Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT Based Scanner For Corona Detection

Human civilisation is observing a very strange and serious time fighting an invisible disastrous enemy, the NOVEL COVID -19 corona virus. Initially observed in the Wuhan province of China, its symptoms range from mild illness to pneumonia. Here we design an IoT based device which can detect the subsistence of this virus presumably based on certain framework of body components. In this context we summarized the latest research process of epidemiology, pathogenesis, clinical sensing and discussed some scientific advancement to combat the epidemic novel COVID -19. This paper proposes a design of an IoT based device which can detect the subsistence of this virus presumably based on certain framework of body components. Presence of corona virus in human body may be detected by measuring an individual’s body temperature with the help of temperature sensors. RQ or respiratory quotient will be used as another determining principle to detect corona virus. Analysis of blood samples also tells us the difference between an infected and a normal sample. From analysing the blood sample we can easily determine the total leukocyte count whereas an infected person can have an abnormal leukocyte count. We can access the data over the wifi and over the internet. Some parts of the device like the breathalyzer will be sanitized automatically and injection needles will be loaded automatically after one use and is convenient for proper detection as it analyses the three components of a. So commercially it would be economically reliable product.
3 Phase Ac Servomotor Switching System

The background of the research is the need for switching ac 3 phase servomotor systems for precise position control purposes, in a fairly short displacement step with ac motor actuators. For this purpose, researchers controlled the direction of torque rotation of the servomotor magnetic field by controlling the switching system of the ac 3 phase bridges. The resultant torque of the resulting stator magnetic field represents the desired motor shaft position. For switching systems, we use space vector pulse wide modulation, with the consideration that the switching step is only 8 steps, where 2 steps are the high voltage short circuit arm step and the low voltage short circuit arm. Because the response of the motion is fast enough, the actuator used must be a winding rotor or permanent magnet rotor, better known as servomotor, for the cage rotor ac motor there are obstacles, where the motor loses torque, due to insufficient magnetic field generated by the rotor to move the rotor shaft in time quite short.
Steady State Simulation of a CO2 Heat pump system for Water Heating

A steady state simulation model for a CO2 heat pump system was developed in order to evaluate system performance of water heating. A simple Macro Excel coding was develop to calculate the refrigerant condition in the heat pump pipe. The REFFPROP 8.0 also use to find initial value of the pressure, enthalpy and refrigerant characteristic. The Macro Excel and REFPROP 8.0 was developed by linked both programme because it provide a similar features. The simulation was compared with experimental data of a laboratory scale CO2 heat pump system. The pressure and temperature estimated by simulation were found to be approximately within 10% error between simulation and experiment. The COP obtained from the simulation was higher than that obtained by experiment and it was reduce as the effect of the mass flow rate of the refrigerant reduction for the performance analysis by the change of the closing angle of expansion valve.
The productivity of onion (Allium cepa L.) in seed crops

The article deals with the issues of selection and seed production of onion (Allium cepa L.). Breeding studies were conducted in the nursery of the Belgorod state agrarian UNIVERSITY in 2016-2017. The influence of the fraction size on the yield and structure of onion crops (Allium cepa L.) was studied. it was found that plants in the 1st and 2nd families performed better when planting a large fraction of sowing. The yield in these variants was 2.76-3.05 kg / m2, the density of crops was 172-191 plants per 1 m2. Skirmished plants were not observed when planting a large fraction of onion-sowing. Thus, it is recommended to use samples of these 2 families to continue the selection process.
Real Time Face Mask Identification Using Deep Learning

Corona virus disease (COVID-19), one of the most life-threatening diseases which is highly harmful to the people in all over the world. The government of all countries in the world are in the situation to make effective steps to prevent the spread of COVID-19 in the society. If this situation continuous means the extinction of human beings may possible. So, the foremost action towards the virus is to stop spreading from the infected to the non-infected persons. To prevent spreading of the virus, one has to maintain social distancing from other people and personal hygiene. The main mode of transmission of the COVID-19 is by the droplets of saliva and the discharge from the nose. The governments laid a strict rule to wear a mask in the public places to prevent the spreading of the virus. Various technological techniques are well applied in pandemic studies. In this case we applied a deep learning algorithm to determine whether or not an individual is wearing a face mask. This research resulted in identification of face mask in the public places which results in the lowering of spread of the virus. This model is developed using the Convolutional Neural Network (CNN). It extracts the facial landmarks of the person and finds the face mask region. It is trained by datasets and predicts the result.
The internet of things (iot) for flood disaster early warning in dki jakarta: prospect and community preparedness

