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Estimating intensity−duration−frequency (IDF) curves requires local historical information of precipitation intensity. When such information is unavailable, as in areas without rain gauges, it is necessary to consider other methods to estimate curve parameters. In this study, three methods were explored to estimate IDF curves in ungauged areas: Kriging (KG), Inverse Distance Weighting (IDW), and Storm Index (SI). To test the viability of these methods, historical data collected from 31 rain gauges distributed in central Chile, 35° S to 38° S, are used. As a result of the reduced number of rain gauges to evaluate the performance of each method, we used LOOCV (Leaving One Out Cross Validation). The results indicate that KG was limited due to the sparse distribution of rain gauges in central Chile. SI (a linear scaling method) showed the smallest prediction error in all of the ungauged locations, and outperformed both KG and IDW. However, the SI method does not provide estimates of uncertainty, as is possible with KG. The simplicity of SI renders it a viable method for extrapolating IDF curves to locations without data in the central zone of Chile.

Diabetes is recognized as a significant factor in both mortality and morbidity worldwide, affecting various demographics regardless of geographic location, age group, or gender. This correspondence aims to express concern and draw the attention of leaders and policymakers worldwide to this critical public health issue. A thorough literature search was conducted utilizing various databases, including Google Scholar, PubMed, Science Direct, and the International Diabetes Federation (IDF) website, to collect the required data. Keywords were strategically applied to enhance search results, with preference given to English-language articles containing pertinent information. According to the 2021 report by the IDF, approximately 537 million individuals globally were affected with diabetes, constituting roughly 10.5% of the world's populace. This condition incurred healthcare expenditures totaling $966 billion. Projections indicate a surge in diabetes cases to 783 million by 2045, with associated healthcare costs estimated to surpass $1054 billion. However, almost half of all people with diabetes are unaware of their medical condition, with the highest prevalence of undiagnosed diabetes Mellitus (DM) found in low and middle-income countries (LMICs) of the regions of Africa, the Western Pacific, and Southeast Asia. Collaborating with the World Health Organization (WHO), LMIC governments should improve healthcare accessibility, including more frequent diabetes screenings for individuals aged ≥ 45 years and younger individuals at elevated risk of having a family history.

PurposeHand-held vital microscopes (HVMs) were introduced to observe sublingual microcirculatory alterations at the bedside in different shock states in critically ill patients. This consensus aims to provide clinicians with guidelines for practical use and interpretation of the sublingual microcirculation. Furthermore, it aims to promote the integration of routine application of HVM microcirculatory monitoring in conventional hemodynamic monitoring of systemic hemodynamic variables.MethodsIn accordance with the Delphi method we organized three international expert meetings to discuss the various aspects of the technology, physiology, measurements, and clinical utility of HVM sublingual microcirculatory monitoring to formulate this consensus document. A task force from the Cardiovascular Dynamics Section of the European Society of Intensive Care Medicine (with endorsement of its Executive Committee) created this consensus as an update of a previous consensus in 2007. We classified consensus statements as definitions, requirements, and/or recommendations, with a minimum requirement of 80% agreement of all participants.ResultsIn this consensus the nature of microcirculatory alterations is described. The nature of variables, which can be extracted from analysis of microcirculatory images, is presented and the needed dataset of variables to identify microcirculatory alterations is defined. Practical aspects of sublingual HVM measurements and the nature of artifacts are described. Eleven statements were formulated that pertained to image acquisitions and quality statements. Fourteen statements addressed the analysis of the images, and 13 statements are related to future developments.ConclusionThis consensus describes 25 statements regarding the acquisition and interpretation of microcirculatory images needed to guide the assessment of the microcirculation in critically ill patients.

Design storms are key components for planning drainage networks and flood risk management. Due to atmospheric processes, precipitation accumulations across multiple temporal intervals are often correlated and can combine to shape flood intensities. However, current design storm guidance overlook the observed correlations between return periods of different duration intervals within storms and may thereby lead to under‐ or over‐estimation of the flood risk. We present a new approach for generating plausible design storms that accounts for joint return periods. Focusing on short‐duration extreme precipitation events, potentially leading to urban pluvial flooding, we analyze the dependencies between critical precipitation intensities over the 10‐min, 30‐min, 1‐hr, 3‐hr, and 6‐hr intervals, for data from Zurich (Switzerland). We then propose a method based on a canonical vine copula model for sampling precipitation intensities that reflect the observations' dependencies. Using this model, we then generate realistic design storms with a constrained micro‐canonical cascade model. Our results shows that the common block methods (e.g., the Chicago and Euler design storms) tend to overestimate total precipitation volumes on average, by up to 56%. Furthermore, we highlight the variability in possible duration‐frequency profiles, leading to both higher and lower total precipitation volumes compared to standard approaches. This underscores the need to switch from traditional block methods to a more realistic sampling of design storms, incorporating multiple design storm scenarios for robust risk assessment. The model is applicable to any time series of precipitation, regardless of its location or climate. The code is freely available.

