Miracle-dz/UniFAR-FTUs huggingface dataset:
Diabetic Retinopathy Detection Using GoogleNet Architecture of Convolutional Neural Network Through Fundus Images

The number of people who have Diabetes is about 422 million in the world. Diabetes is a group of metabolic disease characterized by elevated lev- els of blood glucose. The serious damage of blood vessels caused by Diabetes in the tissue at the retina is called Diabetic Retinopathy. Diabetic Retinopathy can cause severe blindness. Early detection can help patients find a suitable treatment and prevent the blindness. Opthalmologists can detect this disease by screening, but this method takes a long time, is very costly and need pro- fessional skills to perform it. In the big data era, many researchers use deep learning models for medical help. One of the models use image classification. We have designed a tool using image classification to help ophthalmologists detect diabetic retinopathy. In this research, we use image classification to classify Diabetic Retinopathy into two classes which are normal (No DR) and Diabetic Retinopathy. We use 200 datasets of fundus images that we obtain from Kaggle Database. We used deep learning model in this research that is one of Convolutional Neural Network architecture called GoogleNet. For training the model we used Python as programming languange with Pytorch library. GoogleNet has a very good performance for image classification and has an accuracy of 88%.
Density Functional Studies on Crystal Structure and electronic properties of Potassium Alanate as a candidate for Hydrogen storage

Potassium Alanate is one of the goal candidates for hydrogen storage during past decades. In this report, initially the Density Functional Theory was applied to simulate the electronic and structural characteristic of the experimentally known KAlH4 complex hydride. The relaxation of unit cell parameters and atomic positions was performed until the total residual force reduced less than 0.001ev per unit cell. The final deduced cell parameters of this orthorhombic structure were a=8.834, b=5.763, c=7.328A˚. Calculations were carried out by using Projected Augmented Plane wave method via QUANTUM ESPRESSO Package. In the next step, the Density of States calculations together with band structure results, showed that our data coincide with a non-magnetic KAlH4 insulator with a band gap of 5.1ev. In order to investigate the nature of chemical bonds in the crystal structure, the charge density distribution in (100),(010),(001),(110) planes, along with Born Effective charge and Lowdin population was used. The results show the transition of a partial charge from K+ cation to [AlH4]- subunit which leads to an ionic bond.
In silico identification of cis-Regulatory elements and their functional annotations from assembled ESTs of Artemisia annua L. involved in abiotic stress signaling

Salt stress is a common side-effect in plants impacted on plant growth, metabolism and productivity. A. annua L. is one of the well-known antimalarial plants, biosynthesized artemisinin in its leaf, now introduced in all-over the world. In this article, we have analyzed the A. annua L. ESTs under salt stress and predicted cis-regulatory elements, roles in abiotic stress signaling. Further, the predicted abiotic stress responsive factors were analyzed in order to their function annotations as compare to the genome of Arabidopsis thaliana. 11 EST-contigs assembled from 127 were 29 signals elements were identified by CAP3 program. In order to evaluate accuracy of the identified factors, gene ontology functions were performed. GOBP analysis enriched the genes (85.71%) as the response to abiotic signaling. The co-expression analysis was revealed by gene investigators and String 10.0, these factors-oriented genes had at least 0.40 correlations and 0.7 mutual connection. In projected PPI network, the recognized factors belong to plant hormone signaling and diterpene pathways. These factors (ABF1, APX CCC1, CPK6, JAZ1, MYC2) introduced as candidate genes responsive factors could be overexpressed in A. annua L. plants either alone or in a shuttle may led the good metabolism and higher artemisinin content.
COUP-TFI Deletion Affects Angiogenesis and Apoptosis Related Genes Expression in Ex-Vivo Mice Placenta

