A Simple and Comprehensive Saturation Packet Delay Model for Wireless Industrial Networks

With the emerging popularity of the wireless local area network technology, many analytical models for its main medium access control mechanism, Distributed Coordination Function (DCF), have been reported. However, most of them are based on some oversimplifying assumptions, or need very complicated mathematical manipulations. In this paper, a simple and accurate packet delay model has been proposed for the IEEE 802.11 DCF mechanism in saturated traffic and error-prone industrial applications which is based on a modified discrete-time Markov chain model of the DCF mechanism which accounts for the backoff freezing. It estimates various delay parameters including the average, jitter, Cumulative Distribution Function, and the effect of Retry Limit. The simulation results confirm the accuracy of the proposed delay model compared with other similar models in the literature.     

Artificial neural network models for indoor temperature prediction: investigations in two buildings

The problem how to identify prediction models of the indoor climate in buildings is discussed. Identification experiments have been carried out in two buildings and different models, such as linear ARX-, ARMAX- and BJ-models as well as non-linear artificial neural network models (ANN-models) of different orders, have been identified based on these experiments. In the models, many different input signals have been used, such as the outdoor and indoor temperature, heating power, wall temperatures, ventilation flow rate, time of day and sun radiation. For both buildings, it is shown that ANN-models give more accurate temperature predictions than linear models. For the first building, it is shown that a non-linear combination of sun radiation and time of day is important when predicting the indoor temperature. For the second building, it is shown that the indoor temperature is non-linearly dependent on the ventilation flow rate.     

MCR-NAS: A combined hard-soft multivariate curve resolution method based on net analyte signal concept for modeling kinetic data with inert interference and baseline drift

This paper demonstrates a novel implementation of a net analyte signal (NAS) algorithm for resolving kinetic model. The evaluation of spectroscopic kinetics data by the established hard-modeling approach assumes that all the variation related to the response is linked to the species involved in the kinetic process. If effects are present in the data that cannot be explained by the model, such as baseline drift or inert interference, the estimated rate constant might be incorrect. An iterative net analyte signal procedure is shown to yield good rate constant estimates when the spectroscopic data is influenced by additional sources of variance. Simulated and real examples are used to confirm this statement.     

Effects of silver nanoparticles coated with anti‐HER2 on irradiation efficiency of SKBR3 breast cancer cells

Breast cancer is the second cause of death in the world. Ionising radiation is a potent mutagen that can cause DNA damage, chromosomes breakage, and cell death. In the present study, radiotherapy and nanoparticle‐antibodies (ABs) have been combined to enhance the efficacy of cancer cell treatment. Silver nanoparticles (SNP) were synthesised, coated with anti‐HER2, and then characterised with different techniques such as X‐ray diffraction, dynamic light scattering, transmission electron microscopy, Fourier transform infrared, and UV–Vis spectroscopy. SKBR3 cells were irradiated with cobalt‐60 in the presence of nanoparticle‐AB as the drug. Cell viability was measured using the diphenyltetrazolium bromide assay, and the cellular status was assessed by Raman spectroscopy. Irradiation considerably decreased cell viability proportionate to the dose increase and post‐irradiation time. The surface‐enhanced Raman spectroscopy increased the signal in the presence of SNP. Increasing the dose to 2 Gy increased the irradiation resistance, and higher dose increases (4 and 6 Gy) enhanced the irradiation sensitivity. Moreover, the cellular changes induced by irradiation in the presence of the drug were stable after 48 h. The authors results introduced the combination of the drug with radiation as an effective treatment for cancer and Raman spectroscopy as a suitable tool to diagnose effective irradiation doses.Inspec keywords: ultraviolet spectra, X‐ray diffraction, tumours, nanofabrication, silver, cellular biophysics, nanomedicine, cancer, drugs, DNA, light scattering, toxicology, biomagnetism, radiation therapy, Raman spectra, transmission electron microscopy, infrared spectra, nanoparticles, gynaecologyOther keywords: higher dose increases, irradiation sensitivity, drug, effective treatment, effective irradiation doses, silver nanoparticles, irradiation efficiency, SKBR3 breast cancer cells, ionising radiation, potent mutagen, DNA damage, cell death, nanoparticle‐antibodies, cancer cell treatment, SNP, different techniques, X‐ray diffraction, dynamic light scattering, transmission electron microscopy, UV–Vis spectroscopy, SKBR3 cells, nanoparticle‐AB, diphenyltetrazolium bromide assay, cell viability proportionate, dose increase, post‐irradiation time, surface‐enhanced Raman spectroscopy, irradiation resistance, time 48.0 hour, size 60.0 inch, Ag     

