Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing the atmospheric boundary layer using high-order moments obtained from multiwavelength lidar data: impact of wavelength choice

Abstract. The lowest region of the troposphere is a turbulent layer known as the atmospheric boundary layer (ABL) and characterized by high daily variability due to the influence of surface forcings. This is the reason why detecting systems with high spatial and temporal resolution, such as lidar, have been widely applied for researching this region. In this paper, we present a comparative analysis on the use of lidar-backscattered signals at three wavelengths (355, 532 and 1064 nm) to study the ABL by investigating the high-order moments, which give us information about the ABL height (derived by the variance method), aerosol layer movement (skewness) and mixing conditions (kurtosis) at several heights. Previous studies have shown that the 1064 nm wavelength, due to the predominance of particle signature in the total backscattered atmospheric signal and practically null presence of molecular signal (which can represent noise in high-order moments), provides an appropriate description of the turbulence field, and thus in this study it was considered a reference. We analyze two case studies that show us that the backscattered signal at 355 nm, even after applying some corrections, has a limited applicability for turbulence studies using the proposed methodology due to the strong contribution of the molecular signature to the total backscatter signal. This increases the noise associated with the high-order profiles and, consequently, generates misinformation. On the other hand, the information on the turbulence field derived from the backscattered signal at 532 nm is similar to that obtained at 1064 nm due to the appropriate attenuation of the noise, generated by molecular component of backscattered signal by the application of the corrections proposed.
The Development of Android and Web-based Logical Thinking Measurement Tools as an Alternative Solution for Research Instruments

This type of research is a development research or it is known by the term of Research and Development (R & D). The focus of this research is to develop a logical reasoning ability measurement tool based on qualified Web and Android as a research instrument alternative solution. The implementation of this research is conducted on Statistics Department in the Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar. The subject of the study in question is the students of Statistics Department of Universitas Negeri Makassar. Measurement tool that will be developed is a Test of Logical Thinking (TOLT). The development of this information system using PHP programming language based on CodeIgniter Framework with MySQL database.
Haemostatic properties of Vernonia amygdalina and Chromolaena odorata leaf extracts using Wistar rat model

BACKGROUNDThe leaves of African plants are widely employed in Nigeria to control bleeding from wounds. This work is aimed at evaluating the haemostatic effects of Vernonia amygdalina (VA/bitter leaf) and Chromolaena odorata (CO/sunflower) leaves on some haemostatic parameters.METHODSA laboratory study of experimental design was conducted involving 35 Wistar rats that were randomized into seven groups. Groups 1 and 2 received leaf extracts of VA at concentrations of 150 and 250mg/kg BW, respectively; groups 3 and 4 received leaf extracts of CO at 150 and 250mg/kg BW; groups 5 and 6 received combined leaf extracts of VA and CO at 150 and 250mg/kg BW, respectively, for 30 days. The last group 7 as control group received only water. Parameters investigated were clotting time (CT), fibrinogen concentration, prothrombin time (PT), activated partial thromboplastin time test (APTT), factor VII, protein C and D – dimer.ResultsThere was a significant reduction in CT, fibrinogen concentration and PT in the intervention groups compared to controls (p<0.01). There was no significant difference in APTT, factor VII and protein C in the intervention groups compared to the controls (p>0.05). D-dimer levels were observed to increase significantly in rats treated with 150mg/kg BW of VA and 150mg/kgBW of the combined leaf extracts (VA/CO) compared to the controls (p<0.05). ConclusionThe VA and CO extracts reduced CT, PT, fibrinogen concentrations and increased D-dimer levels in rats. This study suggests the possible incorporation of the leave extracts of VA and CO in bleeding diathesis as well as in coagulation studies.
Optimization of the antioxiant peptides production from goat milk casein hydrolyzed by alcalase and papain using respons surface methodology

