Comparative study in chemistry of microbially and electrochemically induced pitting of 316L stainless steel

Ennoblement of stainless steel (SS) by microbially deposited manganese oxides can lead to pitting corrosion at low chloride concentrations, causing unexpected material failures. We exposed 316L SS to manganese oxidizing bacteria Leptothrix discophora under well-defined laboratory conditions, and then placed the ennobled coupons in a 0.5 M sodium chloride solution until pitting developed. Using time-of-flight secondary ion mass spectroscopy we demonstrated that the pits and their immediate vicinity associated with microbial influenced corrosion had different chemical signatures than those associated with electrochemically induced pitting, suggesting a possibility that the microorganisms were directly involved in pit initiation. Based on the differences in the chemical signatures we were able to distinguish the microbially induced pits from those induced by anodic polarization.     

Deep learning frameworks to learn prediction and simulation focused control system models

Deep learning (DL) methods have brought world-shattering breakthroughs, especially in computer vision and classification problems. Yet, the design and deployment of DL methods in time series prediction and nonlinear system identification applications still need more progress. In this paper, we present DL frameworks that are developed to provide novel approaches as solutions to the aforementioned engineering problems. The proposed DL frameworks leverage the advantages of autoencoders and long-short term memory network, which are known being data compression and recurrent structures, respectively, to design Deep Neural Networks (DNN) for modeling time series and nonlinear systems with high performance. We provide recommendations on how deep AEs and LSTMs should be utilized to end up with efficient Prediction-focused (Pf) and Simulation-focused (Sf) DNNs for time series and system identification problems. We present systematic learning methods for the DL frameworks that allow straightforward learning of Pf-DNN and Sf-DNN models in detail. To demonstrate the efficiency of the developed DNNs, we present various comparative results conducted on the benchmark and real-world datasets in comparison with their conventional, shallow, and deep neural network counterparts. The results clearly show that the deployment of the proposed DL frameworks results with DNNs that have high accuracy, even with a low dimensional feature vector.

     

Online tax payment systems as an emergent aspect of governmental transformation

The achievement of governmental transformation through the use of electronically delivered services is a worthy goal that requires significant planning and research to achieve. In order to reach transformational paradigm shifts in governmental operation, it will first be necessary to understand and optimize present governmental e-Service provisions. Of these, the revenue function of taxation is paramount. This paper describes factors related to the use and acceptance by accounting professionals of information technology intended to facilitate electronic tax filing systems. Though tested in the context of governmental tax management systems in Turkey, our findings on the use and acceptance of e-Tax systems are relevant and applicable to a great number of nations and contexts as the ongoing electronic transformation of the governmental revenue system contributes to efforts to transform governments through alternative services delivery venues and channels. We discover that intention to use automated systems as part of the governmental treasury function transformation is hindered by factors that mediate actual plans to do so, mostly in terms of normative pressures and perceptions of behavioral control, which training and education may well ameliorate. Hence, transformation of the treasury function in Turkey is only partially complete and will require additional support, direction and training on the part of the government in its interactions with the tax professionals who interact with the emergent automated system.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

An Objective Deghosting Quality Metric for HDR Images

Reconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.     

Efficient disparity vector prediction schemes with modified P frame for 2D camera arrays

An efficient disparity estimation algorithm for multi-view video sequences, recorded by a two-dimensional camera array in which the cameras are spaced equidistantly, is presented. Because of the strong geometrical relationship among views, the disparity vectors of a certain view can for most blocks be derived from the disparity vectors of other views. A frame constructed using that idea is called a D frame in this work. Three new prediction schemes which contain D frames are proposed for encoding 5 × 3 multi-view video sequences. The schemes are applied to several multi-view image sequences taken from a camera-array and they are compared in terms of quality, bit-rate and complexity. The experimental results show that the proposed prediction schemes significantly decrease the complexity of the encoder at a very low cost of quality and/or bit-rate.     

Dyeing of poly(lactic acid) fibres with synthesised novel heterocyclic disazo disperse dyes

Poly(lactic acid) (PLA) is the first melt‐processable, renewable, sustainable and biodegradable natural‐based synthetic fibre. It has a broad range of uses and combines ecological advantages with outstanding performance in textiles. PLA fibre, as an aliphatic polyester, can be dyed with disperse dyes. Apart from the limited number of commercial disperse dyes, disperse dye exhaustion on PLA is generally lower than that on  poly(ethylene terephthalate) (PET). In this study, new heterocyclic disazo disperse dyes, substituted with methyl, nitro and chloro groups at their ortho‐, meta‐ and para‐ positions, synthesised in our previous study, were applied to PLA and PET fibres to examine their dyeing performance, and colour fastness and dye exhaustion properties. Different shades of yellow, orange, reddish brown and brown were obtained. Most of the synthesised novel heterocyclic disazo disperse dyes exhibited good build‐up properties with high K/S levels on both fibres. Para‐ bonding substituent provided higher K/S values than meta‐ and ortho‐ positions for –NO₂ and –Cl substituents for both fibres. Overall, the most synthesised novel heterocyclic disazo disperse dyes in this study exhibited good build‐up properties with high K/S, exhaustion and wet fastness levels on both PLA and PET fibres.      

Covalent Complex of DNA and Bacterial Topoisomerase: Implications in Antibacterial Drug Development

A topoisomerase-DNA transient covalent complex can be a druggable target for novel topoisomerase poison inhibitors that represent a new class of antibacterial or anticancer drugs. Herein, we have investigated molecular features of the functionally important Escherichia coli topoisomerase I (EctopoI)-DNA covalent complex (EctopoIcc) for molecular simulations, which is very useful in the development of new antibacterial drugs. To demonstrate the usefulness of our approach, we used a model small molecule (SM), NSC76027, obtained from virtual screening. We examined the direct binding of NSC76027 to EctopoI as well as inhibition of EctopoI relaxation activity of this SM via experimental techniques. We then performed molecular dynamics (MD) simulations to investigate the dynamics and stability of EctopoIcc and EctopoI-NSC76027-DNA ternary complex. Our simulation results show that NSC76027 forms a stable ternary complex with EctopoIcc. EctopoI investigated here also serves as a model system for investigating a complex of topoisomerase and DNA in which DNA is covalently attached to the protein.