Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Flow Field on Colloid Deposition in Filtration Process of Polystyrene Latex Particles through Columns Packed with Glass Beads

The effect of flow field on the deposition rate of colloidal particles onto solid surface in a column packed with spherical collectors was investigated theoretically and experimentally. A new analysis for calculating the colloid deposition efficiency was developed. The analysis includes the effect of the flow field on the deposition of a colloidal particle through the interactive potential barrier between the particle and the solid surface. The deposition behavior of polystyrene latex particles (diameter: 102 nm) in transport process through columns (length: 100 mm) packed with glass beads (diameter: 0.35–0.40 mm) was observed. The colloid deposition efficiencies calculated by the new analysis were comparable with those obtained by the experiments. The result indicates that the effect of the flow field would cause the difference between the flow velocity dependence of the efficiency obtained by a classical analysis and that by the experimental results. The colloid deposition efficiencies were also obtained by estimating the deposited particles onto the glass beads surface suspended in the colloidal suspension from the SEM observation of the glass beads surface. The effect of the heterogeneity of the collector surface was discussed by calculating the colloid deposition efficiency using the evaluated interaction potential between the particle and the heterogeneous collector surface.
Hormonal contraceptive use, bone density and biochemical markers of bone metabolism in British Army recruits

Introduction Hormonal contraceptive use might impair bone health and increase the risk of stress fracture by decreasing endogenous oestrogen production, a central regulator of bone metabolism. This cross-sectional study investigated bone density and biochemical markers of bone metabolism in women taking hormonal contraceptives on entry to basic military training. Methods Forty-five female British Army recruits had biochemical markers of bone metabolism, areal bone mineral density (aBMD) and tibial speed of sound (tSOS) measured at the start of basic military training. Participants were compared by their method of hormonal contraception: no hormonal contraception (NONE), combined contraceptive pill (CP) or depot-medroxyprogesterone acetate (DMPA) (20±2.8 years, 1.64±0.63 m, 61.7±6.2 kg). Results aBMD was not different between groups (p≥0.204), but tSOS was higher in NONE (3%, p=0.014) when compared with DMPA users. Beta C-terminal telopeptide was higher in NONE (45%, p=0.037) and DMPA users (90%, p=0.003) compared with CP users. Procollagen type 1 N-terminal propeptide was higher in DMPA users compared with NONE (43%, p=0.045) and CP users (127%, p=0.001), and higher in NONE compared with CP users (59%, p=0.014). Bone alkaline phosphatase was higher in DMPA users compared with CP users (56%, p=0.044). Conclusions DMPA use was associated with increased bone turnover and decreased cortical bone integrity of the tibia. Lower cortical bone integrity in DMPA users was possibly mediated by increased intracortical remodelling, but trabecular bone was not affected by contraceptive use.
Analysis of Output Capacitance Co-Energy and Discharge Losses in Hard-Switched FETs

The important distinction between stored energy and co-energy components of the output capacitance of field-effect transistors (FETs) is not explicitly discussed in the literature, and hence, less understood in relation to the switching losses in power electronic converters. An intuitive and detailed analysis of losses related to these energy components is presented in this work. It is shown that the co-energy component could cause a significant loss in a hard-switched device depending on the nature of circuit topology. A clear connection is established between the recently reported Eqoss loss mechanism in a hard-switched device and the co-energy. Furthermore, contrary to the conventional methods, a simple alternative method is proposed to calculate output-capacitance-related turn-on loss of a hard-switched device. The topology operates in no-load conditions, thus removing the effects of load current from the calculated losses. The method also has the added advantage of decoupling and separately calculating both stored and co-energy components, by a special selection of the complementary device in an inverter leg configuration. The concepts are validated with experimental results for a GaN-HEMT based inverter leg. They enable output-capacitance-related losses in hard-switching circuits to be treated in a more intuitive and straightforward manner.
Digital technologies in the formation the students’ supra professional competencies

This paper presents the results of a study on the use of digital technologies for the development of university students’ supra-professional competencies. The authors present the modern transformations of the didactic tetrahedron and the influence of digital technologies on the organization and results of the educational process at universities. The key problems of the large-scale implementation of ICT in teaching are identified. As a result of analysis of previous studies, the authors give a correlation of the use of ICT for the development and assessment of students' supra-professional competencies. An online survey of 88 university teachers from 10 regions of Russia showed the relevance and importance of using digital technology in shaping the future of a competitive and comprehensively developed specialist. At the same time, a number of difficulties were identified that impeded the use of ICT in the implementation of academic disciplines at the university. The authors conclude that it is necessary to reduce the technological gap between the development of technologies and their implementation in the educational process, including to increase the efficiency of the development of students' supra-professional competencies.
Mode Transition in Capillary Capacitively Coupled Radio Frequency Thruster

A miniature capacitively coupled radio frequency thruster with a capillary tube (C3RFT) is being developed and tested at the Technion. The thruster utilizes a nonsymmetric electrode arrangement and a dedicated radio frequency generator. The small diameter of the dielectric tube allows operation of the device in very low flow rates, < 8 SCCM in Ar. The measured thrust time response and wall temperature of 400 °C confirm an electrothermal mechanism of thrust generation. At constant input power, the thrust is shown to increase linearly with the flow rate. At an input power of 14 W and a flow rate of 8 SCCM, the thrust increases significantly compared to the cold flow case, for a total thrust of $\approx 160~\mu \text{N}$ and specific impulse ≈69 s in Ar. When powering the larger inlet “back” electrode, discharge is observed to diverge from classical theory of asymmetric capacitive coupled plasma, leading to mode transition. This phenomenon is marked by the appearance of bright plasma exhaust and reduction in input power from ≈15.17 to ≈9 W. The thrust to power ratio increased by 25% after the transition. A simple sheath breakdown model is proposed to explain the transition.
Application of Machine learning algorithms in diagnosis and detection of psychological disorders

A psychological disorder can be described as the disturbance of the natural state of the mind that affects the cognitive and social behaviour of the individual. The rapid modernization of society and the lack of social and personal interactions are further assisting in the increasing number new cases of psychological disorders. This paper intends to provides a brief overview of existing research being carried out in the field of machine learning and diagnosis, classification and prediction of psychological disorders and will present a sample framework which uses the data from the electronic health records to extract different text-based documents and reports to produce a tagged list of words relevant to disorder which is matched against the symptoms and signs of different psychological disorders to predict the disorder. To validate this prediction, it is further checked against the output of the machine learning models that will predict the psychological disorder based on the patient’s fMRI image and PET images extracted from the patient’s EHR. Through this paper, readers will be able to get an overview of the recent developments in the field of diagnosis of mental disorders by utilizing the machine learning algorithms and techniques to process the relevant unstructured data for improving the accuracy of the diagnosis to reduce the risk of misdiagnosis.
The stability field of anthophyllite; the effect of experimental uncertainty on permissible phase diagram topologies

We have examined experimental data for reactions among anthophyllite, enstatite, forsterite, quartz, talc, and water, using linear programming methods, in order to examine the range of internally consistent thermodynamic data sets that is permitted by the experiments. Phase diagrams calculated using the derived thermodynamic parameters may have all topologies previously proposed to describe the anthophyllite stabitty field as well as others. Depending upon the experimental data analyzed, phase diagrams in close agreement with calorimetrically measured thermodynamic parameters may have the topology originally proposed by Greenwood (1963) or the topology he sketched (Greenwood, l97l) in revising his original ideas. The experimental data of Chernosky (1976) and Greenwood (1963) appear not to be consistent with the available calorimetric data for the above minerals within their stated uncertainties. This discrepancy is most easily explained if there exists a major difference between the properties of talcs used in the experiments and that used for calorimetric measurements or if the calorimetric measurements are incorrect. A similar di-fference in the properties of anthophyllite may also be indicated.
SELF-REDUCING PELLETS FOR IRONMAKING: REACTION RATE AND PROCESSING

Self-reducing pellets bearing iron oxides and carbonaceous material are considered as an efficient way of promoting the carbothermic reduction of iron ore or iron-containing wastes, as several processes already employ this technology. This paper discusses the reaction rates obtained when this kind of agglomerate is heated at different temperatures. To take advantage of the kinetic features presented by self-reducing pellets, any process has to solve the problem of furnishing a high heat load to the charge without compromising its physical integrity. This paper also presents a discussion on how the different existing or proposed processes tackle this problem.
Challenges and Opportunities for Young Female Learners in STEM from the Perspective of Bangladesh

This work presents an overview of the participation of young female learners in the STEM field in Bangladesh. The female stakeholder percentage in STEM fields in Bangladesh is one of the lowest in South Asia. The gender disparity in this sector is visible from early on in the educational journey of K-12 students. Mathematics Olympiad, a major nationwide celebrated competition in STEM, shows gender disparity, particularly during puberty of young female students. This qualitative study on Math Olympiad participants, parents and mentors, statistical analysis of performance over the years, the intervention of government and private entities, and supporting qualitative analysis on women in computing in Bangladesh shows the ongoing challenges and presents the opportunities for female STEM enthusiasts in the coming days.
Multi-Label Classification of Fundus Images With Graph Convolutional Network and Self-Supervised Learning