Antioxidant peptides are widely used in many fields due to their biological activities such as anti-cancer, anti-induction and anti-aging. However, little information is available concerning the peptides production by enzymatic hydrolysis of casein from goat milk. In this study, response surface methodology (RSM) was applied to optimize the conditions of the hydrolysis of goat milk casein by proteases (alcalase in combination with papain) to increase the antioxidant activity of the peptides. The results showed that the optimal hydrolysis conditions were as follows: temperature of 61°C, enzyme/substrate ratio (E/S) of 5.6%, and protease ratio (papain: alcalase) of 1.8:1. Under the optimal conditions, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity significantly increased (1.17 folds compared to un-optimized conditions). Furthermore, Fe2+ chelating ability and superoxide radical scavenging activity increased 1.02 folds and 1.14 folds, respectively. The antioxidant activity was significantly improved after optimization and provided a basis for the study of the antioxidant effects of hydrolysed goat milk casein.
Streaming in School Mathematics: A Bourdieuian Analysis

Streaming, or ability grouping, is a common practice in many mathematics classrooms. Its negative effects on students have been documented in research spanning two decades or more, and yet it is still widely implemented – in some countries even mandated. Using data from interviews with students in the compulsory years of secondary schooling (14-16year olds), and using the theoretical tools offered by Bourdieu, it is argued that the objective practices of school mathematics create an environment through which students internalise the practices to develop a sense of self (a habitus). This habitus potentially is more or less empowering depending on the experiences within these streamed settings.
Study on structure and phase transition of an eco-friendly ferroelectric composite prepared from cellulose nanoparticles mixed with Rochelle salt

ABSTRACT An eco-friendly ferroelectric nanocomposite from cellulose nanoparticles (CNP) and Rochelle salt (RS) at different composition mass ratios was synthesized for clarifying phase transition anomalies after carefully characterizing by scanning electron microscope (SEM), X-ray diffraction (XRD) and Fourier-transform infrared spectroscopy (FTIR) techniques. The dielectric experiments were conducted in a temperature range from 5°C to 60°C under a weak electric field (2 V/cm) at 1 kHz. The obtained results indicated the presence of the upper Curie point characteristic for volume part of RS in the composite and an additional higher-temperature phase transition which was shifted toward higher temperature with increasing CNP content. Besides, at relatively high CNP content, the ferroelectric phase of RS maintained up to its decomposition temperature. The intermolecular hydrogen bonds formed between cellulose and Rochelle salt is assumed to be responsible for the obtained anomalies.
Measuring Digitalization – Key Metrics

Abstract The article covers an analysis of metrics used to measure digitalization activities. Five main levels are analyzed - moving from the metrics of the digital economy to society, industry, enterprise, and clients. The study is based on leading public and commercial metrics used for the evaluation of the digital progress. The similarities and differences between key performance indicators on each level are discussed, forming a set of conclusions on the scope and maturity of various measurement systems and potential improvement options.
Antibacterial and Urease Inhibitory activity of New Piperazinyl N-4 Functionalized Ciprofloxacin-oxadiazoles

This research includes design of new ciprofloxacin bearing oxadiazole at the N-4 piperazinyl for the purpose of having urease inhibitory activity as well as antibacterial activity. Hence, a group of new N-4 piperazinyl oxdiazole derivatives of ciprofloxacin was prepared and characterized using different spectroscopic and analytical techniques including 1H-NMR, 13C-NMR, MS and elemental analysis. Compounds 5b and 5c experienced moderate activity against the urease producing Klebsiella pneumoniae strains better than the standard drug used chloramphenicol and less than the parent drug ciprofloxacin. On the other hand, most of the tested compounds showed a urease inhibitory activity more than the parent drug, ciprofloxacin and comparable to standard, thiourea. The docked compounds exhibited better binding to urease enzyme of H. pylori than standard, AHA with binding scores for correlate to the anti-urease assay results. Compound 5b was the most potent anti-Klebsiella pneumoniae urease inhibitor with activity higher the standard (IC50 = 67.8 and 78.89 µM, respectively).
GROWTH, YIELD COMPONENTS AND CHEMICAL CONSTITUENTS OF Stevia rebaudiana Bert. AS AFFECTED BY HUMIC ACID AND NPK FERTILIZATION RATES

