Analysis of malware propagation behavior in Social Internet of Things

Social Internet of Things (SIoT) is an evolution of the Internet of Things, where objects interact socially with each other in the sense that they can independently establish new relationships, offer, or discover services, in order to accomplish their tasks with minimum involvement of the user. This additional convenience comes at the expense of higher risk of speeding up malware propagation through the dynamically created relationships. Because of the undesirable effects of malware (eg, disruption of device operation), it is essential to understand their spreading behavior in order to minimize their negative impacts. In this paper, we analyze malware propagation behavior in SIoT and investigate different parameters that influence spreading of malware. Toward that end, a simulator has been developed to simulate the spreading process of malware in SIoT. Many propagation scenarios were analyzed using the proposed simulator. Simulation results show that adding more relationships in the SIoT or increasing the number of owned objects per user has increased malware spreading rate. For example, the time to infect all objects is faster by 45% when objects communicate through four relationships compared with the case when objects communicate through only two relationships in SIoT. We also investigated ways to restrict the malware spreading. Results show that preventing objects from establishing dynamic social relationship slows down the infection by 40% compared with the next best scenario (ie, blocking co‐location relationships), which means more time for vendors to patch up their products.     

Comparison of total ozone column observations from space-borne Ozone Monitoring Instrument with ground-based Dobson Ozone Spectrophotometer measurements at an urban location in Indo-Gangetic Basin

This article presents a comparison analysis of OMIT (Ozone Monitoring Instrument retrieved overpass total ozone column (TOC)), and DOST (Dobson Ozone Spectrophotometer observed TOC) over Delhi during a period from October 2004 to June 2011. Megacity Delhi, located in Indo-Gangetic Basin, is an important site for comparison of ground-based and satellite retrieved TOCs due to significant anthropogenic emissions of ozone precursors, large shift in seasons, and large-scale crop residue burning in the region. DOST and OMIT data show an overall bias of 3.07% and significant correlation with coefficient of determination R² = 0.73. Large seasonal fluctuations in the biases and correlations have been observed ranging from 2.46% (winter) to 3.82% (spring), and R² = 0.84 (winter) to R² = 0.09 (summer), respectively. The large biases are attributed to changes in temperature, cloud cover, pollutants emissions from urban area, and crop-residue burning events. We also find notable variations in correlations between the datasets due to the varying burden of absorbing aerosols from open field crop-residue burning. The R² has changed from 0.67 (for aerosol optical depth, AOD 1.5–3.5) to 0.77 (for AOD 0–0.99). The dependence of the bias on solar zenith angle, cloud fraction, and satellite distance is also discussed. A simple linear regression analysis is applied to check the linkage between DOST and OMIT. The influence of atmospheric air temperature and relative humidity on OMIT at different pressure levels between 1000 and 20 hPa has been discussed.     

Synthesis of surface imprinted polymers based on wrinkled flower-like magnetic graphene microspheres with favorable recognition ability for BSA

Nowadays,the employing of molecular imprinting technique in the analysis and separation of proteins from complex biological samples has been widely favored by researchers.To enrich the types of surface protein imprinted materials and expand the application fields of graphene materials,novel surface molecular imprinted polymers (MIPs) based on magnetic graphene microspheres Fe304@rGO@MIPs are first synthesized in this paper.Fe304@rGO@MIPs are prepared by oxidative self-polymerization of dopamine on the surface of magnetic graphene (Fe304@rGO) composite microspheres.Bovine serum albumin (BSA) is selected as protein template.Fe3O4@rGO microspheres with wrinkled flower-like structure are obtained by compounding Fe3O4 and graphene oxide in an appropriate ratio via the method of high-temperature reduction self-assembly.The microspheres exhibit promising dispersibility,high external surface area,rich pore structure,and sufficient magnetic properties.These advantages not only prevent the agglomeration of imprinted microspheres in the aqueous phase,which is conducive to contact and static adsorption,but also increase the amount of protein imprinting.Additionally,sufficient magnetic properties ensure fast and effective separation of the adsorbents.While the adsorption capacity is increased,the separation procedure becomes simple.The binding capacity of Fe304@rGO@MIPs for BSA can reach 317.58 mg/g within 60 min,and the imprinting factor (IF) is 4.24.More importantly,Fe3O4@rGO@MIPs can specifically recognize the target BSA from the mixed proteins and the actual sample.There is no significant decrease in the adsorption amount,IF,and magnetic properties after eight runs.It is promising to be used in the separation of proteins from the actual biological samples.     

Aluminum‐Tailored Energy Level and Morphology of Co3−xAlxO4 Porous Nanosheets toward Highly Efficient Electrocatalysts for Water Oxidation

Tuning energy levels plays a crucial role in developing cost‐effective, earth‐abundant, and highly active oxygen evolution catalysts. However, to date, little attention has been paid to the effect of using heteroatom‐occupied lattice sites on the energy level to engineer electrocatalytic activity. In order to explore heteroatom‐engineered energy levels of spinel Co₃O₄ for highly‐effective oxygen electrocatalysts, herein Al atoms are directly introduced into the crystal lattice by occupying the Co²⁺ ions in the tetrahedral sites and Co³⁺ ions in the octahedral sites (denoted as Co²⁺_Td and Co³⁺_Oh, respectively). Experimental and theoretical simulations demonstrate that Al³⁺ ions substituting Co²⁺_Td and Co³⁺_Oh active sites, especially Al³⁺ ions occupying the Co²⁺_Td sites, optimizes the adsorption, activation, and desorption features of intermediate species during oxygen evolution reaction (OER) processes. As a result, the optimized Co_1.75Al_1.25O₄ nanosheet exhibit unprecedented OER activity with an ultralow overpotential of 248 mV to deliver a current of 10 mA cm^–2, among the best Co‐based OER electrocatalysts. This work should not only provide fundamental understanding of the effect of Al‐occupied different Co sites in Co_3–xAl_xO₄ composites on OER performance, but also inspire the design of low‐cost, earth‐abundant, and high‐active electrocatalysts toward water oxidation.     

A First-Principles Calculation of Electronic Structure and dHvA Simulation of the Novel Superconductor Re<Subscript>6</Subscript>Hf

In this paper, we performed a density functional theory calculation study on the newly discovered superconductor Re₆Hf containing anti-symmetric spin-orbit coupling (ASOC) properties. The calculated densities of electronic states demonstrate that the d–d interaction of Re₆Hf is much stronger than that in Re₆Zr. A fully relativistic DFT study shows a special performance of densities of states for the spin-up and spin-down electrons. Band structure calculations indicate that the lifting at several high symmetric k points and plenty of splits in band structures yield significant topological transitions in the Fermi surfaces of Re₆Hf. The energy splitting of bands caused by ASOC are estimated to be 34 and 44 meV, respectively. The de Hass-van Alphen effect simulation is demonstrated and analyzed.     

Application of moisture activated dry granulation (MADG) process to develop high dose immediate release (IR) formulations

Moisture activated dry granulation (MADG) method was used to develop IR tablets with cohesive, fluffy and high dose drugs. To evaluate this approach, three drugs: metformin hydrochloride, acetaminophen and ferrous ascorbate were selected as model compound along with three binders: maltodextrin DE16, PVP K 12 and HPC. The granules were generated using MADG method and tablets were prepared using rotary tablet press. The granules and tablets were characterized for particle size analysis, flow properties, tablet hardness, friability, moisture content, dissolution study, disintegration time and stability study. All results were found to be within acceptable limits. Development of all formulation tablets were found as best fitted for an immediate release of Metformin hydrochloride, acetaminophen and ferrous ascorbate. MADG delivered a robust manufacturing process for generation of granules with excellent flowability. The tablets prepared using this method were found to show better content uniformity, good compactability and low friability. Use of this approach aids to lower the amount of excipients used to overcome physiochemical limitation of the drug substances and there side effects. Both drying and milling steps in wet granulation were not required for MADG process. MADG became a cost effective process which could lead to reduced total tablet size and also save time.     

