Use of sea water in the flotation of carbonate rich sedimentary phosphate rock

There is an appreciable amount of carbonaceous sedimentary phosphate deposits in the northern Africa and the Middle East which are expected to gain importance in the near future because of the depletion of the deposits in Florida. In many of the regions where phosphate deposits are found there is a shortage of fresh water, and sea is not very far. The use of sea water instead of fresh water in flotation of carbonaceous phosphate rock is successfully demonstrated. Thus an appreciable reduction in the operating cost and an ample amount of saving in the fresh water resources of the region is expected.     

General solution of one-dimensional,time dependent coupled heat flow systems

A new, powerful method of analysis, involving the combined use of finite integral transform and finite element techniques, is presented for the solution of time dependent heat flow systems composed of many one-dimensional elements connected through the nodes. This method leads to an eigenvalue problem which is not of the conventional Sturm-Liouville type. A procedure for the determination of the eigenvalues is described. The solution obtained is in the form of an infinite series and contains quasi-steady and transient terms. The general solution obtained can be applied in the mathematical modelling of many engineering applications such as the determination of the penetration of the daily temperature cycle into buildings, the analysis of heat transfer in array of extended surfaces in compact heat exchangers, and many others.     

Observing Inequality: Can Ergonomic Observations Help Interventions Transform the Role of Gender in Work Activity?

Computer Supported Cooperative Work (CSCW) - Work activity ergonomics (sometimes called francophone ergonomics) draws heavily on observation in order to support transformation of work to arrive at...     

Prediction of software vulnerability based deep symbiotic genetic algorithms: Phenotyping of dominant-features

The detection of software vulnerabilities is considered a vital problem in the software security area for a long time. Nowadays, it is challenging to manage software security due to its increased complexity and diversity. So, vulnerability detection applications play a significant part in software development and maintenance. The ability of the forecasting techniques in vulnerability detection is still weak. Thus, one of the efficient defining features methods that have been used to determine the software vulnerabilities is the metaheuristic optimization methods. This paper proposes a novel software vulnerability prediction model based on using a deep learning method and SYMbiotic Genetic algorithm. We are first to apply Diploid Genetic algorithms with deep learning networks on software vulnerability prediction to the best of our knowledge. In this proposed method, a deep SYMbiotic-based genetic algorithm model (DNN-SYMbiotic GAs) is used by learning the phenotyping of dominant-features for software vulnerability prediction problems. The proposed method aimed at increasing the detection abilities of vulnerability patterns with vulnerable components in the software. Comprehensive experiments are conducted on several benchmark datasets; these datasets are taken from Drupal, Moodle, and PHPMyAdmin projects. The obtained results revealed that the proposed method (DNN-SYMbiotic GAs) enhanced vulnerability prediction, which reflects improving software quality prediction.

     

Testing usability and trainability of indirect touch interaction: perspective for the next generation of air traffic control systems

This study aims to determine whether indirect touch device can be used to interact with graphical objects displayed on another screen in an air traffic control (ATC) context. The introduction of such a device likely requires an adaptation of the sensory-motor system. The operator has to simultaneously perform movements on the horizontal plane while assessing them on the vertical plane. Thirty-six right-handed participants performed movement training with either constant or variable practice and with or without visual feedback of the displacement of their actions. Participants then performed a test phase without visual feedback. Performance improved in both practice conditions, but accuracy was higher with visual feedback. During the test phase, movement time was longer for those who had practiced with feedback, suggesting an element of dependency. However, this ‘cost’ of feedback did not extend to movement accuracy. Finally, participants who had received variable training performed better in the test phase, but accuracy was still unsatisfactory. We conclude that continuous visual feedback on the stylus position is necessary if tablets are to be introduced in ATC.     

Robust control of variable speed autonomous underwater vehicle

Set point tracking control of autonomous underwater vehicle (AUV) via robust model predictive control (RMPC) is considered. Input-constrained RMPC with integral action, which has been developed in our previous work, is used to control the AUV in this study. In order to derive a RMPC control rule, non-linear dynamics of AUV with six degree of freedom is linearized at certain operating points. So, horizontal and vertical plane dynamics of system are represented by linear models which have polytopic uncertainties. Since the derived control rule will be used in real time, the computation time should be reduced. To overcome this computational time problem and get rid of trial–error step of Algorithm 1, a new algorithm is proposed here. The simulations are carried out using the control rule based on this algorithm and these results are presented.     

Fault-Tolerant Formation Driving Mechanism Designed for Heterogeneous MAVs-UGVs Groups

A fault-tolerant method for stabilization and navigation of 3D heterogeneous formations is proposed in this paper. The presented Model Predictive Control (MPC) based approach enables to deploy compact formations of closely cooperating autonomous aerial and ground robots in surveillance scenarios without the necessity of a precise external localization. Instead, the proposed method relies on a top-view visual relative localization provided by the micro aerial vehicles flying above the ground robots and on a simple yet stable visual based navigation using images from an onboard monocular camera. The MPC based schema together with a fault detection and recovery mechanism provide a robust solution applicable in complex environments with static and dynamic obstacles. The core of the proposed leader-follower based formation driving method consists in a representation of the entire 3D formation as a convex hull projected along a desired path that has to be followed by the group. Such an approach provides non-collision solution and respects requirements of the direct visibility between the team members. The uninterrupted visibility is crucial for the employed top-view localization and therefore for the stabilization of the group. The proposed formation driving method and the fault recovery mechanisms are verified by simulations and hardware experiments presented in the paper.     

Efficient Binary Tomographic Reconstruction

Tomographic reconstruction of a binary image from few projections is considered. A novel heuristic algorithm is proposed, the central element of which is a nonlinear transformation ψ(p)=log(p/(1?p)) of the probability p that a pixel of the sought image be 1-valued. It consists of backprojections based on ψ(p) and iterative corrections. Application of this algorithm to a series of artificial test cases leads to exact binary reconstructions, (i.e., recovery of the binary image for each single pixel) from the knowledge of projection data over a few directions. Images up to 10⁶ pixels are reconstructed in a few seconds. A series of test cases is performed for comparison with previous methods, showing a better efficiency and reduced computation times.     

Characterization of Maximum Likelihood Solutions to Image Reconstruction in Photon Emission Tomography

For photon emission tomography, the maximum likelihood (ML) estimator for image reconstruction is generally solution to a nonlinear equation involving the vector of measured data. No explicit closed-form solution is known in general for such a nonlinear ML equation, and numerical resolution is usually implemented, with a very popular iterative method formed by the expectation-maximization algorithm. The numerical character of such resolutions usually makes it difficult to obtain a general characterization of the performance of the ML solution. We show that the nonlinear ML equation can be replaced by an equivalent system of two dual linear equations nonlinearly coupled. This formulation allows us to exhibit explicit (to some extent) forms for the solutions to the ML equation, in general conditions corresponding to the various possible configurations of the imaging system, and to characterize their performance with expressions for the mean-squared error, bias and Cramér-Rao bound. The approach especially applies to characterize the ML solutions obtained numerically, and offers a theoretical framework to contribute to better appreciation of the capabilities of ML reconstruction in photon emission tomography.     

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.