Search Results (1,998)

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Augmented reality (AR), which overlays digital content within the user’s view, is gaining traction across domains such as education, healthcare, manufacturing, and entertainment. The hardware constraints of commercially available HMDs are well acknowledged, but little work addresses what design or interactions techniques developers can employ or build into experiences to work around these limitations. We conducted a scoping literature review, with the aim of mapping the current landscape of design principles and interaction techniques employed in near-field AR environments. We searched for literature published between 2016 and 2025 across major databases, including the ACM Digital Library and IEEE Xplore. Studies were included if they explicitly employed design or interaction techniques with a commercially available HMD for near-field AR experiences. A total of 780 articles were returned by the search, but just 7 articles met the inclusion criteria. Our review identifies key themes around how existing techniques are employed and the two competing goals of AR experiences, and we highlight the importance of embodiment in interaction efficacy. We present directions for future research based on and justified by our review. The findings offer a comprehensive overview for researchers, designers, and developers aiming to create more intuitive, effective, and context-aware near-field AR experiences. This review also provides a foundation for future research by outlining underexplored areas and recommending research directions for near-field AR interaction design. Full article

Plant genetics and chemotaxonomic analysis are considered key parameters in understanding evolution, plant diversity and adaptation. Korean Peninsula has a unique biogeographical landscape that supports various aromatic plant species, each with considerable ecological, ethnobotanical, and pharmacological significance. This review aims to provide a comprehensive overview of the chemotaxonomic traits, biological activities, phylogenetic relationships and potential applications of Korean aromatic plants, highlighting their significance in more accurate identification. Chemotaxonomic investigations employing techniques such as gas chromatography mass spectrometry, high-performance liquid chromatography, and nuclear magnetic resonance spectroscopy have enabled the identification of essential oils and specialized metabolites that serve as valuable taxonomic and diagnostic markers. These chemical traits play essential roles in species delimitation and in clarifying interspecific variation. The biological activities of selected taxa are reviewed, with emphasis on antimicrobial, antioxidant, anti-inflammatory, and cytotoxic effects, supported by bioassay-guided fractionation and compound isolation. In parallel, recent advances in phylogenetic reconstruction employing DNA barcoding, internal transcribed spacer regions, and chloroplast genes such as rbcL and matK are examined for their role in clarifying taxonomic uncertainties and inferring evolutionary lineages. Overall, the search period was from year 2001 to 2025 and total of 268 records were included in the study. By integrating phytochemical profiling, pharmacological evidence, and molecular systematics, this review highlights the multifaceted significance of Korean endemic aromatic plants. The conclusion highlights the importance of multidisciplinary approaches including metabolomics and phylogenomics in advancing our understanding of species diversity, evolutionary adaptation, and potential applications. Future research directions are proposed to support conservation efforts. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Through a systematic review of sustainable rural dwelling recovery, this study offers a broader reflection on retrofitting practices, viewing eco-efficiency as a means to enhance both cultural heritage and agricultural landscapes. The work is based on the assumption that vernacular architecture in rural contexts embodies historical, cultural, and typological values worthy of preservation, while remaining adaptable to reuse through eco-efficient solutions and technological innovation. Using the PRISMA protocol, 115 scientific contributions were selected from 1711 initial records and classified into four macro-groups: landscape relationships; seismic and energy retrofitting; construction techniques and innovative materials; and morphological–typological analysis. Results show a predominance (over 50%) of passive design strategies, compatible materials, and low-impact techniques, while active systems are applied more selectively to protect cultural integrity. The study identifies replicable methodological models combining sustainability, cultural continuity, and functional adaptation, offering recommendations for future operational guidelines. Conscious eco-efficient retrofitting thus emerges as a strategic tool for the integrated valorization of rural landscapes and heritage. Full article

