Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Unipolar SPWM for Transformerless PV Grid Connected Inverter

The transformerless operation of grid interactive photovoltaic (PV) systems decreases the cost, size, weight and enhances the systems total efficiency. However, if the transformer is eliminated from the PV system, no galvanic connection exists between the local grid and PV system. In this condition, a leakage current flows through the PV parasitic capacitor and the local utility grid. In order to eliminate this leakage current, various transformerless inverter topologies have been proposed. In this paper a modified unipolar sinusoidal pulse width modulation is implemented for H6 DC decoupled grid connected inverter in order to curtail the flow of leakage current and enhance efficiency. The proposed PWM is simulated in MATLAB to certify the theoretical investigation.
Gender differences in household headship and level of awareness on anaemia among Ethiopian women: Evidences from a nationwide cross-sectional survey

Abstract Background: Information on gender difference in household headship in relation to awareness and practice related to anaemia is limited. This study has examined the issue under caption and provides evidence-based information for some program initiatives. Methods: Data from 970 Ethiopian women of reproductive age was extracted from a large data set collected as part of a nation-wide micronutrient programmes surveillance to examine the association between gender difference in household-headship and anaemia awareness. Data were analysed in SPSS version 20.0 for Windows. Proportional differences on some selected variables were determined using χ2 test, and a p-value less than 0.05 was considered statistically significant. Results: Males were found to head more than two third of the households. Significantly lower number of respondents were less educated with just 140 (14.4%) having primary cycle education (grades 1-6th) (P=0.001). About half of the respondents (50.2%) were aware of anaemia, with at least two thirds of the respondents from male-headed households aware of its symptoms (p=0.004) and treatment (p=0.003), and the difference was significant. Fewer women received iron supplementation in female-headed households (28.6%) than male-headed households (71.4%), though the difference was not significant (p=0.9). Unexpectedly, significant number of respondents with low awareness was among those with primary education with some regional variations (p=0.001). Conclusion: Anaemia awareness and treatment seeking behaviour was markedly lower in female-headed households than male-headed households which can be attributed to low levels of education. Thus, to narrow the observed gap, targeted education programs for women headed household is recommended. [Ethiop. J. Health Dev. 2018; 32(2):75-81] Key words: Gender, education, anemia, iron supplementation, women, Ethiopia
Current Derivative based schemes for Fault Location in DC Microgrid

A rapid and accurate fault location is necessary to minimize system downtime and aid in system restoration. Locating a fault in DC microgrid is a challenging and difficult task owing to the low cable impedance and occurrence of fast transients. In this paper, a current differentiation scheme and a hybrid current differentiation scheme are proposed to locate faults in DC microgrid. Based on current differentiation, unknown RLC parameters are estimated using the Moore Penrose pseudo-inverse technique. The estimated RLC parameters are used to find the fault location by a novel mathematical formulation. The proposed method is analyzed with varying sampling time, acceleration factor, fault locations, and fault resistances. Simulation results of the proposed schemes are compared with state of the art method to validate the efficacy to locate the fault. The simulation is carried out in Real-Time Digital Simulator (RTDS) for an ungrounded and grounded Solar Photo-Voltaic (SPV) system connected to the DC microgrid.
Fault locating transmission lines with thyristor-controlled series capacitors By fuzzy logic method

Presently, with the increasing competition due to the privatization of the electricity industry and the desire to provide better services to customers with critical loads. It has led to the development of speed and reliability of fault locating methods for transmission and distribution systems. It can be difficult to locate the fault. There are various methods for locating in series compensated lines with series equipment. This paper presents a novel fault locating algorithm on series compensated lines with the presence of Thyristor Controlled Capacitor Compensator (TCSC). The proposed algorithm was presented and the simulation was modeled in MATLAB software. The results show that the proposed algorithm increases the accuracy and at the same time speeds up the detection of the fault location in the series compensated transmission lines in order to prevent the malfunction of the relay operation.
Using an Azure Machine Learning Approach for Flexible Pavement Maintenance

Machine learning (ML) is one of the intelligent methodologies that has shown promising results in the domains of classification and prediction. In this paper, Azure Machine Learning (AML) systems were applied for flexible pavement maintenance classification problems and solutions. For prediction, four parameters were used as the inputs namely severity, density, road functional, and average daily traffic (ADT) while the output parameters were treatment techniques. This paper provides a critical analysis of classification algorithms: two-class support vector machine, multi-class decision forest, and multi-class neural network. Characteristic significance analysis was carried out to investigate how each classifier utilized the information available in the dataset, focusing on the application of azure machine learning classification (AMLC) to the pavement maintenances results prediction. Overall, the findings showed that predictions using Multi-Class Neural Network (MCNN) were obtaining an accuracy of 0.99. The results indicated that prediction obtained from AML training seemed to be more powerful, with a smaller standard error. Azure ML technique showed high accuracy with satisfactory results and capability of predicting pavement maintenance rigorously.
DCNN-Based Screw Detection for Automated Disassembly Processes

