A Multi-Stage Secure IoT Authentication Protocol

The Internet of Things (IoT) is a network of heterogeneous and smart devices that can make decisions without human intervention. It can connect millions of devices across the universe. Their ability to collect information, perform analysis, and even come to meaningful conclusions without human capital intervention matters. Such circumstances require stringent security measures and, in particular, the extent of authentication. Systems applied in the IoT paradigm point out high-interest levels since enormous damage will occur if a malicious, wrongly authenticated device finds its way into the IoT system. This research provides a clear and updated view of the trends in the IoT authentication area. Among the issues covered include a series of authentication protocols that have remained research gaps in various studies. This study applies a comparative evaluation of authentication protocols, including their strengths and weaknesses. Thus, it forms the foundation in the IoT authentication field of study. In that direction, a multi authentication architecture that involves secured means is proposed for protocol authentication. Informal analysis can affect the security of the protocols. Burrows-Abadi-Needham (BAN) logic provides proof of the attainment of mutual authentication. NS3 simulator tool is used to compare the performance of the proposed protocol to verify the formal security offered by the BAN logic.     

An Indo-Soviet experiment on anmhd generator test section at the Soviet U-02 facility

This paper summarizes the major results of the joint Indo-Soviet experiment for testing the Indianmhd generator channel section, designed and fabricated at the Bhabha Atomic Research Centre, Bombay, which was carried out at the U-02 facility in Moscow, USSR, in May 1980. The total test duration was 65 hours and included electrophysical tests and life tests under applied electric fields. The main purpose of the tests was to substantiate the physical concepts, computer codes, design features and special processing techniques involved in the development ofmhd generators for the Indian pilot plant at Tiruchirapalli. The experimental observations on the phenomena of heat transfer to the walls, gas dynamics in the channel, electrical characteristics of the generator and near-electrode processes including the analysis of arc spots correlate with the theoretical estimates based on present understanding of the physical processes occuring in similarmhd generators. The post-operational inspection of the channel section and extensive investigation of materials through microscopic analysis, chemical analysis and x-ray analysis are also reported in this paper. The joint test programme has clearly demonstrated the definite operating capability of the test section and has given sufficient information and encouragement for building better and improved channels for the future.     

Joint physical and link layer error control analysis for nanonetworks in the Terahertz band

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. The limited capabilities of individual nanomachines and the Terahertz (THz) band channel behavior lead to error-prone wireless links. In this paper, a cross-layer analysis of error-control strategies for nanonetworks in the THz band is presented. A mathematical framework is developed and used to analyze the tradeoffs between Bit Error Rate, Packet Error Rate, energy consumption and latency, for five different error-control strategies, namely, Automatic Repeat reQuest (ARQ), Forward Error Correction (FEC), two types of Error Prevention Codes (EPC) and a hybrid EPC. The cross-layer effects between the physical and the link layers as well as the impact of the nanomachine capabilities in both layers are taken into account. At the physical layer, nanomachines are considered to communicate by following a time-spread on-off keying modulation based on the transmission of femtosecond-long pulses. At the link layer, nanomachines are considered to access the channel in an uncoordinated fashion, by leveraging the possibility to interleave pulse-based transmissions from different nodes. Throughout the analysis, accurate path loss, noise and multi-user interference models, validated by means of electromagnetic simulation, are utilized. In addition, the energy consumption and latency introduced by a hardware implementation of each error control technique, as well as, the additional constraints imposed by the use of energy-harvesting mechanisms to power the nanomachines, are taken into account. The results show that, despite their simplicity, EPCs outperform traditional ARQ and FEC schemes, in terms of error correcting capabilities, which results in further energy savings and reduced latency.     

Carbon Nanotube (10, 0) and Silicon Nanotube (7, 0) as a Novel Material for Drug Delivery of Substituted Eugenols as Antioxidant Drugs

Silicon - The potential of CNT(10, 0) and SiNT(7, 0) and their metal doped CNT(10, 0) and SiNT(7, 0) to adsorb the NH2, OH and Ethyl eugenols are examined. The antioxidant potential of NH2-, OH-...     

