Miracle-dz/UniFAR-FTUs huggingface dataset:
Direct power control of an under-damped grid connected boost inverter

This paper presents a simple and direct power control approach to control a single-phase grid-connected boost inverter (GCBI) for renewable energy applications. A DC voltage source and a single-phase single-stage boost inverter that is connected to the grid by an L filter form the power injection system (PIS). Unlike the conventional voltage source inverters (VSIs), the boost inverter can generate an AC output voltage with amplitude larger than the DC input one, only in a single stage. In comparison with multi-stage power conversion systems, the aforementioned inverter has less number of devices and components which results in low cost and higher efficiency. Nevertheless, the boost inverter suffers from undesirable dynamic behavior and extreme fluctuation response that makes it difficult to control. Thus, the proposed power injection control system consists of two parts. First, a proper state-feedback control scheme is designed to increase damping and also improve the stability of the closed-loop system. Then, the direct power control strategy is employed to control and track the desired powers that should be injected into the grid. Simulation results are presented to validate the effectiveness of the proposed control strategy.
Predicting Type-2 Diabetes Using Machine Learning and Feature Selection Techniques

In medical science, the related data for particular disease have high diversities. So finding relationship between those data has been a challenging work. Nowadays machine learning has been experimenting for finding the relationship and pattern between those data and various systems. Diabetes is one of the most common and deadly disease all around the world. This disease has several complexities and can affect various organs of a human body. But till now there is no prevention or cure for diabetes. So predicting that a person might have high chance to develop diabetes can lead a better option. This paper discusses about various approaches for predicting the Type-2 diabetes and also for determining the most important features for developing diabetes. Data are collected from biographical information and pathological test reports of 15000 people. Gradient Boosting, Decision Tree, Random Forest, K-nearest neighbors and Support Vector Machine have been used in a supervised environment to predict the diabetes. Principal Component Analysis (PCA) is applied to find out the important attributes in predicting diabetes. Ensemble method is also applied for dealing with week predictors to get better results.
Comparison of PCA, LDA, ICA, SVM & HGPP

— In the field of face recognition, this paper explores a comparison of five most popular algorithms. These algorithms are Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA) , Support Vector Machine (SVM) and Histogram of Gabor Phase Patterns(HGPP). The performance of the algorithms have been measured in terms of the accuracy, training time, testing time, total execution time and memory usage for train and test the databases. The algorithms have been tested on the AT&T and IFD face database. The investigation shows that SVM outperforms the rest of the algorithms.
Concept of Variation Detector Used in Video Signal Compression Domain

: - This paper is concerned with a proportion similarity of the substance in images. In particular, we investigate the possibility of finding a cut position in a video sequence. The cut position detector is based on the data comparison when for two successive frames of the video sequence, several parameters are being evaluated. In this contribution we deal only with parameters in time domain, such as mean square and mean absolute error, image fidelity, normalized cross-correlation, Czekanowski distance and normalized distance. All functions are tested with a video sequence containing two cuts. Finally, a qualitative comparison of proposed methods is performed. For the cut detection in time domain is sufficient to use only two parameters: mean absolute error and normalized distance. Besides, for the color input data, the detector may scan only the luminance signal Y of YCbCr representation.
Aalborg Universitet OTA Evaluation of Mobile Phone Antenna Performance for VoLTE

This paper compares Over-The-Air (OTA) performance of traditional voice service and Voice over LTE (VoLTE). The results of the OTA measurements of the antenna performance of 22 mobile phones commonly used over the last two years are presented. VoLTE is a feature included into LTE which allows voice service to be delivered as data flows. Traditional investigations for LTE only include data service and therefore OTA for data services which is measured with the phone held in browsing position by the hand only, whereas the VoLTE uses the same data channel but in a position next to the head held by a hand. Traditional investigations of voice service only include voice service and OTA in voice service. The Figures of Merit (FoMs) taken into account for the ranking of the devices are the Total Radiated Power (TRP) and the Total Isotropic Sensitivity (TIS), related respectively to the transmitter and receiver antennas performance. The investigation includes results for talk mode using left and right hand phantoms next to head to calculate the maximum power radiated by the transmitter. To evaluate the sensitivity of the receiver, performances in talk mode using right hand phantom next to head as well as in data mode using only the hand phantom were considered. The results show a variation in TRP from 9 dB to 17 dB and approximately 9 dB spread in TIS among the phone models in the European LTE bands analyzed. Up to 8 dB difference in TRP between right and left hand usage were found for the worst performing phones, whereas their performances in free space are the same as the best performing phones. Key parameters of our analysis are accuracy, repeatability and testing time, since the equipment and the measurement procedure follow the CTIA Specification.
Control Of Small Wind Turbines In High Wind Speed Areas