Flooding is the most frequent disaster in the last ten years in Jakarta and has occurred since the colonial era. Jakarta’s geographical location is partly below sea level, and its dense population causes Jakarta’s vulnerability increases. The study aims to determine the potential of use and development of IoT in flood disaster mitigation systems as an early warning to the public through cell phones, that have become the needs of the urban community to reduce material and non-material losses. This study uses data elaboration methods, using secondary data from the Central Statistics Agency (BPS) on the Information and Communication Technology Development Index (ICT Development Index) published in 2018, survey data from the Indonesian Internet Service Providers Association (APJII) on internet user profiles in Indonesia published in 2015. The results of this study indicate that internet penetration reaches up to 56%, the highest compared to other regions in Indonesia. The ICT Development Index shows that DKI Jakarta is the highest ICT Development Index, with 7.41 out of 10. It indicates that IoT based on flood disaster mitigation has the potential to be applied in DKI Jakarta.
Design and Operation of Fuzzy Logic Based MPPT Controller under Uncertain Condition

Maximum power point tracking System (MPPT) is so important in PV systems to increase the efficiency of solar cells. So many methods are proposed to generate the maximum voltage from PV modules under different weather conditions. This paper proposed an intelligent method for maximum power point tracking using the P & O algorithm. The model contains a PV module connected to DC to DC boost converter. The PV System is tested under disturbance in the solar irradiation and temperature level. The simulation results show the maximum power tracker could track the maximum power accurately and successfully in all conditions tested. Comparison of various working parameters such as: tracking efficiency and response time of the system shows that the proposed method gives higher efficiency and better performance than the conventional perturbation and observation method. The voltage, current, and power of the Module can be measured through the P&O Method. The Fuzzy logic based Mppt controller is proposed in this method to increase the voltage Pv module. The proposed method used the fuzzy logic-based controlled (FLC) to initiate the control command to the output buck-boost converter as there is a change in the voltage and current across the PV panel. The modeling of the FLC-based MPPT controller is done for the PV module with the help of MATLAB/SIMULINK.
Role of the C4 protein PepYLCV in viral symptom determinant: In silico approach

The C4 protein of the monopartite begomovirus pepper yellow leaf curl virus [PepYLCV] plays a major role in the pathogenicity of plant virus infection. Complement gene [C4], in virus, is overlapped with complement gene 1 [C1] or known as Replicase gene [Rep]. The C1 protein is directly involved in virus DNA replication, but not the C4 protein. Recent studies suggested that the C4 is a viral symptom determinant but the molecular mechanism has not been elucidated. In this study, the C4 gene of the novel PepYLCV Alahan Panjang was isolated. To further investigate the role of C4 in symptom determinants, in silico approached was used. Computational modeling of C4 was constructed with homology and ab initio modeling software and the model was checked with Ramachandran Plot. The optimum model of the C4 was used in the surface analysis to predict binding sites and key residues. The mutant structure of the C4 protein and its prospect for CRISPR-Cas9 utilization are discussed.
Photovoltaic Performance with Heat Sink from Copper and Aluminum Material

Energy becomes a basic human need, so energy reserves are needed. Solar energy is the solution to overcome this problem. Solar energy can be converted into electrical energy through PV panels. However, high temperatures can make PV panels do not work optimally. One way to reduce the temperature is to use a heat sink. The materials used are PV panels without heat sinks, PV panels with aluminum heat sinks, and PV panels with copper heat sinks. This research shows that with the same intensity of 1100 W/m2 PV panels without heat sinks, PV panels with aluminum heat sinks and PV panels with copper heat sinks have an efficiency of 8.76%, 10.27% and 11.14%. The result of temperatures 69.7 °C, 60.8 °C and 52.7 °C and the maximum power produced is 35.19 W, 40.17 W and 43.58 W. The results showed that the average working temperature decreased 8.9 °C and 17 °C, thereby increasing output power by 4.98 W and 8.39 W and increasing efficiency by 1.51% and 2.38% without using a heat sink.
Vespa Mandarinia Recognition and Prediction Strategy Based on Geographic Location and Image Recognition

The paper conduct data analysis and information mining from the following four aspects: the correlation between geographic location and the spread of Vespa mandarinia, image recognition with the help of convolutional neural networks, species richness prediction and accurate recognition optimization, Vespa mandarinia reproduction law and reproduction characteristics, and then propose a reliable prediction model of Vespa mandarinia spread range and a model to predict the possibility of misclassification. First, consider whether the spread of pests in a period of time can be predicted. We arrange the 14 Positive IDs in chronological order and use a time series model to predict the spread range, quantify the results as longitude and latitude, and compare the results. The optimal degree is calculated. The results show that this model can accurately predict the staged emergence of Vespa mandarinias. Next, we select 1/6 high-resolution images based on the provided data set files and image files, based on the SNP selection algorithm of the χ2 test, and perform data labeling preprocessing to create a model that can predict the likelihood of a mistaken classification. Further borrow the geographic location of model one and the image recognition of model two, and use both as factors influencing the possibility of whether the newly submitted report is confirmed as a Vespa mandarinia. With the help of the Euclidean distance within the bounds of the rectangular frame, it is compared with the distance between two adjacent points to determine the impact factor 1, which is conducive to predicting the geographical distribution of the Vespa mandarinia through time series analysis. With the help of the image recognition model, the output of the convolutional neural network is used as the second impact factor, which is conducive to high-performance identification of Vespa mandarinias through real pictures. Further analysis shows that the weight ratio of the two impact factors is 3:7. Finally, we analyzed the Vespa mandarinia’s cycle reproduction law, combined with the possible genetic variation and species evolution within the population, and concluded that if there are other new reports, they should be added to the training set to rebuild the model. training. At the same time, the possibility of the elimination of this pest in Washington State is discussed. Through the statistical historical data of the monthly detection report, and further combining with time prediction, until the forecast report stabilizes to 0 within a certain period, or in a certain period If reports are submitted within the cycle, but all non-Vespa mandarinias are judged, it can be deemed that Washington State has eliminated this pest.
Similarity Detection of Natural-Language Questions and Answers using the VANiLLa dataset

Question Answering refers to the task of providing a matching answer for a given user’s natural-language question. We assume here that the received response from a Question Answering system is also given as a natural-language output. From this scenario the task is raised to validate if the received natural-language answer is valid w.r. t. the given question. In this paper, we will present our approach to compute the similarity of a question to the corresponding natural-language answer while using the features of the surface form of the question and the answer. Our metrics is the distinction between a matching answer and inappropriate answers to a given question. Hence, our research agenda is dedicated to improving the answer quality of Question Answering systems by using the natural-language representation of its input and output.
Stability enhancement of top-gate self-aligned tin-doped indium gallium oxide thin film transistor by low temperature annealing

Top-gate self-aligned IGTO TFTs were used as an example to study the contradiction of uniformity and PBTS stability when developing TFT with high mobility. High intrinsic carrier concentration restricted the tuning of SiO2 deposition. To ensure the uniformity, relatively higher power was employed for GI deposition to reduce donor-type oxygen vacancies. Deep electron traps formed by excess oxygen lead to poor PBTS stability. The PBTS stability was improved without deterioration of uniformity by introducing low temperature (200 °C) annealing, to control hydrogen diffusion from ILD layer which would passivate the electron traps.
Association between exposure to ambient particulate matters and risks of autism spectrum disorder in children: a systematic review and exposure-response meta-analysis

The impact of prenatal and early childhood exposure of ambient particulate matters (PMs) on the risk of autism spectrum disorder (ASD) in children remained inconclusive, particularly at low levels below current National Ambient Air Quality Standards. The study summarizes the epidemiological association between PM exposure and risks of ASD in children. PubMed, Embase, Web of Science, Cochrane Library, Compendex, Biosis Previews, and Agricultural & Environmental Science Databases for studies published before February 2020. Original studies with the following information were included: (a) exposure of ambient PM (including PM2.5 and/or PM10); (b) ASD as the outcome of interest in children; (c) effect estimates of relative risk (RR), odds ratio (OR), or hazard ratio. The risks of ASD are summarized at different exposure windows (i.e. first, second, third trimesters, and early childhood period) by using a random-effects model. Exposure-response meta-regression was performed across various background levels of PM2.5. We used Newcastle–Ottawa Scale for quality assessment. Eleven studies (two cohort and nine case-control studies) and 313 301 children were enrolled. Overall, the risk of ASD increased by 64% (pooled RR = 1.64, 95% CI = 1.16–2.34) and 31% (pooled RR = 1.31, 95% CI = 1.08–1.58), with exposure to 10 μg m−3 increment of PM2.5 during early childhood and prenatal periods, respectively. Stratifying by three trimesters of prenatal period, the risk of ASD increased 35% per 10 μg m−3 difference of PM2.5 exposure during the third trimester (pooled RR = 1.35, 95% CI = 1.18–1.55), but not during the first and second trimesters. The risks of ASD persisted at the background PM2.5 levels from 8 μg m−3 (pooled RR = 1.30, 95% CI = 1.02–1.66) and onward. Our findings suggested an association between PM2.5 exposure and risks of ASD, particularly within specific exposure windows, even at low background levels of PM2.5.
Students’s acceptance toward the use of mobile learning in fractions

