Search Results (900)

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

The Muschelkalk sedimentary cycle in the northwestern region of the Iberian Range (central Spain) lies within a transitional area between the Iberian and Hesperia type Triassic domains. To improve the understanding of its paleopalynological record, fifty samples were analyzed from ten stratigraphic sections corresponding to the Tramacastilla Dolostones Formation (TD Fm.), Cuesta del Castillo Sandstones and Siltstones Formation (CCSS Fm.), and Royuela Dolostones, Marls and Limestones Formation (RDML Fm.). Despite previous studies in the area, palynological data remain scarce or insufficiently detailed, highlighting the need for a systematic reassessment. Based on the identified palynological assemblages, the succession is assigned to an age spanning from the Fassanian to the Longobardian, with a possible extension into the base of the Julian (early Carnian). The results confirm that the siliciclastic unit (CCSS) represents a lateral facies change with respect to the carbonate formations of the upper Muschelkalk (TD and RDML). From a paleoecological perspective, the assemblages indicate a warm and predominantly dry environment, dominated by xerophytic conifers, although evidence of more humid local environments, such as marshes or coastal plains, is also observed. Full article

This study presents a comprehensive inventory of bryophytes in Serra dos Órgãos National Park (PARNASO), aiming to evaluate species richness, floristic composition and threatened taxa. Despite the state of Rio de Janeiro being one of the most extensively sampled regions for bryophytes in Brazil, detailed surveys of its conservation units remain scarce. Data were obtained through bibliographic review, herbarium specimen analysis, and new field collections. A total of 504 species were recorded, belonging to 202 genera and 76 families. The park harbors three locally endemic species, eight endemic to Rio de Janeiro, and sixty-nine species endemic to Brazil. Additionally, eleven species were identified as threatened, comprising seven Endangered (EN), two Critically Endangered (CR), and two Vulnerable (VU) according to the IUCN guidelines. PARNASO includes four distinct ecosystems along an altitudinal gradient: sub-montane forest (up to 500 m), montane forest (500–1500 m), upper-montane forest (1500–2000 m), and high-altitude fields (above 2000 m). Montane Forest showed the highest species richness, followed by high-altitude fields, upper-montane forest, and sub-montane forest. The findings highlight PARNASO’s importance in preserving bryophyte diversity in a highly diverse yet understudied region. This work contributes valuable baseline data to support conservation strategies and future ecological studies in Atlantic Forest remnants. Full article

Papillomaviruses (PVs) are double-stranded DNA viruses known to induce a variety of epithelial lesions in dogs, ranging from benign hyperplasia to malignancies. In regions of rich biodiversity such as the Western Amazon, data on the circulation and genetic composition of canine papillomaviruses (CPVs) remain scarce. This study investigated CPV types present in oral and cutaneous papillomatous lesions in domiciled dogs from Acre and Rondônia States, Brazil. Sixty-one dogs with macroscopically consistent lesions were clinically evaluated, and tissue samples were collected for histopathological examination and PCR targeting the L1 gene. Among these, 37% were histologically diagnosed as squamous papillomas or fibropapillomas, and 49.2% (30/61) tested positive for papillomavirus DNA. Sequencing of the L1 gene revealed that most positive samples belonged to CPV1 (Lambdapapillomavirus 2), while one case was identified as CPV8 (Chipapillomavirus 3). Complete genomes of three CPV1 strains were obtained via high-throughput sequencing and showed high identity with CPV1 strains from other Brazilian regions. Phylogenetic analysis confirmed close genetic relationships among isolates across distinct geographic areas. These findings demonstrate the circulation of genetically conserved CPVs in the Amazon and reinforce the value of molecular and histopathological approaches for the accurate diagnosis and surveillance of viral diseases in domestic dogs, especially in ecologically complex regions. Full article

Background/Objectives: Helicobacter pylori (H. pylori) is a common gastric pathogen linked to gastritis, gastroduodenal ulcers, and gastric cancer. Rising antimicrobial resistance (AMR) poses challenges for effective treatment and has prompted the WHO to classify H. pylori as a high-priority pathogen. This study aimed to detect the prevalence of AMR genes in H. pylori-positive gastric samples from patients in Algarve, Portugal, where regional data is scarce. Methods: Eighteen H. pylori-positive gastric biopsy samples from patients undergoing upper gastrointestinal endoscopy were analyzed. PCR and sequencing were used to identify genes associated with resistance to amoxicillin (Pbp1A), metronidazole (rdxA, frxA), tetracycline (16S rRNA mutation) and clarithromycin (23S rRNA). Sequence identity and homologies were verified using tBLASTx and the Comprehensive Antibiotic Resistance Database (CARD). Results: Out of the 18 H. pylori-positive samples, 16 (88.9%) contained at least one AMR gene. The most frequent genes were rdxA (83.3%) and frxA (66.7%) for metronidazole resistance, and the 16S rRNA mutation (66.7%) for tetracycline. Resistance to amoxicillin and clarithromycin was detected in 27.8% and 16.7% of cases, respectively. Most samples (72.2%) had multiple resistance genes. A significantly strong association was found between female sex and the presence of the rdxA gene (p = 0.043). Conclusions: The study reveals a high prevalence of H. pylori resistance genes in Algarve, particularly against metronidazole and tetracycline. These findings highlight the need for local surveillance and tailored treatment strategies. Further research with larger populations is warranted to assess regional resistance patterns and improve eradication efforts. Full article

This paper presents a Systematic Mapping Study (SMS) on data-driven approaches in flood forecasting from 2019 to 2024, a period marked by transformative developments in Deep Learning (DL) technologies. Analysing 363 selected studies, this paper provides an overview of the technological evolution in this field, methodological approaches, evaluation practices and geographical distribution of studies. The study revealed that meteorological and hydrological factors constitute approximately 76% of input variables, with rainfall/precipitation and water level measurements forming the core predictive basis. Long Short-Term Memory (LSTM) networks emerged as the dominant algorithm (21% of implementations), whilst hybrid and ensemble approaches showed the most dramatic growth (from 2% in 2019 to 10% in 2024). The study also revealed a threefold increase in publications during this period, with significant geographical concentration in East and Southeast Asia (56% of studies), particularly China (36%). Several research gaps were identified, including limited exploration of graph-based approaches for modelling spatial relationships, underutilisation of transfer learning for data-scarce regions, and insufficient uncertainty quantification. This SMS provides researchers and practitioners with actionable insights into current trends, methodological practices, and future directions in data-driven flood forecasting, thereby advancing this critical field for disaster management. Full article

Human exploitation has been a major driver of marine turtle population declines, particularly affecting naturally scarce species such as the pantropical hawksbill turtle. Although hawksbill sea turtles have been documented in the Gulf of California since the early 20th century, data on their historical demography during periods of high exploitation in this region are nonexistent. We investigated the size structure of hawksbill turtles from the Western Central Gulf of California by examining a unique sample of decorative taxidermies, corresponding to 31 specimens captured during fishing operations near Santa Rosalía, Baja California Sur, Mexico, between 1980 and 1990. An analysis of the curved carapace measures revealed a length range (nuchal notch to posterior of supracaudals) of 29.5–59.5 cm (mean = 38.75 ± 6.67 cm) and a width range of 25.0–51.5 cm (mean = 33.63 ± 5.66 cm), with 87% of specimens having lengths between 30 and 45 cm. Based on the carapace length measurements, we estimated the ages to be between 7 and 20 years, indicating that the population included juveniles. Our findings provide baseline data for an understudied period and region, suggesting that this area previously served as an important juvenile habitat. These results contribute essential historical demographic information for conservation planning. Full article

