Lightweight Cryptography Algorithms for IOT Devices

Lightweight cryptography is a new concept for securing data more effectively while using fewer resources and providing greater throughput, conservatism, and low battery consumption. Every fraction second, the Internet of Things (IoT), which connects billions of objects, generates massive amounts of data. As the number of devices grows, so does the amount of data generated, and the security of that data becomes a concern. In IoT architecture, gadgets are essentially smaller and low-powered. Because of their complexity, traditional encryption methods are computationally expensive and take many rounds to encrypt, basically wasting the limited energy of IoT devices. However, a less sophisticated method may jeopardise the intended fidelity. There are various lightweight cryptography techniques available, and we choose one of the symmetric encryption techniques known as Advanced Encryption Standard (AES). The speed of this algorithm is six times that of triple DES.

Uncertainty Quantification of Deep Neural Network-Based Turbulence Model for Reactor Transient Analysis

Deep neural networks (DNNs) have demonstrated good performance in learning highly non-linear relationships in large datasets, thus have been considered as a promising surrogate modeling tool for parametric partial differential equations (PDEs). On the other hand, quantifying the predictive uncertainty in DNNs is still a challenging problem. The Bayesian neural network (BNN), a sophisticated method assuming the weights of the DNNs follow certain uncertainty distributions, is considered as a state-of-the-art method for the UQ of DNNs. However, the method is too computationally expensive to be used in complicated DNN architectures. In this work, we utilized two more methods for the UQ of complicated DNNs, i.e. Monte Carlo dropout and deep ensemble. Both methods are computationally efficient and scalable compared to BNN. We applied these two methods to a densely connected convolutional network, which is developed and trained as a coarse-mesh turbulence closure relation for reactor safety analysis. In comparison, the corresponding BNN with the same architecture is also developed and trained. The computational cost and uncertainty evaluation performance of these three UQ methods are comprehensively investigated. It is found that the deep ensemble method is able to produce reasonable uncertainty estimates with good scalability and relatively low computational cost compared to BNN.

Designing A New Suitable Support System for Deep Virgin Coal Mining Blocks of Godavari Valley Coalfield

Importance of support system in mine design gained pace after modern way of approach took birth through many variants. A suitable support system is designed for deep virgin coal mining blocks of Godavari valley coalfield in India. This is achieved by measuring stress state by sophisticated method followed by geotechnical hazard mapping for identifying potential roof instability, predict hazards in advance and integrating the above parameters for analyze effects on stress due to different mining geometries by using numerical modelling technique. The three-dimensional numerical analysis study pours much light on effects causing instability than the 2D program. The results show that the stresses at an angle to the Level galleries are adverse. The level gallery/dip-raise may be oriented at 200 to 400 to reduce roof problems.

Solvent assisted delamination of the layered copper dithi-enothiophene-dicarboxylate (DUT-134)

The isolation of 2D nanosheets of layered metal-organic frameworks (MOFs) has become an active research field, due to the various advantages resulting from shape anisotropy. The most sophisticated method to...

A patient's temperature remote control system based on NODEMCU ESP8266

With the prompt development in wireless communication, in addition to the evolution of wireless patient monitoring system, and to reduce the death of a big number of patients who endear as a result of heart failure or sudden high temperature in children. This paper proposed a sophisticated method of observation human body parameters like temperature measurements with employ at NodeMcu ESP8266 unit in the Internet of Things (IoT). This prototype is based on NodeMcu module (a static access point that provides the WIFI network, a server, a client and a mobile access point attached to the remote surveillance object) programmed under Arduino IDE and communicating between them via the HTTP protocol.

Role of High Frequency Oscillations of Somatosensory Evoked Potentials in Deciphering Pathophysiology of Migraine

Background Habituation deficit is considered as a neurophysiological abnormality among migraineurs in the interictal period. For clear comprehension and clarity about the mechanism underlying habituation in migraine, a sophisticated method, i.e., high frequency oscillations (HFOs) evoked potentials, have been utilized. However, studies pertaining to this in the Indian context are rare. Objective The aim of the study is to determine the utility of HFO of somatosensory evoked potential (SSEP) in deciphering the pathophysiology of migraine. Materials and Methods Sixty subjects including 30 migraineurs in the interictal period and 30 healthy controls were considered for the study. Median nerve SSEP was recorded in patients and controls by standard protocols. HFO was extracted offline using the Digital zero-phase shift band-pass filtering at 450 and 750 Hz. The early and late HFOs were determined with respect to the N20 peak and were compared between the groups. Results Of total 30 migraineurs, 18 had hemicranial headache and 12 had holocranial headache. N20 latency, P25 latency, N20 onset to peak amplitude, and N20 onset to P25 amplitude were comparable in migraineurs and controls. The intraburst frequency of early HFOs in migraineurs was significantly higher (p = 0.04), whereas the peak-to-peak amplitude was significantly lower (p = 0.001). Conclusion Early HFOs on SSEP represent the thalamocortical excitatory drive in migraineurs. Overall, the study reports that reduced amplitude of early HFOs in the interictal period suggest reduced thalamocortical drive in migraineurs.

