Whale Optimization for Cloud-Edge-Offloading Decision-Making for Smart Grid Services.

As IoT metering devices become increasingly prevalent, the smart energy grid encounters challenges associated with the transmission of large volumes of data affecting the latency of control services and the secure delivery of energy. Offloading computational work towards the edge is a viable option; however, effectively coordinating service execution on edge nodes presents significant challenges due to the vast search space making it difficult to identify optimal decisions within a limited timeframe. In this research paper, we utilize the whale optimization algorithm to decide and select the optimal edge nodes for executing services' computational tasks.

Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms.

Objective: This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation.

Preclinical examination of early-onset thalamic-cortical seizures after hemispheric stroke.

Objective: Ischemic stroke is one of the main causes of death and disability worldwide and currently has limited treatment options. Electroencephalography (EEG) signals are significantly affected in stroke patients during the acute stage. In this study, we preclinically characterized the brain electrical rhythms and seizure activity during the hyperacute and late acute phases in a hemispheric stroke model with no reperfusion.

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering.

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events.