An enhanced healthcare system in mobile cloud computing environment

Abstract Mobile cloud computing (MCC) is a new technology for mobile web services. Accordingly, we assume that MCC is likely to be of the heart of healthcare transformation. MCC offers new kinds of services and facilities for patients and caregivers. In this regard, we have tried to propose a new mobile medical web service system. To this end, we implement a medical cloud multi-agent system (MCMAS) solution for polyclinic ESSALEMA Sfax—TUNISIA, using Google's Android operating system. The developed system has been assessing using the CloudSim Simulator. This paper presents initial results of the system in practice. In fact the proposed solution shows that the MCMAS has a commanding capability to cope with the problem of traditional application. The performance of the MCMAS is compared with the traditional system in polyclinic ESSALEMA which showed that this prototype yields better recital than using usual application

Assessment Of Adaptability And Stability Of Six Tunisian Cereal Genotypes Under Rainfed Conditions And At Two Semi Arid Environments

Three durum wheat (Nasr, Maâli and Salim), two bread wheat (Tahent and Utique) and two barley varieties (Manel and Kounouz) were assessed in two different semi arid locations under rainfed conditions in 2012-2013 growing season for yield related traits performances, stability and adaptability parameters. For determining adaptability and stability of genotypes, regression coefficient (bi) and variance of deviation from regression (S²di) are used. The evaluation was based on five agro-morphological traits: tiller number/plant, spike number/m2, plant number/m2, 1000 kernel weight and grain number/ spike. Variance analysis indicated a highly significant (p<0.05) effect of locations and genotypes for all studied traits. Also, the interaction between the genotypes and environments found to be highly significant (p<0.001) for all studied traits except the tiller number/plant. Analysis of stability showed that there were differences in stability performances among the genotypes for the traits tested. The unstability for spike number/m², plant number/m 2, 1000kernel weight and grain number/spike among the genotypes was originated from the high mean squares of deviation from regression. Analysis of AMMI model showed that Principal Components (PC) Analysis indicated that the two PCs explained 78.17% (PC1 = 41.51% and PC2 = 36.66%) of the total variation. Results showed that the group of genotypes Nasr, Tahent, Kounouz and Manel having wide adaptability and could be recommended for cultivation across diverse environments

Yield of Durum Wheat Cultivar Grown under Different Nitrogen Regimes and Rainfed Conditions

The experiment was conducted in randomized complete block design with three replications in two different locations (Kef and Bousselem). The effect of four nitrogen treatments (0, 75, 100, 120 and 140 kg/ha of N) was assessed for Maali durum wheat variety on five agronomic traits: biological yield, grain yield, harvest index, 1000 kernel weight and nitrogen use efficiency. Analysis of variance revealed significant effect (P< 0.01; P< 0.001) of the nitrogen treatments for all studied traits. However, no nitrogen treatment x site interaction was noted. Except for nitrogen use efficiency, both location exhibited significant variation (p<0.01) for all the traits examined. Biological yield, grain yield, harvest index, 1000 kernel weight and nitrogen use efficiency increased with an increase in nitrogen levels. In comparison to kef site, greatest results were obtained in Bousselem site under all nitrogen levels for all measured traits. Maximum average yield (2157.27 kg/ha) and (3013.11 kg /ha) was unregistered under N4 treatment (140 kg/ha) in Kef and Boussalem site respectively. A significant and positive correlation was noted between nitrogen rates and biological yield (r = 0.74**), grain yield (r = 0.66**), harvest index (r = 0.84**) and 1000 kernel weight (r = 0.85**). In this region, it seems that the application of 140 kg/ha of N fertilization lead to great agronomic performance of Maali durum wheat variety

Interval Type-2 Beta Fuzzy Near Sets Approach to Content-Based Image Retrieval

In computer-based search systems, similarity plays a key role in replicating the human search process. Indeed, the human search process underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. The search for images consists of establishing a correspondence between the available image and that sought by the user, by measuring the similarity between the images. Image search by content is generaly based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends notonly on the criteria of the search but also on the representation of the characteristics of the image. This is the main idea of a content-based image retrieval (CBIR) system. In this article, first, we constructed type-2 beta fuzzy membership of descriptor vectors to help manage inaccuracy and uncertainty of characteristics extracted the feature of images. Subsequently, the retrieved images are ranked according to the novel similarity measure, noted type-2 fuzzy nearness measure (IT2FNM). By analogy to Type-2 Fuzzy Logic and motivated by near sets theory, we advanced a new fuzzy similarity measure (FSM) noted interval type-2 fuzzy nearness measure (IT-2 FNM). Then, we proposed three new IT-2 FSMs and we have provided mathematical justification to demonstrate that the proposed FSMs satisfy proximity properties (i.e. reflexivity, transitivity, symmetry, and overlapping). Experimental results generated using three image databases showing consistent and significant results

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Context Aware Criteria for the Evaluation of Mobile Decision Support Systems

International audienceThe propagation and convergence of wireless communications, Internet, and mobile technology has given rise to new generation of decision support utilities commonly known as Mobile Decision Support Systems. Drawing on technology acceptance and decision-making theories, this study explores critical factors (utility and interestingness) in the use and the performance of Mobile Decision Support Systems and discuss the crucial criteria that should be involved. Our goal is to define interesting criteria for Mobile Decision Support Systems evaluation and emphasize the context aware criteria