Miracle-dz/UniFAR-FTUs huggingface dataset:
A Case Report of Primary Aldosteronism and Extensive Hypertension-Mediated Organ Damage

Primary aldosteronism, the most common curable form of secondary hypertension, is associated with greater hypertension-related organ damage and cardiovascular complications compared to primary essential hypertension. The authors present a case involving a 41-year-old Black male admitted to the emergency department with left hemiparesis and blurred vision persisting for one hour, accompanied by markedly elevated blood pressure (220/140 mmHg). The patient was asymptomatic by then, and, aside from a history of tobacco smoking and occasional cannabis use, lacked significant medical comorbidities. Further investigations revealed a right acute hemorrhagic stroke, bilateral grade 4 hypertensive retinopathy, chronic kidney disease with end-stage renal disease, hypokalemia, and an elevated aldosterone/renin ratio. An abdominal CT scan showed bilateral adrenal hyperplasia. The patient was diagnosed with primary aldosteronism with extensive hypertension-mediated organ damage. This case highlights the significant harm caused by undiagnosed primary aldosteronism-induced secondary hypertension, emphasizing the importance of timely diagnosis and intervention to prevent organ damage.
Outcomes and device use in children with bone-conduction hearing devices in South Africa

Background Bone-conduction hearing devices (BCHD) can provide hearing solutions in settings where middle ear pathology is rife. Objectives Describe functional hearing outcomes and device use of children fitted with BCHD. Method Retrospective review of 79 children fitted with BCHD between January 2017 and May 2022. Outcomes included device use and subjective reports measured with the Parents’ Evaluation of Aural/Oral Performance of Children (PEACH) and the Teachers’ Evaluation of Aural/Oral Performance of Children (TEACH). Analysis of variance established association between mean data logging and type and degree of hearing loss. Thematic analyses were done for qualitative outcomes. Results Average usage was 7.0 h/day (5.4 SD; range 0.1–24). PEACH ratings indicated 93.3% of children wore their BCHD ‘always’ or ‘often’, with 80% displaying Typical auditory performance at 1-month follow-up. TEACH ratings indicated 84.2% of children wore their BCHD ‘always’ or ‘often’, with 78.9% showing typical auditory behaviour. Increased usage was noted for conductive, mixed, moderate and severe hearing losses. There was a mean delay of 17.2 months (23.4 SD; range 0–90) between age of diagnosis and fitting. Thematic analyses identified two main themes: advantages and barriers to BCDH use. Conclusion Average device use fell short of the internationally recommended 10 h/day. Higher BCHD use was associated with higher functional listening performance scores. Long waiting times for medical or surgical intervention for conductive hearing losses can delay BCHD fitting. Contribution Limited information is available to examine outcomes in children fitted with BCHD.
Short-Term Fault Prediction of Wind Turbines Based on Integrated RNN-LSTM

This paper presents a data-driven approach to short-term wind turbine fault prediction and condition monitoring based on a hybrid architecture of recurrent neural network and long short-term memory. The proposed architecture is established by utilizing time series data from the supervisory control and data acquisition system and a Bladed model of a 5 MW wind turbine to predict faults occurring to the wind generator. The recurrent neural network-long short-term memory training procedure is enhanced with self-organizing maps and long short-term memory auto encoder so as to describe the complex interaction between the mechanical system and unpredictable wind speed. To verify the performance of the proposed scheme, we conduct in-depth numerical experiments by applying the hybrid architecture to the Bladed 5 MW wind turbine model with rated wind speed of 11.8 m/s. Experimental results confirm that the proposed scheme has superior accuracy and practicality of fault prediction compared with eminent existing machine learning algorithms such as extreme gradient boost and random forest regressor.
Exploring Working Memory Capacity and Efficiency Processes to Understand Working Memory Training Outcomes in Primary School Children

