Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Phototherapy on Serum Calcium Level in neonatal hyperbilirubinemia

Background: Neonatal hyperbilirubinaemia or jaundice is most common morbidity seen in first week of life. Approximately 60% of term and 80% of preterm newborn develop clinical jaundice. Phototherapy is one of the therapy methods for jaundice. It has few side effects, one of them is hypocalcemia. This study was done to see the effect of phototherapy on serum calcium level on jaundiced neonates. Methods: This was a prospective cross-sectional study, conducted from May 2020 to April 2021 at the Neonatal Intensive Care Unit and Special Newborn Care Unit of Karnali Academy of Health Sciences (KAHS), Jumla. A total of 40 neonates either male or female who had jaundice and placed under phototherapy were enrolled for this study. Blood sample for total serum bilirubin levels and serum calcium levels were sent. They were measured before starting and after stoppage of phototherapy. All the data were recorded in the preformed proforma. It was then, analyzed with the help of SPSS version 16. p-value< 0.05 was considered statistically significant. Results: The mean age and mean gestational age of the neonates was found to be 4.12±1.09 days and 38.55 ± 2.34 weeks respectively. The mean age at which jaundice was noticed in the neonates was 4.13 ± 1.4 days. Pre-phototherapy and post phototherapy level of total serum bilirubin was 14.53 mg/dl ± 2.91 mg/dl and 10.29 mg/dl ± 2.13 mg/dl respectively whereas serum calcium level before and after initiating phototherapy was 9.23 ± 1.11 mg/dl and 8.37 ± 0.69 mg/dl respectively. Hypocalcemia was found in 22.5% of jaundiced neonates receiving phototherapy. Conclusions: Phototherapy which is the mainstay of treating hyperbilirubinemia in neonates decreases serum calcium level in jaundiced neonates.
STUDENT IMPROVEMENT SYSTEM USING NOVEL ALGORITHM

: This paper describes a project which aims at the implementation of an online student attendance management system which can act as an efficient means in maintaining proper correct and updated records. With the help of the various tools and techniques in data base, data mining and web technologies. Currently the amount huge of data stored in educational database these database contain the useful information for predict of students performance. we can implement an attendance management system. Attendance can be accessed anytime and personalized reports can be generated for each student. An educational institution needs to have an approximate prior knowledge of enrolled students to predict their performance in future academics. This helps them to identify promising students and their parents also provide them an opportunity to pay attention to and improve those who would probably get lower attendance. This system which can predict the attendance of students from their previous performances using concepts of data mining techniques under decision tree based novel algorithm. It tracks all the attendance details of a student from the day one to the end of his course which can be used for all reporting purpose, tracking of attendance, completed semester’s years, coming semester year curriculum details.
Hybrid renewable energy photovoltaic and darrieus VAWT as propulsion fuel of prototype catamaran ship

Received Dec 22, 2020 Revised Mar 26, 2021 Accepted Jun 19, 2021 Currently, marine transportation in the world still uses fossil fuels. In addition to running low on supplies, fossil fuels also cause emissions that cause global warming. Sea transportation generates around 1,000 million tonnes of CO2 emissions. Therefore, the exploration of alternative energy is becoming a popular research direction. Several renewable energy sources include solar and wind energy. Indonesia has an average wind speed of above 8 m/s at sea. Also, the energy potential of the sun is around 4.8 kWh/m. Based on the potential of these renewable energy sources, this study discusses the potential of renewable energy sources from sunlight and wind, which are implemented in the prototype catamaran ship. The results obtained from the experiment, the total energy of photovoltaic (PV) and wind turbine generators is 774 Wh. This energy can be used to charge a battery with a battery specification of 35Ah for 6 hours.
AN ANALYSIS OF DEEP LEARNING AND ATTENTION MODELS FOR NATURAL LANGUAGE PROCESSING TASKS

