T-S Controllers For Photovoltaic-Grid Connected System Through DC-DC Boost Converter and Three Phase Inverter

Ce document présente deux contrôleurs flous TS en ligne pour contrôler l'extraction de puissance optimale et son transfert du système PV via deux convertisseurs statiques vers le réseau public. Le premier contrôleur est appliqué sur le convertisseur élévateur pour calculer, à chaque instant, le rapport cyclique permettant de suivre le point de puissance maximale du panneau sous les variations climatiques et d'atteindre un rendement élevé pour la récolte d'énergie solaire. Alors que le second ajuste les états de commutation des branches de l'onduleur triphasé à deux niveauxtransistors pour un transfert maximal de la puissance active produite par le panneau vers le réseau de distribution avec compensation de puissance réactive lors de l'établissement de la synchronisation.Après présentation de la structure du système de connexion au réseau et modélisation mathématique des convertisseurs côté PV et côté réseau, les contrôleurs flous TS sont détaillés. La synthèse de ces contrôleurs est basée sur la subdivision de l'espace d'états du système non linéaire à contrôler en un ensemble de sous-systèmes linéaires. Pour assurer le rejet des perturbations et garantir la stabilité du contrôleur flou,  Le critère et la fonction de stabilité quadratique de Lyapunov sont considérés. Les gains du contrôleur sont calculés en utilisant la solution d'inégalité de matrice linéaire (LMI). Les résultats de la simulation numérique sur l'environnement Matlab-Simulink montrent l'efficacité et les performances des contrôleurs proposés

In vivo nematicidal potential of camel milk on Heligmosomoides polygyrus gastro-intestinal nematode of rodents

Following our previous fi ndings on the in vitro anthelmintic effect of camel milk on Haemonchus contortus, the current study aimed at investigating its in vivo effect. Investigations were carried out using mice infected with Heligmosomoides polygyrus which is a parasite commonly used to test the effi cacy of anthelmintics. Thirty six Swiss white mice of both sexes aged 5 – 6 weeks old, and weighing between 20 and 25 g were orally infected with 0.5 ml dose of 100, 1-week-old H. polygyrus infective larvae (L3 ). After the pre-patent period, infected animals were randomly divided into 6 groups of 6 animals each. The nematicidal effi cacy of camel milk was monitored through faecal egg count reduction (FECR) and total worm count reduction (TWCR). Four doses (8.25; 16.5; 33.0; 66.0 ml/kg body weight (bw)) for fresh camel milk and 22 mg/kg bw for albendazole were studied using a bioassay. Albendazole and 4 % dimethylsulfoxide were included in the protocol as reference drug and placebo, respectively. For all tested doses except 8.25 ml/kg bw, camel milk was effective in vivo against H. polygyrus reducing both faecal egg count and worm count (p < 0.05). The dose 66 ml/kg bw showed the highest nematicidal activity causing a 76.75 % FECR and a 69.62 % TWCR 7 day after initiating the treatment. These results support the possible use of camel milk in the control of gastro-intestinal helminthiasis

Measuring the sales impact of improving inventory records: How improving the accuracy of inventory records can grow sales by 4-8%

There is a growing body of evidence to suggest that retailers’ inventory records are inaccurate to a significant extent. And it is reasonable to assume that the higher the inventory record inaccuracy (IRI), the higher the impact on sales. But what does this mean in real terms? This report describes the outcome of a 3-year project (conducted with the participation of 7 of Europe’s largest retailers) the aim of which is to quantify the IRI problem and demonstrate the sales lift resulting from fixing it. A structured test-control type experiment is used, according to which test stores are subjected to stock counts at some particular point in time, whereas control stores are not, allowing us to measure the effect of reconciling (or not) the stock records on sales. The analysis covers approximately 1 Million stock keeping units (SKUs) sold in about 100 stores; such data is of a different order of magnitude to anything previously attempted in the academic and practitioner literature, leading to important, reliable and trustworthy conclusions. We find that about 60% of the SKUs analysed are affected by inventory record inaccuracies. We also find that positive IRI is as prevalent as negative IRI, with the same detrimental effects though on sales. Very importantly, correcting inventory inaccuracies is found to lead to approximately 4% to 8% of increased sales in the participating retailers. Interestingly, this applies to all retailers including the particularly ‘accurate’ ones. The results demonstrate that the biggest opportunity for improvement comes from high-volume expensive items, and detailed analysis by product category shows which categories should attract most attention. Finally, we discuss and show results on how inventory accuracy deteriorates over time following a stock count. This has implications for deciding how often and when stocktakes should take place. Our findings should be of great value to retailers to: i) inform their decisions on the appropriate levels of resource and investment against improving inventory records accuracy; ii) prioritise investments per product category and class; iii) appreciate the behaviour of positive and negative discrepancies; iv) discuss counting as a sales increase strategy rather than a cost-intensive necessity

Enriching demand forecasts with managerial information to improve inventory replenishment decisions: exploiting judgment and fostering learning

This paper is concerned with analyzing and modelling the effects of judgmental adjustments to replenishment order quantities. Judgmentally adjusting replenishment quantities suggested by specialized (statistical) software packages is the norm in industry. Yet, to date, no studies have attempted to either analytically model this situation or practically characterize its implications in terms of ‘learning’. We consider a newsvendor setting where information available to managers is reflected in the form of a signal that may or may not be correct, and which may or may not be trusted. We show the analytical equivalence of adjusting an order quantity and deriving an entirely new one in light of a necessary update of the estimated demand distribution. Further, we assess the system’s behavior through a simulation experiment on theoretically generated data and we study how to foster learning to efficiently utilize managerial information. Judgmental adjustments are found to be beneficial even when the probability of a correct signal is not known. More generally, some interesting insights emerge into the practice of judgmentally adjusting order quantities

Optimization of community-based breeding programs

Any breeding program undergoes numerous stages of improvement and adjustment and is never completely flawless from the beginning. Initial selection in the community-based breeding programs (CBBPs) were made only based on phenotypic performance, disregarding the relationships between individuals. Following the accumulation of pedigree and performance data, the BLUP animal model has now been employed. Significant genetic gain for selection qualities were achieved in Ethiopian sheep CBBPs. However, there are still ways to maximize genetic advancement and overall advantages of the breeding programs. A method to maximize the current CBBPs was studied, which included increasing selection intensity and sire use strategy, index-based selection, enhancing breeding females' reproductive productivity, and connecting CBBPs with the production unit. The researchers attended planning and training sessions regarding CBBP optimization. The researchers later returned to their respective villages and implemented CBBP optimization as appropriate

Infant Cognitive Scores Prediction With Multi-stream Attention-based Temporal Path Signature Features

There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. The relationship between morphological features and cognitive abilities is also analyzed

Neuropsychiatric Disease Classification Using Functional Connectomics - Results of the Connectomics in NeuroImaging Transfer Learning Challenge

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew’s correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics