Search Results (169)

The wild boar (Sus scrofa) has experienced significant population growth as well as geographic expansion across Europe over the past 15 years, leading to increased concerns regarding its role in the transmission of zoonotic pathogens. Among these, Babesia spp. and Anaplasma spp. are of particular importance due to their impact on both wildlife and domestic animals. This study systematically reviews the prevalence and distribution of Babesia and Anaplasma spp. in wild boars and associated tick vectors across multiple European countries, synthesizing data from literature published between 2010 and 2024. A comprehensive search of Scopus, Google Scholar, and PubMed databases was conducted using predefined keywords related to babesiosis, anaplasmosis, wild boars, Europe, and tick-borne diseases. A total of 281 studies were initially retrieved, of which 19 met the inclusion criteria following relevance assessment. Data extraction focused on pathogen identification, diagnostic methods, sample type, host species, and prevalence rates. Molecular detection methods, primarily PCR and sequencing, were the most used diagnostic tools. Results indicate substantial regional variations in the prevalence of Babesia and Anaplasma spp. A. phagocytophilum was detected in wild boar populations across multiple countries, with the highest prevalence rates observed in Slovakia (28.2%) and Poland (20.34%). Conversely, lower prevalence rates were recorded in France (2%) and Portugal (3.1%). Babesia spp. showed higher prevalence rates in Italy (6.2%), while its detection in other regions such as Romania and Spain was minimal or absent. Notably, spleen and multi-organ samples (spleen/liver/kidney) exhibited higher positivity rates compared to blood samples, suggesting an organotropic localization of these pathogens. The findings underscore the role of wild boars as reservoirs for tick-borne pathogens and highlight their potential to contribute to the epidemiological cycle of these infections. The increasing distribution of wild boars, coupled with climate-driven shifts in tick populations, may further facilitate pathogen transmission. Future studies should focus on integrating molecular, serological, and ecological approaches to improve surveillance and risk assessment. Standardized methodologies across different regions will be essential in enhancing comparative epidemiological insights and informing targeted disease management strategies. Full article

Background: Vaccine research publications play a crucial role in the scientific process by strategically linking the generation of knowledge with its translation into vaccine policy and practice. This study was designed to understand vaccine and immunization research publication trends in Africa to inform strategic directions for vaccine research and innovation efforts in the continent. Methods: We searched PubMed only for vaccine and immunization-related publications from Africa between 1 January 2016 and 8 August 2024. Metrics such as annual growth rates, geographical distribution, international collaboration, and trend topics were analyzed. We conducted separate analyses for general vaccine research, vaccine clinical trials, and vaccine evidence syntheses (systematic reviews and meta-analyses). Results: Vaccine research in Africa demonstrated an annual growth rate of 55.4% (based on the 10,000 records retrieved due to PubMed’s export limit), while vaccine trials saw a decline of 6.08% during the study period. The trend topics analysis across vaccine research, trials, and reviews showed that topics shifted from a focus on general vaccine development, immunization, and malaria pre-2020 to COVID-19-related topics in 2020, with post-2020 research returning to traditional topics like immunization schedules, vaccine safety, and pediatric and maternal vaccines. Additionally, the COVID-19 pandemic had a profound impact on vaccine research, leading to a surge in publications for vaccine research, trials, and reviews. About 65.8% of vaccine research featured international co-authorship. Vaccine trials had a higher rate of international co-authorship at 79.8%. Conclusion: While vaccine research in general in Africa has increased, vaccine trials do not match this increase. The number of clinical trials remained relatively stagnant, reflecting ongoing challenges in the vaccine research ecosystem, particularly in building and sustaining clinical trial capacity across the region. In addition, disparities in research productivity exist between countries. Research prioritization, strategic collaborations, capacity building for research, and improved research infrastructure require critical consideration. Full article

The occurrence of illegal waste activities is a worldwide problem, due to improper actions and inadequate services across many territories. Geographical Information Systems (GISs) software plays a crucial role in optimizing waste management and determining the shortest route paths for waste transportation. This work focuses on the development of a GIS-based workflow for the detection of Illegal Abandoned Waste Sites (IAWSs) and waste management planning. The integration of remote/ground sensing activities, geospatial data, and models within a GIS framework is a useful practice for conducting cost analysis and supporting the development of efficient waste management plans. Firstly, available satellite images are employed in a baseline assessment, combining ancillary and remote sensing data. As a result of satellite monitoring, a ground-piloted survey is carried out by checking the potential-IAWSs density map retrieved from the satellite pre-recognition phase. Hence, a total of 171 ground points are geo-localized and spatialized, according to qualitative on-site products and 2.5D volume analysis. Consequently, distances from illegal dumping sites to proper disposal plants are calculated, achieving the shortest route paths as geospatial information. From these data, a Functional Unit (FU) of 1 ton of mixed waste plus 381.6 kg of inert material is determined, a fundamental stage for comparing different cost analysis processes in similar contexts. By using a GIS-based workflow, a cost analysis assessment is provided, aiming to support principal activities such as waste transportation and disposal to the proper plant (e.g., landfill or incineration). In conclusion, spatial data analysis results are fundamental in managing illegal abandoned waste sites, helping to establish a cost analysis assessment. Full article