: A Language is a means of communication among humans. It has a structure and is defined by the rules of grammar, which govern the constitution of sentences, clauses and words. Linguistics, the scientific study of language concerns itself with a wide variety of topics such as syntax and semantics. It concerns itself with how the words in a language are combined to form a sentence and how meaning and information are derived from the sentence based on the context. Natural Language processing as a computational discipline has the goal of getting computers to perform tasks, which involve language such as sentiment analysis, parts of speech tagging, machine translation and conversational agents. Classical NLP consisted of the symbolic paradigm, which was based on modelling grammar using theoretical computer science and using rules based on logic. Later on, statistical and probabilistic models became the standard with noisy channel models and Bayesian inference methods being prevalent. In the last two decades, the increase in computing power and the large amounts of data that is available over the internet has led to increased focus on Machine Learning. Machine Learning paradigms like Hidden Markov Models were successful. In the few years, there has been a surge in the use of Deep Learning for various tasks including NLP. Deep Learning is that subfield of Machine Learning, which creates representations of data using artificial neural networks based on computational graphs. In this paper, we will review how deep learning architectures can be used for the task of machine translation and building conversational agents.
Stroke Associated with Infective Endocarditis. Frequency and Prognostic Implications

Corresponding Author: Juan José Mengual Chirife Department of Neurology, Complex Hospitalari Moisès Broggi, Spain Email: JuanJose.MengualChirifie@san itatintegral.org Abstract: Stroke can be one of the most severe complications of Infective Endocarditis (IE) and it happens in 14% of patients with IE. Therefore, we aimed to review stroke frequency in patients diagnosed with IE in our hospital from 2010 to 2020. We defined it as IE-Associated Stroke (IEAS) when the stroke happened one month before or after IE's diagnosis. Sixty-six patients were diagnosed with IE during this period time and 15 (22%) suffered IEAS: 13 ischemic strokes, one Subarachnoid Hemorrhage (SAH) and one parenchymal hemorrhage with SAH. Patients with IEAS were more frequently men (80% vs 51% p = 0.042) and had less atrial fibrillation (13.3% vs 37.3%, p = 0.071). Although not statistically significant, Streptococcus sp (53% vs. 44.9%) and Staphylococcus aureus (33.3% vs. 16.3%) were more frequent in patients with IEAS. Mortality was increased in patients with IEAS (60% vs 13.7%, p = 0.001) especially in patients where stroke diagnosis was posterior to IE (88.9% vs 16.7%, p = 0.011). Hypertension (93.8% vs 63%, p = 0.013) and diabetes (53.2% vs 30%, p0 = 0.052) were also more frequent in those patients who died. In multivariate analysis, hypertension (OR 13.7 95% CI 1.4-138.4) and IEAS (OR 12.8 95% CI 2.8-58) were independently associated with death. The frequency of IEAS in our patients is comparable to what has been described in the literature. IEAS was associated with increased mortality.
Storm surges and storm wind waves in the Caspian Sea in the present and future climate

Abstract. This study is devoted to the analysis of the storm surges and wind waves in the Caspian Sea for the period from 1979 to 2017–2020. The models used are the circulation model ADCIRC and the wave model WAVEWATCH III with wind and pressure forcing from the NCEP/CFSR reanalysis. The modeling is performed on the unstructured grid with spacing to 300–700 m in the coastal zone. Mean and extreme values of surges, wave parameters, and storm activity are provided. The maximum significant wave height for the whole period amounts to 8.2 m. The average long-term SWH does not exceed 1.1 m. No significant trend in the storm activity was found. The maximum surges height amounts to 2.7 m. Analysis of the interannual variability of the surges occurrence showed that 7–10 surges with a height of more than 1 meter were obtained per year and the total duration of all these surges was 20–30 days per year. Assessment of the risk of coastal flooding was carried out by calculating the extreme values of the Sea for different return periods 5, 10, 25, 50, and 100 years. The extreme sea level values in the northern part of the Caspian Sea for the return period 100 years is close to 3 m and the areas with big surges are located along the eastern and western coasts. Based on climatic scenarios of CMIP5, a forecast is made for the recurrence of storm wind waves in the 21st century. A statistically significant increase of storm waves recurrence in the future was found, but it is not dramatically growing.
Convolutional Neural Networks and Grammar Rules Analysis for Speech-based Native Language Identiﬁcation

