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A mixed methods study on factors associated with relapse of alcohol use disorder
Background: Alcohol use disorder (AUD) is one of the most concerning mental health issues in India. According to the recent survey, Magnitude of Substance Use in India, 2019, 160 million of the countrys population consumes alcohol. About 35.6% are problem drinkers among those who drink, of which 18% are alcohol dependent. Despite the greater understanding of alcohol use disorder (AUD) and the scientific advancements in treatment, relapse remains to be the main challenge in managing AUD. This study aimed at investigating various factors associated with relapse of AUD and presenting an in-depth understanding of it. Methods: A Sequential Explanatory Mixed Methods design was used. In the quantitative phase, 72 relapsed individuals with AUD currently undergoing treatment were compared with 72 individuals previously treated for AUD who maintain total abstinence for a minimum period of one year. Relapsed participants were selected from three private de-addiction centers in Bangalore and abstaining participants were recruited from various Alcoholics Anonymous meetings in Bangalore. The relapsed and sober groups were matched on gender, AUD diagnosis, and previous inpatient alcohol de-addiction treatment. Cloninger's Temperament and Character Inventory-Revised was used to assess the personality profiles of the participants. A sociodemographic and clinical information form was also used to collect data. Six participants were selected purposively from the same sample for in-depth interviews. Data analysis was conducted using SPSS and NVivo for quantitative and qualitative data, respectively. The study protocol was approved by the institutional ethics committee. Results: Bivariate analyses showed a significant difference in Novelty Seeking, Persistence, Self-Directedness, and Self-Transcendence traits between the relapsed and sober participants. Results also suggested that reported use of other substances, post- discharge follow-ups, and living with drinking or drug-using individuals are significantly associated with relapse. Logistic regression displayed incomplete treatment, use of other substances, and no post-discharge follow-up as predictors of relapse. The qualitative thematic analysis revealed preparedness, motivation, personal exceptionalism, meaning and purpose, and social and interpersonal as the main relapse-related themes. Conclusions: The findings highlight the importance of treatment engagement, discharge planning, aftercare, and special attention to those presenting with multiple substance use. It also displays a few culture-specific aspects to be considered during treatment, such as preparing the individuals entering treatment to effectively engage, assessing and working with their motivation, and addressing the relationship dynamics. -
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Effectiveness of the Services Delivered by Special Schools for Children with Intellectual Disability
Special schools are the most widespread in the country among the various models for the education of children with intellectual disabilities. In India, there are large number of special schools for children with intellectual disabilities, implementing special education programme using various methods and materials. The present research attempts to determine the effectiveness of special schools rendering services for children with intellectual disability. A comprehensive understanding of the practices followed by different special schools would provide more insight into the functioning of special schools that serve children with intellectual disability. This study explores the various practices in special schools and the progress of children in self-care, behaviour and communication after receiving special education. The study also focused on understanding the progress of children with mild, moderate, and severe intellectual disability. The study used mixed research method. A causal design was used to assess special schools' effectiveness with a focus on self-care, behaviour, and communication of children. Both quantitative and qualitative method of data collection and interpretation were done to conclude the study. The self-structured interview schedule was used for qualitative research and collected information from 12 special schools. Cases were developed based on the qualitative data. Within-case and cross-case analysis with thematic analysis were used for analyzing the data. Quantitative data was collected from caretakers of 98 children, using a standardized tool Behavioural Assessment scale for Indian Children with Mental Retardation (BASIC- MR). The impact of special education on the self-care, communication and behaviour of Children with Intellectual Disabilities were analyzed with Wilcoxon Signed Rank test using the baseline data and their progress of the children after five years in special school. The result shows that there are changes in behaviour, self-care and communication of children with ID after they joined special school. The results also highlighted that there is a difference in children's progress based on the level of intellectual disability (mild, moderate and severe). The qualitative analysis explained the best practices exhibited by special schools for children with ID. -
Understanding the impact of designs in visual aids in education /
Visual aids have played a significant role in understanding various messages. Visual aids serve as multipurpose effective tool in understanding concepts that are complex in nature. Not only does visual aids serve as classroom technique in the field of education but also enable the designers to collaborate with the academicians to create deigns that would bridge the gap between study material and better understandings. -
Psychosocial Well-Being of Adolescents : A Social Group Work Intervention