Background: The dirty utility room (DUR) plays a vital role in maintaining and optimizing the safety of patients and healthcare staff. A substantial gap exists in the literature concerning the current topic in terms of empirical studies and reviews. Therefore, this study aims to shed light on the subject and provide reliable evaluations. Methods: A qualitative case study design (observational) was used. We included the DURs of hospitals in multiple regions of the Kingdom of Saudi Arabia/in wards and units of each hospital. To achieve data saturation, visits across wards and ICUs were conducted until no new information was retrieved. NVivo Software version 14 was used for management and analysis of the data. We used our notes to initiate codes and then created themes involving the six steps of thematic analysis for the observational study. Results: Among several main hospitals in the central, western, eastern, southern, and northern geographical locations in Saudi Arabia that included DURs, a total of 24 DURs were explored to capture all relevant aspects related to the observations. Considering the range of items presented in DURs, the majority of hospitals exhibited a substantial lack of equipment. There were disagreements regarding the definition of DURs and the name of DURs. The observers agreed with the practice of urine disposal, which is performed by hand. The observers from all regions mutually agreed that stool disposal methods for patients involved diapers and the cleaning of patients manually with bed sheets. Several risks of infection control were observed related to DUR design and protocols. Conclusions: This national observational study of DURs in Saudi Arabian hospitals revealed major inadequacies in the design, equipment, and processes that are critical for infection control and healthcare quality, emphasizing the critical necessity for standardized methods and appropriate equipment. Full article

With the planning and construction of marine ranching in China, water quality has become one of the critical limiting factors for the development of marine ranching. Due to geographical differences, marine ranches exhibit varying water quality conditions under the influence of the continental shelf. To the best of our knowledge, there is limited research on satellite-based water quality monitoring for marine ranching and the spatiotemporal variations in marine ranches in different geographical locations. Chlorophyll-a (Chl-a) is a key indicator of the ecological health and disaster prevention capacity of marine ranching, as it reflects the conditions of eutrophication and is crucial for the high-quality, sustainable operation of marine ranching. Using a physically based model, this study focuses on the retrieval of Chl-a concentration in Daya Bay. The coefficient of determination (R²) between the model retrieval values and the in situ Chl-a data is 0.69, with a root mean square error (RMSE) of 1.52 μg/L and a mean absolute percentage error (MAPE) of 44.25%. Seasonal variations in Chl-a concentration are observed in Daya Bay and are higher in spring–summer and lower in autumn–winter. In the YangMeikeng waters, Chl-a concentration shows a declining trend with the development of marine ranching. A comparison between the YangMeikeng (nearshore) and XiaoXingshan (offshore) marine ranches suggests that offshore ranching may be less impacted by terrestrial pollutants. The primary sources of Chl-a input in Daya Bay are the Dan’ao River and the aquaculture areas in the northeastern part of the bay. This study can provide valuable information for the protection and management of marine ranching. Full article

Geographical named entity matching, a crucial step in address encoding, aims to enhance address resolution accuracy through the precise identification and linkage of geographical named entity data. However, existing approaches tend to ignore the spatial information of entities, leading to misclassification. Drawing on the human process of searching for addresses, this study proposes a multi-objective learning model named GNEMM that integrates the semantic and spatial information of geographical named entities. To further mimic the human cognitive process during address search, it incorporates the Retrieval-Augmented Generation (RAG) technique. By integrating newly added external address data with an advanced large language model (LLM) like GPT-4, it achieves precise address evaluation and recommendation. The model was tested using a standard geographical named entity dataset from Shandong Province, focusing on three sub-tasks: element segmentation, matching, and spatial similarity score prediction. The experimental results indicate that the method achieves a geographical named entity matching accuracy of up to 99%, with improvements of 10% and 5% in the segmentation and prediction sub-tasks. GNEMM performs best in address-matching tasks of various scales, and the vectors extracted by GNEMM perform best in the downstream retrieval and matching of various address types, which verifies its applicability in geographical named entity recommendation applications. Full article

