Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Control Strategy For Active Power Filter In Grid-Connected PV System Under Unbalanced And Distorted Grid Voltages

Remarkable growth of grid-connected renewable sources like PV systems imposes the use of power electronics converters (mainly PV inverters) in order to ensure efficient and reliable power conversion and grid synchronization. Therefore, under non ideal voltage conditions, the controller design of the PV inverter incorporating Shunt Active Power Filter functionality, becomes a significant challenge for power quality enhancement. On that account, this paper proposes a new concept of Direct Power Control (DPC) with an optimal switching table using extended PQ theory, which is suitable to compensate reactive power and eliminate harmonics drawn by nonlinear loads. Moreover, comparative study with the conventional DPC control strategy is presented to verify the superiority and the performance of the proposed DPC. Simulation results with Matlab/Simulink show that the proposed DPC is able to improve power quality under undesirable voltage supply conditions.
The Importance of Social Canteens in the Portuguese Emergency Food Program: A Case Study of the Holy Houses of Mercy

EnglishThe Portuguese social canteens are a social response to fight the extreme poverty and social exclusion in Portugal. The Solidarity Network of Social Canteens was created under the Portuguese Emergency Nutritional Program in 2012 to ensure individuals and/or families in need have access to free meals. This study aims to assess the contribution of the Social Canteens managed by the Portuguese Holy Houses of Mercy in implementing the Emergency Nutritional Program (particularly in a sample composed of the Holy Houses of Mercy in Northern Portugal). Therefore, we developed a detailed (descriptive and content) analysis upon a survey responded by these institutions. Most of the surveyed institutions revealed a significant increase in the number of daily provided meals (an increase of 300 meals per year since 2012). Despite this increase and the discussion of other obtained indicators, the Social Canteens also identify several criticisms in the linkages of the Program, specifically within the following three vectors: governmental support, interconnection among institutions, and the rules of the Program. portuguesAs cantinas sociais portuguesas constituiram uma resposta social para combater a extrema pobreza e exclusao social em Portugal. A Rede Solidaria de Cantinas Sociais foi criada no âmbito do Programa Nutricional de Emergencia Portugues em 2012 para garantir que individuos e / ou familias necessitadas tivessem acesso a refeicoes gratuitas. Este estudo tem como objetivo avaliar a contribuicao das Cantinas Sociais geridas pelas Santas Casas da Misericordia de Portugal na implementacao do Programa Nutricional de Emergencia (particularmente numa amostra composta pelas Misericordias do Norte de Portugal). A maioria das instituicoes pesquisadas revelou um aumento significativo no numero de refeicoes diarias fornecidas (um aumento de 300 refeicoes por ano desde 2012). Apesar desse aumento e da discussao de outros indicadores obtidos, as Cantinas Sociais tambem identificam diversas criticas nos termos do Programas, especificamente dentro dos tres vetores: apoio governamental, interconexao entre instituicoes e regras do Programa
Changes in selected haematological and biochemical parameters in debeaked pheasant hens during the laying period.

Changes in selected haematological and biochemical parameters during the laying period were monitored in pheasant hens kept in a cage system. Thermal cauterization of one-third of the upper part of the beak was performed before the birds were placed in the cage at the beginning of the laying period. Randomly selected pheasant hens were sampled for haematological and biochemical analysis at the beginning of the laying period, and the same hens were subsequently sampled after 6 and 12 weeks of the laying period. The results showed significant changes in the monitored haematological and biochemical parameters during egg laying. At the time when laying capacity approached a maximum, a fall was seen (P<0.05) in haematocrit, haemoglobin, erythrocytes and plasma glucose values, whereas leukocytes, lymphocytes, basophils and plasma uric acid exhibited an increase (P<0.05). At the end of the laying period, a higher (P<0.05) count of leukocytes, lymphocytes, eosinophils, basophils, monocytes, plasma alanine aminotransferase, uric acid, lactate and calcium concentrations were seen, whereas for haematocrit, heterophil to lymphocyte ratio, plasma total protein, and glucose lower (P<0.05) values were recorded in comparison with the values of the given indicators at the beginning of the laying period. The results determined provide new information about the dynamics of selected haematological and biochemical indicators in clinically healthy common pheasant layers during the course of the laying period
Strategic Planning For The Implementation Of Tourism Information Systems

