Performance of Cornell Assessment of Pediatric Delirium Scale in Mechanically Ventilated Children

Accuracy of delirium diagnosis in mechanically ventilated children is often limited by their varying developmental abilities. The purpose of this study was to examine the performance of the Cornell Assessment of Pediatric Delirium (CAPD) scale in these patients. This is a single-center prospective observational study of patients requiring sedation and mechanical ventilation for 2 days or more. CAPD scale was implemented in our unit for delirium screening. Each CAPD assessment was accompanied by a physician assessment using Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) criteria. Sensitivity analysis was performed to determine the best cut-off score in our target population. We also evaluated ways to improve the accuracy of this scale in patients with and without developmental delay. A total of 837 paired assessments were performed. Prevalence of delirium was 19%. Overall, CAPD score ≥ 9 had sensitivity of 81.8% and specificity of 44.8%. Among typically developed patients, the sensitivity and specificity were 76.7 and 65.4%, respectively, whereas specificity was only 16.5% for developmentally delayed patients. The best cut-off value for CAPD was 9 for typically developed children and 17 for those with developmental delay (sensitivity 74.4%, specificity 63.2%). Some CAPD questions do not apply to patients with sensory and neurocognitive deficits; upon excluding those questions, the best cut-off values were 5 for typically developed and 6 for developmentally delayed children. In mechanically ventilated patients with developmental delay, CAPD ≥ 9 led to a high false-positive rate. This emphasizes the need for either a different cut-off score or development of a delirium scale specific to this patient population.

A novel approach for IoT tasks offloading in edge-cloud environments

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

A Novel Approach for IoT Tasks Offloading in Edge-Cloud Environments

Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT task to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. Although Edge Computing is a promising enabler for latency-sensitive related issues, its deployment produces new challenges. Besides, different service architectures and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents a novel approach for task offloading in an Edge-Cloud system in order to minimize the overall service time for latency-sensitive applications. This approach adopts the fuzzy logic algorithms, considering application characteristics (e.g., CPU demand, network demand and delay sensitivity) as well as resource utilization and resource heterogeneity. A number of simulation experiments are conducted to evaluate the proposed approach with other related approaches, where it was found to improve the overall service time for latency-sensitive applications and utilize the edge-cloud resources effectively. Also, the results show that different offloading decisions within the Edge-Cloud system can lead to various service time due to the computational resources and communications types.

Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge

Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.