Despite the focus on improving cybersecurity awareness, the number of cyberattacks has increased significantly, leading to huge financial losses, with their risks spreading throughout the world. This is due to the techniques deployed in cyberattacks that mainly aim at exploiting humans, the weakest link in any defence system. The existing literature on human factors in phishing attacks is limited and does not live up to the witnessed advances in phishing attacks, which have become exponentially more dangerous with the introduction of generative artificial intelligence (GenAI). This paper studies the implications of AI advancement, specifically the exploitation of GenAI and human factors in phishing attacks. We conduct a systematic literature review to study different human factors exploited in phishing attacks, potential solutions and preventive measures, and the complexity introduced by GenAI-driven phishing attacks. This paper aims to address the gap in the research by providing a deeper understanding of the evolving landscape of phishing attacks with the application of GenAI and associated human implications, thereby contributing to the field of knowledge to defend against phishing attacks by creating secure digital interactions. Full article

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

The dramatic increase in urban construction waste poses severe environmental challenges. Utilizing waste concrete to produce recycled aggregates (RA) for manufacturing recycled concrete (RC) represents an effective strategy for resource utilization. However, inherent defects in RA, such as high porosity, microcracks, and adherent old mortar layers, lead to significant performance degradation of the resulting RC, limiting its widespread application. Traditional methods for enhancing RA often suffer from limitations, including high energy consumption, increased costs, or the introduction of new pollutants. MICP offers an innovative approach for enhancing RC performance. This technique employs the metabolic activity of specific microorganisms to induce the formation of a three-dimensionally interwoven calcium carbonate gel network within the pores and on the surface of RA. This gel network can improve the inherent defects of RA, thereby enhancing the performance of RC. Compared to conventional techniques, this approach demonstrates significant environmental benefits and enhances concrete compressive strength by 5–30%. Furthermore, embedding mineralizing microbial spores within the pores of RA enables the production of self-healing RC. This review systematically explores recent research advances in microbial mineral gel network for improving RC performance. It begins by delineating the fundamental mechanisms underlying microbial mineralization, detailing the key biochemical reactions driving the formation of calcium carbonate (CaCO₃) gel, and introducing the common types of microorganisms involved. Subsequently, it critically discusses the key environmental factors influencing the effectiveness of MICP treatment on RA and strategies for their optimization. The analysis focuses on the enhancement of critical mechanical properties of RC achieved through MICP treatment, elucidating the underlying strengthening mechanisms at the microscale. Furthermore, the review synthesizes findings on the self-healing efficiency of MICP-based RC, including such metrics as crack width healing ratio, permeability recovery, and restoration of mechanical properties. Key factors influencing self-healing effectiveness are also discussed. Finally, building upon the current research landscape, the review provides perspectives on future research directions for advancing microbial mineralization gel techniques to enhance RC performance, offering a theoretical reference for translating this technology into practical engineering applications. Full article

The accurate monitoring of waterbird abundance and their habitat preferences is essential for effective ecological management and conservation planning in aquatic ecosystems. This study explores the efficacy of unmanned aerial vehicle (UAV)-based high-resolution orthomosaics for waterbird monitoring and mapping along the Lieve Canal, Belgium. We systematically classified habitats into residential, industrial, riparian tree, and herbaceous vegetation zones, examining their influence on the spatial distribution of three focal waterbird species: Eurasian coot (Fulica atra), common moorhen (Gallinula chloropus), and wild duck (Anas platyrhynchos). Herbaceous vegetation zones consistently supported the highest waterbird densities, attributed to abundant nesting substrates and minimal human disturbance. UAV-based waterbird counts correlated strongly with ground-based surveys (R² = 0.668), though species-specific detectability varied significantly due to morphological visibility and ecological behaviors. Detection accuracy was highest for coots, intermediate for ducks, and lowest for moorhens, highlighting the crucial role of image resolution ground sampling distance (GSD) in aerial monitoring. Operational challenges, including image occlusion and habitat complexity, underline the need for tailored survey protocols and advanced sensing techniques. Our findings demonstrate that UAV imagery provides a reliable and scalable method for monitoring waterbird habitats, offering critical insights for biodiversity conservation and sustainable management practices in aquatic landscapes. Full article

