Journal for ReAttach Therapy and Developmental Diversities (JRTDD)
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Examining The Saving Behavior Of Working Women: A Comprehensive Analysis
No full textThis study explores the saving behavior of working women, focusing on the factors that influence their financial decisions, the challenges they face and the patterns observed across different demographics. Through a mixed-methods approach involving surveys and in-depth interviews, the research reveals how income level, educational background, financial literacy, age and cultural influences shape the saving habits of working women. The findings highlight that while higher income and financial literacy contribute to better saving practices, cultural expectations and social norms often impose significant constraints. The study underscores the need for targeted financial literacy programs, tailored financial products and policy interventions to support the unique financial needs of working women, ultimately enhancing their economic security and independence
Post-Pandemic Athlete’s Mental Health Status: A Comparative Study
No full textthe Aim of Study the study was conduct to investigate the psychological effect of Post covid-19 pandemic among the individual and team sports players.
Methodology: for the present study a total of 100 athletes (50 athletes, from Individual and 50 athletes from team sports) were selected. The athletes from sports disciplines are Team sports (Football, Hockey, handball, Cricket and Basketball) and Individual sports (Badminton, swimming, Diving, Cycling, Table Tennis, Weightlifting and Tennis with age ranging from 17-24 years. All subjects were selected at LNIPE, Gwalior, M.P., and LNIPE-NERC, Guwahati (Assam). Simple random sampling method was used to draw the sample of the study.
Results: the results obtained from individual and team sports player on DASS revealed that anxiety, depression and stress were higher in individual sports rather than the team sport. The significant differences were found among the individual and team sports athletes in depression, anxiety and stress. It is possible to draw the conclusion that individual gamers had higher levels of stress, anxiety, and depression than did team players. The reason may the team cohesiveness among the individual sports athletes may be lesser than the team sports athletes.
Conclusions: the COVID-19 pandemic has brought to light significant mental health challenges faced by athletes in both individual and team sports. While the nature of these challenges varied, the overall impact on mental well-being was profound
Assess The Knowledge Regarding Prevention Of Myocardial Infarction Among Clients With Hypertension At Medical Op, Kgh, And Visakhapatnam
No full textA study was conducted using Descriptive survey method to to assess the knowledge regarding Prevention of Myocardial Infarction among clients with Hypertension at KGH,VSP by Mrs.K.Sridevi Ph.D Nursing Scholar from Mangalayatan University, Aligharh ,U.P
Objectives:- 1. To assess the knowledge regarding Prevention of Myocardial Infarction among clients with Hypertension. 2. To find out the association between the knowledge of clients with Hypertension and their selected demographic variables. 3. To develop and provide self-instructional module on Prevention of Myocardial Infarction among clients with Hypertension. Methods: The research design adopted for the present study is non experimental design and it is a descriptive design for which the population consists of clients with Hypertension. A structured questionnaire was used to assess the knowledge regarding Prevention of Myocardial Infarction among clients with Hypertension. The pilot study was conducted on five clients with Hypertension with the help of structured questionnaire at medical OP, KGH, VSP. A review of literature helped the investigators to gain insight into present study for the development of the tool. The research approach adopted for the study is descriptive design. The population for the study consists of clients with Hypertension at Medical OP, KGH, VSP. The criteria for sample collection were a purposive sampling method and a sample size is 50 clients with Hypertension. The pilot study was conducted on from 21-09-2023 to 22-09-2023 3 on 5 clients with Hypertension at medical OP, KGH, VSP and the mean value is 9.6 and found that the study is feasible and the tool was appropriate. Results: The study revealed that majority of clients with Hypertension, among 50 respondents 3(6%) respondents having below average level of knowledge, 44 (88%) respondents have average level of knowledge and 3 (6%) respondents having above average level of knowledge regarding Prevention of Myocardial Infarction hence the Investigation mode recommendations in improving their knowledge
Effectiveness of Homoeopathic Medicine in the management of Premenstrual Syndrome
Get PDFPremenstrual Syndrome (PMS) is a group of symptoms covering physical, psychological and behavioral symptoms. The exact cause and etiology of PMS is unknown, but it seems to related to ovarian function or endocrinal factors. The severity of symptoms of PMS varies and it is different in female to female and depends on individuality of female. Homoeopathy is a unique system of medicine where each patient is examined as an individual. In this study we try to see the effectiveness of Homoeopathic medicine in the management of Premenstrual syndrome
A Data-Driven Framework For Real-Time Fraud Detection In Financial Transactions Using Machine Learning And Big Data Analytics
Get PDFThe rapid growth of electronic commerce and the gradual increase in customer confidence in the security of electronic payments have led to a persistent increase in the number of on-line transactions in the past years. Credit and debit cards account for the majority of the on-line payments. Consequently, the credit card financial ecosystem growth has been accompanied by a similar growth of illicit actions, by which con-men try to benefit from this huge financial exchange. Fraud detection is critical for credit institutions, merchants, and national services to minimize money losses. In recent years, several initiatives to enhance systems aimed at detecting fraudulent credit card transactions have been taken. Detecting fraud is very challenging since machine learning approaches rely on training sets limited to the observations that were available at the moment of the training. The newly triggered events, which were never observed in the training phase, can lead to severe issues such as alarm fatigue, in which frauds are detected only after a large amount of incurred losses.
