Search Results (1,751)

Hydrogen is increasingly recognized as a key vector for sustainable energy transitions, deep decarbonization, and enhanced energy security. This review evaluates major hydrogen types, grey, blue, and green, through a comparative assessment of production pathways, cost structures, technological maturity, and market readiness, with a focus on Sub-Saharan Africa (SSA). Grey hydrogen, while currently dominant due to established fossil-based infrastructure and low costs, is associated with high carbon emissions and climate-related risks. Blue hydrogen offers a transitional pathway via carbon capture and storage but faces constraints in SSA from high capital requirements, limited CCS infrastructure, and methane leakage. Green hydrogen, produced through renewable-powered electrolysis, represents the most sustainable long-term option, aligned with global net-zero goals and SSA’s abundant solar and wind resources, despite higher upfront costs. Synthesizing recent techno-economic, policy, and regional studies, the review highlights that prioritizing green hydrogen deployment supported by enabling policy frameworks, targeted investments, and capacity building is critical for unlocking SSA’s hydrogen potential, promoting low-carbon development, and advancing sustainable energy transitions across the region. Full article

Rural territories continue to face persistent structural challenges related to depopulation, limited economic diversification, and unequal access to specialized knowledge. Although scientific research and applied expertise are widely recognized as critical resources for addressing these challenges, their effective transmission to local actors remains fragmented. In recent years, digital platforms have emerged as potential mechanisms to bridge this gap; however, their role within rural development frameworks remains conceptually underdeveloped. This paper proposes a conceptual framework for knowledge transmission platforms oriented towards rural development, integrating scientific research, applied analysis, and structured dissemination within a unified operational architecture. Drawing on a structured review of the literature on rural development, knowledge transfer, and digital platforms, the framework identifies key functional dimensions and design principles that shape platform-based knowledge intermediation. The framework is illustrated through a qualitative case study of CreandoTuProvincia, a Spanish platform focused on territorial analysis and rural knowledge transmission. The findings highlight the relevance of hybrid platforms that combine scientific rigour, accessibility, and territorial embeddedness, offering a scalable model for strengthening evidence-informed rural development strategies. By conceptualizing platforms as structured knowledge intermediaries, this study contributes to the emerging literature on knowledge-based rural development and provides practical insights for policymakers, researchers, and platform designers. Full article

Deep decarbonization strategies at the local level have been extensively documented for cities in the Global North, yet little is known about how cities in Sub-Saharan Africa (SSA) pursue climate mitigation amid infrastructure constraints, limited fiscal autonomy, and pressing developmental needs. Local governments worldwide are recognized as critical actors in addressing urban greenhouse gas (GHG) emissions. However, SSA cities’ decarbonization efforts remain underexplored in academic and policy discourse, despite the region’s acute climate vulnerability and rapid urbanization. However, SSA cities’ decarbonization efforts remain underexplored in academic and policy discourse, despite the region’s acute climate vulnerability and rapid urbanization. This study examines how deep decarbonization pathways in four leading SSA cities (Accra, Addis Ababa, Lagos, and Nairobi) compare with those in the Global North. Using qualitative methods including document analysis and semi-structured interviews, we examine the technical pathways, institutional strategies, governance mechanisms, and actors involved in these cities’ climate mitigation efforts. Our findings reveal that while SSA cities pursue similar technical priorities to Global North cities (renewable energy, building efficiency, sustainable transport), their approaches diverge significantly in implementation. SSA cities innovate through decentralized waste-to-energy systems adapted to informal contexts, rely heavily on donor funding rather than municipal bonds, and uniquely leverage traditional institutions for community engagement. Governance structures are predominantly top-down and centralized, contrasting with the polycentric, multi-level governance observed in the Global North. These findings demonstrate that deep decarbonization in SSA must be reconceptualized not only as a form of climate mitigation but as an integrated strategy that addresses infrastructure gaps and building institutional capacity. This research contributes new knowledge on urban climate governance in developing regions and offers transferable lessons for cities facing similar constraints. Full article