: The strategy developed also addresses a system start-up problem that prevents the generator from accelerating to an uncontrollable operating point in high wind speed conditions. This is accomplished with voltage and current sensors only, and does not require direct measurements of wind speed or generator. The proposed method is applied to a small wind turbine system consisting of a permanent magnet synchronous generator (PMSG) and a simple transducer topology. Experimental and simulation results are included to demonstrate the performance of the proposed method. The document also shows the limitations of using stator driving force to estimate rotor speed in a PMSG connected to a rectifier, due to large d-axis current at high load.
Joint time-frequency analysis : methods and applications

Classical Signal Analysis. Signal Joint-Time Frequency Representations. Wigner-Ville Distribution. Time-Frequency Distribution Series. Adaptive Gabor Expansion and Adaptive Spectrogram. Applications of JFTA. Instantaneous Frequency Estimation. Time-Varying Signal Pattern Recognition and Classification. Non-Linear Time-Varying Filtering.
International Journal of Advanced Research in Computer Science and Software Engineering

: In case of wireless sensor network there exist problem of searching the nodes which are similar to each other. The nodes which are similar and are at minimum distance are selected for data transfer. The distance between nodes is known as localization. In the introduced paper work on APIT is done. The APIT is the distance vector routing based protocol which is used to indicate whether there exist a path from source to destination or not. The infected node can also be present which can take over the actual node causing problems in the transfer process. The Proposed pseudo code is energy efficient and grant extend the lifetime of the sensor network. Additionally the proposed pseudo code has the advantage of being simple and economical. The pretense results show that the introduced pseudo code is a practical, effective and accurate method for sensor nodes location in a WSN.
Impact of phase angle and sarcopenia estimated by bioimpedance analysis on clinical prognosis in patients undergoing hemodialysis

Abstract Bioimpedance analysis (BIA) has been widely used in the evaluation of body composition in patients undergoing maintenance hemodialysis. We conducted this study to evaluate impact of phase angle (PA) and sarcopenia measured by BIA on clinical prognosis in these patients. This longitudinal retrospective study enrolled patients who underwent hemodialysis between January 2016 and March 2019. The patients were stratified into higher (> 4°) and lower (≤ 4.0°) PA groups. Sarcopenia was defined when the appendicular skeletal muscle mass was < 20 kg in men and < 15 kg in women. Of the 191 patients, 63.4% were men. The mean age was 64.2 ± 12.4 years. The lower PA group was older, had a higher proportion of women, a lower body mass index, lower albumin, cholesterol, uric acid, and phosphorus levels, and a higher incidence of history of coronary artery disease than the higher PA group. Linear regression analysis revealed that PA was significantly associated with body mass index (B = 0.18, P = .005), serum albumin (B = 0.23, P = .001), and creatinine levels (B = 0.32, P < .001). During a median follow-up of 16.7 months, 14.1% (n = 27) of patients experienced major adverse cardiovascular events and 11.0% (n = 21) died. Kaplan–Meier survival analysis showed that the higher PA group had significantly better survival, regardless of sarcopenia. Multivariate Cox analyses revealed that lower PA (0.51 [0.31–0.85], P = .010), higher IDWG (1.06 [1.01–1.12], P = .028) and C-reactive protein level (1.01 [1.01–1.02], P < .001), and a history of coronary artery disease (3.02 [1.04–8.77], P = .042) were significantly related to all-cause mortality after adjusting for other covariates. PA measured by BIA was an independent factor in the prediction of mortality in maintenance hemodialysis patients, regardless of sarcopenia. Intervention studies are needed to confirm if the improvement in PA is associated with better clinical outcome.
Advanced strategies in glycosylation prediction and control during biopharmaceutical development: Avenues toward industry 4.0

Glycosylation has been shown to define the safety and efficacy of biopharmaceuticals, thus classified as a critical quality attribute. However, controlling glycan heterogeneity has always been a major challenge owing to the multivariate factors that govern the glycosylation process. Conventional approaches for controlling glycosylation such as gene editing and metabolic control have succeeded in obtaining desired glycan profiles in accordance with the Quality by Design paradigm. Nonetheless, the development of smart algorithms and omics‐enabled complete cell characterization has made it possible to predict glycan profiles beforehand, and manipulate process variables accordingly. This review thus discusses the various approaches available for control and prediction of glycosylation in biopharmaceuticals. Further, the futuristic goal of integrating such technologies is discussed in order to attain an automated and digitized continuous bioprocess for control of glycosylation. Given, control of a process as complex as glycosylation requires intense monitoring intervention, we examine the current technologies that enable automation. Finally, we discuss the challenges and the technological gap that currently limits incorporation of an automated process in routine bio‐manufacturing, with a glimpse into the economic bearing.
Heat inactivated mycobacteria, alpha‐Gal and zebrafish: Insights gained from experiences with two promising trained immunity inductors and a validated animal model

