Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analytical Discontinuous Space-Vector PWM for Three-Level Inverters With Unbalanced DC-Link Voltages

When faced with unbalanced dc-link voltages of three-level inverters, extra harmonics will be generated with reduced linear modulation region if the conventional pulsewidth modulation (PWM) for balanced dc-link voltages is adopted. In this article, in order to suppress output harmonics and maximize the linear modulation region, as well as to reduce switching losses, the discontinuous space-vector PWM (DSVPWM) is investigated and improved for three-level inverters with unbalanced dc-link voltages. First, subsectors in the vector diagram with unbalanced dc-link voltages are optimally redivided for low switching losses, and concisely determined by a cutting-line method. Then, the modulation waves are analytically derived based on the calculated dwell times of the vector states. And an analytical DSVPWM for dealing with unbalanced dc-link voltages is proposed and implemented by a carrier-based algorithm, which can significantly reduce the computation burden. Finally, the proposed analytical DSVPWM is verified through comparative experiments, presenting its merits of high linear modulation region, high efficiency, and relatively low output harmonics.
Progression Control Model to Enhance Performance of Transit Signal Priority

Transit signal priority (TSP) plays an essential role in reducing traffic jams in a multimodal road traffic environment. Traffic experts expect that the demand for cars will be transferred to buses as the bus-service quality improves. This study proposes a multimodal environment TSP model with dedicated median bus lanes to simultaneously increase bus travel speed at signalized intersections and reduce car delay times. The proposed TSP model includes three optimization stages. The TSP is first optimized using a MAXBAND-based bandwidth model after a partitioned TSP control group is defined relative to a median bus stop to support non-stop bus movement. The optimal combination between these groups is selected using the delay-difference-of-offset technique. A kinematic wave-based offset optimization model is proposed to reduce the car delay time. We analyze the performance of this model in improving two-way bus speeds and reducing the car delay time in Seoul’s bus rapid transit system. The proposed TSP model decreases the stop ratio of buses by analyzing the bus trajectory from the micro-simulation. This model can be used as a center-based control to improve the mobility of buses and cars in a multimodal environment.
Applications of modern information technology in physical education

With the rapid development of computer technology and network technology,modern information technology has made a massive impact on physical education.The progressive reform of teaching can not be inseparable from modern information technology which can optimize the physical education.It is imperative to integrate modern information technology with physical education in the new era.With the help of modern information technology,the innovative physical education can substantially improve the quality of education and cultivate the creative ideas of students.A PE teaches should be able to take full advantage of modern information technology-aided teaching in PE.
Determination of Nitrite and Nitrate Level in Wastewater Discharge from Smoked Fish Industry