The determination of an individual’s native language has been previously studied using written text produced by the individual. An emerging approach is to make this classiﬁcation based on speech-based data, such as transcripts or audio characteristics. We present a fusion model, taking into account both a transcript of spoken data and the i-vectors extracted from the recordings. The speech transcript features are captured with a CNN model based on character-level bigrams and with grammatical rule analyses. We report a test accuracy of near 80% in native language identiﬁcation based on this speech data alone.
Data Visualisation for Supporting Linguists in the Analysis of Toxic Messages

: The goal of this research is to provide linguists with visualisations for analysing the results of their hate speech annotation. These visualisations consist of a set of interactive graphs for analysing the global distribution of annotated messages, ﬁnding relationships between features, and detecting inconsistencies in the annotation. We used a corpus that includes 1,262 comments posted in response to different Spanish online new articles. The comments were annotated with features such as sarcasm, mockery, insult, improper language, construc-tivity and argumentation, as well as with level of toxicity (’not-toxic’, ’mildly toxic’, ’toxic’ or ’very toxic’). We evaluated the selected visualisations with users to assess the graphs’ comprehensibility, interpretability and attractiveness. One of the lessons learned from the study is the usefulness of mixed visualisations that include simple graphs (Bar, Heat map) - to facilitate the familiarisation with the results of the annotated corpus together with more complex ones (Sankey, Spider or Chord) - to explore and identify relationships between features and to ﬁnd inconsistencies.
Monoethanolamine-induced glucose deprivation promotes apoptosis through metabolic rewiring in prostate cancer

Rationale: Cancer cells rely on glucose metabolism for fulfilling their high energy demands. We previously reported that monoethanolamine (Etn), an orally deliverable lipid formulation, reduced intracellular glucose and glutamine levels in prostate cancer (PCa). Glucose deprivation upon Etn treatment exacerbated metabolic stress in PCa, thereby enhancing cell death. Moreover, Etn was potent in inhibiting tumor growth in a PCa xenograft model. However, the precise mechanisms underlying Etn-induced metabolic stress in PCa remain elusive. The purpose of the present study was to elucidate the mechanisms contributing to Etn-mediated metabolic rewiring in PCa. Methods: Glucose transporters (GLUTs) facilitate glucose transport across the plasma membrane. Thus, we assessed the expression of GLUTs and the internalization of GLUT1 in PCa. We also evaluated the effects of Etn on membrane dynamics, mitochondrial structure and function, lipid droplet density, autophagy, and apoptosis in PCa cells. Results: Compared to other GLUTs, GLUT1 was highly upregulated in PCa. We observed enhanced GLUT1 internalization, altered membrane dynamics, and perturbed mitochondrial structure and function upon Etn treatment. Etn-induced bioenergetic stress enhanced lipolysis, decreased lipid droplet density, promoted accumulation of autophagosomes, and increased apoptosis. Conclusion: We provide the first evidence that Etn alters GLUT1 trafficking leading to metabolic stress in PCa. By upregulating phosphatidylethanolamine (PE), Etn modulates membrane fluidity and affects mitochondrial structure and function. Etn also induces autophagy in PCa cells, thereby promoting apoptosis. These data strongly suggest that Etn rewires cellular bioenergetics and could serve as a promising anticancer agent for PCa.
Global Balanced Wind Derived from SABER Temperature and Pressure Observations and its Validations

