Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical analysis of plastic die deformation during high temperature copper extrusion

In copper extrusion, billet temperatures of 600°C or more are very common and the dies are therefore exposed to high thermo-mechanical stress. This causes deflection and wear of the dies and thus reduced quality of the extruded profile. In the present study, die deflection and residual deformation after several extrusion cycles was investigated by means of extrusion trials and numerical analyses. Material models of four tool materials (hot-work tool steels 1.2367 and CS1, nickel-based alloy 718, cobalt-based alloy Stellite 1) and the copper alloy CW024A were provided by hot compression tests. Extrusion trials were carried out applying four different dies, each made of another tool material. Using the FEM based software DEFORM 2D, the extrusion trials were modeled and decoupled die stress analyses were performed, which simulated three consecutive load cycles. The focus of the data interpretation was in die deflection in proximity of the die land due to the thermo-mechanical load and residual plastic deformation after relief of the mechanical load. Larger values of deflection close to the die land were observed for the hot-work tool steels, while the deflection of nickel- and cobalt-based alloys was negligibly small. Also, remarkable plastic deformation was only determined for the hot-work tool steels, with increasing values for every simulated load cycle. This analysis characterizes the performance limits of hot-work tool steels and the benefits of nickel- and cobalt-based alloys regarding contour accuracy during high temperature copper extrusion.
A Reliability Assessment of the NCEP/FNL Reanalysis Data in Depicting Key Meteorological Factors on Clean Days and Polluted Days in Beijing

In this study, based on the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data, the reliability and performances of their application on clean days and polluted days (based on the PM2.5 mass concentrations) in Beijing were assessed. Conventional meteorological factors and diagnostic physical quantities from the NCEP/FNL data were compared with the L-band radar observations in Beijing in the autumns and winters of 2017–2019. The results indicate that the prediction reliability of the temperature was the best compared with those of the relative humidity and wind speed. It is worth noting that the relative humidity was lower and the near-surface wind speed was higher on polluted days from the NCEP/FNL data than from the observations. As far as diagnostic physical quantity is concerned, it was revealed that the temperature inversion intensity depicted by the NCEP/FNL data was significantly lower than that from the observations, especially on polluted days. For example, the difference in the temperature inversion intensity between the NCEP/FNL data and the observation ranged from −0.56 to −0.77 °C on polluted days. In addition, the difference in the wind shears between the NCEP/FNL reanalysis data and the observations increased to 0.40 m/s in the lower boundary layer on polluted days compared with that on clean days. Therefore, it is suggested that the underestimation of the relative humidity and temperature inversion intensity, and the overestimation of the near-surface wind speed should be seriously considered in simulating the air quality in the model, particularly on polluted days, which should be focused on more in future model developments.
Microbial Contaminants in Fresh and Extended Turkey Semen and their Sensitivity to Antibiotics

Microorganisms that inhabit the avian cloaca usually contaminate poultry semen which could easily spread throughout an entire flock. This study was conducted to determine the presence of microbial contaminants in turkey semen and evaluate their antibiotic sensitivity. Semen was collected from each tom, pooled and then divided into two aliquots A and B. Aliquot A was immediately evaluated for microbial contaminants and antibiotic sensitivity while aliquot B, was extended and preserved for 24 hours at 4 C and thereafter microbial culture, identification and antibiotic sensitivity were conducted. Escherichia coli, Enterococcus faecalis, Bacillus subtilis, Corynebacteria species and a fungal organism Candida albican were isolated and identified in both aliquots. All the identified organisms were sensitive to pefloxacin, gentamicin and ciprofloxacin, while Enterococcus faecalis, Bacillus subtilis and Corynebacteria species were resistant to Ampicillin-cloxacillin, cefuroxime, amoxicillin and ceftriaxone. Escherichia coli was only resistant to co-trimoxazole, ofloxacin and nalidixic acid. The study concludes that, Escherichia coli, Enterococcus foecalis, Bacillus subtilis, Corynebacteria species and Candida albican were found to be turkey semen contaminants and were resistant to penicillin and streptomycin combination in turkey semen extender but sensitive to pefloxacin, gentamicin and ciprofloxacin.
The Learning Effect of Online Discussions: New Active Form of Learning under COVID-19

Background: COVID-19 created confusion in all areas of society and is no exception in education. In line with the demands of the times, we investigated the applicability of active learning online for the health professional.Methods: This study was conducted in the school of dentistry, a selective university. Eighty-two dentistry students participated in this study in the second semester of 2020. The students were randomly assigned to four different groups and learned according to given experimental procedures. Their performance in the final test scores was analyzed using ANOVAs. Subsequently, we systematically analyzed past offline active learning and online experiments.Results: As with past offline experiments, self-study before discussion groups have achieved high learning outcomes in verbatim type and transfer type items. Online self-study groups scored slightly lower than offline self-study groups, but online self-study groups scored higher than offline and online lecture groups.Conclusions: Our findings denote that active learning for health professionals was applicable online. When self-study precedes the discussion, it becomes richer, thereby increasing students' learning outcomes.
The Passive microwave Empirical cold Surface Classification Algorithm (PESCA): application to GMI and ATMS

This paper describes a new Passive microwave Empirical cold Surface Classification Algorithm (PESCA) developed for snow cover detection and characterization by using passive microwave satellite measurements. The main goal of PESCA is to support the retrieval of falling snow, as several studies have highlighted the influence of snow cover radiative properties on the falling snow passive microwave signature. The developed methodology is based on the exploitation of the lower frequency channels (< 90 GHz), common to most microwave radiometers. The methodology applied to the conically scanning GMI and the cross-track scanning ATMS is described in this paper. PESCA is based on a decision tree developed using an empirical method and verified using the AutoSnow product built from satellite measurements. The algorithm performance appears to be robust for both sensors in dry conditions (TPW < 10 mm), and for mean surface elevation < 2500 m, independently of the cloud cover. The algorithm shows very good performance for cold temperatures (2 m temperature below 270 K) with a rapid decrease of the detection capabilities between 270 K and 280 K, where 280 K is assumed as the maximum temperature limit for PESCA [overall detection statistics: POD=0.98(0.92), FAR=0.01(0.08), HSS=0.72(0.69) for ATMS(GMI)]. Some inconsistencies found between the snow categories identified with the two radiometers are related to their different viewing geometry, spatial resolution, and temporal sampling. The spectral signatures of the different snow classes appear to be different also at high frequency (>90GHz), indicating potential impact for snowfall retrieval. This method can be applied to other conically and cross track scanning radiometers including the future operational EPS-SG mission microwave radiometers.
Some of the Physical and Mechanical Properties of Particleboard Made from Betung Bamboo (Dendrocalamus asper)

The objective of this study was to evaluate various physical and mechanical properties of experimental particleboard panels made from Asian giant bamboo (Dendrocalamus asper). Single layer panels having a density level of 0.75 g/cm3 from coarse and fine particles were used within the scope of this study. Thickness swelling, water absorption, surface roughness, and wettability characteristics of the samples were tested as physical properties while bending, internal bond strength, and screw withdrawal strength of the panels were considered for their mechanical properties. Resistance of the panels against termite and fungus were also determined. Based on the findings in the work both physical and mechanical properties of the panels made from coarse particles resulted in higher values than those made from fine particles with the exception of their internal bond strength. It appears that using fine particles in the panels enhanced their overall surface quality as well as wettability. Regarding biological deterioration of the samples, those made with coarse particles had better resistance. It seems that giant bamboo as a non-wood lignocellulosic species would have potential to be used as raw material to the manufacture value added particleboard with accepted characteristics.
Evaluation of the Serum Visfatin and Adiponectin Levels Related with the Activity of Juvenile Idiopathic Arthritis

Presented in: Congress & LAB. Expo 2018 with International Participation and 1st Hereditary Metabolic Diseases Symposium (01-04 October 2018, Antalya) P4-02. Received Date/Geliş Tarihi: 15.02. 2021 Accepted Date/Kabul Tarihi: 26.03. 2021 Oktay Çalışkan1, Zeynep Banu Güngör1, Özlem Balcı Ekmekçi1, Hakan Ekmekçi1, Kenan Barut2, Sezgin Şahin2, Amra Adroviç Yıldız2, Şeyma Dümür1, Özgür Kasapçopur2, Mine Kucur1
Electrocardiography in post hemodialysed chronic kidney disease patients