Trained immunity (TRAIM) may be defined as a form of memory where innate immune cells such as monocytes, macrophages, dendritic and natural killer (NK) cells undergo an epigenetic reprogramming that enhances their primary defensive capabilities. Cross‐pathogen protective TRAIM can be triggered in different hosts by exposure to live microbes or microbe‐derived products such as heat‐inactivated Mycobacterium bovis or with the glycan α‐Gal to elicit protective responses against several pathogens. We review the TRAIM paradigm using two models representing distinct scales of immune sensitization: the whole bacterial cell and one of its building blocks, the polysaccharides or glycans. Observations point out to macrophage lytic capabilities and cytokine regulation as two key components in non‐specific innate immune responses against infections. The study of the TRAIM response deserves attention to better characterize the evolution of host–pathogen cooperation both for identifying the aetiology of some diseases and for finding new therapeutic strategies. In this field, the zebrafish provides a convenient and complete biological system that could help to deepen in the knowledge of TRAIM‐mediated mechanisms in pathogen–host interactions.
EggLib 3: A python package for population genetics and genomics

Rapid and repeatable polymorphism analyses have become a necessity with the current amount of genomic data that can be collected in many organisms. Traditionally, such analyses are conducted using a variety of tools in combination, often requiring numerous format translation and manipulation. Here, we present a massively updated version of our previous software package egglib, intended to alleviate such costly and error‐prone tinkering with the data. egglib has been streamlined into a python package and thoroughly updated and optimized to accommodate modern‐day sized dataset. We show the main characteristics of the package making it a tool of choice to perform population genetics analyses. Once the data are imported (whatever their encoding), they can be filtered, edited, analysed and compared to coalescent simulations very easily and efficiently. Furthermore, the list of diversity and polymorphism statistics that can now be calculated has been greatly expanded. The software and its full documentation are available at https://egglib.org/.
The Role of Litter in Rainwater Interception

Aims: The function of plants litter on the soil surface in the hydrologic cycle, which is decreasing evaporation, protect of rain interception and surface runoff control. The research aim to know of plant litter rain Methodology: Experiment done in Randomize Blocks Design with 3 replications. For litter observations done on the plot size 40 x 5 m and the interception of rain was measured by using the Rainfall Simulator at high, medium and low intensity. The amount of interception measured by soil water balance approach and gradient of litter moisture. Results: Disturbed forest land (HT) produce the thickest litter and the highest interception value (both by water balance approach and gradient of litter moisture) than the other plot. The thickness of litter have a positive correlation with interception value (R 2 =0.70). Interception value have a negative correlation with runoff (R 2 =-0.99), while it have a positive correlation with soil moisture (R 2 =0.79). rainwater interception. The thickness of litter can intercept more rainwater but reduce surface runoff so it could be increase the soil moisture.
Ef ﬁ cacy and tolerability of oxycodone versus fentanyl for intravenous patient-controlled analgesia after gastrointestinal laparotomy A prospective, randomized, double-blind study

