Influence of Water Temperature on the MXR Activity and P-glycoprotein Expression in the Freshwater Snail, Physa acuta (Draparnaud, 1805)

P-glycoprotein (P-gp) mediated multixenobioticresistance (MXR) is a mechanism analogous to multidrug resistance, which has been extensivelycharacterized in mammalian tumours. The expression and function of the MXR mechanism hasbeen demonstrated in numerous aquatic organisms and has been proposed as a biomarker forpollution assessment. A close relationship between thermal stress and MXR response has beenreported in some aquatic organisms. Seasonal studies in freshwater organisms are scarce andconducted mainly in zebra mussel (Dreissena polymorpha), whose presence has not been reportedin South America. The general purpose of the present study was to evaluate seasonal variation of abiomarker, the MXR mechanism, in the worldwide distributed freshwater snail P. acuta. Weanalyzed the in situ influence of temperature on the biomarker response over an 18-month fieldstudy. MXR defence system was evaluated by a combination of functional assays (RB accumulation)and molecular approaches to analyse P-gp expression. The results demonstrated a linear correlationbetween MXR response, at activity and expression level, and water temperature at sample site, in P.acuta snails. The characterization of the MXR system in worldwide distributed species, includingthe study of their seasonal fluctuations, could contribute to the increasing interest to incorporate thisbiomarker to provide an integrated assessment of mussel health status. This work supports thepossible use of P. acuta snails with this purpose and also highlights that the occurrence of variationsin MXR response related to water temperature has to be taken into account in the interpretation ofin situ monitoring studiesFil: Horak, Cristina Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia ; ArgentinaFil: Assef, Yanina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Centro de Investigación Esquel de Montaña y Estepa Patagóica. Universidad Nacional de la Patagonia ; Argentin

Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK

The Angular Clustering of WISE-Selected AGN: Different Haloes for Obscured and Unobscured AGN

We calculate the angular correlation function for a sample of 170,000 AGN extracted from the Wide-field Infrared Survey Explorer (WISE) catalog, selected to have red mid-IR colors (W1 - W2 > 0.8) and 4.6 micron flux densities brighter than 0.14 mJy). The sample is expected to be >90% reliable at identifying AGN, and to have a mean redshift of z=1.1. In total, the angular clustering of WISE-AGN is roughly similar to that of optical AGN. We cross-match these objects with the photometric SDSS catalog and distinguish obscured sources with (r - W2) > 6 from bluer, unobscured AGN. Obscured sources present a higher clustering signal than unobscured sources. Since the host galaxy morphologies of obscured AGN are not typical red sequence elliptical galaxies and show disks in many cases, it is unlikely that the increased clustering strength of the obscured population is driven by a host galaxy segregation bias. By using relatively complete redshift distributions from the COSMOS survey, we find obscured sources at mean redshift z=0.9 have a bias of b = 2.9 \pm 0.6 and are hosted in dark matter halos with a typical mass of log(M/M_odot)~13.5. In contrast, unobscured AGN at z~1.1 have a bias of b = 1.6 \pm 0.6 and inhabit halos of log(M/M_odot)~12.4. These findings suggest that obscured AGN inhabit denser environments than unobscured AGN, and are difficult to reconcile with the simplest AGN unification models, where obscuration is driven solely by orientation.Comment: Accepted for publication in ApJ. 13 pages, 15 figure

Solution of Two Dimensional Poisson Equation Using Finite Difference Method with Uniform and Non-uniform Mesh Size

