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What drives anthropogenic fine particulate chloride emissions in India? – A quantitative assessment of hotspots
No full textIndigenous healing practices among the Adiyan and Mavilan tribal communities of Kerala: An anthropological study
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Discursive formulations of violence in ethnic conflict: Generation and Transmission of values
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Quantum Effects in Biology
No full textThis book intends to give a systematic exposition of the validity of quantum principles in biological systems. There are two types of applications of quantum theory in physical systems — the "trivial applications" and "non-trivial applications". Since every object in this universe consists of atoms and molecules, they should be described by the laws of quantum theory — which we call trivial applications. On the other hand, there exist some systems where the observational results cannot be explained by the laws of classical physics and this requires a change of paradigm — these are known as non-trivial applications. Many authors pointed out such non-trivial applications of quantum theory to explain how some biological systems function. In this book, we review such kinds of results in a systematic manner which clearly indicates the need to change the paradigm to understand these biological systems better
Sensible heat storage performances of Hytherm 600 oil and energy-harnessing features of glycol-water mixture under simultaneous charging and discharging conditions
No full textA sensible thermal energy storage (TES) system is studied using Hytherm 600 oil as the storage medium and a 30:70 ethylene glycol–water (EG-W) mixture as the discharging fluid, owing to its extended operating temperature range (−14 °C to 105 °C) at atmospheric pressure. The EG-W mixture alleviates the working limitations of pure water by lowering the freezing point and elevating the boiling point, making it suitable for solar thermal systems in colder climates and for low-temperature industrial heat recovery without pressurized systems. An immersed helical coil-aided cylindrical TES tank is used in the experimental setup. The charging-alone and SCAD modes are investigated for different oil-side charging temperatures (60, 75, 90 °C) and discharging flow rates (0.5, 1.25, and 2 L/min). A three-dimensional numerical model is developed, incorporating temperature-dependent thermophysical properties for both fluids. This helped capture the stratification characteristics and thermocline behaviour accurately. Case C1 (charging temperature of 90 °C; charging and discharging flow rates of 0.5 L/min) exhibited the highest thermal energy accumulation of 38,500.1 kJ within the TES tank due to low energy extraction, while Case C3 (charging temperature of 90 °C; charging flow rate of 1.25 L/min, discharging flow rate of 2 L/min) demonstrated increased heat extraction (890 kJ). Case A2 (charging temperature of 60 °C; charging flow rate of 0.5 L/min; discharging flow rate of 2 L/min) exhibited the highest sustained efficiency of 0.26–0.27 in the later stages of SCAD operation. Entropy generation increased notably during SCAD operations due to enhanced thermal mixing. Case C2 (charging temperature of 90 °C; charging flow rate of 0.5 L/min; discharging flow rate of 1.25 L/min) exhibited the highest cumulative entropy generation of 52.49 kJ/kg·K, and Case A1 (charging temperature of 60 °C, charging and discharging flow rates of 0.5 L/min) showed the lowest entropy generation (37.49 kJ/kg·K). The analyses establish that higher charging temperatures strongly promote stratification, effectively countering the mixing induced by higher discharge rates. These insights establish a crucial foundation for configuring effective TES systems with non-conventional discharging fluids for various low-temperature applications
Causal Discovery and Classification Using Lempel–Ziv Complexity
Full text linkInferring causal relationships in the decision-making processes of machine learning models is essential for advancing explainable artificial intelligence. In this work, we propose a novel causality measure and a distance metric derived from Lempel–Ziv (LZ) complexity. We explore how these measures can be integrated into decision tree classifiers by enabling splits based on features that cause the most changes in the target variable. Specifically, we design (i) a causality-based decision tree, where feature selection is driven by the LZ-based causal score; (ii) a distance-based decision tree, using LZ-based distance measure. We compare these models against traditional decision trees constructed using Gini impurity and Shannon entropy as splitting criteria. While all models show comparable classification performance on standard datasets, the causality-based decision tree significantly outperforms all others on the Coupled Auto Regressive (AR) dataset, which is known to exhibit an underlying causal structure. This result highlights the advantage of incorporating causal information in settings where such a structure exists. Furthermore, based on the features selected in the LZ causality-based tree, we define a causal strength score for each input variable, enabling interpretable insights into the most influential causes of the observed outcomes. This makes our approach a promising step toward interpretable and causally grounded decision-making in AI systems
Predicting stock prices using permutation decision trees and strategic trailing
No full textIn this paper, we explore the application of Permutation Decision Trees (PDT) and strategic trailing for predicting stock market movements and executing profitable trades in the Indian stock market. We focus on high-frequency data using 5-minute candlesticks for the top 50 stocks listed in the NIFTY 50 index and Forex pairs such as XAUUSD and EURUSD. We implement a trading strategy that aims to buy stocks at lower prices and sell them at higher prices, capitalizing on short-term market fluctuations. Due to regulatory constraints in India, short selling is not considered in our strategy. The model incorporates various technical indicators and employs hyperparameters such as the trailing stop-loss value and support thresholds to manage risk effectively. We trained and tested data on a 3 month dataset provided by Yahoo Finance. Our bot based on Permutation Decision Tree achieved a profit of 1.1802% over the testing period, where as a bot based on LSTM gave a return of 0.557% over the testing period and a bot based on RNN gave a return of 0.5896% over the testing period. All of the bots outperform the buy-and-hold strategy, which resulted in a loss of 2.29%