Search Results (3,229)

Alzheimer’s disease (AD) is increasingly recognized as a multifactorial disorder driven by a combination of disruptions in proteostasis and organelle communication. The 2020 Lancet commission reported that approximately 10 million people worldwide were affected by AD in the mid-20th century. AD is the most prevalent cause of dementia. By early 2030, the global cost of dementia is projected to rise by USD 2 trillion per year, with up to 85% of that cost attributed to daily patient care. Several factors have been implicated in the progression of neurodegeneration, including increased oxidative stress, the accumulation of misfolded proteins, the formation of amyloid plaques and aggregates, the unfolded protein response (UPR), and mitochondrial–endoplasmic reticulum (ER) calcium homeostasis. However, the exact triggers that initiate these pathological processes remain unclear, in part because clinical symptoms often emerge gradually and subtly, complicating early diagnosis. Among the early hallmarks of neurodegeneration, elevated levels of reactive oxygen species (ROS) and the buildup of misfolded proteins are believed to play pivotal roles in disrupting proteostasis, leading to cognitive deficits and neuronal cell death. The accumulation of amyloid-β (Aβ) plaques and tau neurofibrillary tangles is a characteristic feature of AD. These features contribute to chronic neuroinflammation, which is marked by the release of pro-inflammatory cytokines and chemokines that exacerbate oxidative stress. Given these interconnected mechanisms, targeting stress-related signaling pathways, such as oxidative stress (ROS) generated in the mitochondria and ER, ER stress, UPR, and cytosolic chaperones, represents a promising strategy for therapeutic intervention. This review focuses on the relationship between stress chaperone responses and organelle function, particularly the interaction between mitochondria and the ER, in the development of new therapies for AD and related neurodegenerative disorders. Full article

This study presents the design, fabrication, and clinical validation of a lightweight, body-powered prosthetic index finger actuated via metacarpophalangeal (MCP) joint motion. The proposed system incorporates an underactuated, cable-driven mechanism combining rigid and compliant elements to achieve passive adaptability and embodied intelligence, supporting intuitive user interaction. Results indicate that the prosthesis successfully mimics natural finger flexion and adapts effectively to a variety of grasping tasks with minimal effort. This study was conducted in accordance with ethical standards and approved by the Institutional Review Board (IRB), Project No. 670161, titled “Biologically-Inspired Synthetic Finger: Design, Fabrication, and Application.” The findings suggest that the device offers a viable and practical solution for individuals with partial hand loss, particularly in settings where electrically powered systems are unsuitable or inaccessible. Full article

The Internet of Things (IoT) is transforming how devices communicate, with more than 30 billion connected units today and projections exceeding 40 billion by 2025. Despite this growth, the integration of heterogeneous systems remains a significant challenge, particularly in sensitive domains like healthcare, where proprietary standards and isolated ecosystems hinder interoperability. This paper presents an extended version of the Share design pattern, a lightweight and contract-based mechanism for dynamic service composition, tailored for resource-constrained IoT devices. Share enables decentralized, peer-to-peer integration by exchanging executable code in our examples written in the LUA programming language. This approach avoids reliance on centralized infrastructures and allows services to discover and interact with each other dynamically through pattern-matching and contract validation. To assess its suitability, we developed an emulator that directly implements the system under test in LUA, allowing us to verify both the structural and behavioral constraints of service interactions. Our results demonstrate that Share is scalable and effective, even in constrained environments, and supports formal correctness via design-by-contract principles. This makes it a promising solution for lightweight, interoperable IoT systems that require flexibility, dynamic configuration, and resilience without centralized control. Full article