Within the framework of behavioral ecology and conservation, individual recognition plays a critical role in the research on wild social animals at the individual level. Traditional identification methods often rely on long-term field experience or invasive physical tagging. Recent advances in deep learning enable non-invasive individual recognition under natural conditions; however, the effectiveness of facial detection and identification depends on species-specific facial characteristics, environmental conditions, and dataset scale. In this study, we used 3385 images from 18 identified wild Tibetan macaques (Macaca thibetana) to develop an individual recognition system, TMacaque-FaceNet, integrating You Only Look Once (YOLO) for face detection and a Vision Transformer (ViT) for individual classification. The results showed that the Tibetan macaque face detector achieved a mAP@0.5 of 0.971, with a precision of 0.974 and a recall of 0.931. The individual recognizer for the wild Tibetan macaque social group achieved a top-1 accuracy of 96.33% on the test set. On an event-wise (temporal holdout) validation set comprising 90 images (5 images per individual), the recognizer achieved a top-1 accuracy of 95.56%. Gradient-weighted attention rollout analyses further revealed that the model focused on biologically meaningful facial regions, supporting the interpretability of the recognition process. Our results provide a new automated facial recognition method to non-invasively monitor Tibetan macaque individuals in natural environments. It provides a practical tool to facilitate automated behavioral observation, social network analysis, and long-term population monitoring of wild non-human primates. Full article

Diabetes Mellitus is a chronic metabolic disorder characterized by impaired insulin production and/or action, leading to persistent hyperglycemia and insulin resistance. It has been associated with several comorbidities, including cognitive dysfunction, affecting functions such as attention, memory, and processing speed. Mounting evidence indicates a complex relationship between type 2 Diabetes Mellitus (DM2) and neurodegenerative disorders such as mild cognitive impairment and Alzheimer’s disease (AD). Beyond the conventional hallmarks of each pathology, patients with DM2 face an increased risk of neuronal degeneration, while AD is characterized by a marked reduction in insulin receptor density. Although aging, neuroinflammation, and vascular dysfunction have been recognized as key risk factors in AD, the precise molecular mechanisms driving AD pathogenesis remain incompletely understood. Various studies have been conducted to identify reliable biomarkers that elucidate the connection between DM2 and AD, including insulin dysregulation, neuroinflammation, amyloid-β aggregation, and tau hyperphosphorylation. Investigation of these biomarkers is still ongoing, and they may serve not only as diagnostic tools but also as therapeutic targets. Here, we review the current evidence supporting a convergent biological framework between DM2 and AD. Clarifying these shared pathways may improve early detection and guide the development of targeted therapeutic strategies aimed at reducing neurodegeneration in metabolically vulnerable populations. Full article

Rural architectural heritage sites in Korea, such as rice mills, breweries, and granaries, face increasing risks of neglect, deterioration, and demolition. Most of these structures are not recognized within formal heritage designation systems, and no established evaluation framework exists. Consequently, their conservation and management remain challenging. This study proposes a comprehensive evaluation framework for the preservation and utilization of rural architectural heritage. Based on a literature review and expert consultation, 18 evaluation indicators were derived and grouped into six value criteria: historical, architectural/artistic, social/cultural, landscape, economic, and utilitarian values. The Analytic Hierarchy Process (AHP) was employed to determine the relative importance and priority of these indicators. Historical value received the highest weight, followed by architectural/artistic and social/cultural values. Among the 18 indicators, “representativeness of the period” ranked highest, followed by “rarity,” “historicity,” “local identity,” and “architectural excellence.” However, the indicators associated with economic and utilitarian values had relatively low weights. The framework was validated by applying it to 17 rural architectural heritage sites in Buyeo, South Korea. This study presents a systematic and value-based evaluation framework that reflects the regional and industrial characteristics of rural architectural heritage and provides both policy and practical implications for sustainable conservation and adaptive reuse. Full article

From falcons spotting prey to humans recognizing faces, the ability to rapidly process visual information depends on a foveated retinal organization that provides high-acuity central vision while preserving low-resolution peripheral vision. This organization is conserved along early visual pathways, yet remains under-explored in machine learning. Here, we examine the impact of embedding a foveated retinotopic transformation as a preprocessing layer on convolutional neural networks (CNNs) for image classification. By applying a log-polar mapping to off-the-shelf models and retraining them, we achieve comparable accuracy while improving robustness to scale and rotation. We demonstrate that this architecture is highly sensitive to shifts in the fixation point and that this sensitivity provides an effective proxy for defining saliency maps that facilitate object localization. Our results demonstrate that foveated retinotopy encodes prior geometric knowledge, providing a solution for visual searches and a meaningful classification robustness and localization trade-off. These findings provides a proof of concept in order to connect principles of biological vision with artificial networks, suggesting new, robust and efficient approaches for computer vision systems. Full article

