PENGARUH UPAH DAN MOTIVASI KERJA TERHADAP PRODUKTIVITAS KERJA DI PT KUSUMATEX

This research aimed to find out: 1) Whether or not there is an effect of wage on work productivity in the workers of PT. Kusumatex, 2) Whether or not there is an effect of work motivation on work productivity in the workers of PT. Kusumatex, and 3) Whether or not there is an effect of wage and work motivation on work productivity in the workers of PT. Kusumatex. This study was a quantitative research with a multiple linear regression model use to test the hypothesis proposed. The population of research was the workers in production division of PT. Kusumatex with 125 respondents being the sample. The sampling technique used was random sampling. Considering the result of research, it could be found the coefficient regression Ῠ = 48.627 + 0.355X1 + 0.207X2. From the result of hypothesis testing, it could be found rstatistic > rtable (0.657 > 0.176) so that the first hypothesis stating that “There is a positive relationship between wage and work productivity in PT. Kusumatex” was supported. From the result of hypothesis testing, it could also be found rstatistic > rtable (0.477 > 0.176), so that the second hypothesis stating that “There is a positive relationship between work motivation and work productivity in PT. Kusumatex” was supported. From the result of F test, it could be seen Wage and Work Motivation variables simultaneously affected the Work Productivity in PT. Kusumatex with Fstatistic value = 63.054 and a= 0.000. Keywords: Wage, Work Motivation, Intrinsic, Work Productivit

Modeling Stochasticity and Variability in Gene Regulatory Networks

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This paper contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.Comment: 23 pages, 8 figure

ICAR: endoscopic skull‐base surgery

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Biological interpretation of the steady states of the system under different environmental conditions.

<p>Biological interpretation of the steady states of the system under different environmental conditions.</p

Steady states of the denitrification network under different environmental conditions.

<p>The first condition (low <i>O</i>₂, low <i>PO</i>₄ and high <i>NO</i>₃) corresponds to the perfect condition for denitrification and the second condition (low <i>O</i>₂, high <i>PO</i>₄ and high <i>NO</i>₃) corresponds to the denitrification condition disrupted by <i>PO</i>₄ availability. The remaining conditions can be labeled as aerobic conditions.</p

Summary of the model variables, their discretization, update rules and supportive argument.

<p>The update rules with an asterix (*) means this update rule is very close to the biological correspondence but not quite. The transition tables of the variables having update rules with an asterix (*) can be found in the Supplementary material.</p

Denitrification regulatory network of <i>P. aeruginosa</i>.

<p>Green solid arrows indicate upregulation and red dashed arrows indicate downregulation. Model components are PhoPQ, PmrA, Anr, NarXL, Dnr, NirQ, <i>nar</i>, <i>nir</i>, <i>nor</i>, <i>nos</i>, <i>NO</i>₂, <i>NO</i>, <i>N</i>₂<i>O</i>, and <i>N</i>₂. Our interest lies in perturbation of the external parameters (<i>O</i>₂, <i>PO</i>₄, <i>NO</i>₃) and their effect on the long-term behavior of the network.</p

Nitrous oxide concentration in <i>P. aeruginosa</i> cultures grown in glucose minimal medium at varying phosphate concentrations, normalized to 10⁸ cells.

<p>Nitrous oxide concentration in <i>P. aeruginosa</i> cultures grown in glucose minimal medium at varying phosphate concentrations, normalized to 10⁸ cells.</p