Detecting fraud requires applying scalable learning techniques able to analyze the huge amount of streaming data generated by the transactions and able to mitigate the two main situations complicating the problem: the class imbalance and the concept drift since the world evolves and consequently the fraud patterns change. The need to detect frauds in real-time gives rise to several challenges. Recent advances in analytics, and the availability of open source solutions for storage, processing, and analytics of Big Data, have opened new perspectives for the real-time detection of frauds in massive amounts of transactions. In this paper, the SCAlable Real-time Fraud Finder (SCARFF) framework is presented. SCARFF is a distributed (anomaly) detection machine learning approach for the fraud detection that integrates Big Data tools both for the massive storage and processing of the transactions and for the predictive analysis.
SCARFF makes a contribution to the literature in four directions. First, the integration of the Hadoop and Spark ecosystems and of the sophisticated learning approach, addressing the inherent problems of imbalance, nonstationarity, and feedback latency, is a unique contribution. Second, the capability of handling a never-seen-before massive dataset of real credit card transactions is a unique achievement. Third, the formal description of the methods implemented to tackle data imbalance in real-time is presented. Fourth, the implementation in real-time of an ensemble learning engine capable of detecting credit card frauds at the rate of records−1, with large computational savings compared to batch implementations, is a further unique achievement
Adaptive AI Workflows for Edge-to-Cloud Processing in Decentralized Mobile Infrastructure
Get PDFDeterioration in wireline/mobile communications infrastructure reliability and subsequent service outages with a snowballing trend amount to significant social costs that are likely to spiral. Service providers are reducing the attack surface by decoupling control and service functions, substituting open-source solutions for traditional proprietary service instances. Self-governing agent devices and nodes are intended to be augmented with enhanced sensing, control, and storage capabilities, notably cognitive capabilities. These architecture trends snowball a move to decentralized multicentric cloud infrastructure envisioned by new paradigms. There is a need for resilient decentralized mobile infrastructure based on Avatars that is responsive to large-scale topology dynamics, heterogeneous traffic demands, and cognitive agents with cross-layer perceptions and behaviors. They argue that adaptive AI workflows underpinned by dynamic Spatio-temporal agent/offer modeling augmented with situational spatio-temporal modeling, multi-agent MABs, and RL methods can enable edge-to-cloud processing of nodes and traffic agents for decentralized mobile infrastructure. [1] The proposed notions of situational spatio-temporal agent/offer modeling, multi-agent MAB, RL-based distributed behavioral learning, and cloud-edge creativity enable AI workflows for edge-to-cloud processing in trajectory-aware network infrastructure environments. The enhanced awareness of agents and offers with their situational relevance in dynamic interactions is enhanced, enabling smart cognitive state and intent discovery, unprecedented online autonomous adaptive processing and decision-making, and unexplored creativity at the edge and in the cloud. Low-complexity multi-agent MAB can tune the related prices and processing power in degree-matched multi-hop network architecture for edge-to-cloud decentralized power-rate allocation with learning guarantees. Knowledge transferred by simple tabular RL is discoverable and composable, realizing online autonomous decentralized emergent control even with dynamic topology and changing objective environments
Exploring Shifting Assamese Identities in Contemporary Fiction with a Special Focus of Jahnavi Barua\u27s "Undertow"
Get PDFAssam, a state in Northeast India, possesses a rich and complex history, marked by diverse ethnic groups, linguistic variations, and a vibrant cultural heritage. Historically, Assamese identity has often been portrayed as rooted in a shared language, the legacy of the Ahom kingdom, and a strong connection to the Brahmaputra River. However, in the contemporary era, this seemingly stable notion of identity faces increasing pressures from various internal and external forces. Globalization, increased migration, urbanization, and persistent socio-political issues, including insurgency and debates around indigenous rights, have contributed to a more nuanced and often fragmented sense of self among the people of Assam.
Contemporary Assamese fiction, particularly works written in English, offers a compelling lens through which to explore these shifting identities. These narratives move beyond simplistic or nostalgic representations, delving into the intricacies of individual and collective experiences in a world that is constantly redefining what it means to be Assamese. This paper seeks to investigate this phenomenon by focusing on Jahnavi Barua’s "Undertow" (2020), a novel that poignantly captures the multifaceted dimensions of modern Assamese identity. Through its exploration of family dynamics, displacement, and the search for belonging, "Undertow" serves as a microcosm for the broader shifts occurring within Assamese society.