Agriculture in dryland regions faces increasing pressure from climate variability, water scarcity, and competing urban and environmental demands. A recent basin-wide technical analysis for the Rio Grande/Rio Bravo in the United States of America (USA) and Mexico shows that consumptive water use in the river system overall is on an unsustainable path. The Middle Rio Grande Basin (MRGB) of central New Mexico (USA) exemplifies these sustainability challenges, where irrigated agriculture persists despite low precipitation, high evaporative demand, and prolonged drought. This study provides analytical spatial description of irrigated agriculture in the MRGB, examining farm size distribution, crop composition, groundwater access, and consumptive water use measured by evapotranspiration (ET) and effective ET. Using 2021 remotely sensed crops and ET data, groundwater well records, and GIS-based aggregation to the irrigator farm level, the analysis reveals a highly fragmented agricultural landscape dominated numerically by micro-scale and small farms, which together account for 55.9% of total agricultural ET. Alfalfa and other hay crops occupy nearly three-quarters of irrigated acreage and consume 74% of total ET, reflecting the prevalence of forage production. Groundwater access is highly uneven, with most wells concentrated among large farms, creating resilient disparities. The findings highlight that consumptive agricultural water use in the MRGB is diffuse rather than concentrated: non-commercial farms (<12 hectares) account for 55.9% of basin-wide ET, while commercial farms contribute only 14.4% despite occupying about one-fifth of irrigated land. This complicates water conservation efforts. Resilient management strategies must therefore engage thousands of small, largely non-commercial irrigators through mechanisms that recognize both hydrological and spatial realities. The study provides an empirical basis for designing sustainable irrigation and water-management strategies in dryland agricultural systems facing increasing climatic and institutional pressures. Full article

As data exploitation continues to demonstrate its value, ontologies, thesauri, and other semantic datasets are increasingly recognized for enabling semantically meaningful data integration across disparate domains. With the proliferation of dataset catalogs, the MOD ontology (Metadata for Ontology Description and publication) was adopted, and an associated API was developed to support the future European Open Science Cloud (EOSC). Their aim is to harmonize catalogs of semantic datasets with respect to metadata vocabularies and access mechanisms, thereby ensuring compliance with the FAIR principles. Within an implementation action involving developers of prominent dataset catalogs, we were selected to integrate the MOD-API into ShowVoc, our platform for publishing and consuming ontologies, thesauri, lexicons, and other Semantic Web datasets. However, ShowVoc already relied on an expressive metadata model, the MDR (acronym for “Metadata Registry”), named after the component responsible for managing the platform’s internal catalog. Due to precise dissemination requirements, the MDR provides multiple abstraction levels and detailed specifications concerning the distributions and formats in which a dataset may be made available. In this article, we report on the challenges that we faced and the trade-offs that we made while reconciling these metadata models, highlighting limitations in the current MOD standard that may inform future enhancements. Full article