Abstract. Zonal winds in the stratosphere and mesosphere play important roles in the atmospheric dynamics and aeronomy. However, the direct measurement of winds in this height range is difficult. We present a dataset of the monthly mean zonal wind in the height range of 18–100 km and at latitudes of 50° S–50° N from 2002 to 2019, which is derived by the gradient balance wind theory and the temperature and pressure observed by the SABER instrument. The tide alias above 80 km at the equator is replaced by the monthly mean zonal wind measured by a meteor radar at 0.2° S. The dataset (named as BU) is validated by comparing with the zonal wind from MERRA2 (MerU), UARP (UraU), HWM14 empirical model (HwmU), meteor radar (MetU) and lidar (LidU) at seven stations from 53.5° N to 29.7° S. At 18–70 km, BU and MerU have (1) nearly identical zero wind lines, (2) year-to-year variations of the eastward/westward wind jets at middle and high latitudes, (3) the quasi-biennial oscillation (QBO) and semi-annual oscillation (SAO), especially the anormal QBO in early 2016. The comparisons among BU, UraU and HwmU show good agreement in general below 80 km. Above 80 km, the agreements among BU, UraU, HwmU, MetU and LidU are good in general, except some discrepancies at limited heights and months. The BU data are archived as netCDF files and can be available at https://dx.doi. org/10.12176/01.99. 00574 (Liu et al., 2021).
Effect of different treatments of ethrel on ripening behaviour and post- harvest quality of mango (Mangiferaindica L.) during storage

An experiment was done to assess the effect of various post -harvest treatments of ethrel on ripening behaviour and post -harvest quality of mango cv. Amrapali (Mangiferaindica L.) during storage at Punjab Agricultural University, Regional Research Station, Gurdaspur during the years 2013-14 and 2014-15. The experiment was planned under completely randomized design (CRD) with three replications. The treatment units was six fruits per replication. Total five treatments and control were applied. The objective of this work was to evaluate the effects of various concentrations of ethrel (500 ppm, 750 ppm, 1000 ppm,1250 ppm, 1500 ppm and control ) on shelf life of mango fruits when stored under ambient conditions. The treated fruits were assessed for physiological changes fruit colour, flavour/aroma, organoleptic taste, marketability, ripening, specific gravity, moisture loss, decay, physiological loss in weight (PLW), total soluble solids (TSS), acidity and sugars. The observations were recorded at 5, 8 and 11 days after storage. At 11th day of storage these mangoes exhibited deep yellow fruit colour with excellent flavour, organoleptic taste and highly acceptable marketability at 1000 ppm ethrel treatment as compared to other ethrel doses. Fruit ripening (100%), specific gravity (0.84), TSS(23Â°Brix) and sugars(20.65%) were highest in the fruits treated with 1000ppm ethrel as compared to rest of treatments at 11th day of storage. Lowest moisture loss (8.5%), decay (1%), PLW(12.50%) and acidity(0.12%) were observed in the fruits treated with 1000ppm of ethrel as compared to rest of treatments at 11th day of storage. The beneficial effect of the present study is to overcome the problem of uneven and late ripening of Amrapali mango by postharvest treatment of ethrel at 1000 ppm at their mature green stage to develop typical fruit flavour with excellent fruit quality.
Academic Leagues (AL) are an important learning tool. They are defined as non-profit student organizations with a self-managed learning

vanessameneses.nutri@gmail.com Abstract Introduction: Academic Leagues (AL) are student associations that help strengthen the teaching-service-community relationship. Objective: To analyze the potential and challenges of Academic Leagues as learning devices, based on the perception of students and graduates of health degree programs on two campuses at a public university in northeastern Brazil, which use different teaching approaches. Methods: This is a qualitative study, whose data were collected through Focus Groups and analyzed using the Content Analysis technique. Results: Despite the challenges posed by people management, the standardization of the Leagues and the need for financial resources for full operation, the Academic Leagues, regardless of the teaching model adopted by the institution, play an important role in education and training of health workers so that they can develop greater critical sense, broader vision on health promotion and greater attention to the principles and demands of the Unified Health System. Conclusion: The results showed that students and teachers are encouraged to take action to overcome challenges and to exchange experiences to enhance complementary health education. Educational institutions have the role of monitoring the actions of the Leagues to strengthen and ensure the offer of university extension activities.
Potential effect of hyaluronic acid and triamcinolone acetate, alone or combined, on chondrogenic differentiation of mesenchymal stem cells