This comprehensive review maps the fast-evolving landscape in which artificial intelligence (AI) and deep-learning (DL) techniques converge with the Internet of Things (IoT) to manage energy, comfort, and sustainability across smart environments. A PRISMA-guided search of four databases retrieved 1358 records; after applying inclusion criteria, 143 peer-reviewed studies published between January 2019 and April 2025 were analyzed. This review shows that AI-driven controllers—especially deep-reinforcement-learning agents—deliver median energy savings of 18–35% for HVAC and other major loads, consistently outperforming rule-based and model-predictive baselines. The evidence further reveals a rapid diversification of methods: graph-neural-network models now capture spatial interdependencies in dense sensor grids, federated-learning pilots address data-privacy constraints, and early integrations of large language models hint at natural-language analytics and control interfaces for heterogeneous IoT devices. Yet large-scale deployment remains hindered by fragmented and proprietary datasets, unresolved privacy and cybersecurity risks associated with continuous IoT telemetry, the growing carbon and compute footprints of ever-larger models, and poor interoperability among legacy equipment and modern edge nodes. The authors of researches therefore converges on several priorities: open, high-fidelity benchmarks that marry multivariate IoT sensor data with standardized metadata and occupant feedback; energy-aware, edge-optimized architectures that lower latency and power draw; privacy-centric learning frameworks that satisfy tightening regulations; hybrid physics-informed and explainable models that shorten commissioning time; and digital-twin platforms enriched by language-model reasoning to translate raw telemetry into actionable insights for facility managers and end users. Addressing these gaps will be pivotal to transforming isolated pilots into ubiquitous, trustworthy, and human-centered IoT ecosystems capable of delivering measurable gains in efficiency, resilience, and occupant wellbeing at scale. Full article

Non-melanoma skin cancer (NMSC), comprising basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), represents the most common type of cancer worldwide, particularly among Caucasians. While BCC is locally invasive with minimal metastatic potential, cSCC is a highly aggressive tumor with a significant potential for metastasis, particularly in elderly populations. Tumor development and progression and the metastasis of cSCC are influenced by a complex interplay between tumor cells and the tumor microenvironment. Recent research highlights the importance of various immune cell subsets, including T cells, tumor-associated macrophages (TAMs), and dendritic cells, in influencing tumor progression, immune evasion, and treatment resistance. This review outlines key regulatory mechanisms in the immune tumor microenvironment (TME) of cSCC and explores the role of cytokines, immune checkpoints, and stromal interactions. We further discuss the relevance of three-dimensional (3D) in vitro models such as spheroids, organoids, and tumor-on-chip systems as tools to mimic immune–tumor interactions with higher physiological relevance, such as macrophage activation and polarization against cSCC cells. Globally, 3D models offer new opportunities for immunotherapy screening and mechanistic studies. Understanding the immune landscape in cSCC through advanced modeling techniques holds strong clinical potential for improving diagnostic and therapeutic strategies. Full article

Grassland communities consist primarily of perennial herbaceous species, with grasses forming a dominant or significant component. These ecosystems have been utilised for economic purposes since the earliest periods of human history. In the natural environment, they fulfil numerous critical functions that, despite increasing awareness of climate change, often remain undervalued. Grasslands contribute directly to climate regulation, air purification, soil conservation, flood mitigation, and public health—all of which positively affect the well-being of nearby populations. Moreover, they satisfy higher-order human needs known as “cultural” services, providing aesthetic enjoyment and recreational opportunities. These services, in tangible terms, support the development of rural tourism. The objective of this study was to examine the perception of cultural ecosystem services provided by different types of grassland communities—meadows, pastures, and lawns. The study employed a structured questionnaire to evaluate the perceived significance and functions of these communities. Respondents assessed their aesthetic and recreational value based on land-use type. To quantify these dimensions, the study applies the Recreational and Leisure Attractiveness Index (RLAI), the Aesthetic Attractiveness Index (AAI), ranking methods, and contingent valuation techniques. Based on the respondents’ declared WTP (willingness to pay) and WTA (willingness to accept) values, statistically significant differences in the perceived value of land-use types were identified. Lawns were rated highest in terms of recreational attractiveness, meadows in terms of aesthetics, while pastures achieved the highest economic values. Significant differences were also observed depending on respondents’ place of residence and academic background. The results indicate that the valuation of cultural services encompasses both functional and psychological aspects and should be integrated into local land-use and landscape planning policies. Full article