Application of positron annihilation spectroscopy to study the recovery of commercial pure Al and Al-0.96 wt.% Si alloys

Isochronal annealing of quenched commercial pure Al (99.95%) and Al-0.96 wt.% Si was investigated between room temperature (RT) and 550 °C. The annealing of defects was studied using positron lifetime and Doppler broadening (DB) techniques. The retardation of annealing defects in the two alloys is interpreted in terms of precipitation of impurities and Si atoms, as well as defect properties (size and concentration) in the framework of the two state trapping model.     

A Bipartite Genetic Algorithm for Multi-processor Task Scheduling

Until now, several methods have been presented to optimally solve the multiprocessor task scheduling problem that is an NP-hard one. In this paper, a genetic-based algorithm has been presented to solve this problem with better results in comparison with related methods. The proposed method is a bipartite algorithm in a way that each part is based on different genetic schemes, such as genome presentation and genetic operators. In the first part, it uses a genetic method to find an adequate sequence of tasks and in the second one, it finds the best match processors. To evaluate the proposed method, we applied it on several benchmarks and the results were compared with well known algorithms. The experimental results were satisfactory and in most cases the presented method had a better makespan with at least 10% less iterations compared to related works.     

The corrected induced surface current for arbitrary conductingobjects at resonance frequencies

It is well known that using the method of moments in conjunction with either the electric or magnetic field surface integral equations (EFIE or MFIE) produces inaccurate surface currents on conducting bodies at resonance frequencies. A new technique (based on singular value decomposition) is developed to correct the computed current by adding a correction factor term. This term is seen to be the resonant mode current, obtained by employing the power method in the moment method matrix, multiplied by an unknown complex factor. Applying the condition of vanishing field inside the conducting object results in obtaining the unknown complex factor. Therefore, this technique is hereafter referred to as correction factor technique (CFT). When the computed surface current on a conducting sphere, proposed technique, is compared with the exact one, the numerical results show excellent agreement     

Smart networks for control

Control networks move small packets of data to a large set of nodes, so as to integrate and automate everything from home appliances to entire factories. This is due to the use of embedded microprocessors. The author discusses the control net architecture, network topology and the fast response of the network     

Prediction of thermal conductivities of oxygen,nitrogen and carbon dioxide at the moderate density regime via semi-empirical assessment

Thermal conductivities coefficients for gaseous state of N₂, O₂ and CO₂ at zero density are determined by the inversion technique. The Lennard-Jones 12-6 (LJ 12-6) potential energy function is used as the initial model potential required by the technique. The Wang Chang-Uhlenbeck-de Boer (WCUB) approach of the kinetic theory of gases has been used for calculating the contribution of molecular degree of freedom to the thermal conductivity of N₂, O₂ and CO₂. Also, the initial density dependence of gaseous thermal conductivity according to the Rainwater-Friend theory, which was given by Najafi et al., has been considered for N₂, O₂ and CO₂.     

Identifying Homogeneous Water Quality Regions in the Nile River Using Multivariate Statistical Analysis

Detecting homogeneous regions in the Nile River is essential in carrying mathematical modelling. The aim of this paper is to indentify homogenous regions with respect to water quality. Eight years data were subjected to principal components analysis (PCA) to define the parameters responsible for the variability in water quality. The PCA produced three variates (or principal components). For the Nile stem, variates are related to bacterial pollution, organic pollution and then agricultural/nutrients. As for the Nile branches, variables group as coming from bacterial and organic sources, while the agricultural/nutrients stamp is more visible in summer. Then, cluster analysis (CA) was performed to verify whether the observations could be grouped into spatially coherent patterns. CA grouped sampling sites into three homogenous regions: upper, middle and lower Nile stem. To interpret the subdivision, CA was performed on municipal and demographic data coming from Nile governorates, such as potable water consumption, sewage collection, cultivated areas and population data. The cultivated areas group similarly to nutrients water quality data and the percentages of uncollected sewage group similarly to bacterial data. The consecutive use of PCA and CA enabled to determine the main sources of pollution and to identify homogeneous regions with respect to water quality variables.