Social work practice with children and families is one of the most challenging, skilled and rewarding areas of social work practice. Social workers believe that safeguarding children and preventing them from significant harm is a rewarding and challenging way to make a difference in the life of a child, which involves the corporation, consultation and collaboration of many people working effectively together. As highlighted by the United Nations' data disaggregation against the goal of "no one left behind," the absence of data on adolescents needs research on the "second decade." Furthermore, because India has the world's largest adolescent population, studies and policies aimed at developing adolescents' competencies are critical to the country's development; interventions aimed at instilling confidence in underprivileged adolescents to strive for a better future are critical for mitigating inequity. Adolescents from disadvantaged families and whose parents are no longer able to provide adequate care to children are having various psychosocial problems, high risk of violence, exploitation, abuse and neglect and their psychosocial well-being is often insufficiently monitored. This intervention study adopted a quasi-interventional design to measure the effectiveness of social group work in raising the psychological well-being, self-esteem and coping orientation of adolescents in child sponsorship programs. Social group work intervention with 20 sessions was designed in response to the information garnered through the pilot study and administered to the intervention group (n=20). Conducted pre-test and post-test for both intervention group and control group (n=20) and two follow up tests in three months intervals for the intervention group (n=20) using 42 item version of Ryffs scale for psychological well-being, Rosenbergs 10 item self-esteem scale and 54 items A-COPE scale; and data analyzed using SPSS. Comparison between pre and post measurements carried out using paired sample t-test for the intervention group and control group separately, gave out a p value < 0.05 for the intervention group and, > 0. 05 for the control group. Thus, it was proved that the psychological well-being, self-esteem and coping orientation of participants in the intervention group were raised significantly due to the social group work intervention. Applying refined granularity, this research adds data specifically on adolescents enrolled in child sponsorship programs and sets a blueprint for social group work to raise their psychological well-being, self-esteem and coping orientation. Proposing a conceptual framework for child sponsorship programs, this study recommends the need for operational tie-ups, sustained youth support, training of trainers (ToT) for community animators, preparing individual care plans and training to school social workers and the need of starting walk-in counselling centres and mentoring services. Furthermore, this study suggests additional research in all aspects of its operation, as well as interventions at the group, family, and community levels, for the well-being and empowerment of marginalised adolescents. -
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Impact of Integrated Explicit Instruction on Development of Critical Thinking Skills and Dispositions among Adolescents
Critical thinking is an essential skill that is required for survival in the twenty first newlinecentury. Educational institutions are gearing up to align their curriculum to ensure the development of critical thinking skills and dispositions among their candidates. However, there are few empirical studies that layout a clear road map of instructional strategies for the teaching of critical thinking. Given that adolescents are the most receptive to neurobiological skill and disposition development and that Literature is one of the best platforms that connects to real life, this research uses the educational design research method to develop an Integrated Explicit Instruction (IEI) module that could be used in English classes to teach adolescents critical thinking skills and develop in them critical thinking dispositions. This research not only bridges the gap in an empirically tested instructional strategy to teach critical thinking but also lays the foundation for further longitudinal studies that could measure the development of critical thinking skills and dispositions long term in participants who have been exposed to the intervention. -
Design and development of an efficient model for handwritten modi script recognition
Machine simulation of human reading has caught the attention of computer science newlineresearchers since the introduction of digital computers. Character recognition, a branch of pattern recognition and computer vision, is the process of identifying either printed or handwritten text from document images and converting it into machinecoded text. Character recognition has been successfully implemented for various foreign language scripts like English, Chinese and Latin. In the case of Indian language scripts, the character recognition process is comparatively difficult due to various complexities such as the presence of the vowel modifiers and a large number of characters (class). MODI script is a shorthand form of Devanagari script and it was used as an official script for writing Marathi until 1952. Presently the script is not used officially, but has historical importance. MODI script is a cursive script and the character recognition task is difficult due to various reasons such as variations in the shapes of a character with different individuals and the presence of identical looking characters. MODI documents do not have any word demarcation symbols and that adds to the complexity of the task. The advances in various Machine Learning newlinetechniques have greatly contributed to the success of optical character recognition. newlineThe proposed work is aimed at exploring various Machine Learning techniques/ newlinemethods which can be effectively used in(to) recognizing(recognize) MODI script and newlinebuild a reliable and robust character recognition model for handwritten MODI script. This research work also aims at the development of a Machine Transliteration and text recognition system for MODI manuscripts. -
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Artificial intelligence based system and method for management, recommendation, mapping of skill /
Patent Number: 202111054501, Applicant: Durgansh Sharma.
