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Cognitive Behavioral Therapy for Tinnitus: An Applied Bioanalytical Perspective
Tinnitus affects 1015% of adults worldwide, with 23% experiencing severe and chronic symptoms that impair quality of life. Conventional treatments often provide limited long-term relief, while Cognitive Behavioral Therapy (CBT) has emerged as an evidence-based intervention. The present study evaluated the effectiveness of CBT for tinnitus through an integrated psychometric and bioanalytical approach. A total of 100 participants were randomized into a CBT group (n = 50) and a waitlist control group (n = 50). The CBT program consisted of 12 weekly sessions delivered over 12 weeks. Psychometric assessments included the Tinnitus Handicap Inventory (THI), Hospital Anxiety and Depression Scale (HADS), Pittsburgh Sleep Quality Index (PSQI), and WHOQOL-BREF. Bioanalytical measures comprised functional MRI, EEG, salivary cortisol, heart rate variability (HRV), and inflammatory cytokines (IL-6, TNF-?). The CBT group achieved a 45% reduction in THI scores (58.4 9.2 to 32.1 8.5), while the control group showed only a 5% change. Anxiety and depression scores decreased by 42% and 40%, respectively, and sleep quality improved by 38%. Morning cortisol increased by 48%, HRV improved by 38%, and inflammatory cytokines decreased significantly (IL-6: ?26%; TNF-?: ?19%). Neuroimaging and EEG findings confirmed reduced auditory cortex hyperactivity and abnormal connectivity. These results demonstrate that CBT not only alleviates tinnitus distress but also induces measurable neurophysiological and systemic changes, reinforcing its role as a cornerstone of evidence-based tinnitus management. 2025, Green Publication. All rights reserved. -
Decolonizing the Mind: Invoking the Vernacular Experience in a Postcolonial Language Classroom
This chapter attempts to understand the teaching-learning practices, programmes, courses, and pedagogies of an English department that recently co-opted cultural studies as a means of decolonisation in a private university in India to understand how cultural diversity, learner diversity, teacher experiences, and learner interests became considered factors in language learning pedagogies and selection of learning content. The research will employ mixed methods of qualitative and quantitative techniques of course content analysis, student interviews to gauge the impact of the learning on the decolonisation process, teacher interviews to understand approaches to task design, and the intended outcome and the strategies and perception changes in material production and task development when the learning shifted to the online mode as a result of the pandemic disruption. 2023 by IGI Global. All rights reserved. -
Topophilia in Transition: KeralasSacred Landscapes and Smart CityDevelopment
The rise of smart cities in India presents a complex phenomenon and a significant challenge for the preservation of sacred landscapes. To address thisthe chapterproposes Smart Theology as a conceptual framework that integratesecological ethics and cultural resilience with digital innovation. This approach is examined through fieldwork in Keralas kavussacred sites reflecting Indigenous ecological knowledge and ritual practicefunded by the Centre for Cultural Resources and Training (CCRT), Government of India. Digital interventions includingGIS, Virtual Reality, and community-driven tools serve as a vital bridge mediating the tension between cultural legacy and urban innovation. By investigating these applications the study demonstrates how technology facilitate inclusive preservation while maintaining the spiritual essence of sacred landscapes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Glucose-Urea-Choline chloride: a versatile catalyst and solvent for the Kabachnik-Fields reaction
Kabachnik-fields reaction is a multi-component organic reaction that gives ?-aminophosphonates as products. The reaction between a carbonyl group, an amine, and amino phosphonates is noteworthy due to their antibacterial, antifungal, anti-HIV, anti-cancer, and analgesic characteristics. Low melting mixtures are also good alternatives for toxic catalysts and organic solvents. The use of organic solvent can be reduced in the Kabachnik-fields reaction by using low melting mixtures as a reaction media and catalyst. This method is cost-effective and safe. A practical synthesis of different derivatives of dialkylphosphonates was conducted. The solvent/catalyst is also easily recyclable. 2025 Elsevier B.V. -
A Penalized Maximum Likelihood Estimation for the Log-Logistic Distribution with Complete Data
