Volume 10 | Issue 6 |

 Abstract

The Forward Contracts (Regulation) Act (FCRA), enacted in 1952, aimed to regulate forward trading, with the Forward Market Commission (FMC) established as its regulatory body in 1953. Despite intentions for autonomy, FMC faced limitations due to government interference, restricted powers, and inadequate infrastructure, leading to inefficiencies in commodity market regulation. The merger of FMC with SEBI in 2015 under the Securities Contract Regulation Act (SCRA) marked a significant regulatory shift. This article explores the shortcomings of FMC, the rationale behind the merger, and the potential for SEBI to enhance market integrity, efficiency, and innovation while addressing challenges in spot market integration and pricing mechanisms.

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 Keywords

FCRA, FMC, SEBI, Commodity derivative market, SCRA

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Abstract: Medical tourism involves crossing international borders to avail medical treatment and facilities in another country and enjoy the tourism facilities there. Medical tourism is an important sector in stimulating the economic growth of Kerala. The growth of medical tourism in Kerala is a multifaceted phenomenon driven by the state's high-quality healthcare services, affordability, and integration of traditional and modern medicine. The sector significantly contributes to Kerala's economic growth through revenue generation, job creation, and infrastructure development. More than five lakh foreign patients come to the state for treatment every year. About 25% to 30% of major hospitals' revenue comes from medical tourism (Confederation of Indian Industry). Government initiatives play a crucial role in supporting and enhancing this growth by implementing supportive policies, promoting the state internationally, and investing in healthcare infrastructure. This study explores how Kerala is progressing in medical tourism, why people from other countries choose Kerala as their favorite medical tourism destination and the role of government in promoting medical tourism.

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 Keywords

Keywords: Medical tourism, Market Development Assistance (MDA) Scheme, Indian Healthcare Federation (IHF), Medical Visa, Heal in India Initiative, Kerala Medical Value Travel Society

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A number of methods for solving differential equations are discussed in this article along with the many fields of science and engineering that make use of them. To comprehend both naturally occurring and artificially created systems one must be familiar with differential equations which are mathematical models that depict the change of a quantity over time or space. This article will go over what a differential equation are, the various kinds of differential equations, how to solve them, the order and degree of the differential equation, and some instances of ordinary differential equations with real-world problems and a solved issue. Variable separation, integration factors, homogeneous equations and replacements, the characteristics approach, numerical methods, Laplace and Fourier transforms, and symmetry methods are the main techniques covered. Various methods are applicable to various kinds of differential equations; together they provide a toolbox full of methods for solving these problems either analytically or with reasonable approximations. Various areas rely on differential equations as shown by the highlighted applications. These include engineering, biology, economics, environmental science, technology, medicine, and physics. This study strives to provide a thorough grasp of the techniques used to solve differential equations and their significance in addressing real-world situations by giving both the theoretical foundations and practical implementations.

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differential equations, applications, techniques

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Creative Commons Attribution 4.0 and The Open Definition

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Supply chain management (SCM) that makes use of BDA is becoming more prevalent. The wide range of SCM applications of BDA, including demand prediction, trend analysis, and consumer behaviour, is the reason for this. This study aims to enhance precision and efficiency in demand forecasting and SCM through the application of BDA. Despite advancements, challenges persist such as reliance on isolated datasets, underutilisation of advanced machine learning models, and limited incorporation of external variables. Demand forecasting, crucial for supply chain decisions like production planning and inventory control, is hindered by demand volatility influenced by factors such as promotions and market trends. This study leverages integrated, real-time data sources and sophisticated analytics to improve prediction accuracy and operational efficiency, thereby enabling businesses to better manage uncertainties in demand, supply, and costs. The research also identifies gaps in current literature, including the need for more robust models, comprehensive external variable analysis, and broader validation across industries. By addressing these gaps, the research contributes to a development of more resilient and responsive supply chains

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Big data analytics for supply chain, supply chain through demand forecasting, demand forecasting using supply chain.

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This study explores integrating artificial intelligence (AI) and machine learning (ML) techniques into cloud automation processes, focusing on enhancing auto-scaling mechanisms. Auto-scaling is a critical cloud management component, dynamically adjusting resources to meet fluctuating demands. Traditional auto-scaling methods often rely on static thresholds and reactive policies, which can lead to inefficiencies such as over-provisioning or resource shortages. This research addresses these limitations by employing predictive analytics and machine learning algorithms to create a more adaptive, intelligent, and proactive auto-scaling system. The research utilizes a combination of supervised and unsupervised machine learning models to predict workload patterns and optimize resource allocation in real time. Historical data from cloud infrastructure, including CPU usage, memory consumption, and network traffic, are analyzed to train these models. The study implements various algorithms, such as decision trees, neural networks, and reinforcement learning, to enhance the auto-scaling mechanisms' predictive accuracy and decision-making capabilities. A simulated cloud environment tests and validates the proposed system, ensuring its robustness and scalability. The findings demonstrate that AI-driven auto-scaling mechanisms significantly outperform traditional methods regarding resource utilization, cost efficiency, and response time. The predictive models successfully anticipate workload surges and optimize resource allocation before bottlenecks occur, leading to a smoother and more efficient cloud operation. Additionally, integrating machine learning into the auto-scaling process reduces the reliance on manual configurations and static policies, allowing for more dynamic and flexible cloud management. The implications of this research are far-reaching for cloud service providers and enterprises relying on cloud infrastructure. By leveraging AI and machine learning, organizations can achieve more efficient resource management, leading to cost savings, enhanced performance, and improved user experiences. The study also sets the stage for future advancements in cloud automation, where AI-driven approaches could become the norm, further pushing the boundaries of what cloud computing can achieve. This study discusses the role and capability of AI and machine learning in scaling clouds, focusing on improving auto-scaling dynamic attributes. Therefore, the switch from reactive resource management to predictive and proactive resource management is a welcome publication that provides new angles for increasing the smartness of cloud structures to meet the continuously growing demand.