Deliberative approaches to governance systems analysis and improvement are rare. Australia’s Great Barrier Reef (GBR) provides the context to describe an innovative approach that combines reflexive and interactive engagement processes to (a) develop and design a framework to assess the GBR’s complex governance system health; and (b) undertake a benchmark assessment of governance system health. We drew upon appreciative inquiry and used multiple lines of evidence, including an extensive literature review, governance system mapping, focus group discussions and personal interviews. Together, these approaches allowed us to effectively engage key actors in value judgements about twenty key characteristic attributes of the governance system. These attributes were organised into four clusters which enabled us to broadly describe and benchmark the system. These included the following: (i) system coherence; (ii) connectivity and capacity; (iii) knowledge application; (iv) operational aspects of governance. This process facilitated deliberative discussion and consensus-building around attribute health and priorities for transformative action. This was achieved through the inclusion of diverse perspectives from across the governance system, analysis of rich datasets, and the provision of guidance from the project’s Steering Committee and Technical Working Group. Our inclusive, collaborative and deliberative approach, its analytical depth, and the framework’s repeatability enable continuous monitoring and adaptive improvement of the GBR governance system and can be readily applied to complex governance systems elsewhere. Full article

The purpose of this paper is to use an informatics-based analysis to develop a rational design approach to the accelerated screening of nano-composite materials. Using existing nano-composite data, we develop a quantitative structure–activity relationship (QSAR) as a function of polymer matrix chemistry and nano-additive volume, with the property predicted being electrical conductivity. The development of a QSAR for the electrical conductivity of nano-composites presents challenges in representing the polymer matrix chemistry and backbone structure, the additive content, and the interactions between the components while capturing the non-linearity of electrical conductivity with changing nano-additive volume. An important aspect of this work is designing chemistries with small training data sizes, as the uncertainty in modeling is high, and potentially the representated physics may be minimal. In this work, we explore two important components of this aspect. First, an assessment via Uniform Manifold Approximation and Projection (UMAP) is used to assess the variability provided by new data points and how much information is contributed by data, which is significantly more important than the actual data size (i.e., how much new information is provided by each data point?). The second component involves assessing multiple training/testing splits to ensure that any results are not due to a specific case but rather that the results are statistically meaningful. This work will accelerate the rational design of polymer nano-composites by fully considering the large array of possible variables while providing a high-speed screening of polymer chemistries. Full article

This study explores the potential of Intelligent Geographic Information Systems (GISs) in advancing educational practices through the integration of real-time data models. The objective is to investigate how GIS technology can enhance teaching and learning by providing interactive and dynamic learning environments. The research employs a bibliometric analysis based on the Scopus database, covering the period from 2000 to 2024, to identify key trends, the evolution of GIS applications in education, and their pedagogical impact. Findings reveal that GISs, particularly when incorporating real-time data, enable a more immersive learning experience, facilitate data-driven decision-making, and promote student engagement through project-based learning. However, challenges such as the lack of specialized training for educators and limitations in technological infrastructure remain significant barriers to widespread adoption. The study concludes that Intelligent GISs have the potential to transform education by fostering personalized, interdisciplinary learning and enhancing educational management. It emphasizes the need for further research aimed at developing user-friendly systems and addressing ethical concerns to ensure the benefits of GIS technology are accessible to all students. Future studies should examine the long-term effects of GISs on student outcomes and explore their integration into diverse educational contexts. Full article

African hydrological systems are incredibly complex and highly sensitive to climate variability. This review synthesizes observational data, remote sensing, and climate modeling to understand the interactions between fluvial processes, water cycle dynamics, and anthropogenic pressures. Currently, these systems are experiencing accelerating warming (+0.3 °C/decade), leading to more intense hydrological extremes and regionally varied responses. For example, East Africa has shown reversed temperature–moisture correlations since the Holocene onset, while West African rivers demonstrate nonlinear runoff sensitivity (a threefold reduction per unit decline in rainfall). Land-use and land-cover changes (LULCC) are as impactful as climate change, with analysis from 1959–2014 revealing extensive conversion of primary non-forest land and a more than sixfold increase in the intensity of pastureland expansion by the early 21st century. Future projections, exemplified by studies in basins like Ethiopia’s Gilgel Gibe and Ghana’s Vea, indicate escalating aridity with significant reductions in surface runoff and groundwater recharge, increasing aquifer stress. These findings underscore the need for integrated adaptation strategies that leverage remote sensing, nature-based solutions, and transboundary governance to build resilient water futures across Africa’s diverse basins. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. Full article

