Statistical tests for molecular evolution provide quantifiable insights into the selection pressures that govern a genome′s evolution. Increasing sample sizes used for analysis leads to higher statistical power. However, this requires more computational nodes or longer computational time. CATE (CUDA Accelerated Testing of Evolution) is a computational solution to this problem comprised of two main innovations. The first is a file organization system coupled with a novel search algorithm and the second is a large-scale parallelization of algorithms using both GPU and CPU. CATE is capable of conducting evolutionary tests such as Tajima′s D, Fu and Li′s, and Fay and Wu′s test statistics, McDonald–Kreitman Neutrality Index, Fixation Index, and Extended Haplotype Homozygosity. CATE is magnitudes faster than standard tools with benchmarks estimating it being on average over 180 times faster. For instance, CATE processes all 54,849 human genes for all 22 autosomal chromosomes across the five super populations present in the 1000 Genomes Project in less than thirty minutes while counterpart software took 3.62 days. This proven framework has the potential to be adapted for GPU-accelerated large-scale parallel analyses of many evolutionary and genomic analyses.

The study is intended to explain the effect of Mortality Salience (MS) on consumer behaviors. In a first part, we present a state of the art of Terror Management Theory (TMT) and its contributions in management sciences by focusing on the impact of MS on consumption. In a second part, we illustrate the results of an experiment testing the effect of death reminders on consumption choices. The results of the experiment show that the reminders of death generate, for the most part of participants, pro-materialistic consumption choices. Based on these results, we highlight the effect the death reminders can generate on Lebanese consumers.

Statistical tests for molecular evolution provide quantifiable insights into the selection pressures that govern a genome's evolution. Increasing sample sizes used for analysis leads to higher statistical power. However, this requires more computational nodes or longer computational time. CATE (CUDA Accelerated Testing of Evolution) is a computational solution to this problem comprised of two main innovations. The first is a file organization system coupled with a novel search algorithm and the second is a large‐scale parallelization of algorithms using both graphical processing unit (GPU) and central processing unit. CATE is capable of conducting evolutionary tests such as Tajima's D, Fu and Li's, and Fay and Wu's test statistics, McDonald–Kreitman Neutrality Index, Fixation Index and Extended Haplotype Homozygosity. CATE is magnitudes faster than standard tools with benchmarks estimating it being on average over 180 times faster. For instance, CATE processes all 54,849 human genes for all 22 autosomal chromosomes across the five super populations present in the 1000 Genomes Project in less than 30 min while counterpart software took 3.62 days. This proven framework has the potential to be adapted for GPU‐accelerated large‐scale parallel analyses of many evolutionary and genomic analyses.

More and more, healthcare institutions work to ameliorate the relation supervisor/supervised. In hospitals, transformational leadership proved to influence employee’s motivation and satisfaction (Spinelli, 2006, p.20) thus the hospital’s services. To our knowledge, there is no study conducted on the administrative employees in the healthcare sector in Lebanon that constitute our sample. There is only one study conducted on nurses by El-Jardali et al., (2008) in 69 hospitals in this country. Given this situation, we how can describe the relationship between transformational leadership and employee’s job satisfaction in hospitals? Data processing of a questionnaire administered to 455 employees of 28 over 125 hospitals in Lebanon shows that there is no significant relationship between the employee’s job satisfaction and these two transformational leadership components: leader’s idealized influence and intellectual stimulation. While we found a correlation between employee’s job satisfaction and two other components: inspirational motivation (Training; projects monitoring) and individualized consideration (Active listening to employee’s work issues).

This paper is proposed to clarify the effectiveness of semantic expressions used to designate climate change in France context, i.e. "ré chauffement climatique" ("global warming"); "changement climatique" ("climate change"); and "derangement climatique" ("climate imbalance"). An experimental study (sample size N = 126) based on "linguistic semantics" approach is conducted in order to assess the effect of these expressions on concerns, perceptions risk and sensitivity regarding Climate Change (CC). Our results show that the expression "ré chauffement climatique" ("global warming") is the most appropriate from a statistical standpoint. It increased the importance of the problem (salience of this issue) relative to other societal issues (e.g. unemployment, social justice, crime, etc.); it also enhanced participants' sensitivity (respondents' emotions associated with CC) more than the other expressions. We can still note however a strong difference in impact among the expressions if we were to calculate their impact on the basis of risk perception and communication objective. Results showed that when focusing our communication campaigns on nature, it would be preferable to use the term "changement" ("change"), when focusing our communication on social level, it would be preferable to use the term "ré chauffement" ("warming"), whereas the term "dé rè glement" ("imbalance") becomes the most suitable in seeking to build a communication campaign focusing on economic aspects. Semantics therefore should be selected depending on the communication objective.