Despite the abundance of research evaluating working memory training outcomes in children, few studies have examined the underlying cognitive mechanisms. This study aimed to contribute understanding by exploring whether working memory capacity (maximum span) and/or efficiency (basic and cognitive processing speeds), two proposed cognitive mechanisms, are associated with children’s working memory performance immediately and 6-months post-intervention. We used data from a previous trial in primary school children (7–11 years) who completed working memory training (n = 52) or an active control (n = 36), comprising 10 sessions (each 20-minutes) in class over two weeks. Children completed five working memory measures at baseline, immediately and 6-months post-intervention: two Backwards Span and two Following Instructions measures (same paradigms as training activities), and one n-back measure (different paradigm). Maximum span, basic and cognitive processing speeds, and performance were calculated for each measure. Associations between change in maximum span, processing speeds and change in performance on the working memory measures from baseline to immediately and 6-months post-intervention did not differ between groups (all p < .05). Maximum span, processing speeds and performance on working memory measures did not differ between groups. Findings provide little evidence that the studied capacity or efficiency processes contribute to understanding working memory training outcomes in primary school children. Furthermore, working memory training did not have benefits for children’s working capacity, efficiency or performance up to 6-months post-intervention. It is of interest for future studies to explore cognitive mechanisms, including strategy use, maximum span and information processing, in datasets where training effects are observed.
Pulp and paper mill wastewater and coliform as health hazards: A review

This review article depicts the presence of coliform bacteria in the pulp and paper mill wastewater and also emphasizes their effect in health hazards. Pulp and paper-mill wastewater discharged into freshwater, estuarine and marine ecosystems alter aquatic habitats, affect aquatic life and adversely impact on human health.The wastewater contains many organic and inorganic compounds including large number of coliforms that are severely hazardous to living being. In which Klebsiella is predominately present in pulp and paper mill wastewater while Escherichia coli, Citrobacter, Enterobacter, etc. are in less number. The fecal Streptococci (Enterococci) alternative indicators of fecal health hazards are commonly present in all mills wastewater in the absence of fecal matter. Total coliforms, fecal coliforms and Enterococci or E. coli counts as indicators of fecal contamination, which causes health hazard in human and animals. This review is focused on various aspects such as nature and characteristics of organic and inorganic pollutants, coliform population, detection techniques, diseases caused by coliforms and persistent organic pollutant (POP) in industrial waste.
Análise das Previsões de Precipitação Obtidas com a Utilização do Modelo Eta como Insumo para Modelos de Previsão Semanal de Vazão Natural

In this paper we show the evaluation of the Eta model of ten-day precipitation forecasts in a 6-year study period of three basins in Brazil. For application in inflow forecast models, the precipitation forecasts were accumulated from one to ten days. The results indicate that the Eta precipitation forecasts have no clear systematic errors over these basins that can be treated with traditional statistical techniques. These forecasts generally underestimate the heaviest precipitation and perform better when rain accumulates over three or more days. Despite these forecast errors, the river discharge models have shown improvements when driven by these forecasts. Key-words: Precipitation forecasts, Eta Model, Discharge forecasts
Anti-Microbial Effect of AgBr-NP@CTMAB on Streptococcus Mutans and Assessment of Surface Roughness Hardness and Flexural Strength of PMMA