Two field experiments were carried out during 2016 and 2017 consecutive seasons ata Private Farm at Mit-Ghamr Distrect, Dakahlia Governorate, Egypt, to study the effect of humic acidrates (0.0, 325.60, 651.2 and 976.80 g/fad.), NPK fertilization rates [0.0, 25, 50, 75 and 100% of therecommended rate (RR)] and their interactions on growth, leaves and herb yield and some chemicalconstituents as well as active ingredients of stevia plants. The obtained results referred that themaximum value for each of plant height, branch and leaf number/plant, total dry weight/plant, dryweight of leaves and herb/fad. , total N, P, K and total sugar percentages as well as total chlorophyllcontent (a+b) were detected when plants were applied with the highest rate of humic acid (976.80g/fad.) and fertilized with 75% RR of NPK, in most cases. The main constituents of Stevia rebaudianaleaves as detected by high performance liquid chromatography-mass spectrometry (LC-MS) wereRebaudioside A, Rebaudioside B, Rebaudioside C, Dulcoside and Steviolbioside which increased withtreatment of 100% RR of NPK fertilizers combined with the highest rate of humic acid (976.80 g/fad.)compared to control.
Comparative study of negative electron affinity GaAs nanowire photocathodes with Cs/O and with Cs/NF3 from first‐principles

We perform a detailed comparative study of Cs/O and Cs/NF3 activation mechanism of negative electron affinity (NEA) GaAs nanowire photocathodes through first‐principles calculations based on density functional theory (DFT). The adsorption energy, Mulliken charge distribution, work function, electron affinity, and dipole moment are analyzed. Results show that the stability of Cs/NF3 co‐adsorption nanowire surface is stronger than that of the Cs/O model. The incorporation of O or NF3 on Cs‐covered nanowire surfaces leads to a sharper decline of surface work function. The NEA states can be attained with both O2 and NF3. However, during the Cs/O activation process, it needs to strictly control the Cs/O ratio to achieve the maximum photoemission properties of cathode. Moreover, the work function variation of GaAs nanowire photocathodes activated with Cs, O, and NF3 can be explained by the dipole moment model. This work is expected to provide a theoretical guide for improving Cs/O and Cs/NF3 activation procedure for NEA GaAs nanowire photocathodes.
Performance Assessment of New Dual-Pocket Vertical Heterostructure Tunnel FET-Based Biosensor Considering Steric Hindrance Issue

In this paper, a new dual-pocket (DP), dielectric modulated (DM) heterostructured tunnel field-effect transistor (DM-HTFET)-based biosensor has been reported. First, the efficacy of the DP-hetero-TFET (HTFET) has been analyzed by comparing it with other existing TFET structures. Next, a comprehensive assessment of sensitivity between single-pocket (SP) and DP-DM-HTFET biosensors for pocket thickness (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {pocket}}$ </tex-math></inline-formula>), pocket length (<inline-formula> <tex-math notation="LaTeX">${L}_{\text {pocket}}$ </tex-math></inline-formula>), pocket doping (<inline-formula> <tex-math notation="LaTeX">${N}_{\text {pocket}}$ </tex-math></inline-formula>), work function of the gate metal, molar fraction of Ge, gate oxide layer, and gate oxide layer thickness (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {ox}}$ </tex-math></inline-formula>) was done. Hence, a nonuniform arrangement of biomolecules inside the cavity simulation has been done using ATLAS device simulation software to validate the working ability of the proposed sensor. Significant improvement in the sensitivity due to threshold voltage (ON-current) i.e., 26.78% (78.5%), 60.8% (40.4%), 56% (<inline-formula> <tex-math notation="LaTeX">${2.2} \times {10}^{{2}}$ </tex-math></inline-formula>%), and 40.6% (80.68%) has been observed for the DP-DM-HTFET over SP with the variation of <inline-formula> <tex-math notation="LaTeX">${T}_{\text {pocket}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${L}_{\text {pocket}}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">${N}_{\text {pocket}}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">${T}_{\text {ox}}$ </tex-math></inline-formula>, respectively. DP-DM-HTFET-based current mirror circuit has also been demonstrated at the end. Sensitivity evaluation discloses that the DP-DM-HTFET can be a promising candidate for CMOS-based label-free biosensing applications.
Online Bayesian Transfer Learning for Sequential Data Modeling