Background: It has been suggested that oxycodone is effective in relieving acute postoperative pain. The aim of this study was to investigate the ef ﬁ cacy and tolerability of oxycodone (O) versus fentanyl (F), and the adequate potency ratio of oxycodone and fentanyl in patients with intravenous patient-controlled analgesia after gastric laparotomy. Methods: In this double-blinded, randomized, controlled study, 60 patients undergoing elective gastric laparotomy were allocated to receive either oxycodone or fentanyl for postoperative intravenous patient-controlled analgesia (potency ratio 60:1). The patients received ketorolac 60mg before the end of anesthesia and then continued with patient-controlled analgesia for 48hours postsurgery. Pain severity, side effects and respiration rate were recorded 30 minutes, 3, 6, 12, 24, and 48hours after the surgery. Cumulative opioid requirements and patient satisfaction were also measured. Results: The median consumption more than 48hours after operation of oxycodone was 50mg (range: 40.0 – 62.4mg) and fentanyl was 0.8mg (range: 0.6 – 1.1mg), and the percentage of patients requiring rescue medication was not statistically signi ﬁ cant. Numeric rating scores at rest and upon movement were signi ﬁ cantly lower in group O than in F ( P < 0.05). Whereas the incidences of adverse events were similar between the groups (33.3% vs 27.6%, P = 0.64), a signi ﬁ cant higher sedation scores were found in patients given fentanyl at 30 minutes after the surgery ( P = 0.04). Conclusion: Oxycodone was comparable to fentanyl in the relief of postoperative pain following gastric laparotomy. Oxycodone not only provides better postoperative pain relief and less sedation, but also there was a tendency toward more side effects with oxycodone. Abbreviations: ASA = American Society of Anesthesiologists, BMI = body mass index, F = fentanyl, IV-PCA = intravenous patient-controlled analgesia, NRS = numeric rating scores, O = oxycodone, RR = respiration rate. comprehensive data exist regarding the analgesic ef ﬁ cacy of intravenous oxycodone versus fentanyl in patients after gastric laparotomy. In this prospectively, randomized, double-blind study, we compared the analgesic ef ﬁ cacy, adverse events, and patient satisfaction ratings of fentanyl and oxycodone in patients experiencing moderate to severe pain after gastric laparotomy. In addition, this study also evaluated the conversion dose rate of intravenous PCA (IV-PCA) oxycodone and fentanyl for postoperative analgesia on the basis of adequacy pain control.
PROCESSING ROUTES AND COSTS FOR COPPER PLATING ON BIFACIAL HETEROJUNCTION CELLS

A. Lachowicz, J. Geissbühler, A. Faes, J. Champliaud, J. Horzel, C. Ballif, M. Despeisse to ask for confirmation: M. Sciuto (3SUN), A. Battaglia (3SUN), J.F. Lerat, D. Muñoz, P.J. Ribeyron, P. Papet, B. Strahm, M. Zwegers, J. Hermans CSEM, PV-center, Jaquet-Droz 1, 2000 Neuchâtel, Switzerland 3SUN S.r. l. , Enel Green Power Group, Contrada Blocco Torrazze, 95121 Catania, Italy National Institute for Solar Energy (INES), 50 avenue du Lac Léman, 73375 Le Bourget-du-Lac, France Meco Equipment Engineers BV, Marconilaan 2, 5151 DR Drunen, Netherlands Meyer Burger BV, Luchthavenweg 10, 5657 Eindhoven, Netherlands
Sparse Multi-output Gaussian Processes

We consider the problem of modelling correlated outputs from a single Gaussian process (GP). Modelling multiple output variables is a challenge as we are required to compute cross covariances between the different outputs. These cross covariances allow us to improve our predictions of one output given the others as the correlations between outputs are modelled [1, 2, 3, 4]. One of the most straightforward methods used for this kind of modelling tasks employs a convolution process (CP) in which each output is expressed as the convolution between a smoothing kernel function and a Gaussian white noise [2]. If the white noise is a common input to all convolutions, then the outputs are going to be correlated and will have covariances and cross-covariances that are positive definite. However, the input function doesn’t have to be white noise. In principle, it could be any Gaussian process. Even though the CP framework is an elegant way for constructing dependent output processes, the fact that the full covariance function of the joint Gaussian process must be considered, results in significant computational demand and memory requirements when performing inference as the size of the covariance matrix now scales with the product of the number of outputs and the number of observation points. In this work we propose a sparse approximation for the full covariance matrix involved in the multiple output convolution process. We exploit the fact that each of the outputs is conditional independent of all others given the input process. This leads to an approximation for the covariance matrix which keeps intact the covariances of each output and approximates the cross-covariances terms with a low rank matrix. It has a similar form to the Partially Independent Training Conditional (PITC) [5] approximation for a single output GP.
Motivic decomposition of abelian schemes and the Fourier transform.

such that the /-adic realization of h (A) is H (A, Qt). Recall that Chow motives are obtained from the category of smooth projective varieties over a field by a construction of Grothendieck using äs intersection theory the Chow ring tensored with Q. See [Ma] and [Mur], 1.1 and l . 6, remark 2 for details. By an intricate argument Shermenev also shows how to express h *(A) in terms of h (A). For Chow motives a decomposition äs in (0.1) with /-adic realization äs above is by no means unique. Shermenev's decomposition in particular depends on choices. In this paper we establish a canonicalfunctorial decomposition äs above not only for abelian varieties but also for abelian schemes over a smooth quasiprojective base 5 over a field. In order to formulate such a Statement we extend parts of Manin's paper [Ma] on motives over a field to the case where the base is S. This is straightforward and we obtain a category of relative Chow motives over S.