This research aimed to determine nitrite and nitrate contamination in wastewater discharge from the smoked fish industry around the fishpond area in Penatarsewu village, Sidoarjo East Java. Samples were taken from ten locations along The Alo River from September to November 2020. Nitrate and nitrite levels were determined by the Standard Method for Examination of Water and Wastewater 4500-NO2-B dan 4500-NO3-B. Nitrite formed a reddishpurple azo dye produced by coupling diazotized sulfanilamide with N-(1-naphthyl)-ethylene-diamine dihydrochloride after a visible spectrophotometer measured the complexes nitrite at wavelength 543 nm. Meanwhile, the Nitrate level was determined by ultraviolet spectrophotometer at a wavelength of 220 nm. The standard nitrite calibration curve was shown by the line equation y= 3,30361x-0,0028 and a determination coefficient of 0.9999. Whereas the standard nitrate was showed by the line equation y=0,2507x + 0,0067 and a determination coefficient 0,9996. The level of nitrate and nitrite in the sample ranged from 0.05-0.93mg / L 1,2-1,6 mg / L. According to a quality standard threshold value for nitrite and nitrate-based on the Government Regulation of the Republic of Indonesia (PPRI) No.82 2001 is 0,06mg/L and 10 mg/L, therefore it can be concluded that some samples are above the threshold value for nitrite level.Keywords: Alo River, nitate, nitrite, smoked fish, spectrophotometerPenentuan Kadar Nitrit dan Nitrat Pembuangan Limbah Cair Industri Ikan AsapABSTRAKPenelitian bertujuan untuk menentukan tingkat kontaminasi nitrat dan nitrit pada limbah industri ikan asap di sekitar kawasan tambak ikan asap di Desa Penatarsewu, Sidoardjo, Jawa Timur. Sampel diambil dari 10 titik di areal sekitar tambak sepanjang badan Sungai Alo pada bulan September sampai November 2020. Penentuan kadar nitrat dan nitrit mengacu pada Standard Methods for the Examination Water and Wastewater 4500-NO2-B dan 4500-NO3-B. Nitrit membentuk kompleks ungu kemerahan yang dihasilkan dari kopling sulfanilamida dengan N-(1-naptil)-etilena-diamina dihidroklorida kemudian kadarnya ditentukan dengan spektrofotometer sinar tampak pada panjang gelombang 543 nm. Kadar nitrat ditentukan menggunakan spektrofotometer ultraviolet pada panjang gelombang 220 nm. Hasil pengukuran standar nitrit menghasilkan persamaan garis y=3,30361x-0,0028 dengan koefesien determinasi 0,999 dan standar nitrat menghasilkan persamaan garis y=0,2507x+0,0067 dengan koefesien determinasi 0,9996. Kadar nitrit dan nitrat secara berturut-turut berkisar antara 0,05-0,93 mg/L dan 1,2-1,6 mg/L. Berdasarkan nilai ambang batas baku mutu nitrit dan nitrat berdasarkan Peraturan Pemerintah Republik Indonesia (PPRI) No.82 tahun 2001 adalah 0,06 mg/L dan 10 mg/L, maka dapat disimpulkan bahwa beberapa sampel mengandung kadar nitrit di atas ambang batas.Kata kunci : ikan asap, nitrat, nitrit, Sungai Alo, spektrofotometer
Volume‐of‐fluid based two‐phase flow methods on structured multiblock and overset grids

A geometric volume‐of‐fluid (VOF) method is implemented on the static/dynamic overset grids with a new VOF interpolation algorithm developed. The VOF value cannot be directly interpolated through the overset grid boundaries since the VOF function is a discontinuous step function. In the present study, the interface is reconstructed in the fringe cells of the overset grids using the interpolated distance function and normal vector. The VOF value in the fringe cells is calculated geometrically based on the reconstructed interface on the physical domain. The gravity and surface tension forces are implemented using a VOF based ghost fluid method, where a ghost pressure interpolation scheme is developed to handle the pressure jump across the interface due to the density changes and surface tension effect. Numerical tests and application examples have been performed for the verification and validation of the numerical methods developed in the present study. The advection tests show that the effect of the VOF overset interpolation on the accuracy of the VOF method is small. The numerical test for the static pressure shows that the present VOF based ghost fluid method can greatly reduce the spurious velocity observed in the body force method. The sharp pressure profile due to the surface tension effects can be preserved across the interface. Application examples demonstrate the capability and superiority of the VOF based numerical methods for the prediction of the violent ship flows involving strong air water interface interactions and complex geometries.
Discovery of granulite‐facies metamorphic rocks in the Yilan area, Heilongjiang Province, China: Geochronology, geochemistry, metamorphic characteristics, and geological implications

The Mashan Complex of the Jiamusi Massif was traditionally considered as the oldest stratigraphic sequence in eastern NE China. However, the metamorphic degree of the Mashan Complex is different in different areas, which can be divided into Jixi–Mishan–Hulin granulite facies belt and Luobei–Huanan–Mudanjiang amphibolite facies belt. Here, we investigated the Mashan Complex in the Yilan area and present lots of new petrological, mineral chemical, geochemical, and U–Pb geochronological data, in addition to phase equilibria modelling, to constrain the timing of metamorphism, P–T conditions, and the P–T path of the Mashan Complex, and furthermore to deduce the crustal evolution of the Jiamusi Massif. The protoliths of the Mashan Complex in the Yilan area are greywackes and felsic sandstones, whose provenance is dominantly felsic rocks formed from a continental island arc. Petrographic observations, traditional thermobarometry, and quantitative phase equilibria modelling of the studied samples reveal P–T conditions from M2 to M3 of ~7.2 kbar/760–800°C and 4.5–5.0 kbar/585–610°C, respectively, which together define a clockwise P–T path involving decompressional cooling (M3) that followed the peak granulite‐facies metamorphism (M2). It reveals essential information related to the collision orogeny and post‐collision process. Zircon U–Pb dating of two felsic rocks yielded the peak of the granulite‐facies metamorphism in the Yilan area of the Jiamusi Massif that might have occurred at ~500 Ma. The overall deposition of the metamorphic rocks was later than ~760 Ma and that most of the detrital materials came from late Mesoproterozoic and early Neoproterozoic source rocks.
Touching Distance: The underlying Russophilia in the Skripal Case Coverage in Greek Press

