136 municipalities. The five-level model yields comparable maturity scores and reveals a structural capability gap—governance and strategy outpace foundational technical capacities, especially digital infrastructure and data management. Maturity rises with municipality size, yet leadership, partnerships, and innovation culture act as moderators. The SMMM represents one of the first empirically validated, large-scale maturity assessments tailored to municipal administrations, providing a robust analytical basis for diagnosing capability gaps at scale. Its architecture directly supports municipal policy by translating conceptual smart city ambitions into measurable, comparable operational capacities and by enabling more targeted, evidence-driven interventions. The SMMM provides a low-burden instrument for self-assessment, peer benchmarking, and evidence-based policy design. Closing the identified capability gap requires capability-first investment and more explicit integration of cybersecurity and data privacy in future models and municipal practice. Full article

Semiconductor manufacturing is a resource and energy-intensive industry with a substantial environmental footprint. To address the footprint, we present a methodology for quantifying the environmental impact of semiconductor unit processes using the Environmental Footprint 3.1 Life Cycle Impact Assessment (LCIA) framework, focusing on identifying improvement opportunities in process steps with less sensitivity to defects. We apply this methodology to backside wet cleaning by proposing an alternative single-wafer process that adopts ozonated chemistries. The assessment used primary data from imec’s 300 mm pilot line. Results show that the proposed process reduces the total environmental footprint by 55% compared to the baseline Spin Cleaning with Repetitive use of Ozonated water and Diluted HF process. Key reductions include 67% less electricity for cleaning, 59% less HF use, and a 31% reduction in ultrapure water consumption. When scaled to a facility producing N28 Logic wafers at 50,000 wafer starts per month, with 46 backside clean steps per processed wafer, the process achieves annual savings of approximately 4 million kWh of electricity and 28 million liters (28,000 m³) of tap water per year. A sensitivity analysis revealed that replacing fossil-based electricity with hydroelectric power further reduces total environmental impacts by up to 63%, emphasizing the benefit of combining process innovation with renewable energy sourcing. Full article

Climate change has led to rising temperatures and increasingly extreme weather conditions, largely driven by human activity, including agriculture. The food and agriculture sector is responsible for approximately 21–37% of global greenhouse gas (GHG) emissions. In response to climate change, various innovative agricultural systems have emerged in recent decades. Among them, soilless systems represent revolutionary methods for producing large quantities of vegetables while using fewer inputs, including water, fertilizers, and pesticides. This study assesses the carbon footprint of two greenhouse-based lettuce (cv. Romana) growing systems using a cradle-to-gate life cycle assessment (LCA) approach. The first system employs an aeroponic growing method, whereas the second relies on a soil-based growing method within the greenhouse. To contextualize their environmental performance, the carbon footprints of these greenhouse cultivation systems are compared with those of the outdoor pot system. Results indicate that the highest Global Warming Potential (GWP) is associated with soil-based cultivation in the greenhouse, reaching 7.98 kg CO₂eq per kilogram of fresh weight (FW) lettuce, followed by the outdoor pot system (1.72 kg CO₂eq/kg), while the aeroponic system demonstrates the lowest GWP, achieving 0.98 kg CO₂eq/kg. The greenhouse structure contributed 9357.93 kg CO₂eq to the total GWP, representing 23% of the total impact in the aeroponic system and 22.7% in the soil-based greenhouse system. These findings suggest that soilless cultivation systems can provide a more sustainable and higher-yield alternative to soil-based methods, potentially reducing the environmental impact of vegetable production in the Mediterranean region. Full article

Building extraction from remote sensing imagery plays a pivotal role in applications such as smart cities, urban planning, and disaster assessment. Although deep learning has significantly advanced this task, existing methods still struggle to strike an effective balance among global semantic understanding, local detail recovery, and multi-scale contextual awareness—particularly when confronted with challenges including extreme scale variations, complex spatial distributions, occlusions, and ambiguous boundaries. To address these issues, we propose TriadFlow-Net, an efficient end-to-end network architecture. First, we introduce the Multi-scale Attention Feature Enhancement Module (MAFEM), which employs parallel attention branches with varying neighborhood radii to adaptively capture multi-scale contextual information, thereby alleviating the problem of imbalanced receptive field coverage. Second, to enhance robustness under severe occlusion scenarios, we innovatively integrate a Non-Causal State Space Model (NC-SSD) with a Densely Connected Dynamic Fusion (DCDF) mechanism, enabling linear-complexity modeling of global long-range dependencies. Finally, we incorporate a Multi-scale High-Frequency Detail Extractor (MHFE) along with a channel–spatial attention mechanism to precisely refine boundary details while suppressing noise. Extensive experiments conducted on three publicly available building segmentation benchmarks demonstrate that the proposed TriadFlow-Net achieves state-of-the-art performance across multiple evaluation metrics, while maintaining computational efficiency—offering a novel and effective solution for high-resolution remote sensing building extraction. Full article

