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Compression-Based Complexity Analysis of Thalamic EEG Using Multiscale Preprocessing Techniques
Get PDFQuantifying the complexity of biomedical signals offers critical insight into underlying physiological and pathological dynamics. This study systematically evaluates compression-based complexity measures—Effort-to-Compress (ETC) and Lempel-Ziv Complexity (LZC)—and compares them with classical entropy-based metrics, including Shannon Entropy, Approximate Entropy (ApEn), Sample Entropy (SampEn), and Permutation Entropy (PermEn). Analyses were performed on both synthetic benchmark signals and clinical thalamic EEG recordings acquired during seizure and non-seizure states. Robustness was assessed under Gaussian, Laplacian, and powerline noise, with and without preprocessing via Discrete Wavelet Transform (DWT) and Differential Pulse Code Modulation (DPCM). ETC consistently demonstrated the highest discriminative power and noise resilience, achieving large effect sizes and classification accuracies exceeding 85% when combined with full-scale DWT preprocessing. In contrast, LZC performed reliably in raw data but degraded following multiscale transformations. Entropy-based measures such as SampEn and ApEn remained competitive under clean conditions yet were more sensitive to noise and preprocessing variability. These findings establish that no single complexity metric is universally optimal; rather, performance depends on signal modality, noise structure, and preprocessing design. For thalamic EEG-based seizure detection, ETC with DWT preprocessing provides a robust, interpretable, and parameter-free framework suitable for clinical and real-time neurophysiological applications
Impact of the COVID-19 lockdown on agriculture in a rainfed region in India: Lessons for dealing with natural and economic shocks
Get PDFOn March 25, 2020, India imposed a nationwide lockdown to curb COVID-19, affecting agricultural operations in northern Karnataka’s rainfed districts. A survey of 1004 households revealed that 64% faced challenges accessing inputs, credit, and transport. In Bidar, 54% of respondents struggled with input supply disruptions, compared to 12% in Raichur. Additionally, 29% of respondents in Bidar and 33% in Raichur reported issues with timely credit access. These impacts varied by region, landholding, and social category. The study highlights the need for increased public agricultural expenditure, better technological tools, and strengthened supply chains and market access to mitigate such disruptions in the future
Augmented regression models using neurochaos learning
No full textThis study presents novel Augmented Regression Models using Neurochaos Learning (NL), where Tracemean features derived from the Neurochaos Learning framework are integrated with traditional regression algorithms - Linear Regression, Ridge Regression, Lasso Regression, and Support Vector Regression (SVR). Regression analysis is one of the most fundamental tools in machine learning and data science, yet improving its robustness and accuracy in noisy, real-world settings remains a persistent challenge; this motivates the incorporation of chaos-inspired features. Our approach was evaluated using ten diverse real-world datasets and a synthetically generated noisy dataset of the form
with various levels of additive Gaussian noise. Results show that incorporating the Tracemean feature (mean of the chaotic neural traces of the neurons in the NL architecture) significantly enhances regression performance, particularly in Augmented Lasso Regression and Augmented SVR, where six out of ten real-life datasets exhibited improved predictive accuracy. Among the models, Augmented Chaotic Ridge Regression achieved the highest average performance (R2
) boost (11.35%). Additionally, experiments on the simulated noisy dataset demonstrated that the Mean Squared Error (MSE) of the augmented models consistently decreased and converged towards the Minimum Mean Squared Error (MMSE) as the sample size increased with various levels of additive Gaussian noise. This work demonstrates the potential of chaos-inspired features in regression tasks, offering a pathway to more accurate, robust and computationally efficient prediction models
Bowing to surrender or to fight?: Interpretations of diversities in a Kalaripayattu salutation
No full textSalutation in martial arts overlaps with religious and spiritual modes of paying respect to different deities. This study documents and interprets different modes of a salutation, namely 'poothara (also puttara) thozhal / vandanam' , across various styles of kalaripayattu to learn the variations in and through this indigenous martial arts from the southern regions of India. Using YouTube videos, translations and fieldwork data, we not only map the variations across different styles but also bring out the variations within the same style. We study arappakai and pillathangi styles and argue that the variations between and within these styles show diversity and multiplicity of contemporary kalaripayattu practices, even when this martial tradition is increasingly becoming institutionalized and modernized. We also observed that in certain cases, even when oral commands (vaythari) are different, the actions followed are similar or the same. Through interpretations of customary beliefs, we demonstrate how the poothara salutations have devotional as well as combat training, healing and spiritual dimensions. Lastly, we reflect on the need for documenting and preserving the diversities within kalaripayattu practices
Policy Recommendations for Sustainable Development, Volume 2: Year 2021-2022
No full textThe book contains policy-related publications based on research conducted by the National Institute of Advanced Studies. These publications address and support sustainable development goals, such as energy and the environment, education, inequality, human development, and peace research, among others. This volume fosters comprehensive research focused on these global targets and endeavors to address some of society's greatest grand challenges. The book will be extremely beneficial for researchers, academicians, practitioners, and policymakers working in the areas of the 17 Sustainable Development Goals (SDGs)
Dr M. R. Srinivasan: A Gentle Visionary
No full textDr. M. R. Srinivasan (1930–2025) was a pioneering Indian nuclear engineer and is hailed as the father of India’s civil nuclear programme. He played a key role in developing indigenous nuclear power reactors and establishing institutions like the Nuclear Power Corporation of India Ltd (NPCIL). As former Chairman of the Atomic Energy Commission, his leadership ensured India’s self-reliance in nuclear technology despite global restrictions. For his contributions, he was honoured with the Padma Shri, Padma Bhushan, and Padma Vibhushan
Coastal Tamil urn burial sites and Harappan links: Wrought in iron?
No full textThe writeup explores the implications of the recent early AMS dates of coastal Iron Age urn burial sites of Tamil Nadu