Automation of disassembly processes in electronic waste recycling is progressing but hindered by the lack of automated procedures for screw detection and removal. Here we specifically address the detection problem and implement a universal, generalizable, and extendable screw detector which can be deployed in automated disassembly lines. We selected the best performing state-of-the-art classifiers and compared their performance to that of our architecture, which combines a Hough transform with a novel integrated model of two deep convolutional neural networks for screw detection. We show that our method outperforms currently existing methods, while maintaining the high speed of computation. Data set and code of this study are made public.
Control Strategy for Denitrification Efficiency of Coal-Fired Power Plant Based on Deep Reinforcement Learning

The optimal control of denitrification system in coal-fired power plants in China has recently received widespread attention. The accurate prediction of denitrification efficiency and formulate control strategy of denitrification efficiency can guide the control and operation of the denitrification system better. Meanwhile, it can achieve the effect of energy conservation and Nitrogen oxides (NOx) reduction. In this paper, we take a domestic 1000 MW unit as an example, consider each of the major factors that affect the denitrification efficiency of selective catalytic reduction (SCR). We put forward a deep reinforcement learning (DRL) model by combining the Long short-term memory (LSTM) model and the Asynchronous Advantage Actor - Critic algorithm (A3C). We first use the LSTM to build a prediction model for denitrification efficiency. We then use the DRL model to obtain a control strategy for SCR denitrification efficiency in coal-fired power plants. The experimental results demonstrate that the accuracy of denitrification efficiency prediction model we established is better than other machine learning models, reaching 91.7%. Our control strategy model is industrially feasible and universally applicable.
Improved Palmprint Segmentation for Robust Identification and Verification

This paper introduces an improved approach to palmprint segmentation. The approach enables both contact and contactless palmprints to be segmented regardless of constraining finger positions or whether fingers are even depicted within the image. It is compared with related systems, as well as more comprehensive identification tests, that show consistent results across other datasets. Experiments include contact and contactless palmprint images. The proposed system achieves highly accurate classification results, and highlights the importance of effective image segmentation. The proposed system is practical as it is effective with small or large amounts of training data.
Generative Adversarial Networks for Stochastic Video Prediction With Action Control

The ability of predicting future frames in video sequences, known as video prediction, is an appealing yet challenging task in computer vision. This task requires an in-depth representation of video sequences and a deep understanding of real-word causal rules. Existing approaches for tackling the video prediction problem can be classified into two categories: deterministic and stochastic methods. Deterministic methods lack the ability of generating possible future frames and often yield blurry predictions. On the other hand, although current stochastic approaches can predict possible future frames, their models lack the ability of action control in the sense that they cannot generate the desired future frames conditioned on a specific action. In this paper, we propose new generative adversarial networks (GANs) for stochastic video prediction. Our framework, called VPGAN, employs an adversarial inference model and a cycle-consistency loss function to empower the framework to obtain more accurate predictions. In addition, we incorporate a conformal mapping network structure into VPGAN to enable action control for generating desirable future frames. In this way, VPGAN is able to produce fake videos of an object moving along a specific direction. Experimental results show that the combination of VPGAN with a pre-trained image segmentation model outperforms existing stochastic video prediction methods.
Composite Power System Reliability Evaluation Using Importance Sampling and Convolutional Neural Networks

This paper proposes a new approach for evaluation of power systems reliability using Monte Carlo Simulation. Using standard Monte Carlo Simulation, a composite of Convolutional Neural Networks (CNN) and Importance Sampling (IS) is proposed for computing power system reliability indices. It is shown that the computational efficiency can be dramatically increased if the machine learning techniques are used in conjunction with well-known variance reduction technique of importance sampling. The IEEE Reliability Test System (IEEE-RTS) is used for studying the proposed method. The results of case studies show that CNNs together with importance sampling provide a good classification accuracy while reducing computation time substantially.
Performance of a Multi-Grounded Distribution Network with a Four-Wire Three-Phase Power Conditioner

This paper presents a simplified analysis of a multi-grounded distribution network compensated by a four-wire three-phase power conditioner. The effect of the neutral conductors and grounding resistances on the voltage unbalance factor are demonstrated, in steady-state, performing digital simulations using the OpenDSS package. Then, a power conditioner is connected to the distribution network to compensate for the currents drained by an unbalanced load. The control loops used to control the AC currents and the DC voltage of the power conditioner are also presented. Results of time-domain simulations show that, although the unbalance can not be fully mitigated, the power conditioner can reduce the voltage unbalance and neutral voltages of the distribution network.
A Two-Stage Algorithm for Engagement Detection in Online Learning

