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The Effect of Homoeopathic Medicine in Management of Psychological Distress in Gynecological Problems- A Pilot Study
(The International Journal of Indian Psychology, 2025) Shikha Chouhan, Deepa Pandey, Madhu Pandey, Seema Rani Saraf, Jyotsana Shukla
Women dealing with gynecological issues like PCOS, PMS, dysmenorrhea, and menopause often face more than just physical discomfort—many also experience psychological distress such as anxiety, mood swings, and low self-esteem. While conventional treatments mostly focus on managing physical symptoms, the emotional impact often goes unaddressed. This study explores how individualized homoeopathic treatment can help reduce psychological distress in such cases. A total of 60 women between the ages of 12 and 45 participated in the study. Thirty of them received homoeopathic treatment, while the other thirty did not. Over the course of a few months, their well-being was evaluated using the WHOQOL-BREF questionnaire, which measures physical, psychological, social, and environmental quality of life. Remedies like Pulsatilla, Natrum Muriaticum, Ignatia, and Sepia were prescribed based on each woman’s unique symptom pattern. The results were promising—most of the women who received homoeopathic care showed noticeable improvements in their emotional well- being, mood stability, and overall quality of life. Many also experienced better menstrual regulation and stress relief. In contrast, the group that didn’t receive treatment showed minimal improvement. These findings suggest that homoeopathy may offer a gentle, holistic way to support women’s emotional and psychological health in the context of gynecological problems. However, more large-scale studies are needed to confirm these benefits over the long term.
The Association of Social Media with Anger and Psychological Well-Being Among Users and Non-Users
(The International Journal of Indian Psychology, 2025) Om Pandey, Deepa Pandey, Jyotsana Shukla
This study investigates the impact of social media usage on anger and psychological well- being by comparing users and non-users. Using standardized psychological measures, including the Bergen Social Media Addiction Scale, Buss-Perry Aggression Questionnaire, and Ryff's Psychological Well-Being Scale, the research assesses the relationship between social media engagement, aggression, and emotional health. Data analysis reveals a significant positive correlation between social media usage and aggression (r = 0.499, p < 0.01), indicating that frequent users exhibit higher levels of anger. Additionally, a strong negative correlation between social media use and psychological well-being (r = -0.548, p < 0.01) suggests that excessive online engagement contributes to lower emotional stability and life satisfaction. In contrast, non-users demonstrate lower aggression levels and greater psychological well-being.
Harmful Words: The Influence of Negative Language on Self- esteem and Mental Well-Being
(The International Journal of Indian Psychology, 2025) Priyanshi Singh, Deepa Pandey, Madhu Pandey, Jyotsana Shukla
Language has a significant impact on one's perception towards self as well as mental health which ultimately has an adverse effect on the wellbeing of an individual. (Tanvir & Mitu, 2024). The present research emphasizes the patterns of negative language (harmful words) on self-esteem and mental wellbeing. To meet the aim of the study, a sample population of 200 students were randomly selected which belong to the age group of 15-24 years. The key findings indicated that there was a significant impact on mental wellbeing of males who were exposed to harmful words but had no impact on their self esteem. Whereas, females had no significant impact of harmful words on their self esteem or mental wellbeing.
A study on Depression, Anxiety, Stress, Resilience, and Sleep Quality among Physicians working with COVID’19 Cases in India
(INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH IN TECHNOLOGY, 2025) Ananya Mishra, Jyotsana Shukla
Abstract—This research aimed to study and compare the levels of depression, anxiety, stress, resilience and quality of sleep in Physicians Working with COVID’19 cases and Physicians working with Non COVID’19 cases in private and public hospitals in Lucknow city, India, during the COVID'19 pandemic. Data was collected via Google Form. Participants were required to fill out demographic datasheet and answer standardized questionnaires on depression, anxiety, stress, resilience and quality of sleep. Purposive sampling method was used. Interestingly, no significant difference was found between the two groups on the depression, anxiety, stress, resilience and quality of sleep. However, mean scores indicated that Physicians Working with COVID’19 cases experienced more anxiety, stress and a poorer sleep quality as compared to Physicians working with Non COVID’19 cases. Those physicians who worked with COVID’19 cases scored higher on resilience and lower on depression as compared to those physicians who were working with Non COVID’19 cases. The reasons could be that the former developed more resilience in order perhaps due to the support available from the trained staff, appropriate security/safety measures, family support and teamwork. The variables that would contribute to their better mental health need to be further explored so that the Physicians’ mental health can be further promoted through relevant interventions and psychoeducation, during challenging times such as the COVID’19.
Neuromorphic Computing in Low Power CMOS VLSI Circuits
(CRC Press, 2026) Digima Mustapha, Shrish Bajpai, Naimur Rahman Kidwai
Neuromorphic computing, which attempts to emulate biological neural networks, offers a highly promising way to solve some of the problems with the traditional von Neumann architectures we have known for decades. The chapter explores how some of the principles of neuromorphic computing can be applied to low power CMOS VLSI circuits and spaces to serve the need for pragmatic, energy efficient, high-performance computing systems. Beginning with the concepts of what neuromorphic computing represents and its promise for transforming artificial intelligence and machine learning applications, the chapter continues to look at how to address the design and performance aspects of forming neuromorphic architectures with CMOS technology, including minimizing power consumption, scalability, or performance improvements. The chapter assesses transistor-level approaches to construct the building blocks of neuromorphic computing, including artificial neurons and synapses, all while operating within the restrictions of available CMOS processes. The chapter also looks into some interesting areas, including memristors and phase-change materials for their application into CMOS circuits under neuromorphic computing paradigm. It also examines diving into newer circuit levies and design strategies that emphasize learning and signal processing efficiency perspectives as functionally distributed computational systems. We describe several case study examples of effective CMOS implementations of neuromorphic computing systems, and this chapter suggests types of applications suited for neuromorphic computing in areas including sensory processing and pattern recognition for environments identified as autonomous actors. To sum up, the chapter ends with a discussion of avenues for future research and some anticipated effects of neuromorphic CMOS circuits on next-generation computing paradigms, particularly with regard to ways they might inform the future use of low-power, brain-inspired computing solutions for a variety of uses.