Large Language Models (LLMs) have demonstrated exceptional capabilities in the field of Artificial Intelligence (AI) and are now widely used in various applications globally. However, one of their major challenges is handling high-concurrency workloads, especially under extreme conditions. When too many requests are sent simultaneously, LLMs often become unresponsive which leads to performance degradation and reduced reliability in real-world applications. To address this issue, this paper proposes a queue-based system that separates request handling from direct execution. By implementing a distributed queue, requests are processed in a structured and controlled manner, preventing system overload and ensuring stable performance. This approach also allows for dynamic scalability, meaning additional resources can be allocated as needed to maintain efficiency. Our experimental results show that this method significantly improves resilience under heavy workloads which prevents resource exhaustion and enables linear scalability. The findings highlight the effectiveness of a queue-based web service in ensuring LLMs remain responsive even under extreme workloads.

The impact of diabetes is significantly increasing in the USA in both prevalence and healthcare costs. The Centers for Disease Control and Prevention (CDC) reports that, as of 2020, approximately 34.2 million people in the United States have diabetes. This study analyzes the relationship between food consumption, exercise, and Glycosylated Hemoglobin (HbA1c) levels in non-diabetic individuals. Data from the National Health and Nutrition Examination Survey (NHANES) were used, and statistical analyses were performed using SAS software to identify food types that impact HbA1c levels.

The global spread of the novel coronavirus (COVID-19) has prompted the workforce, business, healthcare service and education along with individual to migrate from traditional work method to remote environment. The shifting from traditional to remote work environment has created many challenges towards security and privacy risks. In this paper, we have surveyed the potential user data security breach and privacy situation, cyber-threat that can affect business, health organization, and solution to protect data security and privacy.

Mobile wearable health devices have expanded prevalent usage and become very popular because of the valuable health monitor system. These devices provide general health tips and monitoring human health parameters as well as generally assisting the user to take better health of themselves. However, these devices are associated with security and privacy risk among the consumers because these devices deal with sensitive data information such as users sleeping arrangements, dieting formula such as eating constraint, pulse rate and so on. In this paper, we analyze the significant security and privacy features of three very popular health tracker devices: Fitbit, Jawbone and Google Glass. We very carefully analyze the devices' strength and how the devices communicate and its Bluetooth pairing process with mobile devices. We explore the possible malicious attack through Bluetooth networking by hacker. The outcomes of this analysis show how these devices allow third parties to gain sensitive information from the device exact location that causes the potential privacy breach for users. We analyze the reasons of user data security and privacy are gained by unauthorized people on wearable devices and the possible challenge to secure user data as well as the comparison of three wearable devices (Fitbit, Jawbone and Google Glass) security vulnerability and attack type.