Employee attrition in selected industries: ITES, Banking, Insurance and Telecommnication in Delhi & NCR

Employee attrition has been seen as across the industries and retaining talented employees has become a challenge for HR managers. This research focsed how selected four industries differe on factors of attrition ..In this research descriptive research design has been used and through non random quota sampling 600 employees from four industries have been interviewd with a structured questionnaire. Thirteen factors came out through factor analysis which is responsible for employee attrition. Telecommunications sector employees feel they are having high job targets and feel unsupportive organization culture.Insurance sector employees feel low perceived value and insecurity for their job, less growth opportunities and have less learning opportunity. IT&ITES sector employees feel they are not provided good compensation and there are high job targets in their job.Banking sector employees there is a role stagnation, stress and office politics in their jobin comparison

Optimal charge/discharge profiles of mechanically constrained lithium-ion batteries

The cost and safety related issues of lithium-ion batteries require proactive charge and discharge profiles that can efficiently utilize the battery. Detailed electrochemical engineering based models that incorporate all of the key physics affecting the internal states of a lithium-ion battery are modeled using a system of coupled nonlinear partial differential equations. Careful choice of numerical discretization schemes and mathematical reformulation approaches can reduce the computational cost of these models to implement them in control relevant applications. The progress made in understanding the capacity fade mechanisms has paved the way for inclusion of that knowledge in deriving optimal charging/discharging profiles. Derivation of optimal charging/discharging profiles using physics based models enable us to provide constraints that can minimize local nonideal behavior and maximize efficiency locally and globally. This presentation will discuss derivation of optimal charging/discharging profiles which restrict various driving forces that accelerate capacity fade in a battery (e.g., temperature rise, over-potential for parasitic side reactions, intercalation induced stresses in solid phase) with minimal compromise on the amount of charge stored

Employee attrition in selected industries: ITES, Banking, Insurance and Telecommnication in Delhi & NCR

Employee attrition has been seen as across the industries and retaining talented employees has become a challenge for HR managers. This research focsed how selected four industries differe on factors of attrition ..In this research descriptive research design has been used and through non random quota sampling 600 employees from four industries have been interviewd with a structured questionnaire. Thirteen factors came out through factor analysis which is responsible for employee attrition. Telecommunications sector employees feel they are having high job targets and feel unsupportive organization culture.Insurance sector employees feel low perceived value and insecurity for their job, less growth opportunities and have less learning opportunity. IT&ITES sector employees feel they are not provided good compensation and there are high job targets in their job.Banking sector employees there is a role stagnation, stress and office politics in their jobin comparison

Firms’ Tweets and Stock Price Discovery

Do firms’ tweets improve stock price discovery at quarterly earnings announcements? We address this question using a comprehensive sample of 148,656 tweets released by 855 SP 1500 firms from 2008 through 2021. Firms’ tweets are associated with stronger stock price and volume reactions to earnings announcements. In addition, firms’ tweets reduce investor uncertainty, increase the timeliness and efficiency with which stock prices reflect information in earnings announcements, and reduce the post-earnings-announcement drift. We document that firms’ tweets improve stock price discovery by enhancing firm visibility and increasing retail investor trading, which facilitates faster incorporation of information into stock prices. Our inferences hold in a propensity score matched sample, where firms that use Twitter are matched with similar firms that do not. Our findings are of interest to regulators who wish to improve the informativeness of security prices, investors who are interested in information that affects prices and volume, and managers who seek channels to communicate with investors

Network Structure Identification using Corrupt Data-Streams

University of Minnesota Ph.D. dissertation. 2021. Major: Electrical Engineering. Advisors: Andrew Lamperski, Murti Salapaka. 1 computer file (PDF); xii, 137 pages.Many complex systems lend themselves to effective modeling described by a network of dynamically interacting agents. Such modeling is prevalent in many application domains that include climate science, neuroscience, internet-of-things, power grids, and econometrics. The evolution of these systems is governed by the interdependencies and interactions between the agents that can contain feedback loops. Identification of the presence or absence of influence pathways among the agents is of primary importance that enables subsequent analytics in networked systems such as identifying central agents and clusters, devising control strategies in distributed systems, and resource allocation. In most application domains, the nature of the relationships and interdependencies cannot be easily modeled using first principles. Furthermore, in many such systems, it is not possible to deliberately affect the system, and thus passive or noninvasive methods are required. The existing methods of network identification do not account for the common ways through which data gets corrupted. In real-world systems, sensor readings can be inaccurate, clocks can get out of sync, and messages can get lost in transmission over a wireless network. The focus of this research is to incorporate realistic modeling assumptions on data streams and characterize the effects of data corruption on network identification using passive means. We show that identifying the structure of networked systems using corrupt measurements results in the inference of spurious links. The effects of data corruption on network reconstruction are characterized with provable guarantees on the quality of construction with respect to the generative models considered. A wide range of generative models that underlie the data streams are considered that include static interactions (Markov random fields), linear time-invariant dynamical systems, and nonlinear dynamical models. We examine both causal and non-causal inference methods. In both cases, we provide an exact characterization of the location of spurious links. Our results show that the spurious links are localized to the neighborhood of the corrupted node. All our solution methodologies utilize only the time-series observations without any knowledge of the system parameters. Our precise characterization of the erroneous links is further exploited when the network has special structural properties. There are several physical systems, especially flow-driven systems like power grids, heat transfer networks, and fluid flow networks, where every dynamic coupling between the agents/nodes is bi-directional. In such systems, identifying unidirectional links in reconstruction lead to the conclusion that such links arise from data corruption. We utilize our precise characterization of spurious links to detect and localize all corrupt nodes in the network. It is imperative that learning the exact network representation of such systems without spurious links is needed for performing accurate state estimation, control, and optimization. To this, we developed methods to remove all spurious links and identify the exact structure of bi-directed networks despite of data corruption.Subramanian, Venkat Ram. (2021). Network Structure Identification using Corrupt Data-Streams. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/224981

Analytical Solution for the Impedance of a Porous Electrode

A macrohomogeneous model is presented for a porous electrode that includes coupled potential and concentration gradients with linear kinetics. The equations are solved to obtain an analytical expression for the impedance of a porous electrode. Complex plane plots are presented that illustrate two well-defined arcs: a kinetic arc and a diffusion arc with their time constants far apart. The effects of parameters such as exchange current density, porosity, diffusion coefficient, thickness, and interfacial area on the impedance spectra are presented. The usefulness of the analytical solution in investigating the effect of solution phase diffusion is also presented