Miracle-dz/UniFAR-FTUs huggingface dataset:
A Low-Cost Cell-Level Differential Power Processing CMOS IC for Single Junction Photovoltaic Cells

This article presents a cell-level differential power processing IC to maximize the power yield under partial shading and mismatch condition in series-connected single junction photovoltaic (PV) cells. A 3-MHz bidirectional buck–boost converter is employed to realize voltage equalization technique forcing the PV cells to constantly operate close to their maximum power points. A novel and simple low-power low-area analog control circuit is proposed to maintain the high system efficiency at low and high levels of mismatch by obviating the need for power hungry blocks such as analog to digital converters, digital to analog converters, op-amps, saw-tooth generator, and regulators. The voltage of adjacent PV cells is used to drive the high-side switches through bootstrap supplies eliminating the need for voltage regulators. The performance of the proposed IC, fabricated in 130 nm CMOS process, is validated through simulation and experimental results. The converter is capable of processing mismatch currents up to 4 A while the control circuitry consumes less than 40 mW and a system efficiency above 95% is achieved.
The acute effects of different resistance training loads on repeated sprint ability in water polo players

Purpose. combining resistance and in-water training is a common practice in water polo. The purpose of this study was to examine the acute effects of different external loads during resistance training on the repeated sprint ability in water polo players. Methods. A total of 9 national level water polo players (age: 21.5 ± 2.0 years) executed, on different occasions, a repeated sprint swimming test (8 × 25 m with a 30-s rest) following a low load (50% of 1-repetition maximum) or a high load (80% of 1-repetition maximum) resistance training that consisted of leg press and bench press exercises. During the control condition, the players only performed the repeated ability test. Indices of repeated sprint ability included the best 25-m time (best), the mean time (mean), and the heart rate after the 4 th and the 8 th 25-m effort. In addition, the performance decrement was calculated. Results. control and low load intensity conditions resulted in significantly faster best times than high load intensity ( p < 0.01 and 0.05, respectively). Mean times were significantly faster during the control compared with the high load intensity condition ( p < 0.05). Performance decrement and heart rate after the 4 th and the 8 th 25-m effort showed no differences between all 3 conditions ( p > 0.05). Conclusions. These results suggest that when scheduling intrasession resistance and in-water training in water polo, low load programs should be preferred when the primary goal is the enhancement of repeated swimming performance.
Combination Analysis of Multi-Wavelength, Multi-Parameter Radar Measurements for Snowfall

Abstract. Radar dual wavelength ratio (DWR) measurements from the Stony Brook Radar Observatory Ka-band Scanning Polarimetric Radar (KASPR, 35 GHz), a profiling W-band (94 GHz) and a next generation K-band (24-GHz) Micro Rain Radar (MRRPro) were exploited for ice particle identification using triple frequency approaches. The results indicated that two of the radar frequencies (K- and Ka-band) are not sufficiently separated, thus, the triple radar frequency approaches had limited success. On the other hand, a joint analysis of DWR, mean vertical Doppler velocity (MDV), and polarimetric radar variables indicated potential in identifying ice particle types and distinguishing among different ice growth processes and even in revealing additional microphysical details.We investigated all DWR pairs in conjunction with MDV from the KASPR profiling measurements and differential reflectivity (ZDR) and specific differential phase (KDP) from the KASPR quasi-vertical profiles. The DWR-versus-MDV diagrams coupled with the polarimetric observables exhibited distinct separations of particle populations attributed to different rime degrees and particle growth processes. In fallstreaks, the 35–94 GHz DWR pair increased with the magnitude of MDV corresponding to the scattering calculations for aggregates with lower degrees of riming. The DWR values further increased at lower altitudes while ZDR slightly decreased, indicating further aggregation. Particle populations with higher rime degrees had a similar increase of DWR, but the 1–1.5 m s−1 larger magnitude of MDV and rapid decreases in KDP and ZDR. The analysis also depicted the early stage of riming where ZDR increased with the MDV magnitude collocated with small increases of DWR. This approach will improve quantitative estimations of snow amount and microphysical quantities such as rime mass fraction.
Topology, Analysis, and Modeling of Voltage Equalizers Based on Reutilization Technique for Supercapacitor Storage System

The voltage equalizers based on the switched capacitor are widely used in energy storage system because of its simple circuit structure and easy realization. However, the voltage stress and the number of bypass capacitors in traditional switched-capacitor equalizers are enormous due to the large number of energy storage cells, and they can only realize balance of a single string. Therefore, three improved switched-capacitor equalizers based on reutilization technique are proposed to solve the problems. The supercapacitors in the circuit can be used as energy storage cells and energy transfer cells through the reutilization of energy, thus avoiding the extensive use of bypass capacitors and achieving the balance between strings. Meanwhile, the design difficulty of equivalent equalization resistance and the switch number are greatly reduced. The circuit configuration, operation principle, and modeling are illustrated in detail. Finally, three experiment prototypes are established. The results verify the feasibility of the proposed topologies.
An impedimeric biosensor based on a composite of graphene nanosheets and polyaniline as a suitable platform for prostate cancer sensing

