Uplift modeling VS conventional predictive model: A reliable machine learning model to solve employee turnover

Employee turnover is the loss of talent in the workforce that can be costly for a company. Uplift modeling is one of the prescriptive methods in machine learning models that not only predict an outcome but also prescribe a solution. Recent studies are focusing on the conventional predictive models to predict employee turnover rather than uplift modeling. In this research, we analyze whether the uplifting model has better performance than the conventional predictive model in solving employee turnover. Performance comparison between the two methods was carried out by experimentation using two synthetic datasets and one real dataset. The results show that despite the conventional predictive model yields an average prediction accuracy of 84%; it only yields a success rate of 50% to target the right employee with a retention program on the three datasets. By contrast, the uplift model only yields an average accuracy of 67% but yields a consistent success rate of 100% in targeting the right employee with a retention program

A Survey on Continuous Time Computations

We provide an overview of theories of continuous time computation. These theories allow us to understand both the hardness of questions related to continuous time dynamical systems and the computational power of continuous time analog models. We survey the existing models, summarizing results, and point to relevant references in the literature

Predicting the Lay Preventive Strategies in Response to Avian Influenza from Perceptions of the Threat

Background: The identification of patterns of behaviors that lay people would engage in to protect themselves from the risk of infection in the case of avian influenza outbreak, as well as the lay perceptions of the threat that underlie these risk reduction strategies. Methodology/Principal Findings: A population-based survey (N = 1003) was conducted in 2008 to understand and describe how the French public might respond to a possible outbreak. Factor analyses highlighted three main categories of risk reduction strategies consisting of food quality assurance, food avoidance, and animal avoidance. In combination with the fear of contracting avian influenza, mental representations associated with the manifestation and/or transmission of the disease were found to significantly and systematically shape the behavioral responses to the perceived threat. Conclusions/Significance: This survey provides insight into the nature and predictors of the protective patterns that might be expected from the general public during a novel domestic outbreak of avian influenza

Modelling reconfigurable manufacturing systems with coloured timed Petri nets

International audienceReconfigurable manufacturing systems (RMSs) have been acknowledged as a promising means of providing manufacturing companies with the required production capacities and capabilities. This is accomplished through reconfiguring system elements over time for a diverse set of individualized products often required in small quantities and with short delivery lead times. Recognizing the importance of dynamic modeling and visualization in decision-making support in RMSs and the limitations of current research, we propose in this work to model RMSs with Petri net (PN) techniques focusing on the process of reconfiguring system elements while considering constraints and system performance. In view of the modeling challenges, including variety handling, production variation accommodation, machine selection, and constraint satisfaction, we develop a new formalism of colored timed PNs. In conjunction with colored tokens and timing in colored and timed PNs, we also define a reconfiguration mechanism to meet modeling challenges. An application case from an electronics company producing mobile phone vibration motors is presented. Also reported are system analysis and application results, which show how the proposed formalism can be used in the reconfiguration decision making process

Internal Jugular Vein Cross-Sectional Area and Cerebrospinal Fluid Pulsatility in the Aqueduct of Sylvius: A Comparative Study between Healthy Subjects and Multiple Sclerosis Patients

Objectives Constricted cerebral venous outflow has been linked with increased cerebrospinal fluid (CSF) pulsatility in the aqueduct of Sylvius in multiple sclerosis (MS) patients and healthy individuals. This study investigates the relationship between CSF pulsatility and internal jugular vein (IJV) cross-sectional area (CSA) in these two groups, something previously unknown. Methods 65 relapsing-remitting MS patients (50.8% female; mean age = 43.8 years) and 74 healthy controls (HCs) (54.1% female; mean age = 43.9 years) were investigated. CSF flow quantification was performed on cine phase-contrast MRI, while IJV-CSA was calculated using magnetic resonance venography. Statistical analysis involved correlation, and partial least squares correlation analysis (PLSCA). Results PLSCA revealed a significant difference (p<0.001; effect size = 1.072) between MS patients and HCs in the positive relationship between CSF pulsatility and IJV-CSA at C5-T1, something not detected at C2-C4. Controlling for age and cardiovascular risk factors, statistical trends were identified in HCs between: increased net positive CSF flow (NPF) and increased IJV-CSA at C5-C6 (left: r = 0.374, p = 0.016; right: r = 0.364, p = 0.019) and C4 (left: r = 0.361, p = 0.020); and increased net negative CSF flow and increased left IJV-CSA at C5-C6 (r = -0.348, p = 0.026) and C4 (r = -0.324, p = 0.039), whereas in MS patients a trend was only identified between increased NPF and increased left IJV-CSA at C5-C6 (r = 0.351, p = 0.021). Overall, correlations were weaker in MS patients (p = 0.015). Conclusions In healthy adults, increased CSF pulsatility is associated with increased IJV-CSA in the lower cervix (independent of age and cardiovascular risk factors), suggesting a biomechanical link between the two. This relationship is altered in MS patients

Angiogenesis gene expression in murine endothelial cells during post-pneumonectomy lung growth

Although blood vessel growth occurs readily in the systemic bronchial circulation, angiogenesis in the pulmonary circulation is rare. Compensatory lung growth after pneumonectomy is an experimental model with presumed alveolar capillary angiogenesis. To investigate the genes participating in murine neoalveolarization, we studied the expression of angiogenesis genes in lung endothelial cells. After left pneumonectomy, the remaining right lung was examined on days 3, 6, 14 and 21days after surgery and compared to both no surgery and sham thoracotomy controls. The lungs were enzymatically digested and CD31+ endothelial cells were isolated using flow cytometry cell sorting. The transcriptional profile of the CD31+ endothelial cells was assessed using quantitative real-time polymerase chain reaction (PCR) arrays. Focusing on 84 angiogenesis-associated genes, we identified 22 genes with greater than 4-fold regulation and significantly enhanced transcription (p <.05) within 21 days of pneumonectomy. Cluster analysis of the 22 genes indicated that changes in gene expression did not occur in a single phase, but in at least four waves of gene expression: a wave demonstrating decreased gene expression more than 3 days after pneumonectomy and 3 sequential waves of increased expression on days 6, 14, and 21 after pneumonectomy. These findings indicate that a network of gene interactions contributes to angiogenesis during compensatory lung growth

Effect of Temperature Gradient Direction in the Catalyst Nanoparticle on CNTs Growth Mode

To improve the understanding on CNT growth modes, the various processes, including thermal CVD, MP-CVD and ECR-CVD, have been used to deposit CNTs on nanoporous SBA-15 and Si wafer substrates with C2H2 and H2 as reaction gases. The experiments to vary process parameter of ΔT, defined as the vector quantities of temperature at catalyst top minus it at catalyst bottom, were carried out to demonstrate its effect on the CNT growth mode. The TEM and TGA analyses were used to characterize their growth modes and carbon yields of the processes. The results show that ΔT can be used to monitor the temperature gradient direction across the catalyst nanoparticle during the growth stage of CNTs. The results also indicate that the tip-growth CNTs, base-growth CNTs and onion-like carbon are generally fabricated under conditions of ΔT > 0, <0 and ~0, respectively. Our proposed growth mechanisms can be successfully adopted to explain why the base- and tip-growth CNTs are common in thermal CVD and plasma-enhanced CVD processes, respectively. Furthermore, our experiments have also successfully demonstrated the possibility to vary ΔT to obtain the desired growth mode of CNTs by thermal or plasma-enhanced CVD systems for different applications