Material encapsulation in poly(methyl methacrylate) shell: A review

Material encapsulation is a relatively new technique for coating a micro/nanosize particle or droplet with polymeric or inorganic shell. Encapsulation technology has many applications in various fields including drug delivery, cosmetic, agriculture, thermal energy storage, textile, and self-healing polymers. Poly(methyl methacrylate) (PMMA) is widely used as shell material in encapsulation due to its high chemical stability, biocompatibility, nontoxicity, and good mechanical properties. The main approach for micro/nanoencapsulation of materials using PMMA as shell comprises emulsion-based techniques such as emulsion polymerization and solvent evaporation from oil-in-water emulsion. In the present review, we first focus on the encapsulation techniques of liquid materials with PMMA shell by analyzing the effective processing parameters influencing the preparation of PMMA micro/nanocapsules. We then describe the morphology of PMMA capsules in emulsion systems according to thermodynamic relations. The techniques to investigation of mechanical properties of capsule shell and the release mechanisms of core material from PMMA capsules were also investigated. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48039.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Synthesis and Characterization of Co Nanocomposite Based on Poly(benzimidazole-amide) Matrix and Their Behavior as Catalyst in Oxidation Reaction

2,6-Bis(5-amino-1H-benzimidazol-2-yl)pyridine was prepared and characterized by Fourier transform infrared spectroscopy, elemental analysis, ¹H-NMR, and ¹³C-NMR spectroscopic methods. Then a new poly(benzimidazole-amide) was synthesized by polymerization of the corresponding diamine and isophthalic acid. The obtained poly(benzimidazole-amide) exhibited good yield and high thermal stability. Due to the existence of benzimidazole moieties in polymer’s structure, it has the tendency to form complexes with metal ions. So, a new poly(benzimidazole-amide)/Co nanocomposite was prepared. Morphological studies revealed that metal nanoparticles were dispersed in the polymer matrix without any aggregation. poly(benzimidazole-amide)/Co nanocomposite was used as a catalyst in the oxidation of ethyl benzene to acetophenone with tert-butyl hydroperoxide.     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

Wireless Personal Communications - The integration of the Internet of Things (IoT) and cloud environment has led to the creation of Cloud of Things, which has given rise to new challenges in IoT...     

Classification of DoS Attacks in Smart Underwater Wireless Sensor Network

As per the most recent literature, Orthogonal Frequency Division Multiplexing (OFDM), a multi access technique, is considered most suitable for the 3G, 4G and 5G techniques in high speed wireless communication. What made OFDM most popular is its ability to deliver high bandwidth efficiency and superior data rate. Besides it, high value of peak to average power ratio (PAPR) and Inter Carrier Interference (ICI) are the challenges to tackle down via appropriate mitigation scheme. As a research contribution in the present work, an improved self-cancellation (SC) technique is designed and simulated through Simulink to mitigate the effect of ICI. This novel proposed technique (Improved SC) is designed over discrete wavelet transform (DWT) based OFDM and compared with conventional SC scheme over different channel conditions i.e. AWGN and Rayleigh fading environments. It is found that proposed DWT-OFDM with Improved SC scheme outperforms conventional SC technique significantly, under both AWGN and Rayleigh channel conditions. Further, in order to justify the novelty in the research contribution, a Split-DWT based Simulink model for Improved SC scheme is investigated to analyse the BER performance. This Split-DWT based Simulink model presented here foretells the future research potential in wavelet hybridization of OFDM to side-line ICI effects more efficiently.

     

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace. Similarly, the field of health informatics is also considered as an extremely important field. This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis. The developed system has two front ends, the first dedicated for the user to perform the photographing of the trace report. Once the photographing is complete, mobile computing is used to extract the signal. Once the signal is extracted, it is uploaded into the server and further analysis is performed on the signal in the cloud. Once this is done, the second interface, intended for the use of the physician, can download and view the trace from the cloud. The data is securely held using a password-based authentication method. The system presented here is one of the first attempts at delivering the total solution, and after further upgrades, it will be possible to deploy the system in a commercial setting.