Synthesis and application of phenylcarbamodithioate compound for steel protection

Phenyldithiocarbamate compound has been synthesized and studied as corrosion inhibitor for steel. Dithiocarbamate (DTC) compounds with linear alkyl groups are good inhibitors, but their stability is quite low in acidic solutions. It should be noted that long-term stability is important for practical applications, in order to avoid excess use of chemicals. So, we have synthesized phenyl substituted DTC which offers strong inhibition efficiency and extra stability. This new inhibitor is chemically adsorbed on steel through its DTC group, while the aromatic ring provides extra stability and long-term efficiency. For the assessment of corrosion kinetics, we have utilized potentiodynamic and ac impedance studies; also solution assay analysis was realized with atomic absorption spectroscopy. It was revealed that inhibitor exhibits remarkably high efficiency, even under elevated temperature conditions. At 55 °C temperature conditions, i_corr value decreased from 5050 to 154 μA cm^?2, with the addition of 500 ppm inhibitor. The long-term stability of inhibitor was also tested and 85.93% efficiency was obtained after three days of exposure period for 500 ppm concentration.     

The influence of different circular hole perforations on interlaminar shear strength of a novel fiber metal laminates

This study characterizes surface treated classic type fiber metal laminates (FMLs) interlaminar shear strength (ILSS) based on a glass mat reinforced polyphenylene sulphide composite and an aluminum alloy. The effect of concentration of γ‐glycidoxypropyltrimethoxysilane surface treatment on ILSS of adhesive bonding between aluminum sheet and composite laminates has been investigated. After determining the silane concentration, novel FML material is manufactured using a compression moulding process which involves aluminum sheets with different circular hole perforations (Array type A and B) with two circular hole diameters (ϕ3 and ϕ5 mm) and two total hole area/closed area: 0.05 and 0.06) to develop mechanical interlocking between aluminum layers and composite laminates. Tensile tests are performed to investigate the effect of different circular hole perforations on ILSS properties of FMLs. Test results show that ILSS is improved with increasing the circular hole diameter and decreased with the number of holes as correlated with undrilled FMLs. Failure modes, damage initiation, and progression of FMLs with different open hole perforations are determined with optical microscope. POLYM. COMPOS., 37:963–973, 2016. © 2014 Society of Plastics Engineers     

Detection of COVID-19 and its pulmonary stage using Bayesian hyperparameter optimization and deep feature selection methods

Since the first case of COVID-19 was reported in December 2019, many studies have been carried out on artificial intelligence for the rapid diagnosis of the disease to support health services. Therefore, in this study, we present a powerful approach to detect COVID-19 and COVID-19 findings from computed tomography images using pre-trained models using two different datasets. COVID-19, influenza A (H1N1) pneumonia, bacterial pneumonia and healthy lung image classes were used in the first dataset. Consolidation, crazy-paving pattern, ground-glass opacity, ground-glass opacity and consolidation, ground-glass opacity and nodule classes were used in the second dataset. The study consists of four steps. In the first two steps, distinctive features were extracted from the final layers of the pre-trained ShuffleNet, GoogLeNet and MobileNetV2 models trained with the datasets. In the next steps, the most relevant features were selected from the models using the Sine–Cosine optimization algorithm. Then, the hyperparameters of the Support Vector Machines were optimized with the Bayesian optimization algorithm and used to reclassify the feature subset that achieved the highest accuracy in the third step. The overall accuracy obtained for the first and second datasets is 99.46% and 99.82%, respectively. Finally, the performance of the results visualized with Occlusion Sensitivity Maps was compared with Gradient-weighted class activation mapping. The approach proposed in this paper outperformed other methods in detecting COVID-19 from multiclass viral pneumonia. Moreover, detecting the stages of COVID-19 in the lungs was an innovative and successful approach.     

Who is LB1? Discriminant analysis for the classification of specimens

Many problems in paleontology reduce to finding those features that best discriminate among a set of classes. A clear example is the classification of new specimens. However, these classifications are generally challenging because the number of discriminant features and the number of samples are limited. This has been the fate of LB1, a new specimen found in the Liang Bua Cave of Flores. Several authors have attributed LB1 to a new species of Homo, H. floresiensis. According to this hypothesis, LB1 is either a member of the early Homo group or a descendent of an ancestor of the Asian H. erectus. Detractors have put forward an alternate hypothesis, which stipulates that LB1 is in fact a microcephalic modern human. In this paper, we show how we can employ a new Bayes optimal discriminant feature extraction technique to help resolve this type of issues. In this process, we present three types of experiments. First, we use this Bayes optimal discriminant technique to develop a model of morphological (shape) evolution from Australopiths to H. sapiens. LB1 fits perfectly in this model as a member of the early Homo group. Second, we build a classifier based on the available cranial and mandibular data appropriately normalized for size and volume. Again, LB1 is most similar to early Homo. Third, we build a brain endocast classifier to show that LB1 is not within the normal range of variation in H. sapiens. These results combined support the hypothesis of a very early shared ancestor for LB1 and H. erectus, and illustrate how discriminant analysis approaches can be successfully used to help classify newly discovered specimens.     