The escalating prevalence of hate speech on social media necessitates effective detection mechanisms to foster a safe and inclusive online community. This research paper aims to enhance hate speech detection accuracy by evaluating the performance of diverse machine learning algorithms: Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbors (KNN). A diverse dataset comprising text samples from various online platforms, encompassing a wide spectrum of hate speech instances, was meticulously collected. The data underwent careful preprocessing involving tokenization, stemming, and stop-word removal to enhance data quality. Additionally, feature extraction techniques such as TF-IDF (Term Frequency-Inverse Document Frequency) and word embeddings were employed to effectively represent the textual content. The dataset was divided into training and testing sets, and the selected machine learning algorithms were trained on the former. Fine-tuning of hyperparameters was performed using crossvalidation techniques to optimize their performance. Evaluation metrics, including accuracy, precision, recall, and F1-score, were employed to assess the models’ effectiveness. The experimental findings revealed promising outcomes for hate speech detection across all three algorithms. Notably, Count Vectorizer features demonstrated excellent performance, with Random Forest achieving an accuracy of 0.942 for binary hate speech analysis and Logistic Regression achieving an accuracy of 0.897 for multi-class hate speech analysis, followed by LR and KNN.

The issues of online fake news have attained an increasing eminence in the diffusion of shaping news stories online. Misleading or unreliable information in the form of videos, posts, articles, URLs is extensively disseminated through popular social media platforms such as Facebook and Twitter. As a result, editors and journalists are in need of new tools that can help them to pace up the verification process for the content that has been originated from social media. Motivated by the need for automated detection of fake news, the goal is to find out which classification model identifies phony features accurately using three feature extraction techniques, Term Frequency–Inverse Document Frequency (TF–IDF), Count-Vectorizer (CV) and Hashing-Vectorizer (HV). Also, in this paper, a novel multi-level voting ensemble model is proposed. The proposed system has been tested on three datasets using twelve classifiers. These ML classifiers are combined based on their false prediction ratio. It has been observed that the Passive Aggressive, Logistic Regression and Linear Support Vector Classifier (LinearSVC) individually perform best using TF-IDF, CV and HV feature extraction approaches, respectively, based on their performance metrics, whereas the proposed model outperforms the Passive Aggressive model by 0.8%, Logistic Regression model by 1.3%, LinearSVC model by 0.4% using TF-IDF, CV and HV, respectively. The proposed system can also be used to predict the fake content (textual form) from online social media websites.

The lack of sub-daily rainfall data to obtain intensity-duration-frequency (IDF) relationships at a local scale is a common limitation in many countries. This study determines rainfall disaggregation coefficients using pluviographic data from several meteorological stations to adjust IDF curves for Rio de Janeiro state, Brazil. The adjusted IDF curves using the coefficients obtained show satisfactory adjustment with Nash and Sutcliffe efficiency values above 0.99 compared to the mean values proposed by Cetesb (1979). The root mean square error (RMSE) of the estimated rainfall intensities using the adjusted IDF relationships for different return periods and rainfall durations varied between 2.55 at the Eletrobrás station and 42.49 at the São Bento station. The disaggregation coefficients obtained for Rio de Janeiro state differ from the values proposed in the literature, which confirms the need to adjust values locally and for hydrologically homogeneous regions. This local and regional scale approach provides more accurate IDF curves. Keywords: extreme rainfall, IDF relationship, Rio de Janeiro.

[This corrects the article DOI: 10.3389/fphar.2025.1569863.].

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

Background Orthogonal polarized spectral (OPS) and sidestream dark field (SDF) imaging video microscope devices were introduced for observation of the microcirculation but, due to technical limitations, have remained as research tools. Recently, a novel handheld microscope based on incident dark field illumination (IDF) has been introduced for clinical use. The Cytocam-IDF imaging device consists of a pen-like probe incorporating IDF illumination with a set of high-resolution lenses projecting images on to a computer controlled image sensor synchronized with very short pulsed illumination light. This study was performed to validate Cytocam-IDF imaging by comparison to SDF imaging in volunteers. Methods This study is a prospective, observational study. The subjects consist of 25 volunteers. Results Sublingual microcirculation was evaluated using both techniques. The main result was that Cytocam-IDF imaging provided better quality images and was able to detect 30% more capillaries than SDF imaging (total vessels density Cytocam-IDF: 21.60 ± 4.30 mm/mm 2 vs SDF: 16.35 ± 2.78 mm/mm 2 , p < 0.0001). Comparison of the images showed increased contrast, sharpness, and image quality of both venules and capillaries. Conclusions Cytocam-IDF imaging detected more capillaries and provided better image quality than SDF imaging. It is concluded that Cytocam-IDF imaging may provide a new improved imaging modality for clinical assessment of microcirculatory alterations.