The worldwide agriculture industry is facing increasing problems due to rapid population increase and increasingly unfavorable weather patterns. In order to reach the projected food production targets, which are essential for guaranteeing global food security, innovative and sustainable agricultural methods must be adopted. Conventional approaches, including traditional breeding procedures, often cannot handle the complex and simultaneous effects of biotic pressures such as pest infestations, disease attacks, and nutritional imbalances, as well as abiotic stresses including heat, salt, drought, and heavy metal toxicity. Applying phytohormonal approaches, particularly those involving hormonal crosstalk, presents a viable way to increase crop resilience in this context. Abscisic acid (ABA), gibberellins (GAs), auxin, cytokinins, salicylic acid (SA), jasmonic acid (JA), ethylene, and GA are among the plant hormones that control plant stress responses. In order to precisely respond to a range of environmental stimuli, these hormones allow plants to control gene expression, signal transduction, and physiological adaptation through intricate networks of antagonistic and constructive interactions. This review focuses on how the principal hormonal signaling pathways (in particular, ABA-ET, ABA-JA, JA-SA, and ABA-auxin) intricately interact and how they affect the plant stress response. For example, ABA-driven drought tolerance controls immunological responses and stomatal behavior through antagonistic interactions with ET and SA, while using SnRK2 kinases to activate genes that react to stress. Similarly, the transcription factor MYC2 is an essential node in ABA–JA crosstalk and mediates the integration of defense and drought signals. Plants’ complex hormonal crosstalk networks are an example of a precisely calibrated regulatory system that strikes a balance between growth and abiotic stress adaptation. ABA, JA, SA, ethylene, auxin, cytokinin, GA, and BR are examples of central nodes that interact dynamically and context-specifically to modify signal transduction, rewire gene expression, and change physiological outcomes. To engineer stress-resilient crops in the face of shifting environmental challenges, a systems-level view of these pathways is provided by a combination of enrichment analyses and STRING-based interaction mapping. These hormonal interactions are directly related to the United Nations Sustainable Development Goals (SDGs), particularly SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action). This review emphasizes the potential of biotechnologies to use hormone signaling to improve agricultural performance and sustainability by uncovering the molecular foundations of hormonal crosstalk. Increasing our understanding of these pathways presents a strategic opportunity to increase crop resilience, reduce environmental degradation, and secure food systems in the face of increasing climate unpredictability. Full article

This article explores how the European Union (EU) promotes gender equality beyond its borders, situating the analysis within broader debates on external Europeanization and normative power. While most studies have examined the domestic impact of EU policies, this paper focuses on how gender equality norms are projected internationally and conceptually investigates the mechanisms involved in their diffusion. Drawing on existing theoretical literature, the article analyzes how EU-level normative strategies may interact with domestic political, institutional, and societal dynamics in third countries, often in complex and contested ways. Rather than providing empirical testing, the study identifies core mechanisms—such as strategic communication, partnerships, and funding tools—and reflects on their limitations and the role of local agency in interpreting or resisting EU-promoted norms. The findings highlight the difficulty of isolating EU influence from other international and transnational drivers of norm diffusion, and the need for caution in attributing policy shifts solely to EU action. The article concludes that a better understanding of these processes requires more attention to domestic contexts, as well as future empirical research to complement conceptual analyses of Europeanization in sensitive policy domains such as gender equality. Full article

The emergence of multidrug-resistant pathogens has driven the development of novel antimicrobial peptides (AMPs) as therapeutic alternatives. Lactolisterin LBU (LBU) is a bacteriocin with promising activity against Gram-positive bacteria, including Staphylococcus aureus. In this study, we designed and evaluated a panel of amino acid variants of LBU to investigate domain–activity relationships and improve activity. Peptides were commercially synthesized, and their effect was evaluated for minimal inhibitory concentration (MIC), minimal bactericidal concentration (MBC), hemolytic activity, cytotoxicity, in vivo toxicity, and virulence modulation. AlphaFold3 structural prediction of LBU revealed a four-helix topology with amphipathic and hydrophobic segments. Helical wheel projections identified helices I and IV as amphipathic, suggesting their potential involvement in membrane interaction and activity. Glycine-to-alanine substitutions at helix I markedly increased antimicrobial activity but altered toxicity profiles. In contrast, changes at helix junctions and kinks reduced antimicrobial activity. We also showed differential regulation of virulence genes upon sub-MIC treatment. Overall, rational substitution enabled identification of residues critical for activity and toxicity, providing insights into therapeutic tuning of lactolisterin-based peptides. Full article