Theories of mediatization and domestication provide an analytical framework in understanding how patterns of interpretation are related to the public opinion’s attitudes towards actors and topics. In this study, we focus on the coverage of the Skripal case by major Greek newspapers in March 2018 in a pursuit to determine the variety of dispositions towards Russia. The Greek media’s coverage seems to comply with a disengaged approach: more news than views. The majority of news reports -instead of opinions- can be regarded as an effect of awkwardness or a choice not to provide analysis on Russia, even though the abundant news reports have the exact opposite effect of maintaining the public’s vivid interest. By performing a content analysis on articles with references to Russia, we seek to look beneath this veneer. We suggest that the Greek newspapers’ coverage is linked with more structural traits, such as Russophilia, as we identify several distinct subcultures in the press when it comes to dispositions towards Russia.
Embedded Machine Learning Using a Multi-Thread Algorithm on a Raspberry Pi Platform to Improve Prosthetic Hand Performance

High accuracy and a real-time system are priorities in the development of a prosthetic hand. This study aimed to develop and evaluate a real-time embedded time-domain feature extraction and machine learning on a system on chip (SoC) Raspberry platform using a multi-thread algorithm to operate a prosthetic hand device. The contribution of this study is that the implementation of the multi-thread in the pattern recognition improves the accuracy and decreases the computation time in the SoC. In this study, ten healthy volunteers were involved. The EMG signal was collected by using two dry electrodes placed on the wrist flexor and wrist extensor muscles. To reduce the complexity, four time-domain features were applied to extract the EMG signal. Furthermore, these features were used as the input of the machine learning. The machine learning evaluated in this study were k-nearest neighbor (k-NN), Naive Bayes (NB), decision tree (DT), and support vector machine (SVM). In the SoC implementation, the data acquisition, feature extraction, machine learning, and motor control process were implemented using a multi-thread algorithm. After the evaluation, the result showed that the pairing of the MAV feature and machine learning DT resulted in higher accuracy among other combinations (98.41%) with a computation time of ~1 ms. The implementation of the multi-thread algorithm in the pattern recognition system resulted in significant impact on the time processing.
Coffee Moisture Classifier Using Convolutional Neural Networks

One of the most important agricultural sectors in Colombia is the coffee industry. The coffee provides important income to the country and is highly recognized for its flavor and quality. To ensure quality production, the drying process is one of the most important factors. This process requires the humidity of the production be in the range of 10% to 12%. Due to the low impact of technification process in the country, many coffee growers measure empirically this characteristic because specialized equipment tends to be highly expensive or unknown. In addition, generating inaccurate estimation of coffee moisture could produce economic losses, due to the cost overrun for hiring an experts, or the loss of weight because of the coffee dehydration, which is fundamental in the sale of coffee. In this work, we developed an algorithm that detects coffee moisture using digital images. Our method uses deep learning from a convolutional network with LeNet CNN topology to classify the image and obtain the measurement. This proposal was validated with images captured by the coffee growers themselves and finally, the results shows the implemented method is robust to outliers and changes in the capture of the photographs.
Hyperspectral Image Classification Promotion Using Clustering Inspired Active Learning

