Evolution of the Kondo resonance feature and its relationship to spin-orbit coupling across the quantum critical point in Ce2Rh{1-x}CoxSi3

We investigate the evolution of the electronic structure of Ce2Rh{1-x}CoxSi3 as a function of x employing high resolution photoemission spectroscopy. Co substitution at the Rh sites in antiferromagnetic Ce2RhSi3 leads to a transition from an antiferromagnetic system to a Kondo system, Ce2CoSi3 via the Quantum Critical Point (QCP). High resolution photoemission spectra reveal distinct signature of the Kondo resonance feature (KRF) and its spin orbit split component (SOC) in the whole composition range indicating finite Kondo temperature scale at the quantum critical point. We observe that the intensity ratio of the Kondo resonance feature and its spin orbit split component, KRF/SOC gradually increases with the decrease in temperature in the strong hybridization limit. The scenario gets reversed if the Kondo temperature becomes lower than the magnetic ordering temperature. While finite Kondo temperature within the magnetically ordered phase indicates applicability of the spin density wave picture at the approach to QCP, the dominant temperature dependence of the spin-orbit coupled feature suggests importance of spin-orbit interactions in this regime.Comment: 6 figure

Performance Analysis of Hoeffding Trees in Data Streams by Using Massive Online Analysis Framewor

Present work is mainly concerned with the understanding of the problem of classification from the data stream perspective on evolving streams using massive online analysis framework with regard to different Hoeffding trees. Advancement of the technology both in the area of hardware and software has led to the rapid storage of data in huge volumes. Such data is referred to as a data stream. Traditional data mining methods are not capable of handling data streams because of the ubiquitous nature of data streams. The challenging task is how to store, analyse and visualise such large volumes of data. Massive data mining is a solution for these challenges. In the present analysis five different Hoeffding trees are used on the available eight dataset generators of massive online analysis framework and the results predict that stagger generator happens to be the best performer for different classifiers

Predicting HR Churn with Python and Machine Learning

Employee turnover imposes a substantial financial burden, necessitating proactive retention strategies. The aim is to leverage HR analytics, specifically employing a systematic machine learning approach, to predict the likelihood of active employees leaving the company. Using a systematic approach for supervised classification, the study leverages data on former employees to predict the probability of current employees leaving. Factors such as recruitment costs, sign-on bonuses, and onboarding productivity loss are analysed to explain when and why employees are prone to leave. The project aims to empower companies to take pre-emptive measures for retention. Contributing to HR Analytics, it provides a methodological framework applicable to various machine learning problems, optimizing human resource management, and enhancing overall workforce stability. This research contributes not only to predicting turnover but also proposes policies and strategies derived from the model's results. By understanding the root causes and timing of employee departures, companies can proactively implement measures to mitigate turnover, thereby minimizing the associated financial and operational burdens

Studies on Stability of Processing-type Genotypes of Tomato (Solanum lycopersicum L.)

To study stability of genotypes under three diverse environments, ten genotypes along with two checks of processingtype tomato were evaluated in Randomized Block Design (RBD) with three replications. Environment included three seasons, viz., kharif (2007), rabi (2007-08) and summer (2008) to identify the most stable varieties. Overall performance of PTR-1, PTR-4, PTR-6 and 'Arka Ashish' was found stable for yield per plant, number of branches per plant, % fruit set, % acidity and lycopene content. PTR-4 and PTR-6 were stable for high yield and for good processing traits