With the continuous development of the construction industry, work pressure faced by construction workers has been increasing, leading to a growing prominence of job burnout that adversely affects workers’ physical and mental health as well as work efficiency. Constantly exposed to high-pressure environments, construction workers are prone to symptoms such as emotional exhaustion, depersonalization, and diminished personal accomplishment, which in turn impair their work performance and quality of life. However, existing literature has largely overlooked the potential role of leisure activities such as music in alleviating job burnout. Although music is widely recognized as an effective tool for emotional regulation, its application and impact among construction workers remain underexplored. Based on a sample of 1086 construction workers (71.09% male, 48.99% aged 36–45), this study examines four dimensions of music engagement, including Time Commitment (TC), Economic Spending (ES), Emotional Investment (EI), and Personal Participation (PP), and investigates how these dimensions, through the mediating role of leisure crafting (LC), negatively influence job burnout (JB) among this population. This study employed covariance-based structural equation modeling (CB-SEM) with a sample of 1086 construction workers (71.09% male, 48.99% aged 36–45) to examine how four dimensions of music engagement, namely Time Commitment (TC), Economic Spending (ES), Emotional Investment (EI), and Personal Participation (PP), influence job burnout through leisure crafting. The results show that: (1) time commitment, economic spending, emotional investment and personal participation all have a negative influence on job burnout; and (2) leisure crafting mediates the effect of music engagement by construction workers on job burnout. This study emphasizes the necessity of incorporating mental health interventions into high-pressure work environments, providing guidance for companies to develop more flexible and effective employee care and welfare policies. This research therefore holds significant theoretical and practical value, as it promotes sustainable development in the construction industry, improves workers’ well-being and enhances the design of related work environments. Full article

The Mediterranean mussel, Mytilus galloprovincialis, is currently facing unprecedented mass mortality events (MMEs) that threaten the economic and ecological stability of Mediterranean aquaculture. The present review gathered and analyzed current knowledge on climate change and environmental disorders that may cause MMEs in Mediterranean mussels, compromising mussel physiology and immune competence. Biological agents, which proliferate under stress conditions, can either trigger direct disease or act as co-factors in mortality. The impact of the economic loss following MMEs in mussel production in the Mediterranean Sea is also described. The main key drivers used in the analysis of the literature were “M. galloprovincialis”, “MMEs”, “environmental stressors”, “climate change”, “pathogens”, “pollutants”, “economical losses”. The One Health–One Welfare framework recognizes the inextricable interconnection between the health of human, mussel, and marine ecosystems. This approach is essential for developing holistic monitoring programs, robust risk assessment strategies, and adaptive management policies capable of ensuring the long-term sustainability of Mediterranean mussel production and the ecological stability of coastal systems. In the future, the development of integrated water monitoring systems where mussels are both farmed species and active biological sentinels is possible. The implementation of a digital monitoring system will offer a transformative strategy for mitigating MMEs in Mediterranean mussel populations. Full article

Background/Objectives: Nasal reconstruction after non-melanoma skin cancer excision remains challenging due to the need to restore both nasal form and function while ensuring oncologic safety. Beyond surgical success, patient-reported outcomes are increasingly recognized as essential components of postoperative evaluation. The aim of this study was to retrospectively assess oncologic, surgical, and patient-reported outcomes in a real-world cohort of patients undergoing nasal reconstruction following skin cancer excision. Methods: A retrospective cohort study was conducted on 60 patients treated at the University Hospital “G. Martino” (Messina, Italy) between 2019 and 2022. Reconstructive techniques included direct closure, full-thickness skin grafts, local or regional flaps. Oncologic outcomes and postoperative complications were recorded during routine follow-up. Patient-reported outcomes were evaluated using a semi-structured PROM-derived questionnaire adapted from the FACE-Q Skin Cancer Module, NOSE, and SCaFF domains. Internal consistency of the questionnaire was assessed using Cronbach’s alpha. Results: Basal cell carcinoma was the most frequent diagnosis (55%), and the nasal ala, dorsum, and tip were the most commonly involved subunits. Local flaps were performed in 42% of cases. No histologically confirmed recurrences were observed in the flap-reconstructed subgroup during the available follow-up, whereas recurrences were observed in patients managed with non-flap reconstructive approaches. Postoperative complications were uncommon; however, one fatal infectious event occurred in a high-risk patient undergoing complex reconstruction for recurrent disease. The PROM-derived questionnaire demonstrated good internal consistency (Cronbach’s α = 0.82). Functional symptoms were rare, with 93% of patients reporting no snoring or nasal obstruction and 97% reporting no nasal voice alteration. Aesthetic satisfaction was rated as satisfactory or very satisfactory by 63% of patients, and social relationships were not affected in 85%. Patient-perceived recurrence risk (38%) exceeded histologically confirmed recurrence (15%). Conclusions: In this elderly real-world cohort, flap-based nasal reconstruction was associated with generally favorable patient-centered outcomes and low complication rates. The discrepancy between patient-perceived and confirmed recurrence highlights the role of oncologic anxiety. Prospective studies using fully validated PROMs are warranted to support standardized outcome comparison and guide clinical decision-making. Full article

