Facile Deposition of Silver Nanoparticles on Photonic Cellulose Nanocrystals Films: A Study on Solvent Stability and Post Antibacterial Activity

Cellulose nanocrystals (CNCs) incorporated with silver nanoparticles (AgNPs) photonic films have drawn considerable attention due to their plasmonic chiroptical activity. However, the exploitation of some fundamental properties for practical use such as the affinity analysis of metal nanoparticles attached to the surface of photonic films according to the solvent compatibility and antibacterial activity under physical conditions has yet not been studied. Hence, a facile process of in situ deposition of AgNPs into the chiral structure of CNC films is proposed. CNC photonic films, cross-linked by glutaraldehyde are prepared. This interaction generated the solvents-stable photonic film with a considerable amount of unreacted aldehyde functional groups that facilitates the reduction of Ag salt to AgNPs. The formed AgNPs in the photonic films show excellent stability over immersion in various polar and non-polar solvents. The post-solvent treated photonic films display excellent contact-based antibacterial behavior against gram-negative Escherichia coli.     

Yet another cybersecurity risk assessment framework

IT systems pervade our society more and more, and we become heavily dependent on them. At the same time, these systems are increasingly targeted in cyberattacks, making us vulnerable. Enterprise and cybersecurity responsibles face the problem of defining techniques that raise the level of security. They need to decide which mechanism provides the most efficient defense with limited resources. Basically, the risks need to be assessed to determine the best cost-to-benefit ratio. One way to achieve this is through threat modeling; however, threat modeling is not commonly used in the enterprise IT risk domain. Furthermore, the existing threat modeling methods have shortcomings. This paper introduces a metamodel-based approach named Yet Another Cybersecurity Risk Assessment Framework (Yacraf). Yacraf aims to enable comprehensive risk assessment for organizations with more decision support. The paper includes a risk calculation formalization and also an example showing how an organization can use and benefit from Yacraf.

     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.     

SAPDS: self-healing attribute-based privacy aware data sharing in cloud

This paper addresses the issue of data governance in a cloud-based storage system. To achieve fine-grained access control over the outsourced data, we propose Self-Healing Attribute-based Privacy Aware Data Sharing in Cloud (SAPDS). The proposed system delegates the key distribution and management process to a cloud server without seeping out any confidential information. It facilitates data owner to restrain access of the user with whom data has been shared. User revocation is achieved by merely changing one attribute associated with the decryption policy, instead of modifying the entire access control policy. It enables authorized users to update their decryption keys followed by each user revocation, making it self-healing, without ever interacting with the data owner. Computation analysis of the proposed system shows that data owner can revoke n′ users with the complexity of O(n′). Besides this, legitimate users can update their decryption keys with the complexity of O(1).     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.     

Heterogeneous Reactor Modeling of an Industrial Multitubular Packed‐Bed Ethylene Oxide Reactor

The one‐dimensional heterogeneous model of an industrial multitubular packed‐bed ethylene oxide (EO) reactor was developed using the equation‐oriented platform Aspen Custom Modeler. Reactor operation was optimized in terms of maximized EO production and selectivity and enhanced safety related to the presence of oxygen in the EO reactor. Good agreement was found between the model results during validation against the available information under identical operating conditions. The model predicts the behavior of the EO reaction and demonstrates the extent of catalyst utilization with product distribution, product yield, by‐product formation, temperature and concentration profiles, over time and along the length of the reactor or catalyst bed. The model sensitivity studies compute the optimum feed flow, oxygen concentration, feed pressure, etc. and suggest the best operational philosophy.     

Attrition behavior of fine particles in a fluidized bed with bimodal particles: Influence of particle density and size ratio

To process the solid particulates in fluidized bed and slurry phase reactors, attrition is an inevitable consequence and is therefore one of the preliminary parameters for the catalyst design. In this paper, the mechanical degradation propensity of the zeolite catalysts (particles) was investigated in a bimodal distribution environment using a Gas Jet Attrition — ASTM standard fluidized bed test (D-5757). The experimentation was conducted in order to explore parameters affecting attrition phenomena in a bimodal fluidization. In a bimodal fluidization system, two different types of particles are co-fluidized isothermally. The air jet attrition index (AJI) showed distinct increases in the attrition rate of small particles in a bimodal fluidization environment under standard operating conditions, in comparison with single particle. A series of experiments were conducted using particles of various sizes, with large particles of different densities and sizes. Experimental results suggest that the relative density and particle size ratio have a significant influence on attrition behavior during co-fluidization. Therefore a generalized relationship has been drawn using Gwyn constants; those defined material properties of small particles. Moreover, distinct attrition incremental phenomenon was observed during co-fluidization owing to the change in collision pattern and impact, which was associated with relative particle density and size ratios.     

Investigation of improved dielectric and thermal properties of ternary nanocomposite PMMA/MXene/ZnO fabricated by in-situ bulk polymerization

Electric power system applications demand for high-temperature dielectric materials. The improved performance of polymer nanocomposites requires improvement in their thermal conductivity & stability, dielectric stability and processing technique. However, they often lose their dielectric properties with a rise in temperature. Here, we offer a solution by incorporating electrically conducting material (MXene) and semiconducting inorganic nanoparticles (ZnO NPs) into an insulating PMMA polymer matrix to maintain high dielectric constant, both at the room and high temperature. Therefore, to achieve desirable thermal and dielectric properties is the main objective of the present study based on the homogeneous distribution of the nanofillers by in-situ bulk polymerization assisted by strong sonication in the corresponding polymer. The introduction of MXene and ZnO NPs into the PMMA not only acquires a substantial increment in the dielectric constant, to attain a value 437, with minimum energy loss of 0.36 at 25 Hz, but also improves the thermal conductivity of PMMA up to 14 times by causing the reduction of thermal resistance, which is actually responsible for the poor thermal conductivity of amorphous pure PMMA polymer. More importantly, hybrid PMMA/4:2 wt% MXene:ZnO nanocomposite leads to an excellent thermal stability. Moreover, further characterization of the synthesized nanocomposites by FTIR, SEM and XRD leads to the evaluation of strong interaction of ternary components with PMMA matrix.