Wind field retrieval, a crucial component of weather forecasting, has been significantly enhanced by recent advances in deep learning. However, existing approaches that are primarily focused on wind speed retrieval are limited by their inability to achieve real-time, full-coverage retrievals at large scales. To address this problem, we propose a novel multi-scale fusion retrieval (MFR) method, leveraging geostationary observation satellites. At the mesoscale, MFR incorporates a cloud-to-wind transformer model, which employs local self-attention mechanisms to extract detailed wind field features. At large scales, MFR incorporates a multi-encoder coordinate U-net model, which incorporates multiple encoders and utilises coordinate information to fuse meso- to large-scale features, enabling accurate and regionally complete wind field retrievals, while reducing the computational resources required. The MFR method was validated using Level 1 data from the Himawari-8 satellite, covering a geographic range of 0–60°N and 100–160°E, at a resolution of 0.25°. Wind field retrieval was accomplished within seconds using a single graphics processing unit. The mean absolute error of wind speed obtained by the MFR was 0.97 m/s, surpassing the accuracy of the CFOSAT and HY-2B Level 2B wind field products. The mean absolute error for wind direction achieved by the MFR was 23.31°, outperforming CFOSAT Level 2B products and aligning closely with HY-2B Level 2B products. The MFR represents a pioneering approach for generating initial fields for large-scale grid forecasting models. Full article

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions. Full article

Chandipura virus (CHPV) is an arthropod-borne virus linked to encephalitis in humans, primarily in India. Its evolutionary dynamics and transmission pathways remain poorly understood due to limited genomic data. This study analyzed 23 publicly available CHPV genomes, including isolates from humans, sandflies, and a hedgehog, retrieved from GenBank. Phylogenetic analyses were conducted to explore host-specific and geographic evolutionary patterns. Phylogenetic analysis revealed distinct evolutionary lineages. Human-derived genomes collected in India between 2003 and 2024 formed a well-supported monophyletic clade, suggesting a unique evolutionary lineage. In contrast, sandfly-derived genomes exhibited diverse clustering patterns. Notably, Kenyan sandfly isolates from 2016–2017 were phylogenetically closer to human-derived sequences, suggesting possible shared evolutionary pressures. These findings provide preliminary insights into CHPV evolution and emphasize the need for enhanced genomic surveillance in both human and non-human populations. Expanding genomic data is essential to validate these observations and inform public health strategies. Full article

Toscana virus (TOSV), a member of the Phlebovirus genus transmitted by sandflies, is acknowledged for its capacity to cause neurological infections and is widely distributed across Mediterranean countries. The potential geographic distribution and risk to the human population remained obscure due to its neglected nature. We searched PubMed and Web of Science for articles published between 1 January 1971 and 30 June 2023 to extract data on TOSV detection in vectors, vertebrates and humans, clinical information of human patients, as well as the occurrence of two identified sandfly vectors for TOSV. We further predicted the global distribution of the two sandfly vectors, based on which the global risk of TOSV was projected, after incorporating the environmental, ecoclimatic, biological, and socioeconomic factors. A total of 1342 unique studies were retrieved, among which 389 met the selection criteria and were included for data extraction. TOSV infections were documented in 10 sandfly species and 14 species of vertebrates, as well as causing a total of 7571 human infections. The occurrence probabilities of two sandfly vectors have demonstrated the greatest contributions to the potential distribution of TOSV infection risk. This study provides a comprehensive overview of global TOSV distribution and potential risk zones. Future surveillance and intervention programs should prioritize high-risk areas based on updated quantitative analyses. Full article

The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating measure values of connected road features, thereby integrating extensive contextual information from the network hierarchy. In our evaluation, conducted across diverse urban landscapes in six cities in Italy and Türkiye, we tested two geometry-, six centrality-, and eight semantic-based features to predict road functional classes stored as a highway = * key in OSM. The findings indicate a marginal impact of geometric features and city identifiers on classification performance. Utilizing centrality attributes alongside semantic features in a direct, non-hierarchical manner results in an F1 score of 80%. However, integrating these features in our network-based hierarchical feature augmentation process remarkably increases the F1 score up to 85%. The success of our approach underlines the importance of network-based feature engineering in capturing the complex dependencies of geographic data, considering a more accurate and contextually aware OSM classification framework. Full article

As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently explored the spatio-temporal derivation relationships of place names, failing to fully utilize these relationships to enhance the connectivity between place names and improve spatial retrieval capabilities. Therefore, this paper conducts research on the spatio-temporal derivation relationships of place names, defines them in a standardized manner, clarifies the boundary conditions and identification methods, and then constructs a spatio-temporal derivation network of place names for expression and uses this network to carry out reasoning research on spatial adjacency relationships. Experiments and results showed that using the theory and methods of this paper to identify the spatio-temporal derivation relationships of Canadian place names achieves an accuracy rate of 98.5% and a recall rate of 93.4%, and the reasoning results can effectively improve the accuracy of query results. The research enriches the theoretical framework of spatio-temporal derivation relationships of place names, solves the current problems of unclear definition and inability to automatically identify spatio-temporal derivation relationships, and provides new perspectives and tools for the application practice in the field of geographical information science. Full article