This paper introduces a biomimetic framework and novel brain-inspired AI (BIAI) models based on spiking neural networks (SNNs) for emotional state recognition from audio (speech), visual (face), and integrated multimodal audio–visual data. The developed framework, named eXCube2, uses a three-dimensional SNN architecture NeuCube that is spatially structured according to a human brain template. The BIAI models developed in eXCube2 are trainable on spatio- and spectro-temporal data using brain-inspired learning rules. Such models are explainable in terms of revealing patterns in data and are adaptable to new data. The eXCube2 models are implemented as software systems and tested on speech and video data of subjects expressing emotional states. The use of a brain template for the SNN structure enables brain-inspired tonotopic and stereo mapping of audio inputs, topographic mapping of visual data, and the combined use of both modalities. This novel approach brings AI-based emotional state recognition closer to human perception, provides a better explainability and adaptability than existing AI systems. It also results in a higher or competitive accuracy, even though this was not the main goal here. This is demonstrated through experiments on benchmark datasets, achieving classification accuracy above 80% on single-modality data and 88.9% when multimodal audio–visual data are used, and a “don’t know” output is introduced. The paper further discusses possible applications of the proposed eXCube2 framework to other audio, visual, and audio–visual data for solving challenging problems, such as recognizing emotional states of people from different origins; brain state diagnosis (e.g., Parkinson’s disease, Alzheimer’s disease, ADHD, dementia); measuring response to treatment over time; evaluating satisfaction responses from online clients; cognitive robotics; human–robot interaction; chatbots; and interactive computer games. The SNN-based implementation of BIAI also enables the use of neuromorphic chips and platforms, leading to reduced power consumption, smaller device size, higher performance accuracy, and improved adaptability and explainability. This research shows a step toward building brain-inspired AI systems. Full article

Small-and-medium-sized enterprises (SMEs) increasingly depend on business partnerships to access markets and scale operations, yet they often face trust barriers during contract formation due to the complexity of the verification of their cybersecurity posture and compliance status by their partners. This problem is intensified by rising regulatory expectations, notably the EU Cyber Resilience Act (CRA), which many SMEs struggle to interpret and operationalize under constraints of budget, skills, and fragmented responsibilities. This study adopts a Design Science Research approach to blueprint and evaluate a lightweight mapping framework that links commonly implemented security controls to CRA requirements and to widely recognized benchmarks (ISO/IEC 27001 and CIS). Grounded in Institutional Theory and Socio-Technical Systems Theory, the artefact translates regulatory obligations into actionable, evidence-backed controls and produces partner-facing outputs that support transparency in negotiations and service level agreements. The framework is iteratively co-created with a multidisciplinary expert community. Expected contributions include a practical mechanism for making cybersecurity maturity visible, accelerating partnership formation, and enabling sustainable interorganizational relationships while remaining feasible for resource-constrained SMEs. Full article

Digital Twins (DTs) are increasingly recognized as a strategic technology for enhancing cybersecurity in industrial environments, particularly in the face of rising threats targeting Operational Technology (OT). After comparatively examining closely related DT–cybersecurity frameworks to position the contribution within the existing research landscape, this paper presents a systematic literature review and comparative analysis of 19 recent DT-based cybersecurity studies, focusing on their relevance to incident detection and response in sectors such as Industrial Internet of Things (IIoT), manufacturing, and energy. The analysis evaluates each study across multiple dimensions, including attack types, detection and response mechanisms, DT integration, and technology stacks. From this review, we derive a consolidated set of requirements, categorized as functional, non-functional, security-specific, and domain-specific. These requirements serve as the foundation for a novel, cybersecurity-focused, ISO 23247-based framework. The proposed architecture formalizes a DT-enabled incident detection and response lifecycle aligned with ISO 23247. It is explicitly mapped to the derived requirements and detailed with practical implementation considerations. This work contributes a structured, evidence-based approach to DT-based security engineering and offers a reference design for researchers and practitioners aiming to build resilient, adaptive cybersecurity solutions in industrial settings. Full article