Robust delay‐dependent guaranteed cost controller design for uncertain nonlinear neutral systems with time‐varying state delays

In this paper, the problem of robust delay‐dependent guaranteed cost control for uncertain nonlinear neutral systems with time‐varying state delay is investigated. The control law is chosen to be a memoryless type one. Neither any model transformation nor bounding of any cross terms are utilized. The system under consideration may be subjected to both norm‐bounded uncertainties and nonlinear parameter perturbations. A delay‐dependent sufficient condition is obtained in terms of a matrix inequality for which a cone complementarity problem is introduced to provide a feasible solution set. Two numerical examples have been demonstrated to show the effective application of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

A Kharitonov‐like theorem for robust stability independent of delay of interval quasipolynomials

In this paper, a Kharitonov‐like theorem is proved for testing robust stability independent of delay of interval quasipolynomials, p(s)+∑eq_k(s), where p and q_k's are interval polynomials with uncertain coefficients. It is shown that the robust stability test of the quasipolynomial basically reduces to the stability test of a set of Kharitonov‐like vertex quasipolynomials, where stability is interpreted as stability independent of delay. As discovered in (IEEE Trans. Autom. Control 2008; 53 :1219–1234), the well‐known vertex‐type robust stability result reported in (IMA J. Math. Contr. Info. 1988; 5 :117–123) (See also (IEEE Trans. Circ. Syst. 1990; 37 (7):969–972; Proc. 34th IEEE Conf. Decision Contr., New Orleans, LA, December 1995; 392–394) does contain a flaw. An alternative approach is proposed in (IEEE Trans. Autom. Control 2008; 53 :1219–1234), and both frequency sweeping and vertex type robust stability tests are developed for quasipolynomials with polytopic coefficient uncertainties. Under a specific assumption, it is shown in (IEEE Trans. Autom. Control 2008; 53 :1219–1234) that robust stability independent of delay of an interval quasipolynomial can be reduced to stability independent of delay of a set of Kharitonov‐like vertex quasipolynomials. In this paper, we show that the assumption made in (IEEE Trans. Autom. Control 2008; 53 :1219–1234) is redundant, and the Kharitonov‐like result reported in (IEEE Trans. Autom. Control 2008; 53 :1219–1234) is true without any additional assumption, and can be applied to all quasipolynomials. The key idea used in (IEEE Trans. Autom. Control 2008; 53 :1219–1234) was the equivalence of Hurwitz stability and ?_‐o‐stability for interval polynomials with constant term never equal to zero. This simple observation implies that the well‐known Kharitonov theorem for Hurwitz stability can be applied for ?_‐o‐stability, provided that the constant term of the interval polynomial never vanishes. However, this line of approach is based on a specific assumption, which we call the CNF‐assumption. In this paper, we follow a different approach: First, robust ?_‐o‐stability problem is studied in a more general framework, including the cases where degree drop is allowed, and the constant term as well as other higher‐orders terms can vanish. Then, generalized Kharitonov‐like theorems are proved for ?_‐o‐stability, and inspired by the techniques used in (IEEE Trans. Autom. Control 2008; 53 :1219–1234), it is shown that robust stability independent of delay of an interval quasipolynomial can be reduced to stability independent of delay of a set of Kharitonov‐like vertex quasipolynomials, even if the assumption adopted in (IEEE Trans. Autom. Control 2008; 53 :1219–1234) is not satisfied. Copyright © 2009 John Wiley & Sons, Ltd.     

Laser-induced fluorescence visualization of ion transport in a pseudo-porous capacitive deionization microstructure

In this paper, a microfluidic experimental set-up is introduced to study the ionic transport in an artificial capacitive deionization (CDI) cell. CDI is a promising desalination technique, which relies on the application of an external electric field and high surface area porous electrodes for ion separation and storage. Photolithography and deep reactive ion etching were used to fabricate a micro-CDI channel with pseudo-porous electrodes on a silicon-on-insulator substrate. Laser-induced fluorescence was performed using cationic Sulforhodamine B (SRB) fluorescent dye to measure ion concentration within the bulk solution and more importantly, within the porous electrodes during the desalination process, with an average normalized root mean square deviation of 8.2 %. Using this set-up, electromigration of ions within the electrode was visualized and the effect of applied electric potential on bulk solution concentration distribution is quantified. In addition, SRB and Fluorescein were used together to visualize anion and cation concentrations simultaneously. The method presented in this study can be used for solution concentrations up to approximately 0.7 mM. The ionic concentration profiles obtained by this approach can be used to test and validate the existing electrosorption models, and pseudo-porous electrodes can be modified to observe the effects of pore size, shape and distribution on electrosorption performance. Furthermore, with proper modifications, the microfabricated structure and experimental set-up can be used for CDI-on-a-chip applications and bio-separation devices.     

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.