Background: Osteoarthritis is a complex degenerative disease with several factors contributing to joint damage. Objective: To compare the potential effect of hyaluronic acid (HA) and triamcinolone acetonide (TA), alone or combined, on the in vitro chondrogenic differentiation process of mesenchymal stem cells (MSCs). Methods: MSCs were divided into four groups: Control, HA, TA, and HA/TA combined. Each treatment group was cultured for 14 days in chondrogenic differentiation medium. The chondrogenic differentiation potential was assessed by histology and immunohistochemistry. Results: The HA and HA/TA-treated MSCs presented histological characteristics similar to native chondrocytes. The extracellular matrix (ECM) of TA-treated MSCs was compact and organized. Glycosaminoglycan staining was intense in Control, moderate in TA, slight in HA/TA, and undetectable in HA. Type II collagen immunoreactivity was high in the TA-treated ECM and MSCs. Conclusions: Histological analysis shows that HA influences morphological development similar to chondrocytes of the MSCs, but with low expression of specific cartilage molecules. The TA promotes formation of a compact and organized ECM.
Big Data Mining: A Comprehensive Analysis

: Big Data is a term used to identify the datasets that cannot be managed due to their large size and complexity, with the current methodologies or data mining software tools. Big Data mining is the ability of extracting useful information from the large datasets or streams of data, that due to its volume, variability, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most important opportunities for the next few years. In this review paper, a broad overview of the topic, its current status, its characteristics through HACE Theorem, Big Data generation and acquisition and Big Data challenges are summarised. A brief overview of the technologies of Big Data Analytics such as Hadoop and MapReduce is also presented.
Separation of Commercially Important Tuna from Other Fishes Using Feature Descriptor and Pre-trained CNN Models

Aims / Objectives: Identification of fish species is essential in export industries. Among the different fish species exported, tuna forms a significant portion and hence the separation of tuna from other fishes is necessary. The work aims to develop automated systems for the separation of commercially important tuna from other fishes. Methodology: The work proposes two models for the classification of commercial fishes. The first model uses conventional feature descriptors, which extract features from both spatial and frequency domain. These features are combined and are reduced by an ensemble dimension reduction method. The combined and reduced feature sets are evaluated using different classifiers. The second proposed model uses four pre-trained convolutional neural networks, VGG16, VGG19, Xception, and MobileNet, for the classification. The models are fine-tuned for the classification process. Results: Results show that for the first model, extreme learning machine classifier with Mercer wavelet kernel gives high accuracy on combined feature set while the polynomial kernel ELM provides better performance with the reduced set. For the second model, a comparison of the performance of four CNN models is done, and results indicate that VGG19 outperforms other networks in the classification task. Conclusion: Among the two proposed models, pre-trained CNN based model shows better performance than the conventional method in the separation task. Different performance measures, accuracy, precision, recall, F-score, and misclassification error are used to evaluate the system. A comparison of performance of the proposed models with the state-of-the-art systems is also reported.
Binary Descriptors for Dense Stereo Matching

: Dense local stereo matching is traditionally based on initial cost evaluation using a simple metric followed by sophisticated support aggregation. There is a high potential of replacing these simple metrics by robust binary descriptors. However, the available studies focus on comparing descriptors for sparse matching rather than the dense case of extracting a descriptor per each pixel. Therefore, this paper studies the design decisions of well-established binary descriptors such as BRIEF (Binary Robust Independent Elementary Features), ORB (Oriented FAST and rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints) and FREAK (Fast Retina Keypoint) to decide their suitablility for the dense matching case. The results shows that support agregation is required for use with binary descriptors to handle edges in local dense matching. Also, BRIEF produced the smoothnest disparity map if geometric transformations is not present. Whereas, FREAK and BRISK achieved the least overall error percentage across all regions. The lastest Middlebury Stereo benchmark is utilized in the experiments.