Context in Mobile learning: the point of view of the learners

Context-awareness is becoming a crucial component in the mobile learning systems due to the dynamic changing of the Mobile learning environment, a Context-aware mobile learning system senses mobile environment and reacts to changing context during the learning process. Some efforts have been made in the area of Context-aware Mobile learning systems in order to propose a user model, most of them are focusing on the technological context such as the network performances, mobile devices capabilities, others are focusing on the learners’ style and preferences, and no one tried to understand the learners’ needs. However, no one tried to study the Learning context from the point of view of the learners. For this purpose, we created a questionnaire, in which we tried to understand which learning contexts are important to the learners in the learning process, and we use it to understand their needs and preferences, to inform the design of a new Context aware Mobile Learning Approach

Diagnostic de réseaux de Petri partiellement observables avec indicateurs algébriques

Dans cet article, nous présentons une approche de diagnostic d’un réseau de Petri partiellement observable basée sur une formalisation des indicateurs de fautes exprimées sous une forme algébrique (min, max, +). Les fautes sont modélisées par certaines transitions non observables et l’occurence d’une faute est équivalente au franchissement de la transition associée. L’algorithme d’élimination de Fourier-Motzkin est appliqué hors-ligne pour la génération des indicateurs de fautes utilisés pour le diagnostic en-ligne dans un temps polynomial. L’approche permet d’anticiper sur l’état du système (défaillant, non-défaillant, incertain) pour chaque transition observée de l’observation. Une comparaison avec la technique de diagnostic, utilisant la programmation linéaire, permet de montrer l’efficacité de notre approche. Le système de diagnostic se présente sous la forme d’un module opérant en parallèle avec l’approche classique

Diagnosis Using an Estimation Method for Partially Observed Petri Net

International audienc

Potential Nutrigenomic Approaches to Reduce the High Incidence of Obesity in Qatar

Obesity prevalence has been growing exponentially over the last few decades, with a high impact in high-income countries, like Qatar. Several approaches are attempting to understand the causes of this phenomenon however more important is what to do to reverse the trends. Obesity is widely studied, mostly in Europe and the Unites States, and a number of studies have demonstrate the role of specific gene patterns, transcriptome and proteome pathways, and gut microbiome strains. The Omics sciences have a great potential to investigate the determinants of non-communicable diseases, such as obesity. Nutritional genomics sciences apply all the Omics approaches to address nutrition-related diseases, investigating the interaction between genes and diet. To date, few data are available from nutrigenomic studies conducted in Middle East and particularly in Qatar to help the design of targeted interventions. The high incidence of obesity and the peculiar genetic make-up of the Qatari population provide opportunities for exploring nutrigenomic approaches to help addressing the problem

Diagnostic Based on Estimation Using Linear Programming for Partially Observable Petri Nets with Indistinguishable Events

In this paper, we design a diagnostic technique for a partially observed labelled Petri net where the faults of the system are modelled by unobservable transitions. The fault detection and isolation uses an on-line count vector estimation associated with the firing of unobservable transitions exploiting the observation of firing occurrences of some observable transitions. The support of the approach is an algebraic description of the process under the form of a polyhedron developed on a receding horizon. We show that a diagnostic can be made despite that different transitions can share the same label and that the unobservable part of the Petri net can contain circuits

EMPIRICAL MODELS FORLOSSES CALCULATION IN A RECTANGULAR DUCT EQUIPPED BYARTIFICIALROUGHNESS

The objective of this work is to find empirical models linking load losses to the geometric characteristics of artificial roughness called baffles and different modes of provisions of these fluids in the vein of a rectangular channel.An experimental design was set up to measure the lossesLoadcausedby baffles between the upstream and downstream of the channel. The experimental measurements made have enabled us to establish empirical relationships to evaluate losses in accordance with load losses configuration and arrangement of baffles and for different flow regimes

Genetic Variation in CCL5 Signaling Genes and Triple Negative Breast Cancer: Susceptibility and Prognosis Implications

Triple-negative breast cancer (TNBC) accounts for ~15\u201320% of breast cancer (BC) and has a higher rate of early relapse and mortality compared to other subtypes. The Chemokine (C-C motif) ligand 5 (CCL5) and its signaling pathway have been linked to TNBC. We aimed to investigate the susceptibility and prognostic implications of genetic variation in CCL5 signaling genes in TNBC in the present study. We characterized variants in CCL5 and that of six other CCL5 signaling genes (CCND1, ZMIZ1, CASP8, NOTCH2, MAP3K21, and HS6ST3) among 1,082 unrelated Tunisian subjects (544 BC patients, including 196 TNBC, and 538 healthy controls), assessed the association of the variants with BC-specific overall survival (OVS) and progression-free survival (PFS), and correlated CCL5 mRNA and serum levels with CCL5 genotypes. We found a highly significant association between the CCND1 rs614367-TT genotype (OR = 5.14; P = 0.004) and TNBC risk, and identified a significant association between the rs614367-T allele and decreased PFS in TNBC. A decreased risk of lymph node metastasis was associated with the MAP3K21 rs1294255-C allele, particularly in rs1294255-GC (OR = 0.47; P = 0.001). CCL5 variants (rs2107538 and rs2280789) were linked to CCL5 serum and mRNA levels. In the TCGA TNBC/Basal-like cohort the MAP3K21 rs1294255-G allele was associated with a decreased OVS. High expression of CCL5 in breast tumors was significantly associated with an increased OVS in all BC patients, but particularly in TNBC/Basal-like patients. In conclusion, genetic variation in CCL5 signaling genes may predict not only TNBC risk but also disease aggressiveness

EMPIRICAL MODELS FOR LOSSES CALCULATION IN A RECTANGULAR DUCT EQUIPPED BY ARTIFICIAL ROUGHNESS

The objective of this work is to find empirical models linking load losses to the geometric characteristics of artificial roughnesscalled baffles and different modes of provisions of these fluids in the vein of a rectangular channel.An experimental design was set up to measure the losses Load caused by baffles between the upstream and downstream of thechannel. The experimental measurements made have enabled us to establish empirical relationships to evaluate losses inaccordance with load losses configuration and arrangement of baffles and for different flow regimes

Precision medicine in the era of artificial intelligence: implications in chronic disease management.

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases