A solar and wind driven energy system for hydrogen and urea production with CO2 capturing

A novel solar PV and wind energy based system is proposed in this study for capturing carbon dioxide as well as producing hydrogen, urea and power. Both Aspen Plus and EES software packages are employed for analyses and simulations. The present system is designed in a way that PEM electrolyzer is powered by the wind turbines for hydrogen production, which is further converted into ammonia and then synthesizes urea by capturing CO₂ and additional power is supplied to the community. The solar PV is employed to power the cryogenic air separation unit and the additional power is used for the industrial purpose. In the proposed system, ammonia does not only capture CO₂ but also synthesizes urea for fertilizer industry. The amount of electrical power produced by the system is 2.14 MW. The designed system produces 518.4 kmol/d of hydrogen and synthesizes 86.4 kmol/d of urea. Furthermore, several parametric studies are employed to investigate the system performance.     

Security and key management in IoT‐based wireless sensor networks: An authentication protocol using symmetric key

Wireless sensor networks (WSN) consist of hundreds of miniature sensor nodes to sense various events in the surrounding environment and report back to the base station. Sensor networks are at the base of internet of things (IoT) and smart computing applications where a function is performed as a result of sensed event or information. However, in resource‐limited WSN authenticating a remote user is a vital security concern. Recently, researchers put forth various authentication protocols to address different security issues. Gope et al presented a protocol claiming resistance against known attacks. A thorough analysis of their protocol shows that it is vulnerable to user traceability, stolen verifier, and denial of service (DoS) attacks. In this article, an enhanced symmetric key‐based authentication protocol for IoT‐based WSN has been presented. The proposed protocol has the ability to counter user traceability, stolen verifier, and DoS attacks. Furthermore, the proposed protocol has been simulated and verified using Proverif and BAN logic. The proposed protocol has the same communication cost as the baseline protocol; however, in computation cost, it has 52.63% efficiency as compared with the baseline protocol.     

Theoretical Investigations of Properties of New Half-Heusler Compounds NiFeZ (Z = Si,Ge)

The half-Heusler alloys NiFeZ (Z = Si, Ge) are investigated theoretically in the general framework of Density Functional Theory (DFT). We have calculated structural, mechanical, elastic, electronic, magnetic, and optical properties using WIEN2k simulation package. To incorporate the electronic exchange-correlation energy we have used the GGA+U approximation. Both the compounds are found structurally stable, however, elastic properties show that NiFeSi is ductile while NiFeGe is brittle. Electronically, the compounds are found to be half-metals with small band gaps in the spin-down channels whereas spin-up channels are conducting. The total magnetic moments for both the compounds are ≈ 2μ _B. Optical properties including dielectric function, refractive index, extinction coefficient, reflectivity, conductivity, and absorption coefficient are also calculated and analyzed.     

Learning and screening errors in an EPQ inventory model for supply chains with stochastic lead time demands

This paper investigates the role of variable lead time, learning in production and screening errors in a vendor–buyer supply chain with defective items. The vendor–buyer supply chain is modelled for supplying a single item considering that the lots from vendor may contain some defective items. It is assumed that demand during lead time follows a normal distribution. Moreover, the production time at vendor’s facility is assumed to follow learning whereas buyer’s screening for defective items is prone to errors as well. Numerical examples are presented to illustrate the impact of different variables in the model. The analysis shows that delay in transportation lead time forces the buyer to carry more inventories to avoid shortages. Further, Type I error has a major impact on this cost. It was found that learning in production keeps on reducing the total cost of the supply chain up to a threshold.     

A Machine Learning Based Algorithm to Process Partial Shading Effects in PV Arrays

Solar energy is a widely used type of renewable energy. Photovoltaic arrays are used to harvest solar energy. The major goal, in harvesting the maximum possible power, is to operate the system at its maximum power point (MPP). If the irradiation conditions are uniform, the P-V curve of the PV array has only one peak that is called its MPP. But when the irradiation conditions are non-uniform, the P-V curve has multiple peaks. Each peak represents an MPP for a specific irradiation condition. The highest of all the peaks is called Global Maximum Power Point (GMPP). Under uniform irradiation conditions, there is zero or no partial shading. But the changing irradiance causes a shading effect which is called Partial Shading. Many conventional and soft computing techniques have been in use to harvest solar energy. These techniques perform well under uniform and weak shading conditions but fail when shading conditions are strong. In this paper, a new method is proposed which uses Machine Learning based algorithm called Opposition-Based-Learning (OBL) to deal with partial shading conditions. Simulation studies on different cases of partial shading have proven this technique effective in attaining MPP.     

An Efficient False-Positive Reduction System for Cerebral Microbleeds Detection

Cerebral Microbleeds (CMBs) are microhemorrhages caused by certain abnormalities of brain vessels. CMBs can be found in people with Traumatic Brain Injury (TBI), Alzheimer’s disease, and in old individuals having a brain injury. Current research reveals that CMBs can be highly dangerous for individuals having dementia and stroke. The CMBs seriously impact individuals’ life which makes it crucial to recognize the CMBs in its initial phase to stop deterioration and to assist individuals to have a normal life. The existing work report good results but often ignores false-positive’s perspective for this research area. In this paper, an efficient approach is presented to detect CMBs from the Susceptibility Weighted Images (SWI). The proposed framework consists of four main phases (i) making clusters of brain Magnetic Resonance Imaging (MRI) using k-mean classifier (ii) reduce false positives for better classification results (iii) discriminative feature extraction specific to CMBs (iv) classification using a five layers convolutional neural network (CNN). The proposed method is evaluated on a public dataset available for 20 subjects. The proposed system shows an accuracy of 98.9% and a 1.1% false-positive rate value. The results show the superiority of the proposed work as compared to existing states of the art methods.     

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.     

Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud Computing

Cloud computing has gained significant use over the last decade due to its several benefits, including cost savings associated with setup, deployments, delivery, physical resource sharing across virtual machines, and availability of on-demand cloud services. However, in addition to usual threats in almost every computing environment, cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location paradigm. Furthermore, since there are a growing number of attacks directed at cloud environments (including dictionary attacks, replay code attacks, denial of service attacks, rootkit attacks, code injection attacks, etc.), customers require additional assurances before adopting cloud services. Moreover, the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security breaches. In this study, the model based on the root of trust for continuous integration and continuous deployment is proposed, instead of only relying on a single sign-on authentication method that typically uses only id and password. The underlying study opted hardware security module by utilizing the Trusted Platform Module (TPM), which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center servers. The preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.     

Local-Tetra-Patterns for Face Recognition Encoded on Spatial Pyramid Matching

Face recognition is a big challenge in the research field with a lot of problems like misalignment, illumination changes, pose variations, occlusion, and expressions. Providing a single solution to solve all these problems at a time is a challenging task. We have put some effort to provide a solution to solving all these issues by introducing a face recognition model based on local tetra patterns and spatial pyramid matching. The technique is based on a procedure where the input image is passed through an algorithm that extracts local features by using spatial pyramid matching and max-pooling. Finally, the input image is recognized using a robust kernel representation method using extracted features. The qualitative and quantitative analysis of the proposed method is carried on benchmark image datasets. Experimental results showed that the proposed method performs better in terms of standard performance evaluation parameters as compared to state-of-the-art methods on AR, ORL, LFW, and FERET face recognition datasets.     

Egocentric visual scene description based on human-object interaction and deep spatial relations among objects

Multimedia Tools and Applications - Visual Scene interpretation is one of the major areas of research in the recent past. Recognition of human object interaction is a fundamental step...