The accurate diagnosis of fundus disease can effectively reduce the disease's further deterioration and provide targeted treatment plans for patients. Fundus image classification is a multi-label classification task due to one fundus image may contain one or more diseases. For multi-label classification of fundus images, we propose two new multi-label classification networks $--$ MCG-Net based on graph convolutional network and MCGS-Net based on graph convolutional network and self-supervised learning. Here, the graph convolutional network is used to capture the relevant information of the multi-label fundus images, and self-supervised learning is used to enhance the generalization ability of the network by learning more unannotated data. We use the ROC curve, Precision score, Recall score, Kappa score, F-1 score, and AUC value as the evaluation metrics and test on two datasets. Compared with other methods, our methods have better classification performance and generalization ability. Our methods can significantly improve classification performance and enhance the generalization ability of multi-label fundus image classification.
Delivery Drone Driving Cycle

Large companies such as Amazon and Google are currently testing deliveries using unmanned drones, intending to use these drones on the market. Topics tackled in this field of research include object collision routing optimization, delivery routing optimization, battery management optimization, and the addition of solar panels on the drones. Little publicly available research has been found to have been conducted on developing the methods to optimize the powertrain of the drones to maximize their delivery radii through modifying their electric motor and propeller parameters. In working towards that goal, this paper puts forth a delivery drone driving cycle simulation written in MATLAB with which to monitor their performance and fine-tune their properties. The driving cycle has been written to accommodate unmanned drones that use any number of propellers and perform vertical take-off and landing maneuvers. This driving cycle algorithm iteratively runs through multiple driving profiles to find the one which produces the maximal delivery radius for the drone. A data processing tool for polynomial interpolation, which is also written in MATLAB, is developed to manipulate the electric motor and propeller data into usable states for the simulation. For the tested drone configurations, no discernible pattern was noticed in the ideal power throttle needed to reach cruise altitude most efficiently. During cruise, an ideal pitch between 27 to 47 degrees which allowed them to displace horizontally while spending the least amount of energy per meter was found for all configurations.
High-Temperature Formation of Titanate Nanotubes and the Transformation Mechanism of Nanotubes into Nanowires

Large-scale pure titanate nanotubes were synthesized through the hydrothermal reaction between TiO2 powders and concentrated NaOH under an unexpected high temperature of 240 °C, while it was generally claimed that it is impossible to form nanotubes at temperatures higher than 180 °C. The titanate nanotube was found to be an inevitable intermediate product, which finally transformed into a nanowire upon increasing the hydrothermal treatment duration. It was proven that the successive appearance of nanosheets, nanotubes, and nanowires are three unavoidable kinetic products of the reaction. Increasing the temperature could only accelerate the nanotube−nanowire transformation process but could not affect the sequence of the reaction events. The transformation kinetics from nanotubes to nanowires under different reaction temperatures was studied. Detailed studies indicate that this transformation process was accompanied by a coarsening process induced by both oriented attachment (OA) and Ostwald ripening (OR) .
Implementation of Low-Energy LSTM with Parallel and Pipelined Algorithm in Small-Scale FPGA

Regression and classification are necessary for biometric systems and carried out using machine learning. A method for regression and classification is long short-term memory (LSTM). We proposed and implemented algorithms for low-energy LSTM for the regression of microwave-sensor signals into a small-scale FPGA. We found that our FPGA-based parallel (including unrolled)-pipelined algorithm decreased the computation time by 95% compared with the FPGA-based sequential algorithm. In addition, the amount of energy consumption with the proposed algorithm was reduced by 92% and 91% compared with that with a high-end GPU and CPU, respectively.
Studying Optimal Configuration of Microwave-Photonics Long-Term Time Delay Circuit Based on Fiber-Optics Recirculating Loop

An updated approach to configure a microwave-photonics-based long-term radio-frequency memory using recirculating delay line structure is proposed and discussed. As clear validated by the results of modeling and experimental research, the proposed circuit in comparison with the known approaches is provided the trade-off between output power drop and noise rise with increasing number of re-circulations. Specifically, more than 5-fold shorter delay step, better transmission gain flatness of super-wide bandwidth radio-frequency signal, smaller dimensions mainly due to multicore fiber application, and lower power consumption using less number of needed optical amplifiers are demonstrated.
Adaptive Protection of Scientific Backbone Networks Using Machine Learning