We consider the problem of inferring a sequence of hidden states associated with a sequence of observations produced by an individual within a population. Instead of learning a single sequence model for the population (which does not account for variations within the population), we learn a set of basis sequence models based on different individuals. The sequence of hidden states for a new individual is inferred in an online fashion by estimating a distribution over the basis models that best explain the sequence of observations of this new individual. We explain how to do this in the context of hidden Markov models with Gaussian mixture models that are learned based on streaming data by online Bayesian moment matching. The resulting transfer learning technique is demonstrated with three real-word applications: activity recognition based on smartphone sensors, sleep classification based on electroencephalography data and the prediction of the direction of future packet flows between a pair of servers in telecommunication networks.
CdS Nanoparticle-based Biosensor Development for Aflatoxin Determination

An anodic stripping voltammetry detection method with lateral flow immunochromatographic assay for determining aflatoxin M1 was developed. Cadmium sulfide nanoparticles (CdSNPs) were employed as a label for aflatoxin antibody. Conjugation between the CdSNPs and aflatoxin antibody was performed as the first step. The CdSNP-antibody conjugate was then immobilized on a test strip on a conjugate pad. When the conjugate interacted with the sample containing aflatoxin, it was captured by the aflatoxin in the test zone. The CdSNPs retained in the test zone were then measured by anodic stripping voltammetry using a boron-doped diamond electrode. A linear concentration range of 0–70 ppb aflatoxin, with a sensitivity of 20 μA/mM and a detection limit of 30 ppb, was achieved by this method.
Design of an Efficient Maximum Power Point Tracker Based on ANFIS Using an Experimental Photovoltaic System Data

Maximum power point tracking (MPPT) techniques are a fundamental part in photovoltaic system design for increasing the generated output power of a photovoltaic array. Whilst varying techniques have been proposed, the adaptive neural-fuzzy inference system (ANFIS) is the most powerful method for an MPPT because of its fast response and less oscillation. However, accurate training data are a big challenge for designing an efficient ANFIS-MPPT. In this paper, an ANFIS-MPPT method based on a large experimental training data is designed to avoid the system from experiencing a high training error. Those data are collected throughout the whole of 2018 from experimental tests of a photovoltaic array installed at Brunel University, London, United Kingdom. Normally, data from experimental tests include errors and therefore are analyzed using a curve fitting technique to optimize the tuning of ANFIS model. To evaluate the performance, the proposed ANFIS-MPPT method is simulated using a MATLAB/Simulink model for a photovoltaic system. A real measurement test of a semi-cloudy day is used to calculate the average efficiency of the proposed method under varying climatic conditions. The results reveal that the proposed method accurately tracks the optimized maximum power point whilst achieving efficiencies of more than 99.3%.
Diagnosis of low-speed bearing degradation using acoustic emission techniques

