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      Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

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          Abstract

          In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

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          A Study to Evaluate Psychological Distress and Self-Esteem Among Patients with Hemodialysis

          Background: Chronic kidney disease (CKD) is a progressive disease that affects more than 800 million people worldwide, representing more than 10% of the global population. It is more common in older people, women, and racial minorities, as well as in people with diabetes mellitus and high blood pressure. CKD has become one of the top causes of mortality worldwide, and is one of the few non-communicable diseases that have seen an increase in related deaths over the last few decades. The high number of affected people and the serious negative consequences of chronic disease should lead to increased efforts to improve prevention and treatment efforts. Around the world, there are an estimated 1,800-1,600 extra deaths per 10000 patients who are on dialysis.  Aim: The aim of the study is to determine the psychological distress and self-esteem among dialysis patients.  Research Methodology: A quantitative cross-sectional investigate plan was utilized to conduct a research study among 30 dialysis patients. Convenient sampling techniques were utilized to collect data from standardized tool using questionnaire techniques.  Result: Study showed that 36.7% of the samples were doing well, 23.3% had mild psychological distress, 23.3% of the samples had moderate psychological distress and severe distress is seen in 16.7% of the samples. 73.3% of them had normal self-esteem, 20% of the study population had low self-esteem and 6.7% had above average self-esteem. Significant relationship is seen between social and family support with self-esteem (p=0.033).  Conclusion: About half of the samples were having psychological distress which is of mild and moderate, severe psychological distress were seen in 16.7% of the samples, ordinal self-esteem were seen in 73% of the samples, 27% of samples had mild and average self- esteem.
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            Enhancements in Immediate Speech Emotion Detection: Harnessing Prosodic and Spectral Characteristics

            Speech is essential to human communication for expressing and understanding feelings. Emotional speech processing has challenges with expert data sampling, dataset organization, and computational complexity in large-scale analysis. This study aims to reduce data redundancy and high dimensionality by introducing a new speech emotion recognition system. The system employs Diffusion Map to reduce dimensionality and includes Decision Trees and K-Nearest Neighbors(KNN)ensemble classifiers. These strategies are suggested to increase voice emotion recognition accuracy. Speech emotion recognition is gaining popularity in affective computing for usage in medical, industry, and academics. This project aims to provide an efficient and robust real-time emotion identification framework. In order to identify emotions using supervised machine learning models, this work makes use of paralinguistic factors such as intensity, pitch, and MFCC. In order to classify data, experimental analysis integrates prosodic and spectral information utilizing methods like Random Forest, Multilayer Perceptron, SVM, KNN, and Gaussian Naïve Bayes. Fast training times make these machine learning models excellent for real-time applications. SVM and MLP have the highest accuracy at 70.86% and 79.52%, respectively. Comparisons to benchmarks show significant improvements over earlier models.
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              Beyond Mobile Payments: Exploring the Evolution and Future of Fintech (BY IJISRT)

              In today's digital age, FinTech’s have become universal, transforming the way individuals conduct financial transactions. However, with the convenience of Fintech also come challenges that users encounter. This book chapter investigates into the changing landscape of Fintech challenges, drawing insights from a comprehensive survey conducted among various respondents The survey finds three primary obstacles faced by users during Fintech: network issues, time consumption, and privacy concerns. Notably, a significant proportion of respondents reported encountering network-related problems, highlighting the critical role of stable connectivity in facilitating seamless transactions. Moreover, the chapter explores into the foiling experienced by users due to the time-and privacy nature of the payment process, shedding light on the need for streamlined and efficient payment mechanisms. Building upon the survey findings, the chapter offers valuable insights into potential strategies and solutions to address these challenges. By elucidating practical approaches and technological innovations aimed at enhancing Fintech systems' efficiency and security, the chapter equips readers with actionable knowledge to navigate the digital payment landscape effectively. This study's findings add to academic discussion and have practical implications for policymakers, industry practitioners, and educators.
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                Author and article information

                Journal
                International Journal of Innovative Science and Research Technology (IJISRT)
                International Journal of Innovative Science and Research Technology (IJISRT)
                International Journal of Innovative Science and Research Technology
                2456-2165
                May 11 2024
                : 23-30
                Article
                10.38124/ijisrt/IJISRT24MAY207
                2114eb28-7489-41fb-93c8-5e406371f78b
                © 2024
                History

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