Mangrove ecotourism is gaining immense popularity in the Gulf Cooperation Council (GCC) countries as a neoliberal conservation tool, and it has contributed significantly to the growth of the tourism sector in the region over the past two decades. However, there is no comprehensive review on the full extent of mangrove ecotourism activities and the contribution to mangrove conservation/restoration and economic growth in the region. A systematic literature review approach was used to examine the evolution of mangrove ecotourism in the GCC countries from 2010 to 2023. A total of 55 articles were retrieved from the Google and Google Scholar search engines, and the Scopus and Web of Science databases were incorporated. We synthesized the results and provided perspectives on the following: (1) the geographical and temporal distribution of studies in relation to mangrove extent, (2) key sites, attractions, and values for mangrove ecotourism activities, (3) the positive and negative impacts of mangrove ecotourism, and (4) existing mangrove conservation and restoration initiatives for the growth of mangrove ecotourism in the GCC countries. The findings underscore the significance of mangrove ecotourism in supporting economic development, protecting coastal ecosystems, and sustaining local livelihoods in the GCC countries. However, this study highlights the crucial need for sustainable coastal environmental management through integrated land use planning and zoning to address the negative impacts of anthropogenic pressures on mangrove ecosystems and ecotourism attractions. The use of remote sensing tools is invaluable in the monitoring of mangrove ecosystems and associated ecotourism impacts for informing evidence-based conservation and restoration management approaches. Thus, harnessing mangrove ecotourism opportunities can help the GCC countries with balancing economic growth, coastal environmental sustainability, and community well-being. Full article

Estimating live fuel moisture content (LFMC) is critical for assessing vegetation flammability and predicting potential fire behaviour, thus providing relevant information for wildfire prevention and management. Previous research has demonstrated that empirical modelling based on spectral data derived from remote sensing is useful for retrieving LFMC. However, these types of models are often very site-specific and generally considered difficult to extrapolate. In the present study, we analysed the performance of empirical models based on Sentinel-2 spectral data for estimating LFMC in fire-prone shrubland dominated by Cistus ladanifer. We used LFMC data collected in the field between June 2021 and September 2022 in 27 plots in the region of Andalusia (southern Spain). The specific objectives of the study included (i) to test previous existing models fitted for the same shrubland species in a different study area in the region of Madrid (central Spain); (ii) to calibrate empirical models with the field data from the region of Andalusia, comparing the model performance with that of existing models; and (iii) to test the capacity of the best empirical models to predict decreases in LFMC to critical threshold values in historical wildfire events. The results showed that the empirical models derived from Sentinel-2 data provided accurate LFMC monitoring, with a mean absolute error (MAE) of 15% in the estimation of LFMC variability throughout the year and with the MAE decreasing to 10% for the critical lower LFMC values (<100%). They also showed that previous models could be easily recalibrated for extrapolation to different geographical areas, yielding similar errors to the specific empirical models fitted in the study area in an independent validation. Finally, the results showed that decreases in LFMC in historical wildfire events were accurately predicted by the empirical models, with LFMC <80% in this fire-prone shrubland species. Full article

Key long-lived greenhouse gases (CO₂, CH₄, and N₂O) are perhaps among the best-studied components of the Earth’s atmosphere today; however, attempts to predict or explain trends or even shorter-term variations of these trace gases are not always successful. Infrared spectroscopy is a recognized technique for the ground-based long-term monitoring of the gaseous composition of the atmosphere. The current paper is focused on the analysis of new data on CO₂, CH₄, and N₂O total columns (TCs) retrieved from high resolution IR solar spectra acquired during 2009–2022 at the NDACC atmospheric monitoring station of St. Petersburg State University (STP station, 59.88°N, 29.83°E, 20 m asl.). The paper provides information on the FTIR system (Fourier-transform infrared) installed at the STP station, and an overview of techniques used for the CO₂, CH₄, and N₂O retrievals. Trends of key greenhouse gases and their confidence levels were evaluated using an original approach which combines the Lomb–Scargle method with the cross-validation and bootstrapping techniques. As a result, the following fourteen-year (2009–2022) trends of TCs have been revealed: (0.56 ± 0.01) % yr⁻¹ for CO₂; (0.46 ± 0.02) % yr⁻¹ for CH₄; (0.28 ± 0.01) % yr⁻¹ for N₂O. A comparison with trends based on the EMAC numerical modeling data was carried out. The trends of greenhouse gases observed at the STP site are consistent with the results of the in situ monitoring performed at the same geographical location, and with the independent estimates of the global volume mixing ratio growth rates obtained by the GAW network and the NOAA Global Monitoring Laboratory. There is reasonable agreement between the CH₄ and N₂O TC trends for 2009–2019, which have been derived from FTIR measurements at three locations: the STP site, Izaña Observatory and the University of Toronto Atmospheric Observatory. Full article