Purpose To investigate the inhibition of Streptococcus mutans (S.mutans) and its biofilm by AgBr-nanoparticles (NP) @CTMAB (cetyltrimethyl-ammonium bromide) and evaluate the changes in Polymethyl methacrylate (PMMA)’s surface roughness (Ra), microhardness, and flexural strength during prolonged immersion in AgBr-NP@CTMAB for application in the denture cleaning industry. Patients and Methods The antibacterial activity of AgBr-NP@CTMAB against S.mutans was measured colony formation assay, OD600 and laser confocal microscopy. Changes in the specimens’ values for surface roughness, microhardness, and flexural strength (MPa) were measured after immersion solutions for 180 or 360 days. Results The AgBr-NP@CTMAB solution exhibited a robust antibacterial effect on planktonic S. mutans, with a minimum bactericidal concentration of 5 µg/mL. The 10 µg/mL AgBr-NP@CTMAB solution efficiently inhibited S. mutans biofilm formation. (2) No significant difference in surface roughness after immersion in AgBr-NP@CTMAB (10 µg/mL and 20 µg/mL) comparing with distilled water (P > 0.05) and Polident had significantly higher than distilled water (P < 0.05). There was a significant decrease in the surface hardness of the PMMA specimens that were immersed in the Polident compared with those in distilled water (P < 0.05). While, no significant differences in surface hardness after immersion in the AgBr-NP@CTMAB (P > 0.05). The result of flexural strength suggested that there was no statistically significant difference (P < 0.05) between AgBr-NP@CTMAB as well as Polident and water. Conclusion AgBrNP@CTMAB can efficiently inhibit the growth of plankton S.mutans and biofilm formation, without affecting the flexural strength, microhardness, or surface roughness of PMMA. Therefore, AgBrNP@CTMAB holds promise as a new denture cleaning agent.
Occurrence of Eimeria spp. and Intestinal Helminths in Free-Range Chickens from Northwest and Central Romania

Simple Summary In the context of backyard poultry raising, a notable concern arises regarding the susceptibility to parasitic infections. The poultry industry holds a crucial position in ensuring food safety and nutritional requirements, emerging as the most rapidly advancing agricultural sub-sector. The aim of our study was to assess the prevalence of gastrointestinal parasites in chickens raised in the backyard system within the northwestern and central regions of Romania. Fecal samples were collected and tested using flotation, McMaster, and PCR (polymerase chain reaction) methods. The overall prevalence of infection with gastrointestinal parasites was 53.1%. Intestinal parasites demonstrate a pronounced prevalence within the context of backyard poultry flocks, and the substantial burdens imposed by these parasites can deleteriously influence both avian productivity and economic considerations. Abstract Chickens raised in backyard free-range systems are confronted with a significant threat of parasitic infections. Among the parasitic agents, protozoa belonging to the genus Eimeria and helminths, including Ascaridia galli, Capillaria spp. , Heterakis gallinarum, and Strongyloides avium, stand out as the most prevalent. The sampling protocol included sixteen localities in four counties within the Transylvania region of Romania. Fecal samples were collected from chickens reared in a backyard system. Fecal samples were screened for oocysts (O) and eggs (E) by flotation method, and their number per gram of feces (OPG/EPG) was calculated after counting them by McMaster method. Positive samples for Eimeria spp. were further analyzed by PCR (polymerase chain reaction) method to identify the Eimeria species. A total of 145 flocks were tested and the overall prevalence of infection was 53.1%. The most prevalent infections were with A. galli/H. gallinarum (25.5%), and Eimeria spp. (24.8%), followed by Capillaria spp. and strongyles. The mean OPG/EPG values were as follows: 63,577 for Eimeria spp. , 157 for Ascaridia/Heterakis, 362 for Capillaria spp. , and 1671 for Strongyle eggs. Identified Eimeria species were E. acervulina (41.7%), E. tenella (27.8%), E. praecox (16.7%), E. brunetti (16.7%), OTUy (operational taxonomic unit y) (8.3%), OTUz (operational taxonomic unit z) (8.3%) and E. mitis (5.6%). Intestinal parasites exhibit a high prevalence among chickens in backyard poultry flocks, and the presence of significant parasite burdens can adversely affect both productive and economic aspects. To the best of our knowledge, this is the first comprehensive study that aimed to analyze the prevalence of gastrointestinal parasites in chickens raised in a backyard free-range system in Romania, and the first report of OTUy species in Europe.
Tourism demand for Mexico and Uruguay