In this article, we propose a new protection scheme for backbone networks to guarantee high service availability. The presented scheme does not require any reconfiguration immediately after the failure (i.e., it is proactive). At the same time, it does not require any reserved backup network resources either. To achieve these seemingly contradictory goals, we utilize the recent advancements in Machine Learning (ML) to implement a network intelligence that periodically re-allocates the unused capacity as protection bandwidth to meet the service availability requirements of each connection. Our goal is achieved by two components (1) predicting the traffic for the next period on each link, and (2) intelligently selecting the best fit dedicated protection scheme for the next period depending on the estimated unused (spare) bandwidth and the previous service availability violations. Note that re-allocating protection bandwidth affects neither the operational connections nor the current best practice of operators to over-provision network bandwidth to support elephant flows. Finally, we provide a case study on the real traffic from Energy Sciences Network (ESnet), a high-speed, international scientific backbone network. The key benefit of our framework is that adaptively utilizing the over-provisioned bandwidth for spare capacity is sufficient to improve the availability from three-nines to five-nines (in ESnet for the 30 examined connections). The drawback is negligible bandwidth limitations; the user perceives a minor and very temporal bandwidth limitation in less than 0.1% of the time.
An Experimental Study of 28 GHz Analog Beamforming with a Uniform Linear Array

This paper reports an over-the-air measurement of a uniform linear antenna array for the radio frequency of 28 GHz and discusses the achieved accuracy of beamforming. Two conventional phased array calibration methods, i.e., rotating element electric field vector method and multi-element phase-toggle method, are performed and compared to confirm calibration validity. Both methods realize fine beam steering accuracy of δθRMS = 0.23° around the boresight direction. In addition, we confirmed the robustness of the calibration values against frequencies within 200 MHz wide around 28 GHz.
A Study of the Physical and Chemical Processes Active in Corona Discharges Fed by Carbon Dioxide

Ozone generation in both positive and negative corona discharges DC corona, both operated in glow regime, feed by dry CO2 has been studied. Higher ozone concentrations were observed in negative corona discharges. Ozone formation was found to be strongly dependent upon both the flow rate of the gas and on the radius of the outer electrode. The physical characteristics of the discharge were monitored through measurement of the discharge current. Small increases in the gas flow rate were observed to cause a significant increase in the discharge current of a negative corona discharge but little/no effect was observed in positive corona.
Using Web-based Visual Aids and Assignment in Enhancing Student’s Learning in Materials Engineering Course

Incorporation of blended learning method in education, particularly engineering education is challenging due to the nature of engineering courses that heavily rely on instructor’s input on the course content and hand-on practice. Transforming traditional classroom-based learning and paper work-based assessment are limited due to the lack of interaction between students and instructor when blended learning system is promoted. As a results, there is a concern raised on the effectiveness of achieving learning outcomes. Furthermore, with the closure of schools and universities campus, conversion of traditional face-to face learning to e-learning is necessary to avoid the spread of COVID-19 virus. This paper shares the experience of using web-based assignment from the instructor’s view and students’ perspective on the usage experience. Qualitative and quantitative methods were employed in data collection and analysis. It was found that the time for instructor used to conduct the course is reduced and average 58.5% of students showed positive feedback towards the implementation of the web-based assignment as compared to traditional method. However, there are limitations in the technical context that required enhancement in the systems.
Horizontal, Stacked or Vertical Silicon Nanowires: Does it Matter from a Low-Frequency Noise Perspective?

This work reviews the low-frequency noise performance of different flavors of silicon Gate-All-Around Nanowire (NW) (or Nanosheet - NS) transistors. For the horizontal devices, the 1/f-like noise is dominated by the number fluctuations mechanism, so that the Power Spectral Density (PSD) is directly proportional with the trap density in the gate stack. The impact of different process options and device architectures (junctionless versus inversion mode) is discussed. Overall, it is found that the average 1/f noise PSD is reducing going from single NW transistors on Silicon-on-Insulator substrates, to stacked horizontal NS devices and, finally, vertical NW FETs. In the latter case, white noise may dominate the low-frequency noise spectrum.
Prediction of Student’s Academic Performance using Feedforward Neural Network Augmented with Stochastic Trainers

The academic performance of students is of great interest to tutors and decision-makers in educational institutions. The extensive use of information technology systems in education generates an enormous amount of data, which is challenging to analyze and extract valuable information. Therefore, Educational Data Mining (EDM) concept emerges to adapt Data Mining (DM) techniques to extract the hidden and valuable educational knowledge that improves the learning process. The primary purpose of this paper is to introduce an efficient student’s performance prediction model. For this purpose, a feed-forward Multi-Layer Perceptron approach boosted with stochastic training algorithms is proposed. The proposed model is benchmarked and assessed using three public educational datasets gathered from UCI and Kaggle repositories. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle the imbalanced data problem. The performance of the proposed model is evaluated by a set of classifiers, namely, Support Vector Machine, Decision Trees, K-Nearest Neighbors, Logistic Regression, Linear Discriminant Analysis, and Random Forest. The comparative study revealed that the MLP achieved promising prediction quality on the majority of datasets compared to other traditional classifiers, as well as those in previous works.