Cognitive behavioral stress management effectively facilitates neurologic recovery, alleviates mental distress, and elevates health status in acute ischemic stroke patients.

Cognitive behavioral stress management (CBSM) relieves physical and psychological burdens in patients with some central nervous system diseases, while its utility in acute ischemic stroke (AIS) patients is unclear. This study aimed to explore the effect of CBSM on neurologic recovery and psychosomatic health in AIS patients. Totally, 176 naive AIS patients were randomized into routine care (RC) group (n=88) and CBSM group (n=88) to receive a 3-month corresponding intervention.

Electrical Properties of Human Lung Nodules In Vitro From 100 Hz to 100 MHz.

Objective: The incidence of pulmonary nodules has been increasing over the past 30 years. Different types of nodules are associated with varying degrees of malignancy, and they engender inconsistent treatment approaches. Therefore, correct distinction is essential for the optimal treatment and recovery of the patients.

Effect of education and muscle relaxation program on anxiety, depression and care burden in caregivers of acute stroke survivors: A randomized, controlled study.

This study intended to discover the effect of education and muscle relaxation (EMR) program on anxiety, depression and care burden among caregivers of acute stroke survivors.This randomized, controlled study enrolled a total of 110 caregivers of first-ever acute stroke patients, and randomly assigned to EMR (N = 55) and control (N = 55) groups. The caregivers in the EMR group received 12-month health education and progressive muscle relaxation, and those in control group were provided common rehabilitation advices.

Clustering-Based Component Fraction Estimation in Solid-Liquid Two-Phase Flow in Dredging Engineering.

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid-liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics.

Data preprocessing methods for electrical impedance tomography: a review.

Objective: Electrical impedance tomography (EIT) is a promising measurement technique in applications, especially in industrial monitoring and clinical diagnosis. However, two major drawbacks exist that limit the spatial resolution of reconstructed EIT images, i.e.

Solid Concentration Estimation by Kalman Filter.

One of the major tasks in process industry is solid concentration (SC) estimation in solid-liquid two-phase flow in any pipeline. The -ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements and inaccurate density baseline. Alternatively, under various conditions there are a tremendous amount of indirect measurements from other sensors that can be used to adjust the accuracy of SC estimation.

Optimal similarity norm for electrical tomography based on Bregman divergence.

Electrical Tomography (ET) is an advanced visualization technique, which can reconstruct all targets in an investigated field based on boundary measurements. Since the spatial resolution in the ET process can be greatly affected by the selected similarity norm, different norms may result in different ET time and spatial resolutions. In the tomographic applications nowadays, Bregman divergence (BD) has attracted increasing attention.

The Association Among Medication Beliefs, Perception of Illness and Medication Adherence in Ischemic Stroke Patients: A Cross-Sectional Study in China.

Purpose: To examine the association and the mediating effect among medication beliefs, perception of illness, and medication adherence in ischemic stroke patients.

Patients And Methods: This is a cross-sectional study, 306 ischemic stroke patients recruited from The Second Affiliated Hospital of Harbin Medical University, China between June 2018 and October 2018. The Beliefs about Medications Questionnaire (BMQ) was used to assess a patient's beliefs about medication.

A general method for evaluating the effects of air pollutants on lung cancer prevalence.

It is widely accepted that some air pollutants are related to lung cancer prevalence. An effective method is proposed to quantitatively evaluate the effects of air pollutants and the interactions between them. The method consisted of three parts: data decomposition, comparable data generation and relationship inference.

Amplitude demodulation for electrical capacitance tomography based on singular value decomposition.

Amplitude demodulation is essential in image reconstruction for electrical capacitance tomography (ECT). In this paper, an amplitude demodulation method is proposed based on singular value decomposition (SVD), which can substitute the role of phase-sensitive demodulation in ECT. First, an M × N Hankel matrix is constructed based on a set of discrete samples.

Singular value decomposition based impulsive noise reduction in multi-frequency phase-sensitive demodulation of electrical impedance tomography.

As an important means in electrical impedance tomography (EIT), multi-frequency phase-sensitive demodulation (PSD) can be viewed as a matched filter for measurement signals and as an optimal linear filter in the case of Gaussian-type noise. However, the additive noise usually possesses impulsive noise characteristics, so it is a challenging task to reduce the impulsive noise in multi-frequency PSD effectively. In this paper, an approach for impulsive noise reduction in multi-frequency PSD of EIT is presented.

Optimal three-dimensional reconstruction for lung cancer tissues.

The existing three-dimensional (3D) x-ray reconstruction methods for lung cancer tissue reconstruct the investigated objects based on a series of two-dimensional (2D) image sections and a chosen 3D reconstruction algorithm. However, because these procedures apply the same segmentation method for all 2D image sections, they may not achieve the optimal segmentation for each section. As a result, the reconstructed 3D images have limited spatial resolution.

Relationships between lung cancer incidences and air pollutants.

Background: Statistics on lung cancer incidences and air pollutants show a strong correlation between air pollutant concentrations and pulmonary diseases. And environmental effects on lung cancer incidences remain highly unknown and uncertain in China.

Objective: This study aims to measure the relationships between different air pollutants and lung cancer incidences in Tianjin.

Classification of normal and cancerous lung tissues by electrical impendence tomography.

Biological tissue impedance spectroscopy can provide rich physiological and pathological information by measuring the variation of the complex impedance of biological tissues under various frequencies of driven current. Electrical Impedance Tomography (EIT) technique can measure the impedance spectroscopy of biological tissue in medical field. Before application, a key problem must be solved on how to generally distinguish normal tissues from the cancerous in terms of measurable EIT data.

An effective measured data preprocessing method in electrical impedance tomography.

As an advanced process detection technology, electrical impedance tomography (EIT) has widely been paid attention to and studied in the industrial fields. But the EIT techniques are greatly limited to the low spatial resolutions. This problem may result from the incorrect preprocessing of measuring data and lack of general criterion to evaluate different preprocessing processes.

Using Greedy algorithm: DBSCAN revisited II.

The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, Greedy algorithm substitutes for R(*)-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it.