Background: Chronic kidney disease (CKD) is associated with higher risk of cardiovascular events and Electrocardiographic changes are common in CKD patients. Electrocardiography (ECG) is cheap and easily available investigation that could pick up abnormalities of heart. Our study focuses on electrocardiographic changes seen within 24 hours in post hemodialysis CKD patients. Methods: It is a cross sectional, observational study of 298 CKD patients who underwent hemodialysis at tertiary care facility of VIMSAR, Burla during the period between November 2018 to November 2020. 12 leads ECG of these patients were taken post hemodialysis and abnormalities were interpreted. Results: Total number of patients included in the study were 298 with mean age distribution of 47±8.4 years. Mean Serum urea level was 126 mg/dl. Creatinine levels ranged between 5-10 mg/dl. Left ventricular hypertrophy (LVH) and left atrial enlargement (LAE) were seen in 14.76% and 8.3% of patients respectively. Atrial fibrillation was present in 4.3% of patients. Left axis deviation (LAD 7%), right axis deviation (RAD 6%), atrial ectopics (2.6%) and tall T waves (7.3%) were the other ECG changes observed. Conclusions: ECG changes are common in CKD patients in post hemodialysis state, so it becomes important to monitor these changes in these patients for early detection of any cardiac abnormalities.
Infant functional networks are modulated by state of consciousness and circadian rhythm

Abstract Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.
Effect of maleic anhydride (MA) and glycerol triacetate (GTA) compatibilisers on the properties of compression moulded pineapple leaf fibre reinforced polylactic acid green composite

Green composites are area of interest among researchers due to its eco-friendliness. This paper investigates the fabrication and characteristics of pineapple leaf fibre (PALF) reinforced polylactic acid (PLA) green composites with and without the addition of maleic anhydride (MA) and glycerol triacetate (GTA). Green composites can be applied in single-use short-term applications, like packaging and biomedicals. In this study, the green composites undergo investigation for thermal, mechanical, dynamic and morphological characteristics to find the effect of PALF loading in PLA, with and without compatibilisers. The compatibilising effects were established in FTIR spectroscopy, dynamic mechanical analysis and differential scanning calorimetry. The impact strength increases by 3.3% and 6.5%, respectively, for MA and GTA compatibilisers while an increase of 3.85% and a decline of 1.4% in case of tensile strength was noticed. The morphological studies show a poor interfacial bonding and dispersion attributed to the failure of the on GTA compatibilised composites.
Determinants of Poverty in South Africa Using the 2018 General Household Survey Data

ABSTRACT The paper analyses poverty in South Africa, using the GHS-data of 2018. STATSSA designates three poverty lines, the upper-bound poverty line is employed in the methodology, which makes the analysis more detailed and unique. The methodology uses descriptive statistics, multivariate analysis and inferential approaches specifically the binary-logistic regression model. The results of the model indicate that socioeconomic factors such as income, and household size, on the household level and head of household characteristics such as gender, marital status, age and population group were significant determinants of poverty. Policies that target girl education would go a long way in addressing poverty.
Embankment breaching at Indian Sundarban – an assessment on altered primary sediment index properties and fluvial flow parameters

ABSTRACT Failure of manmade embankments at the islands of Sundarban estuaries, India recur almost annually and results in the submergence of fertile agricultural land and claim human lives. Field studies were carried out to investigate the primary index properties of the embankment soil collected from three locations at the concave bends of embankment in the Sundarbans area. A correlation is drawn between alteration of natural layout of embankment soil and modification of its index properties following the traditional construction of embankment. Close introspection of embankment soil under scanning electron microscope were carried out to understand the spatial variations of the susceptibility of soil grains towards erosion for different layers of the embankment sediments. The flow velocities at permissible closest vicinity to river facing embankment wall were much higher than estimated threshold velocities for the entrainment of soil grains and resulted in the formation of undercuts at the basal part of the embankments that triggered its breaching. Finally, from the analyses of obtained results, some preventive measures are recommended to retard the rate of embankment breaching processes which may be expected to provide more sustainability to the erected embankments and will reduce the chances of frequent disasters at the Indian Sundarbans.
Assessing the Accuracy of Parameter Estimates in the Presence of Rapid Guessing Misclassifications