In this work, a simple and new electrochemical biosensor based on a glassy carbon electrode (GCE) modified with a nanocomposite of polyaniline (PANI) and graphene oxide (GO) nanosheets for DNA detection was designed. The modification process was characterized by scanning electron microscopy (SEM) and electrochemical methods. The immobilization and hybridization processes were followed using electrochemical techniques and the experimental conditions were optimized, too. The electrochemical response for DNA hybridization was measured using the electrochemical impedance spectroscopy (EIS) method. The obtained calibration graph contains a linear range of 1.0 × 10−16 M to 1.0 × 10−8 M and a detection limit of 3.3 × 10−17 M. The designed biosensor (ssDNA/PANI/GO/GCE) revealed high selectivity for the detection of the complementary sequences from non-complementary sequences, so the fabricated biosensor can be used for the detection of prostate cancer (PCa) DNA sequences.
A novel mechanistic approach to interpret results of chemo-osmotic tests

In this paper, we introduce a new approach to interpret results of chemo-osmotic (or membrane efficiency) tests on shale. In contrast to the conventional approach which determines the two transport parameters called membrane efficiency and diffusion coefficient by experiments, the present approach takes into account micro-scale mechanisms and uses rock and solution properties to model the transport of water and ions through shale. In this paper, we introduce and explain the new approach, evaluate its features, perform a sensibility analysis, and finally run a matching on experimental data by the new approach. The study shows that the new ap-proach has greater flexibility compared with the conventional approach, is sensible for rock and solution properties, and is capable to match with ex-perimental data.
Accuracy of classification poisonous or edible of mushroom using naïve bayes and k-nearest neighbors

Mushrooms are plants that are widely consumed by the general public, but not all mushrooms can be consumed directly, because the types of mushrooms are feasible and it is still too difficult to distinguish, then there are several ways to identify fungi, namely by means of morphology. The morphology referred to in this paper is the morphology of fungi which includes color, habitat, class, and others. We got the morphology of this mushroom from a datasets we get from UCI Machine Learning with the 23 atribut that we use in the program. In determining the classification of this fungus we use the Naive Bayes algorithm which produces an accuracy of around 90,2% which we then improve again so that it reaches 100% accuracy using the K-Nearest Neighbors algorithm. Furthermore, in this case to prove accuracy that we had before, we use calculation accuracy with confusion matrix to show it the accuracy of classification poisonous or edible mushroom.
Adsorption of Pure and Binary CO2, CH4, and N2 Gas Components on Activated Carbon Beads

The adsorption equilibriums of CO2, CH4, and N2 pure gases on pitch-based activated carbon beads have been studied using a magnetic suspension microbalance at (293, 303, 333, and 363) K within a pressure range of 0 kPa to 4000 kPa. It is found that experimental adsorption capacities can be successfully described with both the Sips and Multisite Langmuir (MSL) isotherm models. Afterward, binary competitive adsorption breakthrough experiments (CO2/CH4 and CH4/N2) at 303 K and adsorption isotherms of gas mixtures under different conditions have been measured. Theoretical calculations from Sips model-based ideal adsorbed solution theory are found to have better agreement with experimental data of competitive binary adsorption than that from MSL model. Promising adsorption selectivity (5.5) between CH4 and N2 is obtained at 303 K as the pressure of a binary gas mixture is 100 kPa with yCH4 = 0.5 in the feed. Therefore, the activated carbon beads reported in this study can be considered as a promising adsorbent.
Students’ Thinking Process in Solving Abstraction Problems in Mathematics: A Case Study on Limited Test of Development Research Instrument

Abstraction is an activity performed in daily life and the ability needs to be developed, especially in mathematics learning. The information on how students solve problems is important for teachers to consider the strategies to use in helping the students. This research was carried out due to the answers’ variations discovered in abstraction problems. Therefore, it aimed to investigate students' thinking processes in solving problems. The research was a combination of the Sequential Explanatory Design model that used both quantitative and qualitative data collection and analysis methods, while the subjects used were 33 students of seventh grade. The result showed guessing and drawing was used to solve the abstraction problems, but only one student employed patterns and pictures in the process. Consequently, training students on critical thinking skills utilization to solve abstraction problems easily was suggested in mathematics learning.
An Integrated Simple Weighted Sum Product Method—WISP