Penalized maximum likelihood estimation is specified for estimating parameters of a log-logistic distribution for complete-data situations. This approach addresses the issues of Maximum Likelihood Estimation, wherein Maximum Likelihood Estimation is often unstable when sample sizes are small, and fails with heavy-tailed or asymmetric data. By adding a ridge penalty to the log-likelihood, we derive new score equations, which are solved numerically. The performance is measured for a variety of shape and scale parameters and sample sizes, with bias and Mean Squared Error as the two main measures. The simulation experiment results indicate Penalized maximum likelihood estimation consistently achieves lower bias and Mean Square Error with small sample sizes and particularly strong improvements under skewed or heavy-tailed data. With larger sample size, the differences between Maximum Likelihood Estimation and Penalized maximum likelihood estimation decrease, as we would expect. These results suggest that Penalized maximum likelihood estimation is a viable estimation method using the log-logistic distribution, especially with small or limited datasets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Importance of Sustainable Marketing Initiatives for Supporting the Sustainable Development Goals
Businesses that engage in sustainable marketing can benefit both the world and their bottom line. Earlier, companies could satisfy many customers by simply providing low pricing and high-quality goods. However, peoples concern for the environment and other social concerns have grown, and so has their desire to support groups that share their beliefs. Because they often generate strong market returns and demonstrate durability during economic downturns, many investors want to support businesses that use sustainable business methods. Also, these businesses are more likely to comply with social and environmental laws. Several companies use sustainable marketing to succeed in todays ethical and ecologically sensitive marketplace. Organizations must finance sustainability programs in order to practice sustainable marketing. But, it can also improve employee engagement, promote regulatory compliance, raise revenues, and build brand loyalty. 2023 by IGI Global. -
Sensitization of university students in supporting underprivileged children
Education is the fundamental right of every child and an essential element of human growth. Indian education is divided into two parts: private and government education. Underprivileged children, affected by socio-economic factors, predominantly choose government education. Government primary educational institutions have failed to cope with the requirements of the corporate world. On the other hand, the students studying in private higher educational institutions seldom think of the challenges that socio-economically underprivileged children face. Genesys is the forum that unites these socio-economically underprivileged children with students at Christ University. Teaching and training disadvantaged children are the social responsibilities of young people, especially those in higher education. Each student should teach at least one person to make it a win-win situation for both learner and trainer. It helps University students build social skills and provides them with the opportunity to become part of the nation-building process. The chapter employed the quantitative study method to analyze the students' perception of teaching underprivileged children and how demographic variables impact it. It also identified the factors influencing the student's academic performance and psychological well-being. The study found that the students gained different skills like interpersonal skills, adaptability, teaching skills, and happiness, and these skills positively impacted their academic performance and psychological wellbeing. The expected outcome of the program supported by the university is to develop a win-win model for both University students and schoolchildren from low-income groups in society 2024 Nova Science Publishers, Inc. -
A CONCEPTUAL MODEL FOR SKILL DEVELOPMENT: A KEY DRIVER FOR INCLUSIVE GROWTH AND SUSTAINABLE DEVELOPMENT
Purpose: This chapter explores the two major schemes applicable to skill development in India: Skill Acquisition and Knowledge Awareness for Livelihood Promotion (SANKALP) and Pradhan Mantri Kaushal Vikas Yojana (PMKVY). Need for the Study: The primary objective of this research is to check the role of these schemes in enhancing the skills of socio-economically stressed community members for their livelihoods. The secondary aim is to analyse the outcomes of these schemes through a qualitative inquiry. Methodology: A survey was conducted, and the data was collected from trainees of the skill development programmes. Based on the responses, a qualitative content analysis was performed, which showed that most trainees have the thirst and urge to enhance their life skills for a minimalistic livelihood. Findings: The study concluded that though there are many schemes, only PMKVY is active. They focus on more than just youth communities. Instead, they consider individuals in different age categories. Practical Implications: The Government of India (GOI) is progressing towards a healthy economy to compete with other countries. For this mission to be achieved, skill and labour development is paramount. Appropriate training must be provided and administrated through government schemes. 2024 by P. S. Anuradha, L. Mynavathi and M. Anand Shankar Raja Published under exclusive licence by Emerald Publishing Limited. -
Intelligence in Children Whose Either Parent Is Treated For Schizophrenia.
