A uracil nitroso amine based colorimetric sensor for the detection of Cu²⁺ ions from aqueous environment and its practical applications

A simple uracil nitroso amine based colorimetric chemosensor (UNA-1) has been synthesized and screened for its cation recognition ability. Sensor UNA-1 exhibited a high sensitivity and selectivity towards Cu²⁺ ions in aqueous medium in the presence of a wide range of other competing cations (Ag⁺, Al³⁺, Ba²⁺+, Ca²⁺, Cd²⁺, Co²⁺, Cr³⁺, Cs⁺, Fe²⁺, Fe³⁺, Li⁺, Mg²⁺, Mn²⁺, Na⁺, Ni²⁺, Pb²⁺, Zn²⁺, Hg²⁺ and Sr²⁺). With Cu²⁺, the sensor UNA-1 gave a distinct color change from colorless to dark yellow by forming a complex of 1:1 stoichiometry. Furthermore, sensor UNA-1 was successfully utilized in the preparation of test strips and supported silica for the detection of Cu²⁺ ions from aqueous environment

Viscosity Behaviour of Aqueous Solutions of Sodium Chloroacetate & ɑ-Chloropropionate

1097-109

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

Sugar, acid, and nitrogen in the developing berries of some grape varieties

Periodical data regarding changes in total nitrogen, total acid and total sugar of the developing berries revealed that the total nitrogen in dry weight basis increased very rapidly between 10-20 days after anthesis and decreased subsequently until maturity of berries. The maximum N content 20 days after anthesis is thought to coincide with the period of maximum requirement of N at this stage for building up of the various tissue. The total acidity in developing berries showed the familiar pattern of gradual rise to 40 days after anthesis, followed by a gradual decline until maturity of berries. The maximum coincides with low night temperatures, indicating greater synthesis of acids at low temperatures. The gradual reduction of acidity until maturity corresponcled with the rise in day temperature suggesting the respiration of acids at high temperatures.The sugar accumulation in developing grape berries started 50 days after anthesis. The rate of accumulation was very high from 60-80 days after anthesis. This period coincides with the beginning of the third stage of berry growth.Zucker, Säure und Stickstoff in den wachsenden Beeren einiger RebsortenDer Gehalt an Gesamtstickstoff, Gesamtsäure und Gesamtzucker in wachsenden Beeren wurde in regelmäßigen Abständen ermittelt. Dabei zeigte sich, daß der Gesamtstickstoff, auf das Trockengewicht bezogen, in der Zeitspanne zwischen 10 und 20 Tage nach der Anthese sehr schnell zunahm und danach bis zur vollen Beerenreife wieder absank. Es wird vermutet, daß der Höchstgehalt 20 Tage nach der Anthese mit der Phase eines maximalen Stickstoffbedarfes für den Aufbau der verschiedenen Gewebe zusammenfälllt.Die Gesamtsäure in den wachsenden Beeren zeigte das bekannte Bild eines allmählichen Anstiegs während einer Dauer von 40 Tagen nach der Anthese sowie einer darauffolgenden allmählichen Abnahme bis zur vollen Reife der Beeren. Das Maximum fällt mit niedrigen Nachttemperaturen zusammen und zeigt eine stärkere Säuresynthese bei niedrigen Temperaturen an. Der allmähliche Säureabbau entspricht dem Anstieg der Tagestemperaturen und läßt eine Veratmung der Säuren bei hohen Temperaturen vermuten.Die Zuckerakkumulation in wachsenden Beeren begann 50 Tage nach der Anthese. Die Akkumulationsrate war in der Zeitspanne zwischen 60 und 80 Tage nach der Anthese sehr hoch. Diese Periode fällt mit dem Beginn der dritten Phase des Beerenwachstums zusammen

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

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

Covid-19 Detection For CT-scan Images Using Transfer Learning Models

COVID-19 is a respiratory illness caused by a virus called SARS-CoV-2 which affected around 455 million people around the world. CT-scan is a medical imaging technique that uses X-rays to create detailed images of the body and which can be used to detect many respiratory diseases. Transfer learning models are a type of machine learning model that are trained on a large dataset of images and which can be used for their already trained ability to extract features from image in other tasks. They can then be used to classify new images with similar features.This paper presents a study of different transfer learning models for the task of classifying chest X-ray images into three classes: COVID-19, pneumonia, and normal. The study was implemented using Python and the dataset used was the COVID-19 Chest X-ray Dataset. The train-test split used was 0.2–0.8. The parameters used to test the models were the precision, recall, accuracy, F1 score, and Matthew’s correlation score. Other than these, different optimizers were also compared such as ADAM, SGD with different learning rates of 0.01, 0.001, and 0.0001.The models used in this study are EfficientNetB0, EfficientNetB7, VGG16, and InceptionV3. Out of these models, the most effective model was the EfficientNetB0 model, which achieved an accuracy of 98.6%. This study provides valuable insights into the use of transfer learning for medical image analysis. The results suggest that transfer learning can be used to develop accurate and efficient models that can be used as a secondary option for the diagnosis of COVID-19 using chest X-ray images

Manufacture of the Futuristic Castable Type of Screening Smoke Composition

The present trend abroad is to replace conventional smoke compositions with castable type of smoke compositions because of superior performance of the latter over the former. The technology of castable screening smokes has been recently developed for the first time in India by the Explosives Research & Development Laboratory, Pune. This paper discusses the various advantages in large scale manufacture of castable type of screening smoke composition. A comparison is also made with the conventional method of manufacture of screening smoke composition currently followed