Deep neural networks (DNNs) have promoted much of the recent progress in hyperspectral image (HSI) classification, which depends on extensive labeled samples and deep network structure and has achieved surprisingly good generalization capacity. However, due to the expensive labeling cost, the labeled samples are scarce in most practice cases, which causes these DNN-based methods to be prone to over-fitting and influences the classification result. To mitigate this problem, we present a clustering-inspired active learning method for enhancing the HSI classification result, which mainly contributes to two aspects. On one hand, the modified clustering by fast search and find of peaks clustering method is utilized to select highly informative and diverse samples from unlabeled samples in the candidate set for manual labeling, which empowers us to appropriately augment the limited training set (i.e., labeled samples) and thus improves the generalization capacity of the baseline DNN model. On the other hand, another K-means clustering-based pseudo-labeling scheme is utilized to pre-train the DNN model with all samples in the candidate set. By doing this, the pre-trained model can be effectively generalized to unlabeled samples in the testing set after being fine tuned-based on the augmented training set. The experiment accuracies on two benchmark HSI datasets show the effectiveness of the proposed method.
Dark Triad Traits, Social Position, and Personality: A Cross-Cultural Study

This research explores the Dark Triad traits in 18 cultures from Europe, America, Africa, and Asia. We examined the relationships among Dark Triad traits, as measured by the SD3, with gender, age, social status, and two personality models, HEXACO and Zuckerman’s alternative five factor model (AFFM). There were 10,298 participants (5,410 women and 4,888 men) with a mean age of 40.31 (SD = 17.32) years old. Between 6% and 16% of the variance in the Dark Triad traits was accounted by culture. Men scored higher than women on all three traits in most cultures, but gender differences were generally larger in European countries. The relationship between the Dark Triad traits dimensions and age is negative, but the largest effect size is small (Psychopathy; η2 = .018). Psychopathy is associated with low Social Position, and Narcissism with high Social Position. In regard to Personality traits, Narcissism is positively related to Extraversion, and Psychopathy is negatively related to Conscientiousness for the HEXACO, and Narcissism is positively related to Activity and Sensation Seeking, and Machiavellianism and Psychopathy are positively related to Aggressiveness and Sensation Seeking for the AFFM.
Effect of Vitamin-D on COVID-19: A Review

Vitamin-D deficiency is a common yet understated problem. A number of people are at a risk of its deficiency while many others go undetected with the deficiency. The novel corona virus, an RNA virus, has run its rampant course across the globe affecting millions for the worse. COVID-19, a disease caused as a result of the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has shown to produce severe lower respiratory symptoms. This has so far resulted into a reported death count of more than 4.9 million worldwide. More than 4.5 lacs were affected in India. COVID-19 affects numerous other organs such as the liver, brain, kidney and heart. Dosage of Vitamin-D regularly, during the treatment and as a pre-emptive measure have all shown to reduce respiratory tract inflammation. Low plasma 25(OH)D levels appear to be an independent risk factor for COVID‐19 infection and hospitalization. Administration of a large dose of Calcifediol or what is also known as 25-hydroxyVitamin-D , a main metabolite of the Vitamin-D endocrine system, noticeably lessened the need for ICU treatment of patients requiring hospitalization after being tested positive for COVID-19. Elevated levels of Vitamin-D in body could decrease the length of hospital stays and reduce the intensity of the COVID 19 infection. In many COVID-19 patients the T regulatory lymphocytes (Tregs) levels are found to be low. These low levels can be elevated by Vitamin-D supplementation. Low levels of Vitamin-D have also been associated with increased inflammatory cytokines and a noticeably increased risk of pneumonia and viral upper respiratory tract infections. While further large-scale studies are required to provide concrete answers, the severity of the disease has been shown to be reduced and Vitamin-D can potentially be used as a supplementary treatment against Covid-19 and also as a precaution against the same. These results are significant and should not be ignored.
Evidence-Informed Approach to De-Prescribing of Atypical Antipsychotics (AAP) in the Management of Behavioral Expressions (BE) in Advanced Neurocognitive Disorders (NCD): Results of a Retrospective Study