The conservation and restoration of water-gilded wooden cultural heritage, such as polychrome sculptures, frames, panels, altarpieces, etc., requires the use of fillers that guarantee structural stability, physicochemical and mechanical compatibility with the original support, and the ability to adapt to dimensional movements induced by thermo-hygrometric variations. This study, conducted as part of the DorART Project, analyzed the behavior of nine formulations, both commercial and non-commercial, selected through a review of the state-of-the-art specialized literature, along with the use of participatory science, which focused on the practices and materials most commonly used by professionals in the field. The experimental design was based on three types of specimens: two with wooden supports, selected for evaluating their interaction with the original material and with the traditional water gilding technique, and a third type for analyzing the individual behavior of the tested materials. Analyses of adhesion, tensile strength, Shore C hardness, gloss, abrasion test results, wettability, pH changes, and chemical composition were performed using ATR-FTIR spectroscopy. The results showed significant differences depending on the type of curing used and the composition and aging behavior of the specimen. Some of the fillers demonstrated improved compatibility with water-based gilding, facilitating workability and providing structural strength. M3 and M9 demonstrated an optimal balance of workability and aging stability. The results of this study can help restorers select materials based on their specific needs, considering the requirements of mechanical adaptation to the substrate, compatibility, and durability. Full article

As BIM projects grow in volume and complexity, automated Information Compliance Checking (ICC) is becoming essential to meet demanding regulatory and contractual requirements. This study presents novel controlled vocabularies and processes for the management of information requirements, along with a structured evaluation of the Information Delivery Specification (IDS) and its associated tools. The controlled vocabularies are important as they provide support to standardization, information retrieval, data-driven workflows, and AI integration. Information requirements are classified by input type and project interaction context (phase, origin, project role, and communication), as well as by applicability (data management function, model granularity, BIM usage, and checkability). The ontology comprises seven categories: identity, geometry, design/performance, fabrication/construction, operation/maintenance, cost, and regulatory category, each linked to verification principles such as uniqueness and consistency. This enables systematic implementation of validation checks aligned with company and project needs. We introduce three ICC workflows in relation to the BIM authoring tools (inside, outside, and hybrid) and suggest key criteria for the functional and non-functional evaluation of IDS tools. Empirical results from a real project using five IDS tools reveal implementation issues with the classification facet, regular expressions, and issue reporting. The proposed ontology and framework lay the foundation for a scalable, transparent ICC within openBIM. The results also provide ICC process guidance for practitioners, a SWOT analysis that can inform enhancements to the existing IDS schema, identify possible inputs for certification of IDS tools, and generate innovative ideas for research and development. Full article

Watershed ecosystem resilience (RES) plays a vital role in supporting ecosystem sustainability. However, comprehensive assessments and investigations into the complex mechanisms driving RES remain limited, particularly in ecologically sensitive basins. To address this gap, this study proposes a multidimensional RES evaluation framework tailored to watershed-specific natural characteristics. The framework integrates five core dimensions: ecosystem resistance, ecosystem recovery capacity, ecosystem adaptability, ecosystem services, and ecosystem vitality. RES patterns under 2030 different future scenarios were simulated using the PLUS model combined with CMIP6 climate projections. Spatial and temporal dynamics of RES from 2010 to 2020 were quantified using Geodetector and Partial Least Squares Path Modeling, offering insights into the interactions among natural and anthropogenic drivers. The results reveal that RES in the Upper Yangtze River Basin exhibits a spatial gradient of “high in the east and west, low in the middle” with an overall 2.80% decline during the study period. Vegetation coverage and temperature emerged as dominant natural drivers, while land use change exerted significant indirect effects by altering ecological processes. This study emphasizes the importance of integrated land-climate strategies and offers valuable guidance for enhancing RES and supporting sustainable watershed management in the context of global environmental change. Full article