Background: Chicken Ovalbumin Upstream Promoter-Transcription Factor I (COUP-TFI) is a member of the steroid/thyroid nuclear receptor superfamily. Aim of the study was to investigate whether the absence of this gene affects placental development and fetal growth in a mouse model. For this scope placentas of COUP-TFI-knockout (COUP-TFI –/–) and wild-type (WT) were collected at the 18.5 days post-coitum. mRNA amount of following genes known to be involved in different key molecular pathways were evaluated: Bax and Bcl-2 (apoptosis), p21 and p53 (proliferation), VEGF-A, PlGF, HIF1α, Flt-1, ENG, and INHA (angiogenesis). Mice litter weight at birth was also compared.Results: RT-qPCR analysis showed an increased mRNA expression of VEGF-A and Bax in placental tissue of COUP-TFI –/– mice compared to WT mice. Also, a lost in the positive correlation between Bcl-2 and INHA, p21 and ENG, as well as HIF1α and Flt-1 mRNA expression in COUP-TFI null mutants was found. Finally, the mean weight of WT mice was 1.6 g (±0.14) compared to 1.3 grams (±0.13) of COUP-TFI –/– mice (p<0.05). Conclusions: Our results show that COUP-TFI deletion is associated to a lower birth weight in mouse and an increased placental mRNA expression of pro-apoptotic Bax and pro-angiogenetic VEGF-A genes.
Electromagnetic field prediction – scattered and total field approaches

We summarize recent approaches to estimate electromagnetic fields in an arbitrary two-dimensional scattering domain. Only an estimate of the objects’ spatial support in the scattering environment is required in our approach. Using the Huygens’ principle and compressive sensing, the tangential electromagnetic fields on the scatterers’ surface are estimated by making few field measurements in the scattering domain. Based on the field measurements, two inverse formulations are discussed in the paper: (a) scattered field inverse (SFI) and (b) total field inverse (TFI). For an indoor scattering domain with three scattering objects, the total electric field is recovered with an accuracy of 92%, and the incident field is recovered with an accuracy of 96% when the number of measurements is 0.3 times the number of unknowns in which the problem is formulated.
Analysis and forecast of the production parameters of major cereal crops in Serbia

This paper deals with the production parameters of major cereal crops in Serbia (namely the cultivation area, annual production and yields of the cereal crops considered) in the period 2005–2018. The objective of this study was to determine trends in and the volume and stability of the production parameters of the most important cereal crops in Serbia. Furthermore, a forecast of changes in the parameters observed was made for the five-year period 2019-2023. The following cereal crops were under consideration: corn, wheat, barley, oats, rye and triticale. The area devoted to corn in Serbia was found to be very stable, indicating a slight downward trend. However, the Serbian corn production lacked stability due to variations in yield, suggesting a very slight downward trend. Wheat was found to be far more stable with slightly growing trends in all the parameters considered. Moreover, positive trends in all the parameters considered were also noticed in the Serbian triticale production.
Uncertainty Analysis on Electric Power Consumption

: The analysis of large time-series datasets has profoundly enhanced our abilityto make accurate predictions in many fields. However, unpredictable phenomena, such as extreme weather events or the novel coronavirus 2019 (COVID-19) outbreak, can greatly limit the ability of time-series analyses to establish reliable patterns. The present work addresses this issue by applying uncertainty analysis using a probability distribution function, and applies the proposed scheme within a preliminary study involving the prediction of power consumption for a single hotel in Seoul, South Korea based on an analysis of 53,567 data items collected by the Korea Electric Power Corporation using robotic process automation. We first apply Facebook Prophet for conducting time-series analysis. The results demonstrate that the COVID-19 outbreak seriously compromised the reliability of the time-series analysis. Then, machine learning models are developed in the TensorFlow framework for conducting uncertainty analysis based on modeled relationships between electric power consumption and outdoor temperature. The benefits of the proposed uncertainty analysis for predicting the electricity consumption of the hotel building are demonstrated by comparing the results obtained when considering no uncertainty, aleatory uncertainty, epistemic uncertainty, and mixed aleatory and epistemic uncertainty. The minimum and maximum ranges of predicted electricity consumption are obtained when using mixed uncertainty. Accordingly, the application of uncertainty analysis using a probability distribution function greatly improved the predictive power of the analysis compared to time-series analysis.
Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks

: In Wireless Body Area Networks (WBANs) with respect to health care, sensors are positioned inside the body of an individual to transfer sensed data to a central station periodically. The great challenges posed to healthcare WBANs are the black hole and sink hole attacks. Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path. Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness. This work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score (PCS) and an MK-Means algorithm, which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory issues. First, the gathered training data feature count is reduced through data pre-processing in the PCS. Second, the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification. Third, certain attack detection measures given by the intrusion detection system, such as the number of data packages trans-received, are identified by the MK-Means algorithm. This study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate, low communication overhead, and reduced end-to-end delay in the network and improves the accuracy of biomedical data.
Non Uniform Rational B-Splines and Lagrange approximations for time-harmonic acoustic scattering: accuracy and absorbing boundary conditions

ABSTRACT The paper aims to evaluate the performance of the Lagrange-based finite element method and the non-uniform rational B-splines isogeometric analysis of time-harmonic acoustic exterior scattering problems using high-order local absorbing boundary conditions, in particular based on the Karp’s and Wilcox’s far-field expansions. The analysis of accuracy and convergence of both methods is achieved by observing the effect of the order of the approximating polynomial, the number of degrees of freedom, the wave number, and the absorbing boundary conditions tuning parameters. It is concluded that, regardless of the polynomial order, IGA provides a higher accuracy per degree of freedom compared to the traditional Lagrange-based finite element method.
Nellore cloned calf viability during the postnatal period

ABSTRACT: This study evaluated the viability of Nellore cloned calves derived from somatic cell nuclear transfer (SCNT) and compare their viability with animals of the same breed derived from in vitro fertilization (IVF). Thus, two groups were formed. Group I (GI) consisted of 10 calves derived from SCNT and group II (GII) consisted of 10 calves derived from IVF. The differences detected between the groups were in the physical examination of the respiratory tract in GI, which represented the most common clinical-pathological disturbances. The Apgar index score indicated that 80% of GI animals were depressed and all had pale mucous membranes. Thus, anemia was reported in GI. In GII, this started at 12 h of life and was probably caused by an iron deficiency. Moreover, total calcium and ionized calcium levels were higher in GI immediately after birth. These alterations probably resulted in a high incidence of mortality in GI, reaching 90% of the calves, whereas mortality was only 20% for the calves in GII. In conclusion, cloned calves, which were derived from SCNT, had physiological and metabolic alterations after delivery, leading to a higher mortality rate during the perinatal period.
Reducing the Overlap Between Machiavellianism and Subclinical Psychopathy: The M7 and P7 Scales

Machiavellianism (Mach) and subclinical psychopathy are two widely studied antagonistic personality traits with distinct theoretical conceptualizations. Mach is conceptualized by strategic deviousness, cynicism, and pragmatic morality, whereas subclinical psychopathy is conceptualized by impulsive antisocial tendencies, callousness, and rule-breaking. However, existing measures of the two traits are typically highly correlated and have very similar nomological networks. Notably, even though psychopathy scales should be more strongly positively associated with antisocial impulsivity and more strongly negatively associated with conscientiousness than Mach scales, existing Mach and psychopathy scales tend to be similarly related to these constructs. We created a new Mach scale, the M7, and a new psychopathy scale, the P7, by selecting items from existing Mach and psychopathy scales on the basis of the correlations of these items with antisocial impulsivity and conscientiousness. Across three studies (combined N = 4,607), the M7 and P7 showed acceptable to good psychometric properties in terms of closeness to unidimensionality, measurement precision, temporal stability, measurement invariance across language and gender groups, and convergent and discriminant validity (nomological network, self-other agreement, and interpersonal perceptions in group interactions). Most importantly, the new scales assess clearly distinct latent traits that are more in line with their theoretical conceptualizations than established scales are.
A Robust, High-Temperature Organic Semiconductor