The LuBAIR™ Paradigm is a novel approach to ascribe meaning to behavioral expressions in advanced neurocognitive disorders when the reliability of a clinical assessment is limited. The meaning ascribed to each behavioral category was used to identify those which are likely to respond to the use of atypical antipsychotics, in their management. De-prescribing was attempted on patients who qualified to enter this retrospective study. De-prescribing was defined as successful if individuals were completely withdrawn from AAP and remained off them for 60 days, without the re-emergence of behaviors. The LuBAIR™ Inventory was filled on two occasions. The data collected on the second occasion, in the successful and failed de-prescribed groups, were compared in this retrospective study. MANOVA, Chi-Square paired t-test statistical analyses were used to detect the differences in the behavioral categories between the two cohorts. Cohen d was used to measure effect size. Patients who did not have Mis-Identification and Goal-Directed Expressions were more likely to successfully de-prescribe: X2 (1, N = 40) = 29.119 p < 0.0001 and X2 (1, N = 40) = 32.374, p < 0.0001, respectively. Alternatively, the same behavioral categories were more likely to be present in patients who failed de-prescribing: MANOVA and paired t-test (p < 0.0001). Atypical antipsychotics, in their role as an antipsychotic and mood stabilizer, may be used to manage Mis-Identification and Goal-Directed Expressions, respectively.
Trialogical Model in Data Analysis Using Spreadsheet

The advance of information technology and the availability of data in education management provide an opportunity, to run data analysis to assist top management in decision making. Information technology can transform data analysis in a more concise format, accurate, and in a short time, by using any available office application software which is already installed on the computer. However, even though the management at an operational-level in an organization usually already has the software installed on a computer to support the managing of data, the extent of knowledge and skills to use the software efficiently in decision making is still limited. This study applies the Trialogical Model to implement decision support tools for data analysis of distribution lecturer’s teaching and learning (TnL) hour at IPG Kampus Pendidikan Teknik (IPGKPT) by grade specifications. A literature review using the interpretive paradigm involved the Trialogical Model (includes Monological and Dialogical), which is a knowledge-based model was carried out to run the data analysis using a Microsoft (Ms) Excel spreadsheet with Visual Basic for Application (VBA) and graphical interface. The findings show the usability of the Trialogical Model in the development of the decision support tool for TnL data analysis.
Simulated Performance of an Energy Storage and Return Prosthetic Ankle Based on Cams and Miniature Hydraulics

Prosthetic feet are limited in their ability to mimic the energy-recycling behavior of an intact ankle, negatively affecting lower-limb amputees’ gait in terms of metabolic cost and walking speed. To overcome these weaknesses, a novel prosthetic ankle based on hydraulics is described here. The ankle joint drives two cams, which in turn drive two hydraulic rams. One cam-ram system captures the negative work done from foot-flat until maximum dorsiflexion, by pumping oil into an accumulator, while the other returns positive work during push-off providing forward propulsion through fluid flowing from the accumulator to the ram. Simulation results are promising: of the total negative work done by the prosthetic ankle over the gait cycle (i.e., the maximum amount of energy available to be stored), 78% is returned, mainly during push-off; 14% is carried forward for future gait cycles; and 8% is lost. The estimated prosthesis height and mass are approximately <inline-formula> <tex-math notation="LaTeX">${26}. {5} {cm}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${2}. {3} {kg}$ </tex-math></inline-formula>. Nonetheless, further work is necessary to realise a prototype for bench and in-vivo testing. By mimicking intact ankle torque and efficiently storing and returning energy at the ankle joint, this new design may contribute to reducing amputees’ metabolic cost of walking.
Polarization, Angle, and Delay Estimation for Tri-Polarized Systems in Multipath Environments

A new signal processing algorithm proposed here is aimed at estimating five-dimensional (5-D) polarization-space-time (polarization, angle, delay) channel parameters for a tri-polarized system that works in a multipath environment. Unlike most channel estimation algorithms, where spatially spread antenna arrays are deployed, our algorithm is designed for a spatial col-located tripole antenna. A third-order tensor model is firstly established for the multipath polarization-space-time channel. This tensor is subsequently solved by exploiting low-rank decomposition techniques. After that, a closed-form solution of 2-D angles, 2-D polarizations, and multipath delay estimates for each multipath ray are derived. The proposed algorithm requires no computation for iterative searching and parameter pairing, thus offering an advantage in time-sensitive applications. In addition, it is applicable to either narrowband or wideband systems of arbitrary bandwidth and center-frequency.