Verifying anonymity in voting systems using CSP

We present formal definitions of anonymity properties for voting protocols using the process algebra CSP. We analyse a number of anonymity definitions, and give formal definitions for strong and weak anonymity, highlighting the difference between these definitions. We show that the strong anonymity definition is too strong for practical purposes; the weak anonymity definition, however, turns out to be ideal for analysing voting systems. Two case studies are presented to demonstrate the usefulness of the formal definitions: a conventional voting system, and Prêt à Voter, a paper-based, voter-verifiable scheme. In each case, we give a CSP model of the system, and analyse it against our anonymity definitions by specification checks using the Failures-Divergences Refinement (FDR2) model checker. We give a detailed discussion on the results from the analysis, emphasizing the assumptions that we made in our model as well as the challenges in modelling electronic voting systems using CSP.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

CFD SIMULATION OF THE GAS-SOLID FLOW IN THE RISER OF A CIRCULATING FLUIDIZED BED WITH SECONDARY AIR INJECTION

A comprehensive investigation was carried out to study hydrodynamics aspects of secondary air injection in circulating fluidized beds. This article presents modeling and results of computational fluid dynamics simulations of gas-solid flow in the riser section of a laboratory-scale (ID = 0.23 m, height = 7.6 m) circulating fluidized bed with a radial secondary air injector. The gas-solid flow model is based on the two-fluid (Eulerian-Eulerian) approach, where both gas and solids phases are treated as interpenetrating continua. A granular kinetic theory model is used to describe the solids phase stresses. The simulation results are compared with measured pressure drop and axial particle velocity profiles; reasonable agreement is obtained. Qualitatively, excellent agreement is obtained in predicting the increase in solids volume fraction below secondary air ports, the accumulation of solids around the center of the riser due to momentum of secondary air jets, and the absence of the solids down-flow near the wall above the secondary air injection ports, which are the prominent features of secondary air injection observed in the experiments.     

HYDRODYNAMIC STUDIES IN A HALF SLOT-RECTANGULAR SPOUTED BED COLUMN

Experiments were carried out in a half slot-rectangular spouted bed to investigate the effects of slot width and lower section basal angle on column hydrodynamics. Flow regimes, minimum spouting velocity, spouting and maximum pressure drops, and maximum spoutable bed height were determined for 4 mm diameter polyethylene particles. The results are compared with those for conventional cylindrical and rectangular spouted beds. Correlations for each hydrodynamic parameter are developed and compared with equations available in the literature.     

Kinetics of non-congruent dissolution of E-glass fiber in saturated calcium hydroxide solution

The kinetics of non-congruent dissolution of long E-glass fiber in saturated calcium hydroxide solution has been investigated at 25°C in a closed reactor adopting the rotating disc method for the sample holder. The fiber dissolution rate (), measured from the relative loss weight of the fiber after selective dissolution of the attack layer in acetic acid, is a linear function of time, what implies an interfacial dissolution process. However, the kinetics does not stay regular for a long time due to local detachment of the attack layer. The comparison between = f (t) and ′ = f (t) curves, where ′ is the formation rate of the attack layer, shows that the formation of the layer does not only imply the deposit of a bad-crystalline hydrated calcium silicate but the nucleation and growth of solid calcium hydroxide and calcium carbonate resulting from the transfert, to the interface, of ions coming from the liquid phase. This phenomenon was precedently observed by some other authors in the case of glass-fiber reinforced cement matrix.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

Effect of aging temperature and of retrogression treatment time on the microstructure and mechanical properties of alloy AA 7075

Alloy AA 7075-T6 is studied after retrogression and re-aging. The retrogression heat treatment is performed at various temperatures and hold times, and subsequent aging is performed at 130°C for 12 h. The microstructure and mechanical properties of the alloy are studied depending on the temperature and the hold time of the retrogression heat treatment. Electron microscopic studies are preformed and mechanical characteristics are determined in tensile and impact tests. The HRB microhardness is measured.     

Exploring the Belief Systems of Software Development Professionals

It is commonly accepted that an individual's beliefs and actions are based on his or her assessment and perceptions of the world. In order to determine what practices an individual is likely to follow at any given time, it is necessary to understand the individual's behavioral intention in a given circumstance. From an Information Technology perspective, a software development professional's belief systems are potentially the basis for the adoption and implementation of new and innovative work practices and processes. In this article, we explore the belief systems of software development professionals in order to understand the beliefs underlying intention and practice, and we seek answers about how they adopt or reject new and innovative software development processes and practices. The results point out a strong influence of past experiences, personality types, and repeated behavior on current software development processes and practices in industrial settings.     

A greedy gradient-simulated annealing selection hyper-heuristic

Educational timetabling problem is a challenging real world problem which has been of interest to many researchers and practitioners. There are many variants of this problem which mainly require scheduling of events and resources under various constraints. In this study, a curriculum based course timetabling problem at Yeditepe University is described and an iterative selection hyper-heuristic is presented as a solution method. A selection hyper-heuristic as a high level methodology operates on the space formed by a fixed set of low level heuristics which operate directly on the space of solutions. The move acceptance and heuristic selection methods are the main components of a selection hyper-heuristic. The proposed hyper-heuristic in this study combines a simulated annealing move acceptance method with a learning heuristic selection method and manages a set of low level constraint oriented heuristics. A key goal in hyper-heuristic research is to build low cost methods which are general and can be reused on unseen problem instances as well as other problem domains desirably with no additional human expert intervention. Hence, the proposed method is additionally applied to a high school timetabling problem, as well as six other problem domains from a hyper-heuristic benchmark to test its level of generality. The empirical results show that our easy-to-implement hyper-heuristic is effective in solving the Yeditepe course timetabling problem. Moreover, being sufficiently general, it delivers a reasonable performance across different problem domains.