Accurate forecasting of household waste generation is essential for sustainable urban planning and the development of data-driven environmental policies. Conventional statistical models, while simple and interpretable, often fail to capture the nonlinear and multidimensional relationships inherent in waste production patterns. This study proposes a machine learning-based regression framework utilizing Random Forest and XGBoost algorithms to predict annual household waste generation across four metropolitan regions in South Korea Seoul, Gyeonggi, Incheon, and Jeju over the period from 2000 to 2023. Independent variables include demographic indicators (total population, working-age population, elderly population), economic indicators (Gross Regional Domestic Product), and regional identifiers encoded using One-Hot Encoding. A derived feature, elderly ratio, was introduced to reflect population aging. Model performance was evaluated using R², RMSE, and MAE, with artificial noise added to simulate uncertainty. Random Forest demonstrated superior generalization and robustness to data irregularities, especially in data-scarce regions like Jeju. SHAP-based interpretability analysis revealed total population and GRDP as the most influential features. The findings underscore the importance of incorporating economic indicators in waste forecasting models, as demographic variables alone were insufficient for explaining waste dynamics. This approach provides valuable insights for policymakers and supports the development of adaptive, region-specific strategies for waste reduction and infrastructure investment. Full article

This study applies the Weighted Overlay Analysis (WOA) method integrated with GIS to assess landslide susceptibility along Al Figrah Road in Al-Madinah Al-Munawarah, western Saudi Arabia. Seven key conditioning factors, elevation, slope, aspect, drainage density, lithology, soil type, and precipitation were integrated using high-resolution remote sensing data and expert-assigned weights. The output susceptibility map categorized the region into three zones: low (93.5 million m²), moderate (271.2 million m²), and high risk (33.1 million m²). Approximately 29% of the road corridor lies within the low-risk zone, 48% in the moderate zone, and 23% in the high-risk zone. Ten critical sites with potential landslide activity were detected along the road, correlating well with the high-risk zones on the map. Structural weaknesses in the area, such as faults, joints, foliation planes, and shear zones in both igneous and metamorphic rock units, were key contributors to slope instability. The findings offer practical guidance for infrastructure planning and geohazard mitigation in arid, mountainous environments and demonstrate the applicability of WOA in data-scarce regions. Full article

Accurate and spatially consistent rainfall estimation is essential for hydrological modeling and flood risk mitigation, especially in mountainous tropical regions with sparse observational networks and highly heterogeneous rainfall. This study presents a comparative analysis of six radar–gauge merging methods, including three statistical approaches—Quantile Adaptive Gaussian (QAG), Empirical Quantile Mapping (EQM), and radial basis function (RBF)—and three geostatistical approaches—external drift kriging (EDK), Bayesian Kriging (BAK), and Residual Kriging (REK). The evaluation was conducted over the Huong River Basin in Central Vietnam, a region characterized by steep terrain, monsoonal climate, and frequent hydrometeorological extremes. Two observational scenarios were established: Scenario S1 utilized 13 gauges for merging and 7 for independent validation, while Scenario S2 employed all 20 stations. Hourly radar and gauge data from peak rainy months were used for the evaluation. Each method was assessed using continuous metrics (RMSE, MAE, CC, NSE, and KGE), categorical metrics (POD and CSI), and spatial consistency indicators. Results indicate that all merging methods significantly improved the accuracy of rainfall estimates compared to raw radar data. Among them, RBF consistently achieved the highest accuracy, with the lowest RMSE (1.24 mm/h), highest NSE (0.954), and strongest spatial correlation (CC = 0.978) in Scenario S2. RBF also maintained high classification skills across all rainfall categories, including very heavy rain. EDK and BAK performed better with denser gauge input but required recalibration of variogram parameters. EQM and REK yielded moderate performance and had limitations near basin boundaries where gauge coverage was sparse. The results highlight trade-offs between method complexity, spatial accuracy, and robustness. While complex methods like EDK and BAK offer detailed spatial outputs, they require more calibration. Simpler methods are easier to apply across different conditions. RBF emerged as the most practical and transferable option, offering strong generalization, minimal calibration needs, and computational efficiency. These findings provide useful guidance for integrating radar and gauge data in flood-prone, data-scarce regions. Full article

