Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts.

Recently, there has been an increasing need for new applications and services such as big data, blockchains, vehicle-to-everything (V2X), the Internet of things, 5G, and beyond. Therefore, to maintain quality of service (QoS), accurate network resource planning and forecasting are essential steps for resource allocation. This study proposes a reliable hybrid dynamic bandwidth slice forecasting framework that combines the long short-term memory (LSTM) neural network and local smoothing methods to improve the network forecasting model.

Molecular Analysis of the Antibiotic Resistant NDM-1 Gene in Clinical Isolates of Enterobacteriaceae.

Background: The emergence of the New Dehli metallo-beta-lactamase (NDM) gene in Enterobacteriaceae is responsible for multidrug resistance responsible for severe infections and serious morbidity in patients. Our study aimed to define the molecular characteristics and antibiogram of the NDM-1 producing Enterobacteriaceae.

Methods: We isolated 370 individual enterobacteria from the clinical specimens collected from the two tertiary hospitals in Sakaka, Saudi Arabia.