Most of the fifth-grade students have been familiar with mobile technology and commonly use it to play games. Accordingly, the school do not allow fifth-grade students to bring or operate mobile technology while they are at school in order to keep students’ attention on learning. However, during the pandemic COVID-19 condition, the government demands students to learn from home. Thus, one solution is using mobile technology to maintain communication and interaction between students and teachers during teaching and learning activities from home. The present study developed a mobile application, called U-Fraction, to assist students in the mobile learning process. The developed application was associated with authentic learning so that students could have stronger understanding of the real-world problems. Students’ perception toward the mobile learning supported by U-Fraction were measured using TAM with partial least square analysis. The result shows that students tended to use this mobile application for mobile learning. Students also believed that the use of U-Fraction for mobile learning could help their learning process.
Particle Behavior in Curved Microchannels: Aspect Ratio Effects

The sorting devices of microfluidic systems operate by both passive and active mechanisms. The microchannels of these systems are often curved to cope with secondary flows, which allow sorting or mixing. Therefore, it is essential to characterize the secondary flows in microfluidic systems. In this study, we investigate the particle behavior in a spiral rectangular microchannel and clarify the effects of the aspect ratio and Dean number De on particle sorting by comparing experimental and numerical results. We fabricated Archimedean spiral microchannel models with the rectangular cross-section of millimeter-scale dimensions. Particle sorting in the microchannels was observed by the particle-tracking velocimetry (PTV) method. In addition, the particle-sorting mechanism was analyzed by the particle-tracking method in three-dimensional numerical simulations. When particle density exceeded water density, good sorting was observed when 20 < De < 40. The numerical particle-sorting behaviors agreed well with the experimental results.
Effect of spontaneous condensation on condensation heat transfer in the presence of non-condensable gases

The presence of non condensable gases like nitrogen or air reduces the condensation heat transfer during condensation of binary steam mixtures. The non condensable gas accumulates in the vapor phase boundary layer and causes a high heat transfer resistance. Especially with high pressures and low water temperatures spontaneous condensation reduces heat transfer additionally. Fog forms within the steam-nitrogen boundary layer and the steam condenses on the water droplets of the fog layer. The convective mass transfer to the cooling water interface diminishes. Raman spectroscopy and film theory are used to quantify this effect locally. The calculation of overall condensation rates in large steam nitrogen systems requires to use three dimensional CFD codes. The paper presents equations to predict fog formation in the boundary layer which can be implemented in CFD codes.
Speaker forensic identification using joint factor analysis and i-vector

Speaker Forensic is a process to determine the identity between a person’s voice (known speaker) and the investigated voice (suspect speaker). To improve accuracy in speaker forensic analysis is used a combination of 2 forensic methods, joint factor analysis and i-vector methods. The forensic approach by adding noise signal with SNR value as a representation of a tapping situation to measure speaker identification performance. Classification on verification using i-vector is done by comparing i-vector model tests and targets. Both models calculated vector similarities using cosine similarity score. Verification is done with compare the same speaker and verification between different speakers The testing process on the program performance is indicated by an equal rate error value. While the system’s sensibility is indicated by the threshold value. The results showed that the EER value of the Graz dataset (1.87%) compared to the Indonesia dataset (10%).
Deep Reinforcement Learning based Applications in Smart Power Systems

Due to the complexity, uncertainty and the increase of data dimension in power systems, conventional methods often encounter bottlenecks when trying to solve decision-making and control problems. Therefore, data-driven methods to solve these problems are being widely studied. Deep reinforcement learning (DRL) is one of these data-driven methods and is regarded as true artificial intelligence (AI). It has been applied to solve a series of complex sequential decision-making problems, including those in the power system. This paper first reviews the basic idea, model, algorithm and technology of DRL. Then its application in power systems is considered, such as energy management, demand response, electricity market, and operation control. Also, the latest development of DRL in the power system is discussed.
Freehand Sketching Portrait Recognition with Least Square CycleGAN

Freehand sketching portrait recognition refers to the recognition of sketched portraits and face photos drawn by artists. Existing research mainly involves inputting a given real portrait and converting it into a similar sketch portrait, and then matching the face with the sketch portrait in the real portrait database. This paper starts from the idea of inputting a sketch image to identify the real portrait in the database, focuses on the research based on the method of portrait synthesis, and introduces the existing methods of sketch portrait identification. We use the CycleGAN improved by least squares to achieve the translation from sketch to portrait, and finally PCA was used to complete face matching. The results show that LS_CycleGAN has certain advantages. Compared with other methods, its synthesis results are most close to the portrait. The average score of SSIM and PSNR is 0.844 and 17.331, and the average success rate of recognition is 88.2% for Rank10.