The incorporation of artificial intelligence (AI) and digital technology in healthcare has revolutionized service delivery, improving diagnostic precision, patient outcomes, and operational efficacy. Nonetheless, despite considerable progress, numerous problems persist that impede the realization of full potential. Current reviews predominantly emphasize the advantages of AI in disease detection and health guidance, neglecting significant concerns such as social opposition, regulatory frameworks, and geographical discrepancies. This SLR, executed in accordance with PRISMA principles, examined 21 publications from 2020 to 2025 to assess the present condition of AI and digital technologies inside Saudi Arabia’s healthcare industry. Initially, 863 publications were obtained, from which 21 were chosen for comprehensive examination. Significant discoveries encompass the extensive utilization of telemedicine, data analytics, mobile health applications, Internet of Things, electronic health records, blockchain technology, online platforms, cloud computing, and encryption methods. These technologies augment diagnostic precision, boost patient outcomes, optimize administrative procedures, and foster preventative medicine, contributing to cost-effectiveness, environmental sustainability, and enduring service provision. Nonetheless, issues include data privacy concerns, elevated implementation expenses, opposition to change, interoperability challenge, and regulatory issues persist as substantial barriers. Subsequent investigations must concentrate on the development of culturally relevant AI algorithms, the enhancement of Arabic natural language processing, and the establishment of AI-driven mental health systems. By confronting these challenges and utilizing emerging technologies, Saudi Arabia has the potential to establish its status as a leading nation in medical services innovation, guaranteeing patient-centered, efficient, and accessible healthcare delivery. Recommendations must include augmenting data privacy and security, minimizing implementation expenses, surmounting resistance to change, enhancing interoperability, fortifying regulatory frameworks, addressing regional inequities, and investing in nascent technologies. Full article

The rapid increase in e-waste has become a significant global concern, influenced by swift technological advancements, shorter product lifecycles, and rising consumer demand. This situation leads to considerable environmental and health hazards, primarily due to the presence of toxic materials, energy demands, and the inadvertent loss of valuable resources when waste is not adequately managed. This review synthesises contemporary theories related to sustainable e-waste management, featuring concepts such as principles of the circular economy, energy efficiency and innovative recycling technologies. The review explores a range of actions, including regulatory strategies, mechanical pre-treatment methods, focusing on reagent-free recovery techniques, and the utilisation of digital solutions to enhance traceability and operational efficiency. The findings indicate substantial improvements in formal e-waste collection rates in areas with strong legislative frameworks, enhanced metal recovery efficiencies through refined hydrometallurgical and pyrometallurgical techniques and minimised environmental footprints through reagent-free and energy-conserving practices. The review emphasises the importance of viewing e-waste recycling not just as a waste management issue but as a fundamental element of resource security and sustainable industrial practices. By assessing recent developments, this work advocates for closed-loop recycling as an essential driver in the global shift towards a resilient, low-carbon, energy-efficient and circular economy. Full article

Accurate and non-destructive assessment of plant nutritional status remains a key challenge in precision agriculture, particularly under dynamic physiological conditions such as dehydration. Therefore, this study focused on developing an integrated nutritional assessment framework for avocado (Persea americana Mill.) leaves across progressive dehydration stages using spectral analysis. A novel nutritional function index (NFI) was innovatively constructed using an entropy-weighted multi-criteria decision-making approach. This unified assessment metric integrated critical physiological indicators, such as moisture content, nitrogen content, and chlorophyll content estimated from soil and plant analyzer development (SPAD) readings. To enhance the prediction accuracy and interpretability of NFI, innovative vegetation indices (VIs) specifically tailored to NFI were systematically constructed using exhaustive wavelength-combination screening. Optimal wavelengths identified from short-wave infrared regions (1446, 1455, 1465, 1865, and 1937 nm) were employed to build physiologically meaningful VIs, which were highly sensitive to moisture and biochemical constituents. Feature wavelengths selected via the successive projections algorithm and competitive adaptive reweighted sampling further reduced spectral redundancy and improved modeling efficiency. Both feature-level and algorithm-level data fusion methods effectively combined VIs and selected feature wavelengths, significantly enhancing prediction performance. The stacking algorithm demonstrated robust performance, achieving the highest predictive accuracy (R2V = 0.986, RMSEV = 0.032) for NFI estimation. This fusion-based modeling approach outperformed conventional single-model schemes in terms of accuracy and robustness. Unlike previous studies that focused on isolated spectral predictors, this work introduces an integrative framework combining entropy-weighted feature synthesis and multiscale fusion learning. The developed strategy offers a powerful tool for real-time plant health monitoring and supports precision agricultural decision-making. Full article

Post-disaster reconstruction requires instruments capable of ensuring procedural consistency, administrative transparency, and the systematic integration of geohazards, all of which are essential for safeguarding communities. This study presents the digital platform established under Italian Law 55/2019 for the reconstruction of the areas on Mt. Etna affected by the Mw 4.9 earthquake of 26 December 2018, emphasizing its innovative contribution to current international approaches to reconstruction governance. The platform standardizes the entire administrative workflow and is centered on the Parametric Form, which enables an objective calculation of eligible reconstruction grants based on damage indicators, vulnerability metrics, and parametric cost functions. A defining feature of the Etna model is the structural integration between administrative procedures and geohazard mitigation, achieved through updated hazard maps and protocols that incorporate geological, hydrogeological, and geomorphological conditions. This approach reframes reconstruction as an opportunity to reduce overall territorial vulnerability. The system also includes public monitoring tools (WebGIS and dashboards) that enhance traceability, compliance, and stakeholder engagement. Expected outcomes include shorter administrative timelines, improved interinstitutional coordination, and the potential transferability of the model to other emergency contexts. In comparison with international cases, the Etna experience represents an original integration of digitalization, parametric assessment, and site-specific hazard mitigation. Full article