: This research aims to plan a tourism information system implementation strategy to increase the number of visitors, with a case study on animal park tourism. Based on the evaluation of the organizations business processes with information systems that are running is considered not effective, the strategic planning of information systems is needed to help leaders and managers in carrying out their duties to improve work efficiency by automating the information management process, increasing management effectiveness by satisfying information needs for decision making, and improve competitiveness to improve the organizations competitive advantage by changing the style and manner of doing business. The methodology uses the Ward and Peppard strategic planning model with the stages of the analysis of Value Chain Analysis, PEST Analysis, Analysis of the Five Strength Models. The output stage uses the McFarlan Grid Strategic technique and the Balanced Scorecard to evaluate the results of implementing an information system strategic plan. The results of the research are in the form of a strategic framework and information system implementation plan and technology that is in line with the organization's business strategy plan.
A New Method to Enhance LRU Page Replacement Algorithm Performance

: In modern operating systems, the essential goal of the main memory management is to allow multiprogramming. To achieve this goal, virtual memory and paging scheme are used. The virtual memory needs a powerful page replacement algorithm to decide which page to swap out from the memory when a page fault occurs and the main memory is full. Many algorithms are proposed over the years. These algorithms aim to incur minimum software and hardware overhead. One of the well-known replacement algorithms is the Least Recently Used algorithm (LRU). The LRU has been considered as a common and successful policy. However, it imposes significant time overhead when executed. This paper proposes a novel approximation to the LRU. The new approach will use the counter technique to implement the LRU, but this counter will be evaluated based on extended Not Recently Used (NRU) m-bits orientation. The proposed algorithm has been evaluated and compared to the LRU for different workloads. The evaluation shows promising and good results. On average, the paper’s algorithm reduces the page fault rate by 13% and the execution time by 9.5%.
Extreme rainfall estimation on Bia watershed River in south-eastern of CÃ ́te dâ€TMIvoire

This study objective is to evaluate Gumbel's law exclusive use relative error of the in favor of theoretical and practical laws in extreme rainfall quantile estimation. To do this, an extreme rainfall database from 1941 to 2000 from the Bianouan, Agnibilékro, Adiaké and Ayamé rainfall stations on the Bia River watershed was established. The results analyzes show that the Fréchet and normal Log 2 laws are respectively the ideal laws of the theoretical and practical approach. In addition, a Gumbel's law exclusive use underestimates extreme rainfall quantiles compared to two approaches. Thus, Gumbel's law use could be justified by an economic or operational issue insofar as it understates the structures while minimizing the cost.
EndomEtrial cytology at lutEal and follicular phasEs

the aim of the study was to examine cytological changes in the uterus in cows during the fol- licular and luteal phases of the ovarian cycle, as well as to compare two different methods (brush and flushing) used for cytological material collection and to evaluate their usefulness for monitor - ing of the endometrium. Ovarian cycle phases were confirmed by ultrasound and by the level of sex hormones (17-β-estradiol and progesterone). The following types of cells were identified in the cytological smears: type i - surface cells; type ii - intermediate cells; type iii - basal cells; polymorphonuclear leukocytes (PMNs); L - lymphocytes. The number of type I and III cells was statistically significantly higher in the follicular phase than in the luteal phase, both in smears prepared using a brush (P<0.001) and by uterine flush (P=0.003). The number of type II cells was statistically significantly higher in the luteal phase than in the follicular phase in both methods (P<0.001). The results of the study show that phases of the ovarian cycle in cows can be identified based on changes in the quality and percentage of different types of endometrial cells in a cytologi- cal examination.
Interleukin (IL)‐1 regulates ozone‐induced nerve growth factor (NGF) release in bronchoalveolar lavage fluid (BALF) in mice

Our previous studies showed that both IL‐1 and NGF increase synthesis of substance P (SP) in airway neurons after either direct application or ozone exposure. We hypothesize that NGF mediates IL‐1 effects on SP, similar to the pathway described for sympathetic neurons in culture. The current study specifically focuses on the effect of IL‐1 on NGF levels in mice BALF. Three different experimental groups of adult mice were exposed to 2 ppm ozone for three hours while a control group was exposed to filtered air. The three experimental groups were 1) intratracheal (i.t.) instillation of IL‐1 receptor antagonist (IL‐1Ra) before ozone exposure; 2) i.t. instillation of saline before exposure; and 3) ozone exposure with no i.t. instillation. The following day, BALF was collected and NGF concentration was determined using Enzyme‐Linked ImmunoSorbent Assay (ELISA). The results showed ozone exposure alone or after saline instillation significantly increased the NGF level (3230 ng/ml ± 1039 and 2710 ng/ml ± 773 respectively) compared with the filtered air control group (1426 ng/ml ± 694). Pretreatment with IL‐1Ra attenuated the ozone‐induced increase in NGF levels (1045 ng/ml ± 51 units). These data indicate that IL‐1 regulates the ozone‐induced increase in NGF release in mice. Funded by NIH HL 35812.
MODELING OF NON-LINEAR REVERB EFFECT USING BLACK-BOX APPROACH