G.J.B.A.H.S.,Vol.2(4):119-123- October- December ISSN: 2319-5584 -
Social media addiction, culture code and mediation effect of mindfulness: A structural equation modelling access
This study has been initiated with a view to understand the social media addiction and its influence on culture codes a mediation effect of mindfulness. The social media addiction would be a very common problem in the social and the organizational set up. The employees are getting distracted easily because of social media addiction. The organisations are taking much action to rectify this problem but end up with the talent lose because the employees are ready to quit the job when they are not allowed to use social media in the organisation. It has been an inevitable problem for the organisations so many techniques have been initiated in order to solve this problem. Many studies are concentrating on mindfulness which is a tool to remove the distractions and to be a focused employee in the organisation. The social media addiction many a times create a toxic culture among the employees. It needs a high attention to be cured. The employee relationship are highly under stake when they are social media addict. This study would attempt to understand the empirical evidence on this relationship by examining the association among addiction in social media and its effect on culture code and mediating role of mindfulness. This study also concentrates on the moderating role of gender on the proposed model. BEIESP. -
The Effectiveness of Micro Finance Institutions on Socio-Economic Development of Women in Karnataka
International Journal of Research in Commerce, Economics & Management, Vol-2 (11), pp. 74-77. ISSN-2231-4245 -
Machine Learning Base Model for Waste Management
Waste management is an important global challenge, where increasing urbanization and industrialization lead to high waste production. Traditional waste landfill methods, including manual sorting and fixed collection plans often result in disabilities, increased costs, and environmental decline. Integration of machine learning (ML) and artificial intelligence (AI) in waste management systems provides a transformative approach to solve these problems. This chapter examines the role of ML in automatic waste sorting, future indication analysis, collection passport optimization, and converting wasteto-energy. Case studies from Singapore, San Francisco, and large waste management companies highlight MLs real-world applications to create smart and more sustainable waste management systems. Singapores AIoperated system and San Franciscos policy-based waste management models show MLs ability to adapt to comparative analysis resources and reduce landfill addiction. The findings show that AI-controlled waste management leads to high recycling speeds, reduces greenhouse gas emissions, and more efficient resource allocation. Despite challenges such as high implementation costs, regulatory concerns, and privacy problems, the future of waste management lies in AI-operated automation, blockchain integration for waste tracking, and AI-cleaned waste solutions. 2025 River Publishers. -
E-learning During COVID-19Challenges and Opportunities of the Education Institutions
As part of the COVID-19 lockdown, educational institutions were closed and adopted e-learning to keep the learning process going. Due to the COVID-19 pandemic, e-learning has become a required component of all educational institutions such as schools, colleges, and universities worldwide. This pandemic has thrown the offline teaching process into chaos. This chapter discusses the concept and role of e-learning during the pandemic and various challenges and opportunities of e-learning encountered by educational institutions. Three broad challenges identified in e-learning are inaccessibility, self-inefficacy, and technical incompetency. E-learning opportunities are no geographic barriers, flexibility, creativity, and critical learning incorporation increased utilization of online resources and reinforced distance learning. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Identification of new classical Ae stars in the Galaxy using LAMOST DR5
We report the first systematic study to identify and characterize a sample of classical Ae stars in the Galaxy. The spectra of these stars were retrieved from the A-star catalogue using the Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) survey. We identified the emission-line stars in this catalogue from which 159 are confirmed as classical Ae stars. This increases the sample of known classical Ae stars by about nine times from the previously identified 21 stars. The evolutionary phase of classical Ae stars in this study is confirmed from the relatively small mid- and far-infrared excess and from their location in the optical colour-magnitude diagram. We estimated the spectral type using MILES spectral templates and identified classical Ae stars beyond A3, for the first time. The prominent emission lines in the spectra within the wavelength range 3700-9000 are identified and compared with the features present in classical Be stars. The H ? emission strength of the stars in our sample show a steady decrease from late-B type to Ae stars, suggesting that the disc size may be dependent on the spectral type. Interestingly, we noticed emission lines of Fe ii, O i, and Paschen series in the spectrum of some classical Ae stars. These lines are supposed to fade out by late B-type and should not be present in Ae stars. Further studies, including spectra with better resolution, is needed to correlate these results with the rotation rates of classical Ae stars. 2021 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
