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.     

Thermal characterization of n‐alkyl maleate‐styrene‐allyl propionate terpolymers

Thermal characterization of maleic anhydride‐styrene‐allyl propionate (MA‐St‐AP) terpolymer and its ester derivatives named as n‐alkyl maleate and shown as nPr MA‐St‐AP, nBu MA‐St‐AP, nPn MA‐St‐AP, and nBz MA‐St‐AP was carried out. The thermal characterization was performed using thermal analysis techniques such as TGA, DTA, DSC, and TMA. Different results were observed between the original terpolymer and its ester derivatives. Thermal stabilities of the terpolymer and its ester derivatives were compared by using various measurements plotted as TGA, DTA, DSC, and TMA curves. The increase in the alcohols' carbon numbers added to the original terpolymer results in ester derivatives with different thermal stability behavior. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 600–604, 2007     

A web-based platform for biosignal visualization and annotation

With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework.     

AHP and multichoice goal programming integration for course planning

Course planning is one of the important problems in the education systems of universities. The processes cannot continue efficiently without planning, and various interruptions can appear in the system. This way decisions concerning which courses, when, how, and for what purposes should be answered by considering the available resources and stakeholders’ preferences. Besides, universities aiming to be in the European Higher Education Area (EHEA) have to adapt their systems to the Bologna process in order to create a lifelong student‐centered, learning‐oriented area based on quality assurance. In this study, an integrated approach based on the analytic hierarchy process (AHP) and multichoice goal programming (MCGP) model was proposed to construct an efficient course plan following the Bologna process. The proposed approach was applied in an industrial engineering department.     

Properties of Induction Plasma Sprayed Iron Based Nanostructured Alloy Coatings for Metal Based Thermal Barrier Coatings

Metal-based thermal barrier coatings (MBTBCs) have been produced using high frequency induction plasma spraying (IPS) of iron-based nanostructured alloy powders. The study of MBTBCs has been initiated to challenge issues associated with current TBC materials such as difficult prediction of their “in-service” lifetime. Reliability of TBCs is an important aspect besides the economical consideration. Therefore, the study of MBTBCs, which should posses higher toughness than the current TBC materials, has been initiated to challenge the mechanical problems of ceramic-based TBCs (CBTBCs) to create a new generation of TBCs. The thermal diffusivity (TD) (α) properties of the MBTBCs were measured using a laser flash method, and density (ρ) and specific heat (C _p) of the MBTBCs were also measured for their thermal conductivity (k) calculation (k = αρ C _p).     

Stabilised finite element methods for a bending moment formulation of the Reissner-Mindlin plate model

This work presents new stabilised finite element methods for a bending moments formulation of the Reissner-Mindlin plate model. The introduction of the bending moment as an extra unknown leads to a new weak formulation, where the symmetry of this variable is imposed strongly in the space. This weak problem is proved to be well-posed, and stabilised Galerkin schemes for its discretisation are presented and analysed. The finite element methods are such that the bending moment tensor is sought in a finite element space constituted of piecewise linear continuos and symmetric tensors. Optimal error estimates are proved, and these findings are illustrated by representative numerical experiments.     

Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...     

GaN‐based emissive microdisplays: A very promising technology for compact,ultra‐high brightness display systems

High‐brightness GaN‐based emissive microdisplays can be fabricated with different approaches that are listed and described. They consist either of hybridizing a GaN LED array on a CMOS circuit or building a monolithic component on a single substrate. Using the hybridization approach, two types of 10‐μm pixel pitch GaN microdisplay prototypes were developed: (1) directly driven, 300 × 252 pixels and (2) active‐matrix, 873 × 500 pixels. Brightness as high as 1 × 10⁶ and 1 × 10⁷ cd/m² for blue and green arrays, respectively, were reached. GaN‐based emissive microdisplays are suitable for augmented reality systems or head‐up displays, but some challenges remain before they can be put in production.     

A projected gradient method for optimization over density matrices

An ensemble of quantum states can be described by a Hermitian, positive semidefinite and unit trace matrix called density matrix. Thus, the study of methods for optimizing a certain function (energy, entropy) over the set of density matrices has a direct application to important problems in quantum information and computation. We propose a projected gradient method for solving such problems. By exploiting the geometry of the feasible set, which is the intersection of the cone of Hermitian positive semidefinite matrices with the hyperplane defined by the unit trace constraint, we describe an efficient procedure to compute the projection onto this set using the Frobenius norm. Some important applications, such as quantum state tomography, are described and numerical experiments illustrate the effectiveness of the method when compared to previous methods based on fixed-point iterations or semidefinite programming.