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Background: Breast cancer patients report difficulties with concentration, multi‑tasking, and memory. Cognitive dysfunction can impact the quality of life by affecting activities of daily living, treatment compliance, interpersonal relationships, and profession. Aims: The objective of this study was to evaluate and compare the cognitive functions and psychological complications in breast cancer patients with a control population. Settings and Design: This study was conducted in the outpatient department of Bharath Hospital and Institute of Oncology, Mysore, where the cases were recruited. The study participants were 110 female breast cancer patients and 100 noncancer healthy females as controls. Materials and Methods: Demographic details of the participants were collected through a questionnaire. Clinical data were obtained from clinical records. Tools used were Montreal Cognitive Assessment (MoCA), Hamilton Depression Rating Scale (HAM‑D), and Hamilton Anxiety Rating Scale (HAM‑A) to evaluate cognitive functions, depression, and anxiety, respectively. Statistical Analysis: The various findings were analyzed using mean, frequency, Pearson’s correlation, and two‑sample t‑test. Results: Mild cognitive impairment was observed in 88% of the cancer patients, and more than 95% were under severe anxiety and depression. There is a highly significant difference in all three tests (MoCA Test, HAM‑D test, HAM‑A test) with a P < 0.001. Conclusion: Breast cancer patients show statistically significant cognitive deficits as compared to noncancer individuals. [ABSTRACT FROM AUTHOR]

Globally, electricity systems are undergoing transitions from robust, carbon-intensive, and firm power conventional systems to uncertain, intermittent, and variable renewable energy integrated low carbon systems. These transitioning electricity systems have moved from a situation of "matching available supply with dynamic demand" to "matching dynamic supply with dynamic demand." These transformations have led to several new challenges - significant mismatch in periods of high supply and high demand, the shift in the method of accessing energy resources for electricity generation from "procure, store and generate when needed" to "generate when available," continuous struggle to match variable supply with variable demand, and installed capacity redundancy, temporal as well as permanent leading to low plant load factors. Actions on the supply-side alone will not be enough to address these challenges and achieve optimal functioning of the electricity system. We need effective demand-side solutions, too, to manage variations in both supply and demand. In this paper, it is proposed to study the effectiveness of demand-side interventions as potential solutions for managing the variabilities introduced by renewable energy mainstreaming. Towards this, we develop a mixed-integer linear programming model to implement and validate emergency and economic demand response (DR) programs. DR options like load curtailment, short-, medium- and long-term load shifting are considered with both incentive-based and penalty-based pricing strategies to influence consumer participation. The Karnataka electricity system is used as a case study for model implementation and validation. The findings suggest that DR interventions are very effective in moderating variability in electricity demand by chopping the peak loads and topping the valleys. Further, benefits include postponement of installed capacity additions, enhanced utilization of available capacity, and minimization of demand variability. • Demand response for managing supply variations due to renewable energy integration. • Studying effectiveness of DR in supply-demand matching using a modelling approach. • Addressing supply variabilities using strategies from demand-side. • Model is validated with a case study of renewable energy dominated system. • Demand response is a win-win intervention for all the stakeholders. [ABSTRACT FROM AUTHOR]

• Simulation approach for load curve classification and demand representation. • Seven Representative Load Curves to characterize 365 load curves. • Characterizing demand based on weather, economic, agricultural & socio-religious factors. • Knowing factors influencing demand variations helps in prioritizing interventions. • 90% of hourly loads fall in the range of ±12% error when compared to actual data. Till recently, in the case of electricity systems with dominant conventional generation technologies, the supply was largely driven by dynamic demand and thus, it was characterized by fixed supply and variable demand. However, the ongoing transformations in electricity systems leading to mainstreaming of variable renewable energy sources and technologies have introduced variability even on the supply-side. This variability in both supply and demand for power have presented several challenges for both planning and operating such systems. On the supply-side, these variabilities need to be accounted while planning installed capacity additions in the long-term, resource allocations and generation scheduling in the medium-term, and system operation in the short-term. Similarly, demand-side-management strategies need to consider variations in both demand and supply while moderating the load curves. To perform these tasks effectively, it is important to have a better understanding of the dynamic demand in terms of patterns of demand variability, magnitude of variability, span of variability, factors influencing variability, and temporal variability. In this research, we propose to use simulation-based approach as an alternative method to classify load curves and verify its effectiveness for accurate and simpler representation of variability in electricity demand. Initially, logical clustering is performed to segment daily load curves, and then probability distribution-based Monte Carlo simulation is employed to establish seven load curves that represent seven groups of 365 load curves. The results show that these seven groups of load curves represent different weather patterns, lifestyles, and economic and socio-religious activities. [ABSTRACT FROM AUTHOR]