COVID-19 Pandemic: Review on Emerging Technology Involvement with Cloud Computing
Cloud computing is the latest technology that has a significant influence on everyones life. During the COVID-19 crisis, cloud computing aids cooperation, communication, and vital Internet services. The pandemic situation made the people switch to online mode. The technology helped to bridge the gap between the work space and personal space. A quick evaluation of cloud computing services to health care is conducted through this study in COVID situation. A short overview on how cloud computing technologies are critical for addressing the current predicament has been held. The paper also discusses distant working of cloud computing in health care. Moreover, cloud infrastructure provides a way to connect with different aid personnel. The patient data can be transferred to the cloud for monitoring, surveillance, and diagnosis. Thus, health care is provided instantaneously to all the individuals. Additionally, the study addresses the privacy and security-related issues with appropriate solutions. The paper also briefs on the different kind of services are provided by different CSPs that are cloud service providers to confront this epidemic. This article primarily focuses on cloud computing technology involvement in COVID, and secondary focus is on other technology like blockchain, drones, machine learning and Internet of things in COVID-19. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Analytical Methods of Machine Learning Model for E-Commerce Sales Analysis and Prediction
In the commercial market, E-commerce sales show a significant trend and have attracted many consumers. Ecommerce sales forecasting has a significant role in an organization's growth and aids in improved operation. Many studies have been conducted in the past using statistical, fundamental, and data mining techniques for better analysis and prediction of sales. However, the current scenario calls for a better study that combines the available information to propose different machine-learning techniques. The sole motive of the study is to analyze and determine different machine learning models to predict accurate results. The research observed that the Extreme Gradient Boosting model outperformed all other models and brought a good result. It produced an RMSE value of 0.0004 and Explained Variance score of 0.99. Decision Tree algorithm also shows an exemplary result. 2023 IEEE. -
An Efficient Machine Learning Framework for Flood Forecasting
Floods, a significant natural disaster which has an impact on the whole world present major risks to ecosystems and humans, particularly in semi-arid areas with variable rainfall patterns. With the help of ICRISATs historical meteorological data and machine learning algorithms, this study has developed a customized flood prediction model for use. After evaluating and contrasting various models, including the proposed model Stacked Gradient Boosting with Random Forest (SGB-RAF), KNN, Decision Tree, Random Forest, and Linear Regression, it shows that SGB-RAF has the highest R2 score and lowest RMSE comparatively to other models. While other enhancements such as Ridge Regression and polynomial feature creation were studied, SGB-RAF remained effective. Overall, this study highlights how machine learning may improve flood prediction accuracy, which is important for disaster management and for improving the semi-arid regions adaptability to climatic variability. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Maximum Decision Support Regression-Based Advance Secure Data Encrypt Transmission for Healthcare Data Sharing in the Cloud Computing
The recent growth of cloud computing has led to most companies storing their data in the cloud and sharing it efficiently with authorized users. Health care is one of the initiatives to adopt cloud computing for services. Both patients and healthcare providers need to have access to patient health information. Healthcare data must be shared and maintained more securely. While transmitting health data from sender to receiver through intermediate nodes, intruders can create falsified data at intermediate nodes. Therefore, security is a primary concern when sharing sensitive medical data. It is thus challenging to share sensitive data in the cloud because of limitations in resource availability and concerns about data privacy. Healthcare records struggle to meet the needs of security, privacy, and other regulatory constraints. To address these difficulties, this novel proposes a machine learning-based Maximum Decision Support Regression (MDSR)-based Advanced Secure Data Encrypt Transmission (ASDET) approach for efficient data communication in cloud storage. Initially, the proposed method analyzed the node's trust, energy, delay, and mobility using Node Efficiency Hit Rate (NEHR) method. Then identify the efficient route using an Efficient Spider Optimization Scheme (ESOS) for healthcare data sharing. After that, MDSR analyzes the malicious node for efficient data transmission in the cloud. The proposed Advanced Secure Data Encrypt Transmission (ASDET) algorithm is used to encrypt the data. ASDET achieved 92% in security performance. The proposed simulation result produces better performance compared with PPDT and FAHP methods. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.