The presence of rapid guessing (RG) presents a challenge to practitioners in obtaining accurate estimates of measurement properties and examinee ability. In response to this concern, researchers have utilized response times as a proxy of RG and have attempted to improve parameter estimation accuracy by filtering RG responses using popular scoring approaches, such as the effort-moderated item response theory (EM-IRT) model. However, such an approach assumes that RG can be correctly identified based on an indirect proxy of examinee behavior. A failure to meet this assumption leads to the inclusion of distortive and psychometrically uninformative information in parameter estimates. To address this issue, a simulation study was conducted to examine how violations to the assumption of correct RG classification influences EM-IRT item and ability parameter estimation accuracy and compares these results with parameter estimates from the three-parameter logistic (3PL) model, which includes RG responses in scoring. Two RG misclassification factors were manipulated: type (underclassification vs. overclassification) and rate (10%, 30%, and 50%). Results indicated that the EM-IRT model provided improved item parameter estimation over the 3PL model regardless of misclassification type and rate. Furthermore, under most conditions, increased rates of RG underclassification were associated with the greatest bias in ability parameter estimates from the EM-IRT model. In spite of this, the EM-IRT model with RG misclassifications demonstrated more accurate ability parameter estimation than the 3PL model when the mean ability of RG subgroups did not differ. This suggests that in certain situations it may be better for practitioners to (a) imperfectly identify RG than to ignore the presence of such invalid responses and (b) select liberal over conservative response time thresholds to mitigate bias from underclassified RG.
Probing adsorbates on La1−x Sr x NiO3−δ surfaces under humid conditions: implications for the oxygen evolution reaction

Earth-abundant nickel-containing perovskite oxides (ANiO3) are highly active materials for the oxygen evolution reaction (OER). The strong nickel-oxygen (Ni–O) covalency, tunable by A-site chemical substitution, contributes to both bulk and surface material properties like the formation of oxygen vacancies (v •• O ) and OER activity. Here we quantify the coverage of OER relevant adsorbates on a series of La1−x Sr x NiO3−δ (LSNO, 0 ⩽ x ⩽ 0.5) epitaxial thin films exposed to humid environments by ambient pressure x-ray photoelectron spectroscopy. While all LSNO film compositions investigated here have comparable hydroxide coverages at the relative humidities (RHs) probed (1.5 × 10−5 – 0.2%RH), the amount of under-coordinated surface oxygen increases notably with Sr content. We interpret differences in the free energy of adsorption (ΔG ads,i ) of these OER intermediates, inferred from adsorption isotherms, in the context of proposed v •• O -mediated OER mechanisms, consistent with the pH-dependent OER activity observed here for LSNO. We find that Sr incorporation enhances the affinity of LSNO surfaces for these under-coordinated oxygen species, in line with calculations in the literature.
Materials Strategies for Organic Neuromorphic Devices

Neuromorphic computing is becoming increasingly prominent as artificial intelligence (AI) facilitates progressively seamless interaction between humans and machines. The conventional von Neumann architecture and complementary metal-oxide-semiconductor transistor scaling are unable to meet the highly demanding computational density and energy efficiency requirements of AI. Neuromorphic computing aims to address these challenges by using brain-like computing architectures and novel synaptic memories that coallocate information storage and computation, thereby enabling low latency at high energy efficiency and high memory density. Though various emerging memory devices have been extensively studied to emulate the functionality of biological synapses, there is currently no material/device system that encompasses both the needed metrics for high-performance neuromorphic computing and the required biocompatibility for potential body-computer integration. In this review, we aim to equip the reader with general design principles and materials requirements for realizing high-performance organic neuromorphic devices. We use instructive examples from recent literature to discuss each requirement, illustrating the challenges as well as future research opportunities. Though organic devices still face many challenges to become major players in neuromorphic computing, mostly due to their lack of compliance with back-end-of-line processes required for integration with digital logic, we propose that their biocompatibility and mechanical conformability give them an advantage for creating adaptive biointerfaces, brain-machine interfaces, and biology-inspired prosthetics.