Food safety concerns related to pesticide residues in fruits and vegetables have increased globally, particularly in regions where monitoring programs are scarce or inconsistent. This study provides the first multi-year evaluation of pesticide contamination and associated dietary risks in Cape Verde, an African island nation increasingly reliant on imported produce. A total of 570 samples of fruits and vegetables—both locally produced and imported—were collected from major markets across the country between 2017 and 2020 and analyzed using validated multiresidue methods based on gas chromatography coupled to Ion Trap mass spectrometry (GC-IT-MS/MS), and both gas and liquid chromatography coupled to triple quadrupole tandem mass spectrometry (GC-QqQ-MS/MS and LC-QqQ-MS/MS). Residues were detected in 63.9% of fruits and 13.2% of vegetables, with imported fruits showing the highest contamination levels and diversity of compounds. Although only one sample exceeded the maximum residue limits (MRLs) set by the European Union, 80 different active substances were quantified—many of them not authorized under the current EU pesticide residue legislation. Dietary exposure was estimated using median residue levels and real consumption data from the national nutrition survey (ENCAVE 2019), enabling a refined risk assessment based on actual consumption patterns. The cumulative hazard index for the adult population was 0.416, below the toxicological threshold of concern. However, when adjusted for children aged 6–11 years—taking into account body weight and relative consumption—the cumulative index approached 1.0, suggesting a potential health risk for this vulnerable group. A limited number of compounds, including omethoate, oxamyl, imazalil, and dithiocarbamates, accounted for most of the risk. Many are banned or heavily restricted in the EU, highlighting regulatory asymmetries in global food trade. These findings underscore the urgent need for strengthened residue monitoring in Cape Verde, particularly for imported products, and support the adoption of risk-based food safety policies that consider population-specific vulnerabilities and mixture effects. The methodological framework used here can serve as a model for other low-resource countries seeking to integrate analytical data with dietary exposure in a One Health context. Full article

Accurate river discharge forecasting remains a critical challenge in hydrology, particularly in data-scarce mountainous regions where in situ observations are limited. This study investigated the potential of long short-term memory (LSTM) networks to improve discharge prediction by leveraging spatially distributed reanalysis data. Using the ERA5-Land dataset, we developed an LSTM model that integrates grid-based meteorological inputs and assesses their relative importance. We conducted experiments on two snow-dominated basins with contrasting physiographic characteristics, the Uba River basin in Kazakhstan and the Flathead River basin in the USA, to answer three research questions: (1) whether full-grid input outperforms reduced configurations and models trained on Caravan, (2) the impact of spatial resolution on accuracy and efficiency, and (3) the effect of partial spatial coverage on prediction reliability. Specifically, we compared the full-grid LSTM with a single-cell LSTM, a basin-average LSTM, a Caravan-trained LSTM, and coarser cell aggregations. The results demonstrate that the full-grid LSTM consistently yields the highest forecasting performance, achieving a median Nash–Sutcliffe efficiency of 0.905 for Uba and 0.93 for Middle Fork Flathead, while using coarser grids and random subsets reduces performance. Our findings highlight the critical importance of spatial input richness and provide a reproducible framework for grid selection in flood-prone basins lacking dense observation networks. Full article