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

The main goal of this research is to develop and apply a robust Artificial Neural Networks (ANNs) model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer. Concentrated salt and sugar solutions were used as the osmotic solutions at 27C. Series of experiments were performed at various temperatures of 35C, 40C, and 55C for conduction heat input under vacuum ( −760 mm Hg) condition. Some experiments were also performed in a pure vacuum without heat addition. Dimensionless moisture content (DMC), effective moisture diffusivity, and mass flux were considered as the performance parameters in this study. Results revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87% compared to 5.3% in the salt solution. Furthermore, a significant enhancement of drying performance of about 27% in DMC was observed for both samples at vacuum and 40C compared to pure vacuum drying conditions. Using the experimental data, a robust artificial neural network (ANN) was proposed to describe the osmotic dehydration’s behavior on the drying process. The ANN model outputs are the dimensionless moisture contents (DMC), the diffusivity, and the mass flux. Whereas the ANN inputs were the drying time, the percent of sugar solution, and the percent of salt solution. For the ANN apple’s model, the minimum root mean square error (RMSE) values were 0.0261, 0.0349 and 0.0406, for DMC, diffusivity, and mass flux, respectively. Whereas the best correlation coefficients of the above three parameters’ determination values were 0.9909, 0.9867 and 0.9744, respectively. For the ANN potato’s model, the minimum RMSE values were 0.0124, 0.0140 and 0.0333, for DMC, diffusivity, and mass flux, respectively. And the best correlation coefficients of the parameters’ values were found 0.9969, 0.9968 and 0.9736, respectively. Accordingly, the ANN model’s prediction has a perfect agreement with the experimental dataset, which confirmed the ANN model’s accuracy.     

Profiling Casualty Severity Levels of Road Accident Using Weighted Majority Voting

To determine the individual circumstances that account for a road traffic accident, it is crucial to consider the unplanned connections amongst various factors related to a crash that results in high casualty levels. Analysis of the road accident data concentrated mainly on categorizing accidents into different types using individually built classification methods which limit the prediction accuracy and fitness of the model. In this article, we proposed a multi-model hybrid framework of the weighted majority voting (WMV) scheme with parallel structure, which is designed by integrating individually implemented multinomial logistic regression (MLR) and multilayer perceptron (MLP) classifiers using three different accident datasets i.e., IRTAD, NCDB, and FARS. The proposed WMV hybrid scheme overtook individual classifiers in terms of modern evaluation measures like ROC, RMSE, Kappa rate, classification accuracy, and performs better than state-of-the-art approaches for the prediction of casualty severity level. Moreover, the proposed WMV hybrid scheme adds up to accident severity analysis through knowledge representation by revealing the role of different accident-related factors which expand the risk of casualty in a road crash. Critical aspects related to casualty severity recognized by the proposed WMV hybrid approach can surely support the traffic enforcement agencies to develop better road safety plans and ultimately save lives.     

Deep Learning and Machine Learning for Early Detection of Stroke and Haemorrhage

Stroke and cerebral haemorrhage are the second leading causes of death in the world after ischaemic heart disease. In this work, a dataset containing medical, physiological and environmental tests for stroke was used to evaluate the efficacy of machine learning, deep learning and a hybrid technique between deep learning and machine learning on the Magnetic Resonance Imaging (MRI) dataset for cerebral haemorrhage. In the first dataset (medical records), two features, namely, diabetes and obesity, were created on the basis of the values of the corresponding features. The t-Distributed Stochastic Neighbour Embedding algorithm was applied to represent the high-dimensional dataset in a low-dimensional data space. Meanwhile,the Recursive Feature Elimination algorithm (RFE) was applied to rank the features according to priority and their correlation to the target feature and to remove the unimportant features. The features are fed into the various classification algorithms, namely, Support Vector Machine (SVM), K Nearest Neighbours (KNN), Decision Tree, Random Forest, and Multilayer Perceptron. All algorithms achieved superior results. The Random Forest algorithm achieved the best performance amongst the algorithms; it reached an overall accuracy of 99%. This algorithm classified stroke cases with Precision, Recall and F1 score of 98%, 100% and 99%, respectively. In the second dataset, the MRI image dataset was evaluated by using the AlexNet model and AlexNet + SVM hybrid technique. The hybrid model AlexNet + SVM performed is better than the AlexNet model; it reached accuracy, sensitivity, specificity and Area Under the Curve (AUC) of 99.9%, 100%, 99.80% and 99.86%, respectively.