Tourism is frequently viewed as an important engine for the economic growth and country’s development. In Mexico, the domestic trips have become a notable feature but the main tourism exports are from internationals travelers. In 2014 Mexico was the 10th most attractive country for travelers and 58.3% came from the United States.For Uruguay, total yearly tourists represent about 90% of its population, and Argentinean tourists represent nearly 60% of this total and historically they have been themain visitors. Tourist activities have a great impact on both economies, and in this paper, we try to measure tourism demand comparing Mexico and Uruguay, two very different countries, but for both tourism is an important industry, generating employment and income. Therefore, it is central to analyze the determinants behind tourism demand. We study the relationship between the number of US tourists for Mexico and Argentinean tourists for Uruguay, analyzing the relationship with the income and the real exchange rate (RER) of each country. We studied long-run cointegration vectors between variables, following Johansen methodology. We found one cointegration relationship for each country, through Vector error correction models (VECM). We found an income-elasticity greater than 2 for American tourists in Mexico, and near 3 for Argentinean tourists in Uruguay. Bilateral RER also were significant in both models.
Device Modeling for Admittance Spectroscopy of PMOSFETs at Cryogenic Temperatures

Large area PMOSFETs were characterized at room and cryogenic temperatures by admittance spectroscopy to determine transport parameters (e.g., hole mobility in the inversion layer). The devices were simulated in the linear response regime by an efficient drift-diffusion (DD) model including Fermi-Dirac statistics, band tailing and impurity freezeout that shows excellent numerical stability under cryogenic conditions. By inverse modeling accurate device models of the complex PMOSFETs were derived avoiding the stringent approximations of the analytical approaches. A high degree of consistency of simulations and measurements for the small-signal capacitance, conductance and drain current was obtained, by which the hole mobility extracted from the drain current could be verified by independent conductance measurements. In a next step the model could be used to extract data of interface states from measurements at 4.2 K, for which the usual methods based on MOS capacitors fail due to impurity freezeout.
Different Targeting Ligands-Mediated Drug Delivery Systems for Tumor Therapy

Traditional tumor treatments have the drawback of harming both tumor cells and normal cells, leading to significant systemic toxic side effects. As a result, there is a pressing need for targeted drug delivery methods that can specifically target cells or tissues. Currently, researchers have made significant progress in developing targeted drug delivery systems for tumor therapy using various targeting ligands. This review aims to summarize recent advancements in targeted drug delivery systems for tumor therapy, focusing on different targeting ligands such as folic acid, carbohydrates, peptides, aptamers, and antibodies. The review also discusses the advantages, challenges, and future prospects of these targeted drug delivery systems.
LAND USE/LAND COVER CHANGE DETECTION AND EFFICACY OF ARTIFICIAL RECHARGE STRUCTURES IN VANIYAR SUB BASIN OF THE PONNAIYAR RIVER, SOUTH INDIA USING REMOTE SENSING AND GIS TECHNIQUES

Spatial-temporal changes in land use (LU) and land cover (LC) using remote sensing and GIS buffer analysis techniques were carried out in the Vaniyar sub basin of the Ponnaiyar River in South India. Land uses within buffer distances from Artificial Recharge Structures (ARS) were calculated to quantify the effects of artificial recharge structures. Generally, some land use types were decreased with the increased ARS distances. The objective of this work is to notify the significant changes caused by artificial recharge structures and quantifying land use, land cover changes in the Vaniyar sub basin. Most importantly, the growth of population and domestic product accompanied by the growth in industrial and agricultural activities which led to LULC changes with increased built up land and cropland 20% and decreased water body from 2005 to 2015. Furthermore, the simulated based on results land use scenarios in 2005 and 2015, respectively, for assessing the impact of ARS on LULC change detection analysis were carried out in the sub basin. The results shown that land use pattern changed greatly after ARS construction. Since forest land and cropland are two main types of land use in the study area, the forest land occupied over 35.4% in the study area, during the year 2005-2000, water body had 16 % in the year 1985 and 8% in 2000 decreased. In the major check dam shows that indirect effect within 500 m buffered based on temporal, changing aspects of land use. This interpreted result was derived with the help of satellite data for the periods before and after the construction of artificial recharge structures. Generated land use/cover maps in and around the structures for both pre and post ARS with special reference to vegetation changes. The results reveal that, accompanied by climate change, the LULC changes and human water consumption to be the important factors contributing to the significant changes in the sub basin.
Comparison of OPPO Watch Sleep Analyzer and Polysomnography for Obstructive Sleep Apnea Screening