We introduce a new pentacene-based organic semiconductor, 5,6,7-trithiapentacene-13-one (TTPO). TTPO is a small-molecule organic semiconductor that is simple to synthesize and purify, readily crystallizes, melts in air from 386–388 °C without decomposition, and is indefinitely stable against degradation in acid-free solution. TTPO has a high molar absorptivity, optical and electrochemical HOMO–LUMO gaps of 1.90 and 1.71 eV, respectively, and can be thermally evaporated to produce highly uniform thin films. Its cyclic voltammogram reveals one reversible oxidation and two reversible reductions between +1.5 and −1.5 V. The crystal structure for TTPO has been solved and its unique parallel displaced, and head-to-tail packing arrangement has been examined and explained using high-level density functional theory. High-resolution scanning tunneling microscopy (STM) was used to image individual TTPO molecules upon assembly on a pristine Au(111) surface in ultrahigh vacuum. STM images reveal that vapor-deposited T.
A Novel Technique for Early Detection of COVID-19

COVID-19 is a global pandemic disease, which results from a dangerous coronavirus attack, and spreads aggressively through close contacts with infected people and artifacts So far, there is not any prescribed line of treatment for COVID-19 patients Measures to control the disease are very limited, partly due to the lack of knowledge about technologies which could be effectively used for early detection and control the disease Early detection of positive cases is critical in preventing further spread, achieving the herd immunity, and saving lives Unfortunately, so far we do not have effective toolkits to diagnose very early detection of the disease Recent research findings have suggested that radiology images, such as X-rays, contain significant information to detect the presence of COVID-19 virus in early stages However, to detect the presence of the disease in in very early stages from the X-ray images by the naked eye is not possible Artificial Intelligence (AI) techniques, machine learning in particular, are known to be very helpful in accurately diagnosing many diseases from radiology images This paper proposes an automatic technique to classifyCOVID-19 patients from their computerized tomography (CT) scan images The technique is known as Advanced Inception based Recurrent Residual Convolution Neural Network (AIRRCNN), which uses machine learning techniques for classifying data We focus on the Advanced Inception based Recurrent Residual Convolution Neural Network, because we do not find it being used in the literature Also, we conduct principal component analysis, which is used for dimensional deduction Experimental results of our method have demonstrated an accuracy of about 99%, which is regarded to be very efficient © 2021 Tech Science Press All rights reserved
Dependency of Evaporation and Class A Pan Coefficient on Meteorological Parameters

In this article, the relation of evaporation deriving meteorological parameters particularly wind speed, solar radiation and vapor pressure deficit with evaporation schemes namely Class A pan evaporation (Ep), potential evapotranspiration (PET) and reference evapotranspiration (ETo) at Tharandt, Germany for the years from 2004 to 2013 was investigated. PET was calculated using three methods: 1. Haude (2005), 2. Wendling (1991), 3. Penman (1963); whereas, ETo was calculated according to Food and Agricultural Organization-Penman Monteith method. The results showed that the evaporatin schemes were mainly driven by solar radiation (R2 â‰¥ 0.69, RMSE â‰¤ 0.76mm d-1) and vapor pressure deficit (R2 â‰¥ 0.53, RMSE â‰¤ 0.92mm d-1).
Association Between Harm Reduction Strategies and Healthcare Utilization and Outcomes in Patients on Long-term Prescribed Opioid Therapy Presenting to Acute Healthcare Settings: A Systematic Review and Meta-analysis

