Risk factors and predictive model of cerebral edema after road traffic accidents-related traumatic brain injury.

Purpose: Cerebral edema (CE) is the main secondary injury following traumatic brain injury (TBI) caused by road traffic accidents (RTAs). It is challenging to be predicted timely. In this study, we aimed to develop a prediction model for CE by identifying its risk factors and comparing the timing of edema occurrence in TBI patients with varying levels of injuries.

Investigating neuropathological changes and underlying neurobiological mechanisms in the early stages of primary blast-induced traumatic brain injury: Insights from a rat model.

The utilization of explosives and chemicals has resulted in a rise in blast-induced traumatic brain injury (bTBI) in recent times. However, there is a dearth of diagnostic biomarkers and therapeutic targets for bTBI due to a limited understanding of biological mechanisms, particularly in the early stages. The objective of this study was to examine the early neuropathological characteristics and underlying biological mechanisms of primary bTBI.

A Survey on Autism Care, Diagnosis, and Intervention Based on Mobile Apps Focusing on Usability and Software Design.

This article presents a systematic review on autism care, diagnosis, and intervention based on mobile apps running on smartphones and tablets. Here, the term "intervention" means a carefully planned set of activities with the objective of improving autism symptoms. We guide our review on related studies using five research questions.

A Hybrid Deep Transfer Learning Model With Kernel Metric for COVID-19 Pneumonia Classification Using Chest CT Images.

Coronavirus disease-2019 (COVID-19) as a new pneumonia which is extremely infectious, the classification of this coronavirus is essential to effectively control the development of the epidemic. Pathological changes in the chest computed tomography (CT) scans are often used as one of the diagnostic criteria of COVID-19. Meanwhile, deep learning-based transfer learning is currently an effective strategy for computer-aided diagnosis (CAD).

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts.

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively.

A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability.

Altered Patterns of Phase Position Connectivity in Default Mode Subnetwork of Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment.

Alzheimer's disease (AD), which most commonly occurs in the elder, is a chronic neurodegenerative disease with no agreed drugs or treatment protocols at present. Amnestic mild cognitive impairment (aMCI), earlier than AD onset and later than subjective cognitive decline (SCD) onset, has a serious probability of converting into AD. The SCD, which can last for decades, subjectively complains of decline impairment in memory.

Kernel Mixture Correntropy Conjugate Gradient Algorithm for Time Series Prediction.

Kernel adaptive filtering (KAF) is an effective nonlinear learning algorithm, which has been widely used in time series prediction. The traditional KAF is based on the stochastic gradient descent (SGD) method, which has slow convergence speed and low filtering accuracy. Hence, a kernel conjugate gradient (KCG) algorithm has been proposed with low computational complexity, while achieving comparable performance to some KAF algorithms, e.

Comparative Study of Pore Formation Energy by High Intensity, Nanosecond Electrical Pulse.

Nanosecond, high intensity electric pulses create nanopores in the cell membrane. Pore formation energy is probed by taking account of the strain energy based on the continuum model. Maxwell stress acting on the cell membrane is included in the 3D model calculation as well as the effect of membrane curvature.

Simulation of pH-Regulated Electrokinetic Ion Transport in Nanopores with Polyelectrolyte Brushes.

Nanopores with polyelectrolyte brushes in a pHregulated ion channel have been simulated. Electrokinetic ion transport was studied taking account of the effects of pH, presence of multiple ionic species and bulk ionic concentration. A continuum-based model that composed of coupled Poisson-Nernst- Planck equations for the ionic mass transport with electric potential, and Navier-Stokes and Brinkman equations for the fluid flow, were employed.

Ionic Conduction in Biological Nanopores Created by Ultrashort9 High-Intensity Pulses.

Ultrashort, high-intensity electric pulses open nanopores in biological cell membranes. Ion transport in nanopore is analyzed using a numerical method that couples the Nernst-Planck equations for ionic concentrations, the Poisson equation for the electric potential, and Navier-Stokes equations for the fluid flow. Roles of the applied bias, pore size, as well as the surface charge lining the membrane are comprehensively examined through I-V characteristics, conductance variations of the pore.

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm.

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems.

The stability of memristive multidirectional associative memory neural networks with time-varying delays in the leakage terms via sampled-data control.

In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model.

Dependability enhancing mechanisms for integrated clinical environments.

In this article, we present a set of lightweight mechanisms to enhance the dependability of a safety-critical real-time distributed system referred to as an integrated clinical environment (ICE). In an ICE, medical devices are interconnected and work together with the help of a supervisory computer system to enhance patient safety during clinical operations. Inevitably, there are strong dependability requirements on the ICE.

High-throughput state-machine replication using software transactional memory.

State-machine replication is a common way of constructing general purpose fault tolerance systems. To ensure replica consistency, requests must be executed sequentially according to some total order at all non-faulty replicas. Unfortunately, this could severely limit the system throughput.

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme.

Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field.