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 Keywords

AI-powered cloud automation, Auto-scaling mechanisms, Predictive analytics, Machine learning, Cloud infrastructure

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This research aims to examine the effect of a weight training program on the serving performance of high school volleyball players. Twenty-seven players from Vijayapur, Karnataka, India, who were also high school volleyball players, were randomly assigned to three equal groups of nine. The groups included: Experimental Group I (WR), which engaged in resistance exercises using their own body weight; Experimental Group II (WRS), which combined resistance exercises with weights and specific volleyball skills; and Group III (CG), which served as the control group with no additional training. The participants underwent pre-test evaluations of their serving abilities using the AAHPER Volleyball Serving Test, with performance measured in points. Over twelve weeks, the experimental groups followed their respective weight training programs. Post-test serving performance scores were collected after the training period. Statistical analyses using ANOVA and ANCOVA were conducted to determine significant differences in serving skills among the groups. The LSD post hoc test was employed when the ANOVA F-value indicated significance, with a 0.05 level of confidence set for all statistical tests. The findings highlight the value of combining resistance training with skill-specific practice to enhance serving performance in volleyball. Coaches and trainers are encouraged to integrate both resistance training and skill drills into their programs to achieve optimal performance improvements. Future research could explore the long-term effects of these training methods and their applicability to actual game performance.

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Serving, Weight training, Resistance exercises, Volleyball, Skill performance, High school players

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Power quality is one of major concerns in the present era. It has become important, with the introduction of sophisticated devices, whose performance is very sensitive to the quality of power supply that results in a failure of end user equipment's. One of the major problems dealt here is the voltage sag. To solve this problem, custom power devices are used. One of those devices is the Dynamic Voltage Restorer (DVR), which is the most efficient and effective modern custom power device used in power distribution networks. Its appeal includes lower cost, smaller size, and its fast dynamic response to the disturbance. It can provide the most commercial solution to mitigation voltage sag by injecting voltage as well as power into the system. This paper presents modeling, analysis and simulation of a Dynamic Voltage Restorer (DVR) using MATLAB. The efficiency of the DVR depends on the performance of the efficiency control technique involved in switching the inverters. In this model a PI controller and Discrete PWM pulse generator is used.

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Dynamic Voltage Regulator & Power Quality Distribution System

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This study focuses on the impact of private schools in improving educational opportunities for tribal children in the Palamu region of Jharkhand, India. The research highlights the key role played by specific private schools in increasing enrollment and reducing dropout rates among Scheduled Tribe students. These schools, such as St. Joseph School, St Teresa School and St. Xavier School, Mahuadanr of Palamu division have significantly improved access to quality education for tribal communities, contributing to their socio-economic advancement. The study recommends concerted efforts to address educational disparities faced by Scheduled Caste, Scheduled Tribe, and tribal children, emphasizing the need for tailored interventions to support educational equity in rural India. Overall, the research provides insights into effective strategies for promoting educational equity and empowering tribal communities in the Palamu region.

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 Keywords

quality, educational opportunity, private schools, enrolment, drop-out

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

????? ??? ???? ?? (Goods and Service Tax- GST) ?? ?? ?????????? ?? ??? ?? ?????? 1 ???? 2017 ???? ???? ??????? ??? ????. ?????? ????? ??? ???? ?? ???? ???? ?? ?? ???? ???? ?????????? ??? ????????? ?? ????????? ???????? ?????????? ?????? ?? ???????????? ???? ????. ?????, ????? ??? ???? ?? ?? ?????????? ?????? ???? ???? ??? ????? ??? ???? ?? ?????? ????? ?????? ????? ??? ???? ?? ?????? ?????? ???? ??????? ??? ????????? ????? ????? ?????. ???? ?? ????, ?????????????? ??????, ???? ??????? ????, ITC ??????, ????? ??? ???? ?? ?? ??? ?-?? ??? ??????? ????????? ???????????, ??????? SMEs ?? ??????? ?????? ???? ???. ?? ????? ?????? ?????? ??? ??????? ??????????? ?????? ?????? ???? ????? ??????? ??????? ???? ???.

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GST Good and Services Tax

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The evolution and growth of social networking and modern web technology have made an individual's online presence permanent. People frequently express their thoughts, ideas, and emotions through social networking links, with the most popular activity being the discussion of everyday events, which may include private or public conversations. Bullying that involves technology is referred to as cyber-bullying. Bullying attacks target teenagers and young people who spent lot of time on social networking sites. The rise of social media, especially Twitter, has caused confusion about the meaning of free expression, which has given rise to a number of worries. Cyber-bullying is one of these issues, a severe global problem that has an impact on both people and societies. There have been numerous reported attempts to intervene, prevent, or lessen cyber-bullying; however, these efforts are unworkable since they depend on the victims' interactions. Victims of this behavior may experience hopelessness and other potentially fatal issues. It is necessary to create monitoring and detection procedures for potentially hazardous Internet behavior. By using machine learning, we can create algorithms to automatically identify cyber-bullying content and recognize language patterns used by bullies and their victims. Therefore, it is crucial to identify cyber-bullying without the victims' participation. Additionally, numerous machine learning classifiers were applied, including the K-nearest neighbor technique, linear regression, decision trees, and the Support Vector Machine classifiers, Random Forest, Naive Bayes, and AdaBoost. The most precise supervised learning algorithm was used to identify communications that contained cyber-bullying.

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 Keywords

cyber-bully, cybercrime, social media, traditional bullying, social Networking sites, Harassment.

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