Introduction: Leptospirosis is an under-recognized zoonosis that affects both tropical and temperate regions. While it is often associated with exposure to contaminated water or infected animals, its presentation and epidemiology in Mediterranean countries remain incompletely understood. This retrospective cohort study investigates the clinical and epidemiological profile of leptospirosis in Crete, Greece, a region where data are scarce. Methods: All adult patients with laboratory-confirmed leptospirosis admitted to three major public hospitals in Crete, Greece, between January 2019 and December 2023 were included in the analysis. Diagnosis was made through serologic testing along with compatible clinical symptoms. Results: A total of 17 patients were included. Their median age was 48 years, with a predominance of males (70.6%). Notably, more than half of the patients had no documented exposure to classic risk factors such as rodents or standing water. Clinical presentations were varied but commonly included fever, fatigue, acute kidney injury, and jaundice. Of the patients who underwent imaging, most showed hepatomegaly. The median delay from symptom onset to diagnosis was 11 days, underscoring the diagnostic challenge in non-endemic areas. Ceftriaxone was the most frequently administered antibiotic (76.5%), often in combination with tetracyclines or quinolones. Despite treatment, three patients (17.6%) died, all presenting with severe manifestations such as ARDS, liver failure, or shock. A concerning increase in cases was noted in 2023. Conclusions: Leptospirosis can present with severe and potentially fatal outcomes even in previously healthy individuals and in regions not traditionally considered endemic. The relatively high mortality and disease frequency noted emphasize the importance of maintaining a high index of suspicion. Timely diagnosis and appropriate antimicrobial therapy are essential to improving patient outcomes. Additionally, the need for enhanced public health awareness, diagnostic capacity, and possibly environmental surveillance to control this neglected but impactful disease better, should be emphasized. Full article

Achieving optimal agricultural yields and promoting sustainable farming relies on accurate crop recommendations. However, the applicability of many current systems is limited by their considerable computational requirements and dependence on comprehensive datasets, especially in resource-limited contexts. This paper presents HOLISTIQ RS, a novel crop recommendation system explicitly designed for operation on low-specification hardware and in data-scarce regions. HOLISTIQ RS combines collaborative filtering with a Markov model to predict appropriate crop choices, drawing upon user profiles, regional agricultural data, and past crop performance. Results indicate that HOLISTIQ RS provides a significant increase in recommendation accuracy, achieving a MAP@5 of 0.31 and nDCG@5 of 0.41, outperforming standard collaborative filtering methods (the KNN achieved MAP@5 of 0.28 and nDCG@5 of 0.38, and the ANN achieved MAP@5 of 0.25 and nDCG@5 of 0.35). Significantly, the system also demonstrates enhanced recommendation diversity, achieving an Item Variety (IV@5) of 23%, which is absent in deterministic baselines. Significantly, the system is engineered for reduced energy consumption and can be deployed on low-cost hardware. This provides a feasible and adaptable method for encouraging informed decision-making and promoting sustainable agricultural practices in areas where resources are constrained, with an emphasis on lower energy usage. Full article

The Mirim–Patos–Mangueira coastal lagoon system provides a wide range of ecosystem services. However, its vast territorial extent and the political boundaries that divide it hinder integrated assessments, especially during extreme hydrological events. This study is divided into two parts. First, we assessed the spatial and temporal patterns of water quality in the lagoon system using Sentinel-3/OLCI satellite imagery. Atmospheric correction was performed using ACOLITE, followed by spectral grouping and classification into optical water types (OWTs) using the Sentinel Applications Platform (SNAP). To explore the behavior of water quality parameters across OWTs, Chlorophyll-a and turbidity were estimated using semi-empirical algorithms specifically designed for complex inland and coastal waters. Results showed a gradual increase in mean turbidity from OWT 2 to OWT 6 and a rise in chlorophyll-a from OWT 2 to OWT 4, with a decline at OWT 6. These OWTs correspond, in general terms, to distinct water masses: OWT 2 to clearer waters, OWT 3 and 4 to intermediate/mixed conditions, and OWT 6 to turbid environments. In the second part, we analyzed the response of the Patos Lagoon to flooding in Rio Grande do Sul during an extreme weather event in May 2024. Satellite-derived turbidity estimates were compared with in situ measurements, revealing a systematic underestimation, with a negative bias of 2.6%, a mean relative error of 78%, and a correlation coefficient of 0.85. The findings highlight the utility of OWT classification for tracking changes in water quality and support the use of remote sensing tools to improve environmental monitoring in data-scarce regions, particularly under extreme hydrometeorological conditions. Full article