Prediction-based Dynamic Capacity Alloction for Traffic Cost Minimization

Department of Computer Science and EngineeringRecent advances in network virtualization techniques have shed light on dynamic resource allocation according to traffic usage. In particular, the minimum total network usage cost is achievable by using on-the-fly capacity allocation with accurate traffic estimation. In practice, there is an unavoidable delay for system reconfiguration, and thus a precise prediction on the traffic usage is required, which is, however, challenging due to unexpected system dynamics such as mobility and time-varying wireless channels. In this work, we address the prediction-based capacity allocation to minimize traffic cost by exploiting deep learning techniques. We develop an MLP model for accurate prediction of traffic usage, which is trained with real-world system logs obtained in a firewall. Taking into account the prediction errors and asymmetric structure of capacity pricing, we develop an efficient online capacity allocation scheme that achieves low traffic cost. We also evaluate the performance of our solution using the real-world data.clos

Physician-Customized Strategies for Reducing Outpatient Waiting Time in South Korea Using Queueing Theory and Probabilistic Metamodels

The time a patient spends waiting to be seen by a healthcare professional is an important determinant of patient satisfaction in outpatient care. Hence, it is crucial to identify parameters that affect the waiting time and optimize it accordingly. First, statistical analysis was used to validate the effective parameters. However, no parameters were found to have significant effects with respect to the entire outpatient department or to each department. Therefore, we studied the improvement of patient waiting times by analyzing and optimizing effective parameters for each physician. Queueing theory was used to calculate the probability that patients would wait for more than 30 min for a consultation session. Using this result, we built metamodels for each physician, formulated an effective method to optimize the problem, and found a solution to minimize waiting time using a non-dominated sorting genetic algorithm (NSGA-II). On average, we obtained a 30% decrease in the probability that patients would wait for a long period. This study shows the importance of customized improvement strategies for each physician

Timosaponin A3 Induces Anti-Obesity and Anti-Diabetic Effects In Vitro and In Vivo

Obesity is a serious global health challenge, closely associated with numerous chronic conditions including type 2 diabetes. Anemarrhena asphodeloides Bunge (AA) known as Jimo has been used to address conditions associated with pathogenic heat such as wasting-thirst in Korean Medicine. Timosaponin A3 (TA3), a natural compound extracted from AA, has demonstrated potential therapeutic effects in various disease models. However, its effects on diabetes and obesity remain largely unexplored. We investigated the anti-obesity and anti-diabetic properties of TA3 using in vitro and in vivo models. TA3 treatment in NCI-H716 cells stimulated the secretion of glucagon-like peptide 1 (GLP-1) through the activation of phosphorylation of protein kinase A catalytic subunit (PKAc) and 5′-AMP-activated protein kinase (AMPK). In 3T3-L1 adipocytes, TA3 effectively inhibited lipid accumulation by regulating adipogenesis and lipogenesis. In a high-fat diet (HFD)-induced mice model, TA3 administration significantly reduced body weight gain and food intake. Furthermore, TA3 improved glucose tolerance, lipid profiles, and mitigated hepatic steatosis in HFD-fed mice. Histological analysis revealed that TA3 reduced the size of white adipocytes and inhibited adipose tissue generation. Notably, TA3 downregulated the expression of lipogenic factor, including fatty-acid synthase (FAS) and sterol regulatory element-binding protein 1c (SREBP1c), emphasizing its potential as an anti-obesity agent. These findings revealed that TA3 may be efficiently used as a natural compound for tackling obesity, diabetes, and associated metabolic disorders, providing a novel approach for therapeutic intervention