Volunteer State Community College

The use of microservice architectures to provide scalable, resilient and high-performance applications is becoming more popular in modern digital platforms. These architectures break down large systems into smaller independent services which interact via API and distributed networks. Microservices enhance the agility and scalability of applications, but they also create complicated security issues because of distributed communication, containerised deployments, and dynamic scaling capabilities. The established security models that have been used to support monolithic systems cannot be easily applied to the dynamic and distributed nature of microservice ecosystems. Consequently, this has led to problems with organizations in terms of tracking of service interactions, detection of real time threats, and ensuring secure communication among services without affecting performance. Artificial Intelligence (AI) has become one of the weapons to overcome these challenges. Through a combination of the AI-controlled orchestration and microservice security models, systems will be able to automatically identify anomalies, anticipate possible threats, and dynamically assign security resources without negatively affecting system performance. AI-coordinated security allows smart tracking of API traffic, threat mitigation policies, policy adaptation to access control and life-long learning about the behavior of the system. These functions lead to important high-resilience of the system, and provide the security of scaling in the cloud native environment. The proposed research suggests an AI-based microservice security architecture that can be used in high-performance scalable systems. The suggested framework integrates machine learning-driven anomaly identification, smart orchestration engine, secure API gateways, and distributed monitoring systems. The architecture monitors service interactions and identifies abnormal patterns, takes mitigation measures including rate limiting, service isolation, and automated firewall configuration. The system has the ability to use predictive analytics and behavioral models to ensure that the accuracy of threat detection remains low and the latency and throughput remain low. The experiment analyzes the validity of the suggested framework using the simulated workloads and the work performance benchmarking. The experiment outcomes reveal a high level of performance and security resilience of the system. The AI-planned architecture has a better threat detection accuracy, decreased incident recovery time, better resource use, and scalability than the conventional rule-based security systems. The findings suggest that the orchestration with the assistance of AI has the potential to be essential in ensuring the modern distributed systems and facilitating the large scale digital infrastructures. The results of the study add to the existing research on smart cybersecurity systems of cloud-native systems. The solution to the problem proposed is effective in the case of organizations that are running high-performance microservice systems such as financial, e-commerce and telecommunications networks and other large enterprise services. Combining AI with microservice orchestration systems, companies can develop responsive, self-protecting systems that can react to changing cyber risks without any impact on their performance.

Cloud computing environments today have an extensive range of distributed applications, which are dependent on the use of RESTful web services. These services are required to accommodate millions of simultaneous calls, as well as be highly performance, availability, and scalable. Conventional resource distribution systems of a cloud system are usually based on fixed policies or threshold-based auto-scaling strategies. Although these approaches offer a minimum scaleability, they are usually not efficient at managing unpredictable workloads and dynamic workloads typical in modern cloud systems. Consequently, the resources can be either underutilized or over-provisioned resulting in higher operational costs and poor performance of the system. Machine learning (ML) can be an effective solution to the problem of cloud resource allocation optimization in scalable REST services. ML models can predict resource utilization in the future by examining past workload data and determine intricate trends in traffic behavior to doom and refer moving computing resources like CPU, memory, and network bandwidth. This predictability enables cloud systems to make proactive resource allocation prior to the deterioration of performance. This paper presents an intelligent resource allocation framework on scalable cloud resource REST architecture based on machine learning algorithms. The framework combines predictive modeling of workload, resource scheduling by reinforcements of learning and real-time performance monitor. The system predicts the patterns of demand of the API using regression models and neural networks, which are supervised learning techniques. An agent of reinforcement learning then identifies ideal policies of resource allocation to achieve balance between system performance, latency and cost-efficiency. Experimental assessment shows that response time, system throughput and infrastructure usage is improved significantly as compared to the conventional rule based scaling mechanisms. The suggested system is more efficient in resources utilization and minimizes system failure and overhead. These findings reveal the possibility of machine learning based resource management in the next generation cloud based infrastructure as well as the high performance REST service ecologies.

This article describes a practical example of a social & community engagement work and involvement of students with a focus on learning process activities. In 2008, the Gonçalinho project in Cáceres, Brazil, started a cooperation with the Brazilian AG at “Metzinger Gymnasium” in Germany. The result of a cooperation agreement signed between UNEMAT and the Educating and Cultural Society of Brazilian German Integration in 2012 provided the opportunity to promote the exchange of solidarity and human experiences between young people from both countries. This project has a particular historical background regarding the involvement of children with drugs and dealers. In this regard, the project aims to find a way to move children from those risks and involvement with criminality to get a clear perspective on life with more dignity. Therefore, this paper shows the strategies and challenges behind this community service and the students’ contribution based on a five-pillar methodology. Further opportunities regarding the next steps will be highlighted, along with practical lessons learned.

This is a feature release with 1 new feature: Add support for MIDAS volatility processes with Hyperbolic weighting

This paper is about Poincare’s view of the foundations of geometry. According to the established view, which has been inherited from the logical positivists, Poincare, like Hilbert, held that axioms in geometry are schemata that provide implicit definitions of geometric terms, a view he expresses by stating that the axioms of geometry are “definitions in disguise.” I argue that this view does not accord well with Poincare’s core commitment in the philosophy of geometry: the view that geometry is the study of groups of operations. In place of the established view I offer a revised view, according to which Poincare held that axioms in geometry are in fact assertions about invariants of groups. Groups, as forms of the understanding, are prior in conception to the objects of geometry and afford the proper definition of those objects, according to Poincare. Poincare’s view therefore contrasts sharply with Kant’s foundation of geometry in a unique form of sensibility. According to my interpretation, axioms are not definitions in disguise because they themselves implicitly define their terms, but rather because they disguise the definitions which imply them.

Between 2007 and 2012 global food price volatility affected millions of people on low and precarious incomes. Research partners from ten developing countries accompanied households in rural and urban sites, from just after the first price spike in 2008, through a second spike in 2011 and into a period of relative price stability until 2014. In this IDS Bulletin we show how a multitude of micro-reactions to rising and unpredictable prices has laid the foundations for transformed societies. As food has been increasingly commodified, as people on low incomes have struggled to pay for life’s necessities, as they have responded by changing their ways of making a living, residences, diets, family relationships and ways of caring for one another, we map out how food price volatility has played a part in global social change.

Lois Gerber is a volunteer nurse at a school for pregnant or parenting teens and is a guardian ad litem for the state of Florida. Caring for older adults

Louis M. Kyriakoudes. The Social Origins of the Urban South: Race, Gender, and Migration in Nashville and Middle Tennessee, 1890–1930. Chapel Hill: University of North Carolina Press. 2003. Pp. xviii, 226. Cloth $59.95, paper $19.95 Kyriakoudes Louis M.. The Social Origins of the Urban South: Race, Gender, and Migration in Nashville and Middle Tennessee, 1890–1930. Chapel Hill: University of North Carolina Press. 2003. Pp. xviii, 226. Cloth $59.95, paper $19.95. Carole Stanford Bucy Carole Stanford Bucy Volunteer State Community College Search for other works by this author on: Oxford Academic Google Scholar The American Historical Review, Volume 109, Issue 5, December 2004, Pages 1579–1580, https://doi.org/10.1086/ahr/109.5.1579 Published: 01 December 2004

American Methodist Pioneer: The Life and Journals of the Rev. Freeborn Garrettson, 1752–1826. Edited by Robert Drew Simpson. Rutland, Vermont: Academy Books, 1984. ix + 433 pp. $25.00. - Volume 54 Issue 3