A Sophisticated Method of the Mechanical Design of Cable Accessories Focusing on Interface Contact Pressure

The most critical positions of a prefabricated cable accessory, from the electrical point of view, are the interfaces between the stress cone and its surroundings. Accordingly, the contact pressure on those interfaces needs to be carefully designed to assure both good dielectric strength and smooth installation of the stress cone. Nevertheless, since stress cones made from rubber are under large deformation after installation, their internal stress distribution is neither practical to measure directly by planting sensors, nor feasible to compute accurately with the conventional theory of linear structural mechanics. This paper presents one sophisticated method for computing the mechanical stress distribution in rubber stress cones of cable accessories by employing hyperelastic models in a computation model based on the finite element method. This method offers accurate results for rubber bodies of complex geometries and large deformations. Based on the method, a case study of a composite prefabricated termination for extruded cables is presented, and the sensitivity analysis is given as well.

Bovine Cryptosporidiosis and the Egyptian Diarrheic Buffalo Calves

The negative impact of diarrhea on livestock health is well known, Cryptosporidium, is one of the protozoan that causes diarrhea in calves especially buffalo calves. Some species of Cryptosporidium represent a zoonotic hazard. This study aimed to distinguish the potential species of Cryptosporidium in affected buffalo calves and evaluate a modified technique to improve the molecular detection and identification of Cryptosporidium. Twenty buffalo calves suffered from diarrhea were enrolled in the study. The enrollment criteria depended on the results of Ziehl–Neelsen stain. Sugar floatation technique was performed followed by oocyst concentration to form a pellet for DNA extraction. Multiplex PCR was performed for identification and differentiation of Cryptosporidium Spp. The results showed a mixed infection in 4 samples; the most common type of Cryptosporidium affecting the examined buffalo calves was C.parvum (10), followed by C.bovis (7 samples) and C.andersoni (6 samples) while no C.reyne was detected in the examined samples. The zoonotic type, C.parvum was found in 50% of the total affected animals. The current study detects three Cryptosporidium spp. namely C.parvum, C. andersoni, and C.bovis linked to diarrhea in the studied buffalo calves. Mixed infection with more than one species of Cryptosporidium was present. Standard detection of oocyte in fecal samples using modified Ziehl-Neelsen stain is a simple way for diagnosis of Cryptosporidium. However, a more sophisticated method is recommended to detect and differentiate the zoonotic species in calves, as they symbolize a crucial source of human infection.

Robust Method for Diagnosis and Detection of Faults in Photovoltaic Systems Using Artificial Neural Networks

During their operation, PV systems can be subject of various faults and anomalies that could lead to a reduction in the effectiveness and the profitability of the PV systems. These faults can crash, cause a fire or stop the whole system. The main objective of this work is to present a sophisticated method based on artificial neural networks ANN for diagnosing; detecting and precisely classifying the fault in the solar panels in order to avoid a fall in the production and performance of the photovoltaic system. The work established in this paper intends in first place to propose a method to detect possible various faults in PV module using the Multilayer Perceptron (MLP) ANN network. The developed artificial neural network requires a large database and periodic training to evaluate the output parameters with good accuracy. To evaluate the accuracy and the performance of the proposed approach, a comparison is carried out with the classic method (the method of thresholding). To test the effectiveness of the proposed approach in detecting and classifying different faults, an extensive simulation is carried out using Matlab SIMULINK.

New insider threat detection method based on recurrent neural networks

<p>Insider threat is a significant challenge in cybersecurity. In comparison with outside attackers, inside attackers have more privileges and legitimate access to information and facilities that can cause considerable damage to an organization. Most organizations that implement traditional cybersecurity techniques, such as intrusion detection systems, fail to detect insider threats given the lack of extensive knowledge on insider behavior patterns. However, a sophisticated method is necessary for an in-depth understanding of insider activities that the insider performs in the organization. In this study, we propose a new conceptual method for insider threat detection on the basis of the behaviors of an insider. In addition, gated recurrent unit neural network will be explored further to enhance the insider threat detector. This method will identify the optimal behavioral pattern of insider actions.</p>