Copper-decorated core-shell structured ordered mesoporous containing cobalt ferrite nanoparticles as high-performance heterogeneous catalyst toward synthesis of tetrazole.

The present work describes the synthesis of copper immobilization on CoFe2O4/MCM-41 as a catalyst, which is created by attaching copper and ligand (N-phenyl anthranilic acid (PA)) on the surface of CoFe2O4/MCM-41 (CoFe2O4/MCM-41/PA/Cu). The synthesized CoFe2O4/MCM-41 support and immobilized copper were identified by FTIR, TEM, VSM, SEM XRD, and nitrogen adsorption-desorption analysis. The results showed that MCM-41 silica was coated with magnetite nanoparticles and copper was successfully immobilized on this structure. The catalytic performance of synthesized catalyst was tested in the synthesis of tetrazole. It was shown that the solid catalyst exhibited a strong magnetic response and showed good catalytic activity in the synthesis of tetrazole. The catalytic test showed that copper supported on CoFe2O4/MCM-41 hybrid showed much better catalytic activity than copper supported on CoFe2O4, indicating that MCM-41 plays an important role in CoFe2O4/MCM-41 hybrid for the synthesis of tetrazole. Separation of the solid catalyst from the reaction mixture was easily performed by external magnetism without apparent mass loss. And the catalyst could be reused six times for the synthesis of heterogeneous tetrazole.

An extended Kalman filter with inequality constraints for real-time detection of intradialytic hypotension.

Intradialytic hypotension (IDH) is the most common complication of hemodialysis, affecting 15-50% of all dialysis sessions. Previously, we had presented a non-invasive Polyvinylidene Fluoride (PVDF) based sensor in the form of a ring to measure vascular tone and we showed that the morphology of the signal can be utilized to predict IDH. This paper presents an approach for analyzing the PVDF signal using extended Kalman filter (EKF) and a synthetic model that has previously been used to model the ECG signal with Gaussian functions. Moreover, a novel approach for incorporating state inequality constraints into the EKF process using a gradient projection method is introduced. The taut string algorithm was first used to estimate the outline of the signal and remove it to highlight the reflection waves. Then, the EKF was used to characterize the morphology of the signal using Gaussian functions. The amplitudes of the Gaussian functions were used as features to train a classifier. The results indicated that the PPV and NPV for the prediction were 83.33% and 100%, respectively.

A machine learning based approach for identifying traumatic brain injury patients for whom a head CT scan can be avoided.

Head CT scan is more often used to evaluate patients with suspected traumatic brain injury (TBI). However, the use of head CT scans in evaluating TBI is costly with low value endeavor. In this paper, we propose a new algorithm and a set of features to help clinicians determine which patients evaluated for TBI need a head CT scan using cost sensitive random forest (CSRF) classifier. We show that random forest (RF) and CSRF are useful methods for identifying patients likely to have a positive head CT scan. The proposed algorithm has superior diagnostic accuracy in comparison to the Canadian head CT algorithm, which is currently the most accurate and widely used algorithm for determining which TBI patients need a head CT scan. In the highest sensitivity (i.e. 100%), our method outperforms the Canadian rule in terms of specificity, accuracy and area under ROC curve using cost sensitive classifier. Clinical implementation of this algorithm can help decrease financial costs associated with Emergency Department evaluations for traumatic brain injury, while decreasing patient exposure to avoidable ionizing radiation.

Spleen Segmentation and Assessment in CT Images for Traumatic Abdominal Injuries.

Spleen segmentation is especially challenging as the majority of solid organs in the abdomen region have similar gray level range. Physician analysis of computed tomography (CT) images to assess abdominal trauma could be very time consuming and hence, automating this process can reduce time to treatment. The proposed method presented in this paper is a fully automated and knowledge based technique that employs anatomical information to accurately segment the spleen in CT images. The spleen detection procedure is proposed to locate the spleen in both healthy and injured cases. In the presence of hemorrhage and laceration, the edge merging technique is used. The accuracy of the method is measured by some criteria such as mis-segmented area, accuracy, specificity and sensitivity. The results show that the proposed spleen segmentation method performs well and outperforms other methods.