This study focus on the finite difference approximation of two dimensional Poisson equation with uniform and non-uniform mesh size. The Poisson equation with uniform and non-uniform mesh size is a very powerful tool for modeling the behavior of electro-static systems, but unfortunately may not be solved analytically for very simplified models. Consequently, numerical simulation must be utilized in order to model the behavior of complex geometries with practical value. In most engineering problems are also coming from steady reaction-diffusion and heat transfer equation, in elasticity, fluid mechanics, electrostatics etc. the solution of meshing grid is non-uniform and uniform where fine grid is identified at the sensitive area of the simulation and coarse grid at the normal area.The discretization of non-uniform grid is done using Taylor expansion series. The purpose of such discretization is to transform the calculus problem to numerical form (as discrete equation). Therefore, in this study the two dimensional Poisson equation is discretazi with uniform and non-uniform mesh size using finite difference method for the comparison purpose. More over we also examine the ways that the two dimensional Poisson equation can be approximated by finite difference over non-uniform meshes, As result we obtain that for uniformly distributed gird point the finite difference method is very simple and sufficiently stable and converge to the exact solution whereas in non-uniformly distributed grid point the finite difference method is less stable, convergent and time consuming than the uniformly distributed grid points. Keywords: Finite difference method, two dimensional Poisson equations, Uniform mesh size, Non-uniform mesh size, Convergence, Stability, Consistence. DOI: 10.7176/APTA/79-01 Publication date:September 30th 2019

De FC à MAC : un algorithme paramétrable pour la résolution des CSP

http://www710.univ-lyon1.fr/~csolnonBeaucoup d'algorithmes de résolution de Problèmes de Satisfaction de Contraintes ont été proposés ces dernières années. Parmi ces algorithmes nous pouvons mentionner les deux les plus populaires et les plus étudiés : le Forward-Checking (FC) et Maintaining Arc-Consistency (MAC). Dans ce papier, nous étudions ces deux algorithmes et nous réévaluons leurs performances sur des problèmes générés aléatoirement. Précisément, nous montrons expérimentalement que FC est meilleur que MAC sur des CSP difficiles dont le graphe de contraintes est très dense et la dureté des contraintes est faible. En revanche, MAC se montre plus performant que FC sur des problèmes difficiles avec un graphe de contraintes peu dense et une dureté élevée. Ces résultats montrent que le maintien de l'Arc-consistance pendant la recherche peut être une perte de temps. Ensuite, Nous proposons une approche générique qui permet, pendant la recherche, un maintien partiel et paramétrable de la consistance locale

Gap and Overlap Detection in Automated Fiber Placement

The identification and correction of manufacturing defects, particularly gaps and overlaps, are crucial for ensuring high-quality composite parts produced through Automated Fiber Placement (AFP). These imperfections are the most commonly observed issues that can significantly impact the overall quality of the composite parts. Manual inspection is both time-consuming and labor-intensive, making it an inefficient approach. To overcome this challenge, the implementation of an automated defect detection system serves as the optimal solution. In this paper, we introduce a novel method that uses an Optical Coherence Tomography (OCT) sensor and computer vision techniques to detect and locate gaps and overlaps in composite parts. Our approach involves generating a depth map image of the composite surface that highlights the elevation of composite tapes (or tows) on the surface. By detecting the boundaries of each tow, our algorithm can compare consecutive tows and identify gaps or overlaps that may exist between them. Any gaps or overlaps exceeding a predefined tolerance threshold are considered manufacturing defects. To evaluate the performance of our approach, we compare the detected defects with the ground truth annotated by experts. The results demonstrate a high level of accuracy and efficiency in gap and overlap segmentation

Evolution of the UV Excess in Early-Type Galaxies

We examine the UV emission from luminous early-type galaxies as a function of redshift. We perform a stacking analysis using Galaxy Evolution Explorer (GALEX) images of galaxies in the NOAO Deep Wide Field Survey (NDWFS) Bo\"otes field and examine the evolution in the UV colors of the average galaxy. Our sample, selected to have minimal ongoing star formation based on the optical to mid-IR SEDs of the galaxies, includes 1843 galaxies spanning the redshift range $0.05\leq z\leq0.65$. We find evidence that the strength of the UV excess decreases, on average, with redshift, and our measurements also show moderate disagreement with previous models of the UV excess. Our results show little evolution in the shape of the UV continuum with redshift, consistent either with the binary model for the formation of Extreme Horizontal Branch (EHB) stars or with no evolution in EHB morphology with look-back time. However, the binary formation model predicts that the strength of the UV excess should also be relatively constant, in contradiction with our measured results. Finally, we see no significant influence of a galaxy's environment on the strength of its UV excess.Comment: 30 pages, 10 figures; accepted by ApJ. Modified from original version to reflect referee's comment