Until now, many different multiple criteria decision-making methods have been proposed for the facilitation of different business problems such as entrepreneurial. In this article, a new approach for selecting the most acceptable alternative is considered, which is based on the integration of weighted sum and weighted product approaches. The basic idea of the newly proposed approach is partly based on the multiobjective optimization by ratio analysis plus the full multiplicative form method, but also includes some features of the weighted aggregated sum product assessment and combined compromise solution methods. The proposed method is tested by using two illustrative examples that are related to the contractor and industrial robot selection. Additionally, to test its reliability, the obtained results are compared with the ones gained by using the multiple-criteria decision-making methods of the first and second generations. The proposed method predicts the application of a much simpler normalization procedure, involves four utility measures for defining the overall utility of alternatives, and enables a much easier ranking of them. The final conclusion is that the simple weighted sum product method is easy to use and contributes to the increasing of the reliability of decisions that are made. Therefore, the proposal uses a much simpler normalization procedure, i.e., four utility measures for determining the overall utility of alternatives, and allows a much easier ranking of alternatives.
The optimal parameters of spline regression for SNP-set analysis in genome-wide association study

This research aims to develop a method that is capable and reliable for identifying significant regions in Genome-Wide Association Study based on Spline regression. We evaluate the optimal parameters in the Splines by smoothing and tuning p-values obtained from two methods, Sequence Kernel Association Test using normal weight (SKAT normal weight) and Generalized Higher Criticism (GHC) for testing SNP-set. False positive (FP) and True positive (TP) rates were evaluated under different genetic models for disease with significance thresholds adjusted for multiple hypothesis testing based on the permutation method. The simulated data used in this research are constructed from a control data set in a study of Crohn’s disease which is repeated 1,500 replicates for studies of size 3,000 cases and 3,000 controls. The simulation result shows that the optimal parameter in the Splines on the p-value of SKAT normal weight and GHC under the one disease SNP model simulation are at the degree of freedom 1,000. GHC is shown to be preferable in terms of comparing FP and TP rates but it is disadvantageous compared to SKAT in terms of computational burden time. Finally, the optimal parameter of both methods was applied to real data on Crohn’s disease. Both methods found the important regions of genes NOD2 which are strongly associated with the development and the importance of gene NOD2 which causes Crohn’s disease.
A Low-Profile Broadband Dual-Polarized Base Station Antenna Array With Well-Suppressed Cross-Polarization

Next-generation wireless communication requires antennas with excellent performance and low-profile structures. A low-profile broadband dual-polarized base station antenna array with an artificial magnetic conductor (AMC) reflector and cross-polarization suppression structures is proposed. Cross-polarization is effectively suppressed by introducing the structures among the antenna elements with almost no impact on the other antenna performances, and the cross-polarization suppression mechanism is analyzed theoretically. The AMC reflector reduces the antenna profile only $0.12\lambda _{0}$ ( $\lambda _{0}$ is the free-space wavelength at 4 GHz). Prototypes of the antenna element and the five-element antenna array are fabricated, and the simulated and measured results indicate that both achieve good performance over the entire operating frequency band. The antenna array has high isolation (>28 dB), a wide operating bandwidth (3.14–5.32 GHz, 51.5%), and stable radiation performances (XPD >29 dB, gain >15 dBi, FBR >37 dB). The proposed antenna array with good performance and a low profile may be a candidate for 5G communication applications.
Design and modelling of the screw cartridge rotary direct-drive servo valve

A direct-drive rotary servo valve body structure with compact structure and strong anti-pollution ability is proposed, and the jet angle of the orifice is analysed using computational fluid dynamics technology (CFD). Considering the effects of steady-state fluid torque and friction damping, a mechanical model of the valve body of the Screw Cartridge Rotary Direct-drive Servo Valve is established. Then, a MATLAB system model of the screw cartridge valve body is built, and the impact of the spool's angular acceleration, speed, and angle changes on the system are included. A Screw Cartridge Rotary Direct-drive Servo Valve testing platform is established to verify the simulation results. The results show that the Screw Cartridge Rotary Direct-drive Servo Valve has good static characteristics. Compared with the traditional servo valve, the jet angle of the Screw Cartridge Rotary Direct-drive Servo Valve is relatively small, so the hydraulic torque has a small effect on the Screw Cartridge Rotary Direct-drive Servo Valve. The maximum resistance torque of the Screw Cartridge Rotary Direct-drive Servo Valve comes from the friction resistance torque of the valve spool to the plate.
Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images

This paper presents a three-stage approach for the automated analysis of close-range optical images containing ice objects. The proposed system is based on an ensemble of deep learning models and conditional random field postprocessing. The following surface ice formations were considered: Icebergs, Deformed ice, Level ice, Broken ice, Ice floes, Floebergs, Floebits, Pancake ice, and Brash ice. Additionally, five non-surface ice categories were considered: Sky, Open water, Shore, Underwater ice, and Melt ponds. To find input parameters for the approach, the performance of 12 different neural network architectures was explored and evaluated using a 5-fold cross-validation scheme. The best performance was achieved using an ensemble of models having pyramid pooling layers (PSPNet, PSPDenseNet, DeepLabV3+, and UPerNet) and convolutional conditional random field postprocessing with a mean intersection over union score of 0.799, and this outperformed the best single-model approach. The results of this study show that when per-class performance was considered, the Sky was the easiest class to predict, followed by Deformed ice and Open water. Melt pond was the most challenging class to predict. Furthermore, we have extensively explored the strengths and weaknesses of our approach and, in the process, discovered the types of scenes that pose a more significant challenge to the underlying neural networks. When coupled with optical sensors and AIS, the proposed approach can serve as a supplementary source of large-scale ‘ground truth’ data for validation of satellite-based sea-ice products. We have provided an implementation of the approach at https://github.com/panchinabil/sea_ice_segmentation.
Deep Reinforcement Learning-Based Content Migration for Edge Content Delivery Networks With Vehicular Nodes

With the explosive demands for data, content delivery networks are facing ever-increasing challenges to meet end-users’ quality-of-experience requirements, especially in terms of delay. Content can be migrated from surrogate servers to local caches closer to end-users to address delay challenges. Unfortunately, these local caches have limited capacities, and when they are fully occupied, it may sometimes be necessary to remove their lower-priority content to accommodate higher-priority content. At other times, it may be necessary to return previously removed content to local caches. Downloading this content from surrogate servers is costly from the perspective of network usage, and potentially detrimental to the end-user QoE in terms of delay. In this paper, we consider an edge content delivery network with vehicular nodes and propose a content migration strategy in which local caches offload their contents to neighboring edge caches whenever feasible, instead of removing their contents when they are fully occupied. This process ensures that more contents remain in the vicinity of end-users. However, selecting which contents to migrate and to which neighboring cache to migrate is a complicated problem. This paper proposes a deep reinforcement learning approach to minimize the cost. Our simulation scenarios realized up to a 70% reduction of content access delay cost compared to conventional strategies with and without content migration.
Emotional Intelligence among Undergraduate Medical Students at University of Baghdad

Objectives: The study aims to assess the level of emotional intelligence among undergraduate medicalstudents and to identify the relationship between the level of emotional intelligence and some demographicvariables of the students such as: academic year, gender, residency, and monthly income.Methodology: A descriptive, cross-sectional design that is initiated for the period of January 1st to May 1st,2018 on a sample consisted of (200) students which was selected by convenient sampling method from fourmedical colleges at University of Baghdad that are: College of medicine, college of dental medicine, collegeof pharmacy, and college of nursing. The used questionnaire is divided into two parts; one of them containsthe demographic variable of the students and the other one deals with the Scale of Emotional Intelligence(EIS). The data have been collected through the utilization of the self-administrative report as a mean of datacollection and analyzed by application of statistical package for social science IBM SPSS (v. 24). Results: The result revealed that student are showing moderate level of emotional intelligence (63.5%). A relationship was detected between students’ gender and his emotional intelligence, while there are nosignificant relationships between emotional intelligence and academic year, residency, and monthly income.Conclusions: The study concluded that students at medical colleges group are emotionally stable andemotionally intelligent. There is difference in gender of students with regard to emotional intelligence.Recommendations: The study recommended for conducting future related studies with various variablesand re-conducting the study on a large sample and different specialties is necessary
Microbial Communities and Nitrogen-Utilizing Bacteria of Rotating Biological Contactors and Activated Sludge Treating Public Sewage and Night Soil/Johkasou Sludge

The public sewage (PS) and night soil mixed with johkasou sludge (JO) have similar chemical compositions; however, the concentrations of organic matter and nitrogen compounds were different. We investigated the microbial community of the rotating biological contactor (RBC) units treating PS and JO, in which the RBC was submerged in the mixed liquor of activated sludge. Here, we observed that the microbial community compositions at the phylum and class levels were similar between the PSRBC and JO-RBC, whereas the relative abundances of several phyla (Euryarchaeota, Acidobacteria, Chloroflexi, Firmicutes, Patescibacteria, and Betaproteobacteria) significantly differed between them. The microbial community composition of RBC (an attached growth process) was similar to that of the activated sludge (a suspended growth process). The microbial community of activated sludge likely affected that of RBC. The relative abundance of total denitrifying bacteria in the PS-RBC was twice as much as that in JO-RBC, while nitrifying bacterial phylotypes had a similar relative abundance. The predominant denitrifying genera were different between the PS-RBC and JO-RBC, as well as in the cross-sectional layers of the PS-RBC, suggesting the functional diversity of denitrifying bacterial genera inhabiting the RBC.