Objective To evaluate the clinical performance of the OPPO Watch (OW) Sleep Analyzer (OWSA) on OSA screening with polysomnography reference. Methods We recruited 350 participants using OWSA and PSG simultaneously in a sleep laboratory. The respiratory event index (REI) derived from OWSA and the apnea-hypopnea index (AHI) provided by PSG were compared. SHapley Additive exPlanation (SHAP) values were calculated to explain the model of OWSA. Results The OWSA-REI (26.5±18.5 events/h) correlated well with PSG-AHI (33.2±25.7 events/h; r = 0.91, p < 0.001), with an intraclass correlation coefficient (ICC) of 0.83. Using a threshold of AHI ≥15 events/h, the sensitivity, specificity, accuracy, and area under the curve (AUC) were 86.1%, 86.7%, 86.3%, and 0.94, respectively. Bland-Altman analysis showed that OWSA-REI and PSG-AHI were in good agreement (Mean Difference: −6.7, 95% CI:16.0 to −29.3 events/h). In addition, the effectiveness of the models in OWSA were also explained by visualizing SHAP values. Conclusion The OWSA demonstrated a reasonable performance for OSA screening in the clinical setting. In light of this, it is possible for smartwatches to become a complementary tool to PSG, which is particularly useful for larger-scale preliminary screenings.
An Experimental Investigation of Pore Pressure Generation in Moving Debris

Debris flow is a typical type of landslide and has huge impact on human life. The moving debris in traditional numerical modeling is assumed as hydrostatic along the flow depth, which is not the actual case when the generation of excess pore pressure in the shear zone significantly influences the flow mobility. The thesis studies the excess pore pressure build-up in the shear zone of the moving debris by using a rotating drum apparatus, which can rotate itself to provide the shear rate and generate the shear stress on the basal layer of the debris material. Samples with different silt content were tested under different speeds, and the result shows the excess pore pressure build-up is highly considerable related to the fine grain content. Results were also verified by comparing with two other studies using coaxial cylinder (Kyoji Sassa & Gonghui Wang, 2003; Xiang Yu, 2015), proving the excess pore pressure in the shear zone of the moving debris is highly considerable.
Realtime Feature Engineering for Anomaly Detection in IoT Based MQTT Networks

The MQTTset dataset has been extensively investigated for enhancing anomaly detection in IoT-based systems, with a focus on identifying Denial of Service (DoS) attacks. The research addresses a critical gap in MQTT traffic anomaly detection by proposing the incorporation of the ‘source’ attribute from PCAP files and utilizing hand-crafted feature engineering techniques. Various filtering methods, including data conversion, attribute filtering, handling missing values, and scaling, are employed. Anomalies are categorized and prioritized based on frequency of occurrence, with a specific emphasis on DoS attacks. The study compares the performance of the decision tree and its eight variant models (ID3, C4.5, Random Forest, CatBoost, LightGBM, XGBoost, CART, and Gradient Boosting) for anomaly detection in IoT-based systems. Evaluation metrics such as prediction accuracy, F1 score, and computational times (training and testing) are utilized. Hyperparameter fine-tuning techniques like grid search and random search are applied to enhance model performance, accuracy, and reduce computational costs. Results indicate that the benchmark Decision Tree model achieved 92.57% accuracy and a 92.38% F1 score with training and testing times of 2.95 seconds and 0.86 seconds, respectively. The Feature Engineering (Modified) dataset demonstrated a substantial improvement, reaching 98.56% accuracy and a 98.50% F1 score, with comparable training and testing times of 0.70 seconds and 0.02 seconds. Furthermore, the Modified Decision Tree Algorithm significantly improved accuracy to 99.27%, F1 score to 99.26%, and reduced training time to 0.73 seconds and testing time to 0.14 seconds. The research contributes valuable insights into feature engineering and guides the selection of effective approaches for anomaly detection in IoT-based systems, providing early threat warnings and enhancing overall system security and reliability.