A New Multi-Agent Feature Wrapper Machine Learning Approach for Heart Disease Diagnosis

Heart disease (HD) is a serious widespread life-threatening disease. The heart of patients with HD fails to pump sufficient amounts of blood to the entire body. Diagnosing the occurrence of HD early and efficiently may prevent the manifestation of the debilitating effects of this disease and aid in its effective treatment. Classical methods for diagnosing HD are sometimes unreliable and insufficient in analyzing the related symptoms. As an alternative, noninvasive medical procedures based on machine learning (ML) methods provide reliable HD diagnosis and efficient prediction of HD conditions. However, the existing models of automated ML-based HD diagnostic methods cannot satisfy clinical evaluation criteria because of their inability to recognize anomalies in extracted symptoms represented as classification features from patients with HD. In this study, we propose an automated heart disease diagnosis (AHDD) system that integrates a binary convolutional neural network (CNN) with a new multi-agent feature wrapper (MAFW) model. The MAFW model consists of four software agents that operate a genetic algorithm (GA), a support vector machine (SVM), and Naïve Bayes (NB). The agents instruct the GA to perform a global search on HD features and adjust the weights of SVM and BN during initial classification. A final tuning to CNN is then performed to ensure that the best set of features are included in HD identification. The CNN consists of five layers that categorize patients as healthy or with HD according to the analysis of optimized HD features. We evaluate the classification performance of the proposed AHDD system via 12 common ML techniques and conventional CNN models by using a cross-validation technique and by assessing six evaluation criteria. The AHDD system achieves the highest accuracy of 90.1%, whereas the other ML and conventional CNN models attain only 72.3%–83.8% accuracy on average. Therefore, the AHDD system proposed herein has the highest capability to identify patients with HD. This system can be used by medical practitioners to diagnose HD efficiently.     

Distributed Competitive Decision Making Using Multi-Armed Bandit Algorithms

Wireless Personal Communications - This paper tackles the problem of Opportunistic Spectrum Access (OSA) in the Cognitive Radio (CR). The main challenge of a Secondary User (SU) in OSA is...     

Transformations of DHEA and its metabolites by rat liver

Because dehydroepiandrosterone (DHEA) has a wide variety of weak beneficial effects in experimental animals and humans, we searched for metabolites of this steroid in the hope of finding more active compounds that might qualify for the title "steroid hormone." Incubation of DHEA with rat liver homogenate fortified with energy-yielding substrates resulted in rapid hydroxylation at the 7alpha-position of the molecule and subsequent conversion to other 7-oxygenated steroids in the sequence DHEA --> 7alpha-hydroxyDHEA --> 7-oxoDHEA --> 7beta-hydroxyDHEA, with branching to diols, triols, and sulfate esters. The ability of these metabolites to induce the formation of liver thermogenic enzyme activity increased from left to right in that sequence. A total of 25 different steroids were characterized, and at least six additional structures that are currently under study were produced from DHEA. 7-OxoDHEA is more effective than DHEA in enhancing memory performance in old mice and in reversing the amnesic effects of scopolamine.     

The effect of transport on air quality in urban areas of Syria

Statistics show that the number of cars per capita in Syria is still low, but that the figure has more than doubled since 2004. Syria also suffers from inadequate public transport provision, poor infrastructure and the absence of suitable traffic management systems, with the average speed of road transport in Damascus at about 4–5 km/h. Only until very recently, a comprehensive network for the continuous monitoring of air pollutants has been lacking. This paper reviews, collates and synthesises the results of numerous studies of Syrian road transport, with an emphasis on air pollution from Syria's transport and energy production sectors. It is revealed that what studies that have been done show that the air quality in Syrian urban areas falls below established national air quality standards, especially during winter when the demand for heating is high. The paper proposes a number of suggestions to improve air quality in Syria by using greener and more public transport, promoting and incentivising rational and efficient energy consumption in all sectors, taking advantage of available renewable energy resources, establishing an active network for routine measurement of pollution, setting local emissions standards that are in line with international standards and which are supported by the imposition of penalties, fines or taxation on polluting agents.