BackgroundLong-term prescription of opioids by healthcare professionals has been linked to poor individual patient outcomes and high resource utilization. Harm reduction strategies in this population in regard to acute healthcare settings may have substantial impact.MethodsWe performed a systematic review and meta-analysis of primary studies. The studies were included according to the following criteria: 1) age 18 and older; 2) long-term/chronic prescribed opioid therapy; 3) acute healthcare setting presentation from a complication of opioid therapy; 4) evaluating a harm reduction strategy; 5) comparing the effectiveness of different interventions; 6) addressing patient or healthcare related outcomes. We performed a qualitative analysis of harm reduction strategies identified. We pooled patient and system related outcome data for each harm reduction strategy.ResultsA total of 5664 studies were screened and 21 studies were included. A total of 11 broad categories of harm reduction strategies were identified. Meta-analysis was performed for the “supports for patients in pain” harm reduction strategy on two system-related outcomes using a ratio of means. The number of emergency department (ED) visits were significantly reduced for cohort studies (n=6, 0.36, 95% CI [0.20-0.62], I2 = 87%) and randomized controlled trials (RCTs) (n=3, 0.71, 95% CI [0.61-0.82], I2 = 0%). The number of opioid prescriptions at ED discharge was significantly reduced for RCTs (n=3, 0.34, 95% CI [0.14-0.82], I2 = 78%). InterpretationFor patients presenting to acute healthcare settings with complications related to long-term opioid therapy, the intervention with the most robust data is “supports for patients in pain”. Study registrationCRD42018088962
GENDER RELATIONS IN THE MUNICIPAL NETWORK OF BELO HORIZONTE: CONTINUOUS TEACHER TRAINING

This text presents the reflective report of the project of continuous teacher training in the Municipal Education Network of Belo Horizonte, between 2015 and 2018, in the field of gender relations. Partnership between the Municipal Department of Education and the Pontifical Catholic University of Minas Gerais promoted the necessary conditions for the implementation and integration of academic activities of extension, education and research at undergraduate and graduate levels. Despite the national political context of gender offensives in the educational field, the demand for training actions came from teachers and education managers when they faced the impacts of asymmetries in learning between girls and boys, as well as situations of gender violence at school. In the light of the post-structuralist theoretical assumptions of gender, training uses diverse methodologies such as study groups, seminars, pedagogical workshops and case studies in different spaces of the city, such as schools, universities and museums. The analysis of the four years of the project revealed the importance of an interdisciplinary approach to gender relations of a permanent nature, since isolated training actions have proved ineffective.
Changes in age and maturity of anadromous whitefish (Coregonus lavaretus) in the northern Baltic Sea from 1998 to 2014

The maturation of anadromous whitefish (Coregonus lavaretus) was analysed from samples taken from commercial coastal fishing in 1998–2014 in the Gulf of Bothnia. Whitefish matured at a younger age from year to year. The proportion of older (5–12 sea years) mature males decreased from 79% to 39% in the northern Gulf of Bothnia (66°N–64°N) and from 76% to 14% in southern (64°N–60°30'N) during the study period. At the same time, the proportion of young males (2–4 sea years) increased. Whitefish matured younger: the proportion of mature fish at age four increased in both the north and south among females (13% → 98%; 6% → 85%) and males (68% → 99%; 29% → 89%). The catch length of four-year-old fish increased during the study period in both sexes. In contrast, the length of six-year-old females decreased from year to year. Sea surface temperatures increased during the study period, and were possibly associated with a decrease in the age of maturation and faster growth.
Stability Enhancement of a Single-Stage Transonic Axial Compressor Using Inclined Oblique Slots

A casing treatment using inclined oblique slots (INOS) is proposed to improve the stability of the single-stage transonic axial compressor, NASA Stage 37, during operation. The slots are installed on the casing of the rotor blades. The aerodynamic performance was estimated using three-dimensional steady Reynolds-Averaged Navier-Stokes analysis. The results showed that the slots effectively increased the stall margin of the compressor with slight reductions in the pressure ratio and adiabatic efficiency. Three geometric parameters were tested in a parametric study. A single-objective optimization to maximize the stall margin was carried out using a Genetic Algorithm coupled with a surrogate model created by a radial basis neural network. The optimized design increased the stall margin by 37.1% compared to that of the smooth casing with little impacts on the efficiency and pressure ratio.