: In this paper we propose a method for modeling hardware reverb effects that manifests non-linear character. The method is based on an identiﬁcation of device under a test using input-output measurements utilizing a synchronized swept-sine signal. The model is implemented on a personal computer and on digital signal processor SHARC SC589. Both implementations are compared on the basis of time complexity.
Anti bacterial toxicity of Sodium tellurite (ST) against selected bacterial strains

The objective of this in vestigation was to predict the toxic properties of Sodium tellurite against selected human pathogens. A group of six human bacterial pathogens Staphylococcus aureus , Strepto pyogenes, Bacillus subtilis, Proteus vulgaris, Escherichia coli and Klebsiella pla nticola were utilized for screening. The spectrum of toxic properties was compared by means formation of zone. Our study demonstrated that sodium tellurite exhibit a strong toxic effect on B. subtilis (30±2mm), P. vulgaris (27 ± 2 mm) and S. aureus (25 ± 0 .5 mm). The activity was compared with standard C iprofloxacin disc (5µg/disc) and the zone of inhibition was 24 ± 1.4 mm. However, Gram negative bacteria exhibited resistance to Sodium tellurite except proteus vulgaris that we screened in our study. Our st udy suggested that the anti bacterial effect of sodium tellurite is not wider spectrum but selectively more significant even when compared with standard C iprofloxacin.
Comparison of the Atmospheric 200 hPa Jet’s Analyses between Proper Orthogonal Decomposition and Advanced Dynamic Mode Decomposition Method

In this paper, a frequently employed technique named the sparsity-promoting dynamic mode decomposition (SPDMD) is proposed to analyze the velocity fields of atmospheric motion. The dynamic mode decomposition method (DMD) is an effective technique to extract dynamic information from flow fields that is generated from direct experiment measurements or numerical simulation and has been broadly employed to study the dynamics of the flow, to achieve a reduced-order model (ROM) of the complex high dimensional flow field, and even to predict the evolution of the flow in a short time in the future. However, for standard DMD, it is hard to determine which modes are the most physically relevant, unlike the proper orthogonal decomposition (POD) method which ranks the decomposed modes according to their energy content. The advanced modal decomposition method SPDMD is a variant of the standard DMD, which is capable of determining the modes that can be used to achieve a high-quality approximation of the given field. It is novel to introduce the SPDMD to analyze the atmospheric flow field. In this study, SPDMD is applied to extract essential dynamic information from the 200 hPa jet flow, and the decomposed results are compared with the POD method. To further demonstrate the extraction effect of POD/SPDMD methods on the 200 hPa jet flow characteristics, the POD/SPDMD reduced-order models are constructed, respectively. The results show that both modal decomposition methods successfully extract the underlying coherent structures from the 200 hPa jet flow. And the DMD method provides additional information on the modal properties, such as temporal frequency and growth rate of each mode which can be used to identify the stability of the modes. It is also found that a fewer order of modes determined by the SPDMD method can capture nearly the same dynamic information of the jet flow as the POD method. Furthermore, from the quantitative comparisons between the POD and SPDMD reduced-order models, the latter provides a higher precision than the former, especially when the number of modes is small.
Eye Movements Do Not Affect Perceived Size

Since Kepler (1604) and Descartes (1637), it’s been suggested that ‘vergence’ (the angular rotation of the eyes) plays a key role in size constancy (objects appearing to have a constant size despite changes in distance radically altering the size of the retinal image). However, this has never been tested divorced from confounding cues such as changes in the retinal image. In our experiment participants viewed a target which grew or shrank over 5 seconds. At the same time their vergence was increased from 50cm to 25cm. The question was whether this vergence increase biased the participants’ judgements of whether the target grew or shrank? We found no evidence of any bias, and therefore no evidence that eye movements affect perceived size. This finding has three important implications: First, perceived size is much more reliant on cognitive influences than previously thought. This is consistent with the argument that visual scale is purely cognitive in nature (Linton, 2017; 2018). Second, vergence modulation of V1 should no longer be thought of as a candidate mechanism for size constancy. Third, the influence of multisensory integration on visual size appears to be reliant on subjective knowledge about changes in hand and gaze position.
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading

Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment reasoning network to strengthen the connection and enhance the understanding for both document and dialog. Specifically, we split the document into clause-like elementary discourse units (EDU) using a pre-trained discourse segmentation model, and we train our model in a weakly-supervised manner to predict whether each EDU is entailed by the user feedback in a conversation. Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information. Our experiments on the ShARC benchmark (blind, held-out test set) show that Discern achieves state-of-the-art results of 78.3% macro-averaged accuracy on decision making and 64.0 BLEU1 on follow-up question generation. Code and models are released at https://github.com/Yifan-Gao/Discern.
Assessing Robustness of Text Classification through Maximal Safe Radius Computation

Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. In this paper, we focus on robustness of text classification against word substitutions, aiming to provide guarantees that the model prediction does not change if a word is replaced with a plausible alternative, such as a synonym. As a measure of robustness, we adopt the notion of the maximal safe radius for a given input text, which is the minimum distance in the embedding space to the decision boundary. Since computing the exact maximal safe radius is not feasible in practice, we instead approximate it by computing a lower and upper bound. For the upper bound computation, we employ Monte Carlo Tree Search in conjunction with syntactic filtering to analyse the effect of single and multiple word substitutions. The lower bound computation is achieved through an adaptation of the linear bounding techniques implemented in tools CNN-Cert and POPQORN, respectively for convolutional and recurrent network models. We evaluate the methods on sentiment analysis and news classification models for four datasets (IMDB, SST, AG News and NEWS) and a range of embeddings, and provide an analysis of robustness trends. We also apply our framework to interpretability analysis and compare it with LIME.
Could High Salinity Be Used to Control Bullfrogs in Small Ponds?

Abstract We examined survival of bullfrog (Rana catesbeiana) eggs and tadpoles at 3 ppt and 6 ppt salinity in the laboratory to determine if low-level salinity could be used to eradicate bullfrogs from small ponds that contain native fishes. Bullfrog eggs and tadpoles <10 days old experienced 100% mortality when held at 6 ppt salinity for 10 days. Bullfrog tadpoles 10–15 days old experienced significantly reduced survival when exposed to salinity of 6 ppt for 10 days. Older bullfrog tadpoles (>9 months old) appeared unaffected by 14 days of 6 ppt salinity. Salinity of 3 ppt did not impact survival of bullfrog tadpole eggs or tadpoles at any of the life stages we tested. Adding salt to ponds in the early spring to increase salinity to 6 ppt may be a cost effective way to eradicate bullfrogs from small ponds without harming native fishes.
MDReg‐Net: Multi‐resolution diffeomorphic image registration using fully convolutional networks with deep self‐supervision

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self‐supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image‐wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi‐resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self‐supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high‐resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state‐of‐the‐art image registration algorithms.
The FaceChannelS: Strike of the Sequences for the AffWild 2 Challenge

Predicting affective information from human faces became a popular task for most of the machine learning community in the past years. The development of immense and dense deep neural networks was backed by the availability of numerous labeled datasets. These models, most of the time, present state-of-the-art results in such benchmarks, but are very difficult to adapt to other scenarios. In this paper, we present one more chapter of benchmarking different versions of the FaceChannel neural network: we demonstrate how our little model can predict affective information from the facial expression on the novel AffWild2 dataset.
Physics-based reconstruction methods for magnetic resonance imaging

Conventional magnetic resonance imaging (MRI) is hampered by long scan times and only qualitative image contrasts that prohibit a direct comparison between different systems. To address these limitations, model-based reconstructions explicitly model the physical laws that govern the MRI signal generation. By formulating image reconstruction as an inverse problem, quantitative maps of the underlying physical parameters can then be extracted directly from efficiently acquired k-space signals without intermediate image reconstruction—addressing both shortcomings of conventional MRI at the same time. This review will discuss basic concepts of model-based reconstructions and report on our experience in developing several model-based methods over the last decade using selected examples that are provided complete with data and code. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.
DEMI: Discriminative Estimator of Mutual Information

Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data. Recent progress has leveraged neural networks to optimize variational lower bounds on mutual information. Although showing promise for this difficult problem, the variational methods have been theoretically and empirically proven to have serious statistical limitations: 1) most of the approaches cannot make accurate estimates when the underlying mutual information is either low or high; 2) the resulting estimators may suffer from high variance. Our approach is based on training a classifier that provides the probability whether a data sample pair is drawn from the joint distribution or from the product of its marginal distributions. We use this probabilistic prediction to estimate mutual information. We show theoretically that our method and other variational approaches are equivalent when they achieve their optimum, while our approach does not optimize a variational bound. Empirical results demonstrate high accuracy and a good bias/variance tradeoff using our approach.