Online learning plays a key role in current education system. Engagement detection in online learning is crucial as the student's success in online courses heavily depends on his/her state of mind. In our previous work, we used facial expressions labeled as engaged and not-engaged for student's engagement detection. In this paper, we use student's behavioral (on-task and off-task) and emotional (satisfied, bored, and confused) information for engagement detection. Five different convolutional neural network models have been tested for the behavioral and the emotional dimensions detection to detect student's engagement in online learning. The models are All Convolutional Network, Network in Network, Very Deep Convolutional Network, Conv-Pool Convolutional Network, and a proposed model combing some special features from the above models. We used the dataset ─ Dataset for the Affective States in E-Environments — for the performance evaluation. Experimental results show that the behavioral and emotional dimensions─based engagement detection provides a high accuracy.
La protéine ING2 : nouvelles fonctions suppressives de tumeurs et régulation par sumoylation

Les genes de la famille ING : « INhibitor of Growth » (ING1-5) jouent un role crucial dans l'inhibition de la proliferation cellulaire, en regulant notamment le cycle cellulaire, l'apoptose et la senescence. De plus, plusieurs etudes (portant majoritairement sur ING1) montrent que ces genes sont frequemment perdus dans de nombreux cancers. Ils pourraient donc etre impliques dans l'emergence et le developpement de tumeurs. Ainsi, l'objectif de mon projet de these etait d'etudier le gene ING2, afin d'evaluer son interet en cancerogenese. Nous avons tout d'abord montre que l'expression d'ING2 (ARN et proteique) est perdue dans plus de la moitie des cancers bronchiques non a petites cellules, confortant ainsi un role d'ING2 comme gene suppresseur de tumeurs. Par ailleurs, nous avons montre que l'inhibition de l'expression d'ING2 conduit a des defauts de replication et a une forte augmentation de l'instabilite genomique, mettant ainsi en evidence pour la premiere fois qu'ING2 est un gene suppresseur de tumeurs de type « caretaker ». Ceci permet aussi pour la premiere fois d'expliquer comment l'inactivation des ING, observee dans les tumeurs, pourrait contribuer a la cancerogenese. Enfin, nous avons mis en evidence le premier mecanisme de regulation post-traductionnelle d'ING2. En effet, ING2 peut etre sumoylee, et cette sumoylation est necessaire pour son association avec le complexe de regulation Sin3A/HDAC afin de cibler ce dernier au niveau des promoteurs de genes pour reguler leur expression. Ces travaux ont donc contribue a demontrer l'interet d'ING2 en cancerogenese et a mieux comprendre ses fonctions suppressives de tumeurs. De plus, ils ont permis d'ouvrir plusieurs voies d'investigation sur les fonctions et les mecanismes de regulation des proteines ING.
Consequences of undetected olfactory loss for human chemosensory communication and well-being

Olfactory perception has implications for human chemosensory communication and in a broader context, it affects well-being. However, most of the studies investigating the consequences of olfactory loss have recruited patients who have already been categorized as having a dysfunctional sense of smell and sought help in an ENT clinic. We revisit these findings by distinguishing subjects with olfactory impairment from a group of subjects who all declared a normal sense of smell when enrolling for this study. In the initial sample of 203 individuals, we found 59 to have impaired olfaction and four with marginal olfactory performance, not useful in daily life. Interestingly, we found a significant between-group difference in cognitive functioning, further supporting the notion of the relationship between cognition and olfactory performance. However, their chemosensory communication and well-being appeared not to be different from subjects with normosmia. Impaired olfactory function certainly has a severe impact on daily life but more so in individuals who are bothered with it and decide to seek treatment. The limited-to-no olfactory perception in the fraction of subjects who neither complain about it nor seek help in ENT clinics does not seem to have a major effect on their social, cognitive, emotional and health functioning. This article is part of the Theo Murphy meeting issue ‘Olfactory communication in humans’.
The specific biochemistry of human axilla odour formation viewed in an evolutionary context

Human body odour is dominated by the scent of specific odourants emanating from specialized glands in the axillary region. These specific odourants are produced by an intricate interplay between biochemical pathways in the host and odour-releasing enzymes present in commensal microorganisms of the axillary microbiome. Key biochemical steps for the release of highly odouriferous carboxylic acids and sulfur compounds have been elucidated over the past 15 years. Based on the profound molecular understanding and specific analytical methods developed, evolutionary questions could be asked for the first time with small population studies: (i) a genetic basis for body odour could be shown with a twin study, (ii) no effect of genes in the human leukocyte antigen complex on the pattern of odourant carboxylic acid was found, and (iii) loss of odour precursor secretion by a mutation in the ABCC11 gene could explain why a large fraction of the population in the Far East lack body odour formation. This review summarizes what is currently known at the molecular level on the biochemistry of the formation of key odourants in the human axilla. At the same time, we present for the first time the crystal structure of the Nα-acyl-aminoacylase, a key human odour-releasing enzyme, thus describing at the molecular level how bacteria on the skin surface have adapted their enzyme to the specific substrates secreted by the human host. This article is part of the Theo Murphy meeting issue ‘Olfactory communication in humans’.
Which Symptoms, Complaints and Complications of the Gastrointestinal Tract Occur in Patients With Eating Disorders? A Systematic Review and Quantitative Analysis