 
"Role of Resistance and SAQ Training in Improving Lower Limb Strength and Ball Control in Collegiate Football Players"
Get PDFThis study aims to examine the effectiveness of Resistance and SAQ (Speed, Agility, and Quickness) training in enhancing lower limb strength and ball control among collegiate football players. A total of 80 male intercollegiate football players were selected and randomly divided into four groups (n=20 per group): Group I underwent resistance training, Group II received SAQ training, Group III participated in a combined resistance and SAQ training program, and Group IV served as the control group with no specialized intervention. The training was conducted over a 12-week period, with sessions held thrice weekly. Standardized tests were used to measure lower limb strength (such as the vertical jump test and leg press) and ball control performance (including dribbling, juggling, and passing accuracy) before and after the intervention.
The results demonstrated significant improvements in lower limb strength and ball control in all experimental groups compared to the control group. Notably, the group receiving combined resistance and SAQ training showed the greatest gains in both strength and ball control skills, suggesting a synergistic effect of the two modalities. These findings indicate that a structured 12-week program incorporating both resistance and SAQ training can be highly effective in improving key performance variables in football. The study underscores the importance of integrating targeted strength and agility training into regular conditioning programs for collegiate-level football athletes
High-Throughput LC-MS/MS Technique for Reliable Measurement of Posaconazole in Human Plasma
Get PDFA rapid and sensitive LC-MS/MS bioanalytical method was developed and validated for quantifying Posaconazole in human plasma using posaconazole D5 as the internal standard, per USFDA guidelines. Chromatographic separation employed a Waters Atlantis dC18 column (3µ, 4.6×50 mm) with a mobile phase of acetonitrile:5 mM ammonium formate containing 0.01% formic acid (95:5 v/v) at 0.6 mL/min flow rate, yielding retention times of 2.00 min for both analyte and IS. The method demonstrated excellent selectivity with no interference, linearity from 0.80 to 801.48 ng/mL, and robust in-system suitability (area ratio and S/N at LLOQ within limits). Precision and accuracy across six QC replicates met acceptance criteria, with consistent recovery at low, mid, and high levels, negligible matrix effects, and stability up to 18 hr for stock solutions, 3 days frozen for working solutions, 2 days/20 hr bench-top for spiked plasma, and 5 freeze-thaw cycles. This validated method supports reliable pharmacokinetic assessments and therapeutic drug monitoring of Posaconazole in clinical plasma samples. This LC-MS/MS approach addresses limitations of existing methods by offering superior sensitivity, high throughput, and cost-effectiveness for Posaconazole quantification, vital for ensuring therapeutic efficacy in antifungal therapy amid variable bioavailability challenges
Fiscal Strain and Public Health: How Ethiopia’s Debt Crisis is Reshaping Healthcare Financing – A Systematic Review to Evolve a Hybrid Model
No full textThis review critically examines the interplay between Ethiopia’s escalating national debt and its healthcare system, focusing on how debt servicing priorities erode healthcare financing and exacerbate public health challenges. Over the past decade, Ethiopia’s debt-to-GDP ratio surged from 35% in 2013 to over 57% in 2021, diverting crucial resources away from healthcare. With nearly 25% of government revenue allocated to external debt repayments by 2020, healthcare spending remains a mere 4% of GDP, far below the Abuja Declaration’s 15% target. This disproportionate focus on debt servicing results in underfunding, especially for rural healthcare, which serves 80% of the population yet receives only 25% of the national health budget. The review highlights that rising debt service pressure not only restricts healthcare budgets but also stagnates key health outcomes, such as maternal and infant mortality rates. Ethiopia’s reliance on foreign aid for 40% of its healthcare financing makes the system vulnerable to donor shifts, further complicating the country’s healthcare crisis. Governance inefficiencies, misallocation of resources, and corruption amplify the adverse impacts of limited healthcare funding, contributing to inequitable access to healthcare services, particularly in rural areas.
It advocates a future research model that transcends static analyses by proposing a hybrid empirical approach integrating Structural Equation Modeling (SEM) and Vector Autoregression (VAR). This model will explore the causal relationships between debt servicing, healthcare financing, and public health outcomes, while capturing the dynamic, time-lagged effects of fiscal shocks on healthcare delivery. By investigating how debt restructuring, governance improvements, and public-private partnerships (PPPs) could alleviate healthcare financing gaps, this approach provides actionable insights for policymakers aiming to balance debt management with essential healthcare investments. This review underscores the urgent need for structural reforms in governance and debt management to prioritize healthcare investments in debt-stricken countries like Ethiopia. The proposed hybrid model advances the extant literature by providing a comprehensive framework for understanding the long-term impacts of national debt on healthcare systems, offering a pathway toward sustainable healthcare financing solutions