Image analysis is a valuable approach in a wide array of environmental applications. Mapping land cover categories depicted from satellite images enables the monitoring of landscape dynamics. Such a technique plays a key role for land management and predictive ecosystem modelling. Satellite-based mapping of environmental dynamics enables us to define factors that trigger these processes and are crucial for our understanding of Earth system processes. In this study, a reclassification scheme of image analysis was developed for mapping the adjusted categorisation of land cover types using multispectral remote sensing datasets and Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS) software. The data included four Landsat 8–9 satellite images on 2015, 2019, 2021 and 2023. The sequence of time series was used to determine land cover dynamics. The classification scheme consisting of 17 initial land cover classes was employed by logical workflow to extract 10 key land cover types of the coastal areas of Bab-el-Mandeb Strait, southern Red Sea. Special attention is placed to identify changes in the land categories regarding the thermal saline lake, Lake Assal, with fluctuating salinity and water levels. The methodology included the use of machine learning (ML) image analysis GRASS GIS modules ‘r.reclass’ for the reclassification of a raster map based on category values. Other modules included ‘r.random’, ‘r.learn.train’ and ‘r.learn.predict’ for gradient boosting ML classifier and ‘i.cluster’ and ‘i.maxlik’ for clustering and maximum-likelihood discriminant analysis. To reveal changes in the land cover categories around the Lake of Assal, this study uses ML and reclassification methods for image analysis. Auxiliary modules included ‘i.group’, ‘r.import’ and other GRASS GIS scripting techniques applied to Landsat image processing and for the identification of land cover variables. The results of image processing demonstrated annual fluctuations in the landscapes around the saline lake and changes in semi-arid and desert land cover types over Djibouti. The increase in the extent of semi-desert areas and the decrease in natural vegetation proved the processes of desertification of the arid environment in Djibouti caused by climate effects. The developed land cover maps provided information for assessing spatial–temporal changes in Djibouti. The proposed ML-based methodology using GRASS GIS can be employed for integrating techniques of image analysis for land management in other arid regions of Africa. Full article

The blood–brain barrier (BBB) is a highly selective interface between the bloodstream and the brain that prevents systemically administered therapeutics from effectively reaching tumor cells. As tumors progress, this barrier undergoes structural and functional alterations, giving rise to the blood–tumor barrier (BTB)—a pathologically modified structure that, despite increased permeability, often exhibits heterogeneous and clinically insufficient drug transport. Although a new generation of therapies is promising, their therapeutic potential cannot be realized unless the challenges posed by these barriers are effectively addressed. Various pharmacological strategies were explored to enhance brain tumor drug delivery. These include receptor-mediated disruption, inhibition of efflux transporters, and the engineering of delivery platforms that leverage endogenous transport pathways—such as carrier-mediated, adsorptive-mediated, and receptor-mediated mechanisms—as well as cell-mediated drug delivery. This review synthesizes (1) the BBB and BTB’s structural characteristics; (2) the influence of the tumor microenvironment (TME) on drug delivery; (3) pharmacological strategies to enhance drug accumulation within brain tumors; (4) the integration of pharmacological methods with neurosurgical techniques to enhance drug delivery. As efforts to improve drug delivery across the BBB and BTB accelerate, this review aims to map the current landscape of pharmacological approaches for enhancing drug penetration into brain tumors. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains a formidable malignancy with rising incidence and dismal long-term survival, largely due to late-stage presentation and intrinsic resistance to therapy. Recent advances in the multidisciplinary management of PDAC have reshaped treatment paradigms across disease stages. For localized disease, innovations in surgical techniques and the adoption of neoadjuvant strategies have improved resection rates and survival outcomes. In metastatic settings, multiagent chemotherapy regimens and precision therapies targeting BRCA mutations and rare gene fusions are expanding treatment options. Immunotherapeutic modalities, including checkpoint inhibitors, adoptive cell therapies, and mRNA vaccines, show emerging promise despite PDAC’s traditionally immunosuppressive microenvironment. This review synthesizes the current evidence on established therapies and critically evaluates novel and investigational approaches poised to redefine the therapeutic landscape of pancreatic cancer. Full article