Control of swelling properties of polyvinyl alcohol/hyaluronic acid hydrogels for the encapsulation of chondrocyte cells

Hydrogel scaffolds for tissue engineering are important biomaterials. The target in this study was to prepare polyvinyl alcohol/hyaluronic acid hydrogels for the encapsulation of chondrocyte cells by a simple cross‐linking reaction. Control of the swelling properties and morphology of the hydrogels for cultivation of chondrocytes was studied. The hydrogels were prepared from polyvinyl alcohol and hyaluronic acid derivatives bearing primary amine and aldehyde functionalities, respectively. The formation of the hydrogel upon mixing the aqueous solutions of the polymer derivatives took place at room temperature in a few seconds. The swelling properties of the hydrogels were found to depend on the polymer concentration and degree of substitution of the modified polymers. Scanning electron microscopy studies showed that the hydrogels had a suitable porous morphology for cell encapsulation. Furthermore, in vitro cell viability tests with the hydrogels showed no cytotoxicity for chondrocytes and that the cells grew well in the hydrogel scaffolds. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42272.     

Comparison of Parallel Kinematic Machines with Three Translational Degrees of Freedom and Linear Actuation

The development of new robot structures, in particular of parallel kinematic machines(PKM), is widely systematized by different structure synthesis methods. Recent research increasingly focuses on PKM ...     

Finite Element Model Updating Using Evolutionary Strategy for Damage Detection

Abstract: Structural health monitoring through the use of finite element model updating techniques for dispersed civil infrastructures usually deals with minimizing a complex, nonlinear, nonconvex, high‐dimensional cost function with several local minima. Hence, stochastic optimization algorithms with promising performance in solving global optimization problems have received considerable attention for finite element model updating purposes in recent years. In this study, the performance of an evolutionary strategy in the finite element model updating approach was investigated for damage detection in a quarter‐scale two‐span reinforced concrete bridge system which was tested experimentally at the University of Nevada, Reno. The damage sequence in the structure was induced by a range of progressively increasing excitations in the transverse direction of the specimen. Intermediate nondestructive white noise excitations and response measurements were used for system identification and damage detection purposes. It is shown that, when evaluated together with the strain gauge measurements and visual inspection results, the applied finite element model updating algorithm of this article could accurately detect, localize, and quantify the damage in the tested bridge columns throughout the different phases of the experiment.     

An innovative methodology for detection and quantification of cracks through incorporation of depth perception

Visual inspection of structures is a highly qualitative method in which inspectors visually assess a structure’s condition. If a region is inaccessible, binoculars must be used to detect and characterize defects. Although several Non-Destructive Testing methods have been proposed for inspection purposes, they are nonadaptive and cannot quantify crack thickness reliably. In this paper, a contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced. The proposed approach utilizes depth perception to detect cracks and quantify their thickness, thereby giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution. This unique adaptive feature is especially useful for incorporating mobile systems, such as unmanned aerial vehicles, into structural inspection methods since it would allow inaccessible regions to be properly inspected for cracks. Guidelines are presented for optimizing the acquisition and processing of images, thereby enhancing the quality and reliability of the damage detection approach and allowing the capture of even the slightest cracks (e.g., detection of 0.1 mm cracks from a distance of 20 m), which are routinely encountered in realistic field applications where the camera-object distance and image contrast are not controllable.     

Color image segmentation using mean shift and improved ant clustering

To improve the segmentation quality and efficiency of color image, a novel approach which combines the advantages of the mean shift (MS) segmentation and improved ant clustering method is proposed. The regions which can preserve the discontinuity characteristics of an image are segmented by MS algorithm, and then they are represented by a graph in which every region is represented by a node. In order to solve the graph partition problem, an improved ant clustering algorithm, called similarity carrying ant model (SCAM-ant), is proposed, in which a new similarity calculation method is given. Using SCAM-ant, the maximum number of items that each ant can carry will increase, the clustering time will be effectively reduced, and globally optimized clustering can also be realized. Because the graph is not based on the pixels of original image but on the segmentation result of MS algorithm, the computational complexity is greatly reduced. Experiments show that the proposed method can realize color image segmentation efficiently, and compared with the conventional methods based on the image pixels, it improves the image segmentation quality and the anti-interference ability.     