It is widely acknowledged that bearing failures are the primary reason for breakdowns in rotating machinery. These failures are extremely costly, particularly in terms of lost production. Roller bearings are widely used in industrial machinery and need to be maintained in good condition to ensure the continuing efficiency, effectiveness, and profitability of the production process. The research presented here is an investigation of the use of acoustic emission (AE) to monitor bearing conditions at low speeds. Many machines, particularly large, expensive machines operate at speeds below 100 rpm, and such machines are important to the industry. However, the overwhelming proportion of studies have investigated the use of AE techniques for condition monitoring of higher-speed machines (typically several hundred rpm, or even higher). Few researchers have investigated the application of these techniques to low-speed machines (<100 rpm), This PhD addressed this omission and has established which, of the available, AE techniques are suitable for the detection of incipient faults and measurement of fault growth in low-speed bearings. The first objective of this research program was to assess the applicability of AE techniques to monitor low-speed bearings. It was found that the measured statistical parameters successfully monitored bearing conditions at low speeds (10-100 rpm). The second objective was to identify which commonly used statistical parameters derived from the AE signal (RMS, kurtosis, amplitude and counts) could identify the onset of a fault in either race. It was found that the change in AE amplitude and AE RMS could identify the presence of a small fault seeded into either the inner or the outer races. However, the severe attenuation of the signal from the inner race meant that, while AE amplitude and RMS could readily identify the incipient fault, kurtosis and the AE counts could not. Thus, more attention needs to be given to analysing the signal from the inner race. The third objective was to identify a measure that would assess the degree of severity of the fault. However, once the defect was established, it was found that of the parameters used only AE RMS was sensitive to defect size. The fourth objective was to assess whether the AE signal is able to detect defects located at either the centre or edge of the outer race of a bearing rotating at low speeds. It is found that all the measured AE parameters had higher values when the defect was seeded in the middle of the outer race, possibly due to the shorter path traversed by the signal between source and sensor which gave a lower attenuation than when the defect was on the edge of the outer race. Moreover, AE can detect the defect at both locations, which confirmed the applicability of the AE to monitor the defects at any location on the outer race.
Experimental determination of the main features of the viscous flow in the wake of a circular cylinder in uniform translation. Part 1. Steady flow

A visualization method is used to obtain the main features of the hydrodynamic field for flow past a circular cylinder moving at a uniform speed in a direction perpendicular to its generating lines in a tank filled with a viscous liquid. Photographs are presented to show the particular fineness of the experimental technique. More especially, the closed wake and the velocity distribution behind the obstacle are investigated; the changes in the geometrical parameters describing the eddies with Reynolds number (5 < Re < 40) and with the ratio λ between the diameters of the cylinder and tank are given. A comparison with existing numerical and experimental results is presented and some remarks are made about the calculation techniques proposed up to the present. The limits of the Reynolds-number range for which the twin vortices exist and adhere stably to the cylinder are determined.
Can Informal Learning and Academic Engagement mediate the negative Impact of Social Networking on Academic Performance?

College students are the prominent users of social networking sites. Some studies revealed negative effect of social networking on academic performance, so finding some variables which can mediate this negative effect is important. This descriptive-correlational study examined the mediating role of informal learning and engagement in relationship between social networking and academic performance. To this aim, 380 university students were selected using cluster sampling. Social Network Sites Usage, Student’s Engagement in School Scale, perceived educational outcome of social networks and student self-reported GPA were used as instruments. Findings from path analysis revealed that the direct effect of social networking on academic performance and engagement was negative but its impact on informal learning was positive. Path analysis showed neither informal learning nor academic engagement can mediate negative effect of social networking on academic performance.
Self-Supervised Drivable Area and Road Anomaly Segmentation Using RGB-D Data For Robotic Wheelchairs

The segmentation of drivable areas and road anomalies are critical capabilities to achieve autonomous navigation for robotic wheelchairs. The recent progress of semantic segmentation using deep learning techniques has presented effective results. However, the acquisition of large-scale datasets with hand-labeled ground truth is time-consuming and labor-intensive, making the deep learning-based methods often hard to implement in practice. We contribute to the solution of this problem for the task of drivable area and road anomaly segmentation by proposing a self-supervised learning approach. We develop a pipeline that can automatically generate segmentation labels for drivable areas and road anomalies. Then, we train RGB-D data-based semantic segmentation neural networks and get predicted labels. Experimental results show that our proposed automatic labeling pipeline achieves an impressive speed-up compared to manual labeling. In addition, our proposed self-supervised approach exhibits more robust and accurate results than the state-of-the-art traditional algorithms as well as the state-of-the-art self-supervised algorithms.