Design and Implementation of a State-Driven Operating System for Highly Reconfigurable Sensor Networks

Due to the low-cost and low-power requirement in an individual sensor node, the available computing resources turn out to be very limited like small memory footprint and irreplaceable battery power. Sensed data fusion might be needed before being transmitted as a tradeoff between procession and transmission in consideration of saving power consumption. Even worse, the application program needs to be complicated enough to be self-organizing and dynamically reconfigurable because changes in an operating environment continue even after deployment. State-driven operating system platform offers numerous benefits in this challenging situation. It provides a powerful way to accommodate complex reactive systems like diverse wireless sensor network applications. The memory usage can be bounded within a state transition table. The complicated issues like concurrency control and asynchronous event handling capabilities can be easily achieved in a well-defined behavior of state transition diagram. In this paper, we present an efficient and effective design of the state-driven operating system for wireless sensor nodes. We describe that the new platform can operate in an extremely resource constrained situation while providing the desired concurrency, reactivity, and reconfigurability. We also compare the executing results after comparing some benchmark test results with those on TinyOS

Novel ZnO-Ag/MWCNT nanocomposite for the photocatalytic degradation of phenol

A novel Ag-doped ZnO nanoparticle with different amount of multi-walled carbon nano tubes (MWCNTs) was developed, aiming to shift the band edge toward longer wavelength. New particles were produced in a simple wet synthesis method and assessed toward the removal of phenol under UV-A illumination. The photocatalysts were characterized by X-ray diffraction (XRD), Raman spectroscopy, scanning electron microscopy (SEM) with an energy-dispersive X-ray (EDX) spectroscopy analysis, transition electron microscopy (TEM), BET surface area analyzer, UV–vis diffuse reflectance spectroscopy (DRS) and photoluminescence spectroscopy (PL). The results indicated that all the samples containing MWCNTs exhibit higher photocatalytic activity than the bare ZnO and Ag doped ZnO nanoparticles. At a 10% MWCNT addition, the novel particle achieved a photocatalytic conversion rate of 81% in removing 100 ppm phenol under UV-A light irradiation after 240 min. The pH value, initial concentration and catalyst dosage were also found to influence the particle’s photocatalytic performance significantly

QSAR modelling using combined simple competitive learning networks and RBF neural networks

<p>The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.</p

A novel cutting strategy for measuring two components of residual stresses using slitting method

An exact knowledge of residual stresses that exist within the engineering components is essential to maintain the structural integrity. All mechanical strain relief (MSR) techniques to measure residual stresses rely on removing a section of material that contains residual stresses. Therefore, these techniques are destructive as the integrity of the components is compromised. In slitting method, as a MSR technique, a slot with an increasing depth is introduced to the part incrementally that results in deformations. By measuring these deformations the residual stress component normal to the cut can be determined. Two orthogonal components of residual stresses were measured using the slitting method both experimentally and numerically. Different levels of residual stresses were induced into beam shaped specimens using quenching process at different temperatures. The experimental results were then compared with those numerically predicted. It was shown that while the first component of residual stress was being measured, its effect on the second direction that was normal to the first cut was inevitable. Finally, a new cutting configuration was proposed in which two components of residual stresses were measured simultaneously. The results of the proposed method indicated a good agreement with the conventional slitting

A Novel Design Framework for Tightly Coupled IμGNSS Sensor Fusion Using Inverse-Kinematics, Symbolic Engines, and Genetic Algorithms

Tightly-coupled (TC) fusion of Inertial Measurement Units (IMUs) with Global Navigation Satellite Systems (GNSSs) is a common technique that provides high-rate positioning even under GNSS interruptions. In order to provide accurate positioning, errors of IMU and GNSS must be modelled and estimated by filtering techniques such as Extended Kalman Filter (EKF). Due to nonlinearity and stochastic characteristics of IMU and GNSS system and measurement models, robust filter design has been a challenge. Conventional design techniques use mission-specific fixed models and trial-and-error noise parameter tuning to design IμGNSS filters. These conventional techniques are inflexible and do not always lead to accurate designs as there are no ways to verify the filter ability to estimate sensors errors accurately. To address this challenge, this paper presents a flexible design framework and a systematic procedure for TC IμGNSS fusion. The framework utilizes symbolic engines to represent and linearize system and measurement models. Symbolic engines are flexible in new models and fusion algorithms development. In order to evaluate the estimation of sensors errors, an Inverse-Kinematics module is developed to generate error-free sensors measurements which can be contaminated by known errors. The filter parameters are tuned using Genetic Algorithms and the performance is evaluated based on the accuracy of estimating all states including the added known errors. The framework has been used to develop a quaternion-based EKF design and verified on real raw IμGNSS data. The results showed that the developed framework greatly reduces efforts to design robust and accurate fusion systems for TC IμGNSS integration

The impact of ACE2 polymorphisms (rs1978124, rs2285666, and rs2074192) and ACE1 rs1799752 in the mortality rate of COVID-19 in different SARS-CoV-2 variants

BackgroundClinical severity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) outcomes could be influenced by genetic polymorphisms in angiotensin I-converting enzyme (ACE1) and ACE2. This study aims to examine three polymorphisms (rs1978124, rs2285666, and rs2074192) on the ACE2 gene and ACE1 rs1799752 (I/D) in patients who have coronavirus disease 2019 (COVID-19) with various SARS-CoV-2 variants.MethodsBased on polymerase chain reaction-based genotyping, four polymorphisms in the ACE1 and ACE2 genes have been identified in 2023 deceased patients and 2307 recovered patients.ResultsThe ACE2 rs2074192 TT genotype was associated with the COVID-19 mortality in all three variants, whereas the CT genotype was associated with the Omicron BA.5 and Delta variants. ACE2 rs1978124 TC genotypes were related to COVID-19 mortality in the Omicron BA.5 and Alpha variants, but TT genotypes were related to COVID-19 mortality in the Delta variant. It was found that ACE2 rs2285666 CC genotypes were associated with COVID-19 mortality in Delta and Alpha variants, and CT genotypes in Delta variants. There was an association between ACE1 rs1799752 DD and ID genotypes in the Delta variant and COVID-19 mortality, whereas there was no association in the Alpha or Omicron BA.5 variants. In all variants of SARS-CoV-2, CDCT and TDCT haplotypes were more common. In Omicron BA.5 and Delta, CDCC and TDCC haplotypes were linked with COVID-19 mortality. In addition to COVID-19 mortality, the CICT, TICT, and TICC were significantly correlated.ConclusionThe ACE1/ACE2 polymorphisms had an impact on COVID-19 infection, and these polymorphisms had different effects in various SARS-CoV-2 variants. To confirm these results, however, more research needs to be conducted