Background Eating disorders (ED) such as anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED) are often accompanied by a variety of psychological and physical comorbidities. Gastrointestinal (GI) symptoms are a classical feature in most patients with ED. The heterogeneity of studies on this topic is high, making it difficult to have a clear overview. The aim of this systematic review is therefore to provide an overview of subjectively and objectively measured differences and changes in the GI tract in patients with EDs, along with the occurrence of GI complications. Methods A systematic literature search was conducted in PubMed, Web of Science, and Google Scholar to find all relevant studies examining GI problems in AN, BN, and BED. Quantitative analyses were performed for objective GI physiology measures where applicable. Results The review differentiated between ED types and also between studies that report GI outcomes of ED in (i) human studies with an ED diagnosis excluding case reports that provide an overview of GI problems in ED and (ii) case reports with an ED diagnosis describing rare GI complications in ED. GI symptoms and impaired gastric transit times were frequent features of EDs with specific differences found for the ED types. During the time course of treatment, GI symptoms changed and/or improved but not completely. GI complications extended the range of GI problems observed, including a variety of serious complications such as gastric dilatation. Conclusions Problems of the GI tract are frequent in patients with ED and it is likely that they complicate therapy, especially in patients with AN. Systematic Review Registration PROSPERO registration number: CRD42019100585.
Assessment of Pyrogenic Carbonaceous Materials for Effective Removal of Radiocesium

Pyrogenic carbonaceous materials produced by pyrolysis process of various waste feedstocks are increasingly used in non-soil applications such as water purification tools. Pyrogenic carbonaceous materials thermochemically converted from wood chips, corn cobs, garden green waste, cherry pits, walnut shells, pine cones and municipal sewage sludge in slow pyrolysis under N2 atmosphere, were characterized by total C analysis, specific surface area, volumes of micro- and mesopores and tested in batch experiments as potential radiocesium sorption materials. Cesium adsorption-desorption experiments were conducted using the 137Cs radioisotope. Although tested materials significantly differ in Cs removal, Cs+ ions uptake could be attributed to the mechanisms of Freundlich surface adsorption. The highest maximum sorption capacities were reached for pine cones, wood chips and garden waste pyrogenic materials and reached 95.9 to 126 μmol g-1. These results demonstrate suitability of selected Pyrogenic carbonaceous materials as Cs adsorbents potentially useable as reactive barriers for 137Cs contaminated effluents.
Lipopolysaccharide-Deficient Acinetobacter baumannii Due to Colistin Resistance Is Killed by Neutrophil-Produced Lysozyme

Acinetobacter baumannii causes nosocomial infections due to its multidrug resistance and high environmental adaptability. Colistin is a polypeptide antibacterial agent that targets lipopolysaccharide (LPS) and is currently used to control serious multidrug-resistant Gram-negative bacterial infections, including those caused by A. baumannii. However, A. baumannii may acquire colistin resistance by losing their LPS. In mouse models, LPS-deficient A. baumannii have attenuated virulence. Nevertheless, the mechanism through which the pathogen is cleared by host immune cells is unknown. Here, we established colistin-resistant A. baumannii strains and analyzed possible mechanisms through which they are cleared by neutrophils. Colistin-resistant, LPS-deficient strains harbor mutations or insertion sequence (IS) in lpx genes, and introduction of intact lpx genes restored LPS deficiency. Analysis of interactions between these strains and neutrophils revealed that compared with wild type, LPS-deficient A. baumannii only weakly stimulated neutrophils, with consequent reduced levels of reactive oxygen species (ROS) and inflammatory cytokine production. Nonetheless, neutrophils preferentially killed LPS-deficient A. baumannii compared to wild-type strains. Moreover, LPS-deficient A. baumannii strains presented with increased sensitivities to antibacterial lysozyme and lactoferrin. We revealed that neutrophil-secreted lysozyme was the antimicrobial factor during clearance of LPS-deficient A. baumannii strains. These findings may inform the development of targeted therapeutics aimed to treat multidrug-resistant infections in immunocompromised patients who are unable to mount an appropriate cell-mediated immune response.