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The visual scrutinization process for detecting epileptic seizures (ictal patterns) is time-consuming and prone to manual errors, which can have serious consequences, including drug abuse and life-threatening situations. To address these challenges, expert systems for automated detection of ictal patterns have been developed, yet feature engineering remains problematic due to variability within and between subjects. Single-objective optimization approaches yield less reliable results. This study proposes a novel expert system using the nondominated sorting genetic algorithm (NSGA)-II to detect ictal patterns in brain signals. Employing an evolutionary multi-objective optimization (EMO) approach, the classifier minimizes both the number of features and the error rate simultaneously. Input features include statistical features derived from phase space transformations, singular values, and energy values of time– frequency domain wavelet packet transform coefficients. Through evolutionary transfer optimization (ETO), the optimal feature set is determined from training datasets and passed through a generalized regression neural network (GRNN) model for pattern detection of testing datasets. The results demonstrate high accuracy with minimal computation time (<0.5  s), and EMO reduces the feature set matrix by more than half, suggesting reliability for clinical applications. In conclusion, the proposed model offers promising advancements in automating ictal pattern recognition in EEG data, with potential implications for improving epilepsy diagnosis and treatment. Further research is warranted to validate its performance across diverse datasets and investigate potential limitations. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to suggest an accounting framework for India for implementation of its nationally determined contributions (NDCs) and design the appropriate modality for reporting its capacity building needs and priorities in a manner that is in consonance with the provisions of the Paris Rule book from the perspective of a developing country. The suggested accounting methodology considers India's national greenhouse gas (GHG) inventory and climate mitigation policies. The Key Category Analysis concept of the Intergovernmental Panel on Climate Change for accounting GHG inventory estimation at the national level is used to identify and rank the key inventory categories for India. Accounting of various national level mitigation policies is undertaken by assessing their impact on the key categories, subsequently recognizing 'Key Policies' in accordance with the provisions of the Paris rulebook. 'Key Policies' are found to have a definite role in driving the progress of implementation of NDCs. The study recommends a modality that developing countries can use to report their needs for capacity building support for full implementation of their 'key policies' while accounting their future NDCs, using the flexibilities provided under the Paris Rule book. [ABSTRACT FROM AUTHOR]

Residential sector accounted for 24% of the total electricity consumption in India during 2009–10 and has almost remained around one-quarter over the last decade. A total of 163 same urban households were surveyed in 2018 to ascertain the change in penetration and use of energy-efficient (EE) appliances therein over 2010–18. There was a significant increase in their penetration across each enduse category with main changes being in air conditioners (ACs) (47%) and LED lights (over 38 times) over 2010–18. The weighted average relative electricity consumption decreased from 0.89 to 0.44 for ACs (1.5TR), 0.42 to 0.23 for lighting lamps, 0.95 to 0.54 for tubelights, 0.98 to 0.68 for refrigerators, and 0.94 to 0.69 for TVs, indicating the lowering of average energy consumption due to EE appliances. The annual electricity savings at household level were found statistically significant for transitions to EE ACs and ceiling fans. [ABSTRACT FROM AUTHOR]

Purpose: Energy-efficient retrofitting of existing buildings is an inexpensive way of reducing energy consumption and mitigating climate change impacts. The purpose of this study is to examine electricity savings and carbon dioxide (CO2) emission reduction potentials of energy-efficient retrofit measures for surveyed two large shopping malls in India. Design/methodology/approach: A techno-economic model was developed to estimate the electricity savings achieved due to energy-efficient retrofit measures in shopping malls that were surveyed in 2017. Alternative scenarios were constructed based on capital cost and cost of conserved energy (CCE) value for retrofit measures: cheapest replacement, best available technology and best value for money. The life-cycle electricity and CO2 emission savings and payback period for end-use retrofit measures were evaluated. Findings: The estimated average electricity savings were around 39–56% for various retrofit measures across all three scenarios while the average CO2 emission reductions were around 50–125 kt-CO2. Retrofits to light-emitting diode lights and air conditioners with inverter technology offered more life-cycle electricity savings. Paybacks for most lighting end-use measures were estimated to be within 1.5 years while for most space conditioning end-use measures were between 1 and 4 years. Originality/value: The primary survey-based comprehensive research makes an exclusive contribution by estimating life-cycle electricity savings and CO2 emission reductions for energy-efficient retrofit measures of lighting and space cooling end-use appliances for existing shopping malls. The present research methodology can also be deployed in other types of commercial buildings and in residential buildings to estimate electricity savings from energy-efficient retrofit measures. [ABSTRACT FROM AUTHOR]