Artificial intelligence based system (100) and method for management, recommendation, mapping of skill comprising EmpNet (101), recommender system (102), automated machine learning system (103), skillset dataset (104), optimization system (105), industry interface system (106). The method for management, recommendation, mapping of skill comprising the steps of: a) capturing the required skillset personal data by the panchayat system (701); b) verify the skillset (702). -
Linear and non linear electroconvection in a micropolar fluid
This thesis presents a theoretical study of linear and non-linear analyses of Rayleigh Bard Marangoni/Rayleigh Bard electro newlineconvection in a micropolar fluid. The effects of non-uniform basic temperature gradient, suction injection combination and gravity newlinemodulation have been studied in the presence of electric field. The effect of heat transfer in a micropolar fluid in the presence of electric field is also studied and results are presented graphically and discussed qualitatively. These problems assume greater importance in geophysics, newlineastrophysics, oceanography, and engineering and in space situations with g-jitter connected with gravity stimulation study. newlineKeeping in mind the importance and relevance of externally controlled internal convection in a micropolar liquid. We deal with four newlineproblems, details of which are given below. newline(i) Effect of non uniform basic temperature gradient on the onset of Rayleigh Bard Marangoni electro convection in a micropolar fluid. The non-uniform temperature gradient finds its origin in the transient heating or cooling at the boundaries and as a result the basic temperature profile depends explicitly on position and time. This has to be determined by solving the coupled momentum and energy equations. This coupling also makes the problem very complicated. In the present study, therefore, we adopt a series of temperature profiles based on a newlinesimplification in the form of a quasi-static approximation that consists of freezing the temperature distribution at a given instant of time. In this method, we assume that the perturbation grows much faster than the newlineinitial state and hence freeze the initial state into some spatial distribution. newlineTherefore the effects of these non-uniform basic temperature gradient and electric field are studied on the onset of Rayleigh Bard Marangoni convection in micropolar fluid. -
Development and standardiztion of a tool to assess spirituality in families for family based interventions
The aim of the study was to develop and standardize a tool for family spiritual assessment. The sample consisted of 1502 Indian participants which included members from three religious backgrounds namely: Christianity, Hinduism and Islam. The data collected through face-to-face interview was analyzed using exploratory factor analysis (EFA), t-test and ANOVA. A five-item Likert-type tool developed was named as Family Spiritual Assessment Scale (FSAS) through a process of item development. EFA revealed that the 26-item tool with 5-factor solution had an excellent internal consistency of and#945;= .816. Religious factor, Spiritual factor, Mental health factor I (Positive emotions), Mental health factor II (Forgiveness) and Mental health factor III (Negative emotions) are the five important factors of the scale. Gender differences were found in the Spiritual factor, Mental Health, and Total newlinescore of the Scale, where females had higher scores than males. Post-hoc analysis newline(Bonferroni) revealed that total scores of all the three religions differed significantly. The results provide a sound foundation for the future research on spirituality. Family Spiritual newlineAssessment Scale, being the first in India, can be very beneficial to mental health newlineprofessionals and practitioners. -
Development and standardization of a tool to assess spirituality in families for family based interventions /
The aim of the study was to develop and standardize a tool for family spiritual assessment. The sample consisted of 1502 Indian participants which included members from three religious backgrounds namely: Christianity, Hinduism and Islam. The data collected through face-to-face interview was analyzed using exploratory factor analysis (EFA), t-test and ANOVA. A five-item Likert-type tool developed was named as Family Spiritual Assessment Scale (FSAS) through a process of item development. -
Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem
A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE. -
Hybrid Bacterial Foraging Optimization with Sparse Autoencoder for Energy Systems
The Internet of Things (IoT) technologies has gained significant interest in the design of smart grids (SGs). The increasing amount of distributed generations, maturity of existing grid infrastructures, and demand network transformation have received maximum attention. An essential energy storing model mostly the electrical energy stored methods are developing as the diagnoses for its procedure was becoming further compelling. The dynamic electrical energy stored model using Electric Vehicles (EVs) is comparatively standard because of its excellent electrical property and flexibility however the chance of damage to its battery was there in event of overcharging or deep discharging and its mass penetration deeply influences the grids. This paper offers a new Hybridization of Bacterial foraging optimization with Sparse Autoencoder (HBFOA-SAE) model for IoT Enabled energy systems. The proposed HBFOA-SAE model majorly intends to effectually estimate the state of charge (SOC) values in the IoT based energy system. To accomplish this, the SAE technique was executed to proper determination of the SOC values in the energy systems. Next, for improving the performance of the SOC estimation process, the HBFOA is employed. In addition, the HBFOA technique is derived by the integration of the hill climbing (HC) concepts with the BFOA to improve the overall efficiency. For ensuring better outcomes for the HBFOA-SAE model, a comprehensive set of simulations were performed and the outcomes are inspected under several aspects. The experimental results reported the supremacy of the HBFOA-SAE model over the recent state of art approaches. 2023 CRL Publishing. All rights reserved. -
Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
Brain Tumor Localization Using Deep Ensemble Classification and Fast Marching Segmentation
A brain tumor is an unusual and excessive growth of brain cells, which can be cancerous (malignant) or noncancerous (benign). These growths can be risky as they press on healthy brain tissue or expand in the brain. Detecting brain tumors early is tough for radiologists. A typical brain tumor can double in size in just 25days, and without the right treatment, patients often have limited chances of survival, about six months. Initial symptoms can be confused with other illnesses, and brain cancer is difficult to diagnose because of the complex nature of the brain and tumor locations. In this study, we propose a strategy where we first sort medical images based on the presence of a brain tumor. Then, we pinpoint the part of the image containing the tumor through segmentation. We use a combined model of MobileNet-V3 and EfficientNetV2 for image classification. To segment the tumor in the image, we use a fast marching method. The combined model's classification accuracy is 98%, and the segmentation accuracy is 99.6%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Implementation of Integration of AI and IOT Along with Metaverse Technology in the Field of Healthcare Industry
In the evolving panorama of healthcare, the appearance of Metaverse technology emerges as a transformative pressure, redefining traditional paradigms of healthcare shipping and education. This systematic assessment delves into the multifaceted impact of Metaverse technology, encapsulating their role in revolutionizing healthcare through modern-day academic frameworks, patient care interventions, and groundbreaking enhancements in medical imaging. Through an in-depth assessment of present-day literature, this observe illuminates the Metaverse's potential to facilitate immersive mastering tales, allow far flung interventions, and enhance the pleasant of scientific diagnostics and treatment making plans with its 3 -dimensional virtual environments. The findings underscore a burgeoning growth in Metaverse packages inner healthcare, highlighting its capability to noticeably beautify healthcare outcomes, affected person engagement, and expert abilities. Consequently, this evaluate advocates for the prolonged integration of Metaverse generation in healthcare, urging stakeholders to embody the ones enhancements and adapt to the following digital transformation in healthcare services and education. 2024 IEEE. -
GLANCEGuided Language Through Autoregression Establishing Natural and Classifier-Free Editing
In this study, researchers aimed to simplify text conversion into images using the latest text-to-image generation methods. While these methods have improved the quality and relevance of generated images, certain crucial questions remained unanswered, limiting their practicality and overall quality. To address these issues, the researchers introduced a novel text-to-image method. This method allows for better control of the scene depicted in the image through text, enhances the tokenization process by incorporating specific knowledge about key image regions such as faces and important objects, and provides guidance to the transformer model without needing a classifier. The outcome of this work was a model that achieved state-of-the-art results in terms of image quality and human evaluation, enabling the generation of high-fidelity 512?512-pixel images. Moreover, this method introduced new capabilities, including scene editing, text editing with reference scenes, handling out-of-distribution text prompts, and generating story illustrations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
EFFECTIVENESS OF COGNITIVE BEHAVIOURAL THERAPY FOR ADULTS WITH DEPRESSION AND ANXIETY DURING COVID-19: A Systematic Review of Randomised Controlled Trials
Introduction: The COVID-19 pandemic has forced the administration of Cognitive Behavioural Therapy (CBT) either face-to-face or online. This systematic review aims to assess the effectiveness of CBT and Internet-Delivered CBT (iCBT) in treating depression and anxiety disorders during the COVID-19 outbreak. Methods: Three independent reviewers searched the Web of Science, PubMed, Cochrane Library, and Clinical Trial Databases using specific search phrases. PubMed searches included Cognitive Behavioural Therapy/Intervention and COVID-19 and 2019 Coronavirus Disease or 2019-nCoV, internet-administered/internet-based cognitive behavioural therapy, CBT, cognitive behavioural treatment. Two independent reviewers evaluated the risk of bias at the study level, with disagreements settled through discussion with other research team members. The study findings were reported as per the PRISMA guidelines. Results: Thirty-one studies met the inclusion criteria, and 17 were randomised controlled trials. The studies demonstrated that CBT and iCBT effectively treated depression and anxiety disorders during the COVID-19 pandemic. However, a hybrid CBT modality was more beneficial from a long-term perspective. Conclusion: The findings suggest that CBT and iCBT effectively treat depression and anxiety disorders during the COVID-19 pandemic. However, further research is needed to establish these interventions long-term effectiveness and identify the optimal mode of delivery for different populations. 2024 selection and editorial matter, Dr Rajesh Verma, Dr Uzaina, Dr Tushar Singh, Dr Gyanesh Kumar Tiwari, and Prof Leister Sam Sudheer Manickam.