Synergistic Functionality of Dopants and Defects in Co-Phthalocyanine/B-CN Z-Scheme Photocatalysts for Promoting Photocatalytic CO2 Reduction Reactions

The realization of solar-light-driven CO₂ reduction reactions (CO₂ RR) is essential for the commercial development of renewable energy modules and the reduction of global CO₂ emissions. Combining experimental measurements and theoretical calculations, to introduce boron dopants and nitrogen defects in graphitic carbon nitride (g-C₃N₄), sodium borohydride is simply calcined with the mixture of g-C₃N₄ (CN), followed by the introduction of ultrathin Co phthalocyanine through phosphate groups. By strengthening H-bonding interactions, the resultant CoPc/P-BNDCN nanocomposite showed excellent photocatalytic CO₂ reduction activity, releasing 197.76 and 130.32 µmol h⁻¹ g⁻¹ CO and CH₄, respectively, and conveying an unprecedented 10-26-time improvement under visible-light irradiation. The substantial tuning is performed towards the conduction and valance band locations by B-dopants and N-defects to modulate the band structure for significantly accelerated CO₂ RR. Through the use of ultrathin metal phthalocyanine assemblies that have a lot of single-atom sites, this work demonstrates a sustainable approach for achieving effective photocatalytic CO₂ activation. More importantly, the excellent photoactivity is attributed to the fast charge separation via Z-scheme transfer mechanism formed by the universally facile strategy of dimension-matched ultrathin (≈4 nm) metal phthalocyanine-assisted nanocomposites.     

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods. Because of the dynamic nature of electrocardiogram (ECG) data, traditional handcrafted approaches are difficult to execute, making the machine learning (ML) solutions more appealing. Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives. Cardiac arrhythmia classification and prediction have greatly improved in recent years. Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish. Every year, it is one of the main reasons of mortality for both men and women, worldwide. For the classification of arrhythmias, this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors (KNN) classifier. The proposed method makes advantage of the UCI repository, which has a 279-attribute high-dimensional cardiac arrhythmia dataset. The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features. The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients. This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature. The achieved classification accuracy using the proposed approach is 99.8%.     

Minimal Architecture Lithium Batteries: Toward High Energy Density Storage Solutions

The coupling of thick and dense cathodes with anode-free lithium metal configuration is a promising path to enable the next generation of high energy density solid-state batteries. In this work, LiCoO₂ (30 µm)/LiPON/Ti is considered as a model system to study the correlation between fundamental electrode properties and cell electrochemical performance, and a physical model is proposed to understand the governing phenomena. The first cycle loss is demonstrated to be constant and independent of both cathode thickness and anode configuration, and only ascribed to the diffusion coefficient's abrupt fall at high lithium contents. Subsequent cycles achieve close to 100% coulombic efficiency. The examination of the effect of cathode thickness demonstrate a nearly linear correlation with areal specific capacity for sub-100 µm LiCoO₂ and 0.1 mA cm⁻² current density. These findings bring new insights to better understand the energy density limiting factors and to suggest potential optimization approaches.     

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.     

Sailfish Optimization with Deep Learning Based Oral Cancer Classification Model

Recently, computer aided diagnosis (CAD) model becomes an effective tool for decision making in healthcare sector. The advances in computer vision and artificial intelligence (AI) techniques have resulted in the effective design of CAD models, which enables to detection of the existence of diseases using various imaging modalities. Oral cancer (OC) has commonly occurred in head and neck globally. Earlier identification of OC enables to improve survival rate and reduce mortality rate. Therefore, the design of CAD model for OC detection and classification becomes essential. Therefore, this study introduces a novel Computer Aided Diagnosis for OC using Sailfish Optimization with Fusion based Classification (CADOC-SFOFC) model. The proposed CADOC-SFOFC model determines the existence of OC on the medical images. To accomplish this, a fusion based feature extraction process is carried out by the use of VGGNet-16 and Residual Network (ResNet) model. Besides, feature vectors are fused and passed into the extreme learning machine (ELM) model for classification process. Moreover, SFO algorithm is utilized for effective parameter selection of the ELM model, consequently resulting in enhanced performance. The experimental analysis of the CADOC-SFOFC model was tested on Kaggle dataset and the results reported the betterment of the CADOC-SFOFC model over the compared methods with maximum accuracy of 98.11%. Therefore, the CADOC-SFOFC model has maximum potential as an inexpensive and non-invasive tool which supports screening process and enhances the detection efficiency.