Fast and robust estimation of the multivariate errors in variables model.

In the multivariate errors in variable models one wishes to retrieve a linear relationship of the form y = ß x + a, where both x and y can be multivariate. The variables y and x are not directly measurable, but observed with measurement error. The classical approach to estimate the multivariate errors in variable model is based on an eigenvector analysis of the joint covariance matrix of the observations. In this paper a projection-pursuit approach is proposed to estimate the unknown parameters. Focus is on projection indices based on half-samples. These will lead to robust estimators, which can be computed using fast algorithms. Consistency of the procedure is shown, without needing to make distributional assumptions on the x-variables. A simulation study gives insight in the robustness and the efficiency of the procedure.Algorithms; Consistency; Covariance; Efficiency; Errors in variables; Estimator; Matrix; Measurement; Model; Models; Multivariate statistics; Principal component analysis; Projection-pursuit; Robust estimation; Robustness; Simulation; Studies; Variables;

Toward A Generic Vehicular Cloud Network Architecture: A Case of Virtual Vehicle As A Service

In recent years, cloud computing has gained more and more popularity. The motivation towards implementing cloud computing in vehicular networks is due to the availability of communication, storage, and computing resources represented by communication, vehicles, roadside units (RSUs), and central servers. These resources can be utilized and provided to vehicles, drivers on the road, travellers, and customers on the internet. Intelligent Transportation System (ITS) applications can utilize vehicular cloud computing to provide efficient real-time services, as well as to improve transportation safety, mobility, and comfort levels for drivers. In this paper, all possible vehicular cloud models are presented. Each vehicular cloud model offers different services. Integrating all vehicular cloud models into one integrated system will provide all services and serve internet users, passengers, and vehicles. Therefore, a generic vehicular cloud model is proposed. After that, a new service called Virtual Vehicle is proposed in vehicular cloud computing. The virtual vehicle is a virtual machine that migrates from one physical vehicle to another. It provides the same services as the physical vehicle according to the consumer's requirements.Comment: 14 page, 7 figure

Removing Thallium (I) Ion from Aqueous Solutions Using Modified ZnO Nanopowder

In this study, the adsorption of thallium (I) ion as a dangerous pollutant from aqueous solution onto modified ZnO nanopowder as a fairly cheap adsorbent has been examined in batch mode. It was known that modification of the adsorbent was necessary to reach a significant adsorption percentage. The adsorbent used here was modified by sodium phosphate solution. The effect of experimental conditions such as initial pH of solution, contact time, adsorbent dosage, initial concentration of thallium and temperature is studied. The results showed the dependence of the adsorption percentage to these conditions specially its pH. The maximum adsorption percentage of Tl (I) ions at 25±1oC was 92.8%. Freundlich isotherm model provided a better fit with the experimental data than Langmuir and Temkin isotherm models by high correlation. Separation factor, RL, values showed that modified ZnO nanopowder was favorable for the adsorption of Tl (I) ion. The negative value of ΔH0 showed that Tl (I) sorption is an exothermic process and the negative value of ΔS0 represented that there is a little decrease of randomness at the solid-solution interface during sorption

Interaction of Dioxovanadium (V) ion with L-alanine at Different Ionic Strengths

The formation constants of species formed in the system H+ + alanine and VO2+ +alanine have been determined in aqueous solution for 1.0<pH<7.0 and at different ionic strengths ranging from 0.1 to 1.0 mol dm-3 NaClO4, using a combination of potentiometric and spectrophotometric techniques.The compositions of the formd complexes and their stability constants were determined by curve fitting method and it was shown that dioxivanadium(V) forms two mononuclear 1:1 and 1:2 species with alanineof type VO2L and VO2L2-.The porotonation constant of the amino group of alanine has been determined using potentiometric techniques and calculated using a computer program wich employ a least-squares method. The dependence of the porotonation of alanine and the stability constants of the species on ionic strength are described by a deby-huckel type equation

Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data,Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models

Highly selective potentiometric determination of Fe(III) ions using Tris-(1,2-diiminocyclohexylmethyl-5-Cl-2-hydroxyl benzaldehyde) based membrane electrode

A novel ion-selective poly(vinyl chloride) membrane sensor for Fe(III) ions based on Tris-(1,2-diiminocyclohexylmethyl-5-Cl-2-hydroxyl benzaldehyde) are reported in this paper. The electrode exhibits a good potentiometric response for Fe(III), response time ≤ 20 s, over a wide concentration range 1.0 × 10-5 to 1.0 × 10-1 M with a slope 19.4 ± 0.5 mV/decade. The potentiometric response is independent on the pH of solution in the range of 1.5-5.0. The proposed electrode can be used for at least two months without any considerable divergence in potentials.It exhibits very good selectivity relative to a wide variety of alkali, alkaline earth, transition and heavy metal ions. The electrode assembly was also used as indicator electrode in the potentiometric titration of Fe(III) with EDTA

The first case series of malaria overlapped with COVID-19 in Iran

Introduction: Although indigenous malaria cases have dramatically declined over the past decades, the COVID pandemic has continued to affect the programs designed to combat malaria, particularly in those countries where hydroxychloroquine and chloroquine have been used as medications for treating COVID. Two immigrants entered Iran illegally from neighboring countries (i.e., Afghanistan and Pakistan). This study mainly aimed to assess the effects of coronavirus disease (COVID-19) on these cases from all aspects (i.e., case-finding, diagnosis, and treatment). Case Presentation: Both cases presented with common symptoms such as fever and shaking chills. In addition, they had no sign of COVID-19, and their oxygen level and CT images were normal in some cases, but they were mistakenly treated as COVID-19 patients long after the onset of malaria symptoms. One of the suspected coronavirus cases was given chloroquine on a voluntary basis for one day, which may have been responsible for the possible relapse in vivax or resistance of plasmodium vivax to chloroquine and the recurrence of parasitemia in falciparum. Conclusions: The active case detection of malaria was affected by the COVID-19 pandemic. Case finding was dramatically decreased with the onset of coronavirus, thereby causing a spurt in malaria incidence. Moreover, the malaria treatment strategy was negatively affected by the misdiagnosis of COVID-19

Cutaneous Leishmaniasis in Bam: A Comparative Evaluation of Pre- and Post- Earthquake Years (1999-2008)

Background: The recent devastating earthquake of December 26 in Bam, 2003 created various risk factors; caused a sharp increase in incidence of anthroponotic cutaneous leishmaniasis (ACL) cases and reached to an epidemic proportion. The objective of this study was to evaluate the status of ACL cases five years before the earthquake compared to the cases occurred five years after the earthquake (1999-2008). Methods: Status of disease was assessed retrospectively for the five years before the earthquake and prospectively for the five years after the earthquake. Identification was confirmed by smear and polymerase chain reaction (PCR). Results: The mean annual incidence of ACL for the period from 1999 to 2003 was 1.9 per 1000 comparing to post earthquake period, which was 7.6 per 1000. Most of the infection was in individuals of <20 years, more frequently in females before the earthquake, whilst in contrast, there was a progressive rise in the number of cases, significantly in male individuals of >20 years (P< 0.0001) in post earthquake era. The anatomical distribution of lesions considerably changed during the two periods. Most of the cases were limited to three zones within the city prior to the earthquake, whereas it was spread throughout different zones after the earthquake. PCR indicated that the CL was due to Leishmania tropica in the city. Conclusion: The results strongly suggest that in natural disasters such as earthquakes various precipitating factors in favor of disease will be created, which in turn provide a suitable condition for propagation of the vector and the transmission of the parasite