Towards more active and stable PdAgCr electrocatalysts for formic acid electrooxidation: The role of optimization via response surface methodology

In this study, multiwall carbon nanotube (MCNT)‐supported Pd (Pd/MWCNT) catalysts are prepared by using NaBH₄ reduction method. In order to maximize the oxidation and reduction of H₂SO₄, synthesis conditions (Pd ratio, molar ratio of NaBH₄/K₂PdCl₄, volume of deionized water, and duration of agitation) are optimized by using response surface methodology (RSM). The optimum synthesis conditions are determined as 58.2% of Pd by weight, 154.6 molar ratio of NaBH₄ to K₂PdCl₄, 19.48 mL of deionized water, and 186.16 min of agitation duration. The effect of electrochemical measurement conditions on the oxidation kinetics of Pd/MWCNT is also investigated by RSM. The optimum electrochemical measurement conditions are found as 10 μL of catalyst mixture, 90°C of H₂SO₄ solution, and 5.5 M H₂SO₄. The Pd/MWCNT, Pd₅₀Ag₅₀/MWCNT, and Pd_65.6Ag_33.6Cr_0.80/MWCNT catalysts prepared under optimized conditions are characterized by using X‐ray diffraction, transmission electron microscopy, N₂ adsorption‐desorption, and inductively coupled plasma mass spectrometry. The crystallite sizes of these catalysts are found as 4.85, 5.66, and 5.26 nm for Pd/MWCNT, Pd₅₀Ag₅₀/MWCNT, and Pd_65.6Ag_33.6Cr_0.80/MWCNT catalysts, respectively. Isotherms of all these catalysts are found to be similar to Type V isotherms with H3 hysteresis loop. The average particle size of Pd₅₀Ag₅₀/MWCNT and Pd_65.6Ag_33.6Cr_0.80/MWCNT catalysts are determined as 5.2 and 9.2 nm, respectively. Electrochemical performance of as‐prepared catalysts is evaluated by using cyclic voltammetry and chronoamperometry. The formic acid electrooxidation (FAEO) activities are found as 18.9, 27.8, and 51.6 mA/cm² for Pd/MWCNT, Pd₅₀Ag₅₀/MWCNT, and Pd_65.6Ag_33.6Cr_0.80/MWCNT, respectively. Pd_65.6Ag_33.6Cr_0.80/MWCNT shows the highest activity and stability. Optimization of synthesis conditions and electrochemical measurement parameters allow us to obtain very good electrochemical activity and stability for FAEO reaction compared with anode catalysts in the literature.     

The effect of liposome on dyeing mohair/wool blends

The aim of this study was to examine the use of liposome in the dyeing of wool and mohair fibres with acid dyestuffs. Soybean lecithin and cholesterol were used to form the liposome membrane utilised in the dyebath. Liposome production was performed according to the thin lipid layer method (Bangham Method) using a rotary evaporator. Two different forms of liposome were used for dyeing wool and mohair fibres. In its first form, liposome was utilised as an auxiliary agent, where it was added to a conventional dyebath at the beginning of the process. In its second form, dyes were encapsulated with liposome and then used in dyeing. The effects of these two different forms of liposome were compared with conventional dyeing. Dyeing was carried out at depths of shade of 0.5%, 1.0% and 2.0% using three different concentrations of liposome (0.33%, 0.66% and 1.33%). An analysis of K/S values, fastness to washing, and the alkali solubility of fibres was conducted. The fibre samples dyed in the presence of liposome exhibited very good fastness to light (grade 8). The wash fastness test results of the liposomal‐dyed samples were significantly better (grade 4‐5) than for those samples which were conventionally dyed. In the presence of liposome, the tensile strength of fibres was 20 gf, whereas it was 11 gf without liposomes.     

An optimum feature extraction method for texture classification

Texture can be defined as a local statistical pattern of texture primitives in observer’s domain of interest. Texture classification aims to assign texture labels to unknown textures, according to training samples and classification rules. In this paper a novel method, which is an intelligent system for texture classification is introduced. It used a combination of genetic algorithm, discrete wavelet transform and neural network for optimum feature extraction from texture images. An algorithm called the intelligent system, which processes the pattern recognition approximation, is developed. We tested the proposed method with several texture images. The overall success rate is about 95%.     

Forecasting of entropy production due to buoyant convection using support vector machines (SVM) in a partially cooled square cross-sectional room

Forecasting of entropy generation of laminar natural convection in a partially cooled square cross-sectional room has been performed using support vector machines (SVM). The two-dimensional room was modeled as floor heating story with a window. Values of temperature and velocities were obtained by solving governing equations of natural convection with finite difference technique and using these values entropy generation was calculated with its definition. Forecasting of entropy generation due to fluid friction irreversibility (FFI) and heat transfer irreversibility (HTI) were made with known values for unknown parameters using SVM. Thus, calculation time was extremely reduced and values were obtained even for non-convergence cases. It was observed that the SVM was a strong method to predict the entropy generation without computational fluid dynamic analysis for all cases.     

A Comparison of Various Artificial Intelligence Methods in the Prediction of Bank Failures

The strong relationship between bank failure and economic growth attaches far more importance to the predictability of bank failures. Consequently, numerous statistical prediction models exist in the literature focusing on this particular subject. Besides, artificial intelligence techniques began to attain an increasing level of importance in the literature due to their predictive success. This study distinguishes itself from the similar ones in the sense that it presents a comparison of three different artificial intelligence methods, namely support vector machines (SVMs), radial basis function neural network (RBF-NN) and multilayer perceptrons (MLPs); in addition to subjecting the explanatory variables to principal component analysis (PCA). The extent of this study encompasses 37 privately owned commercial banks (17 failed, 20 non-failed) that were operating in Turkey for the period of 1997–2001. The main conclusions drawn from the study can be summarized as follows: (i) PCA does not appear to be an effective method with respect to the improvement of predictive power; (ii) SVMs and RBF demonstrated similar levels of predictive power; albeit SVMs was found to be the best model in terms of total predictive power; (iii) MLPs method stood out among the SVMs and RBF methods in a negative sense and exhibits the lowest predictive power.     

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.     

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.     

Synthesis and photopolymerization of novel,highly reactive phosphonated-urea-methacrylates for dental materials

An urea methacrylate (1) and two phosphonated methacrylates (2–3) were synthesized from 2-isocyanatoethyl methacrylate (IEM) and benzyl amine (1), diethyl aminomethylphosphonate (2) and diethyl amino(phenyl)methylphosphonate (3). Their photopolymerization rates are notably higher than commercial monomers, despite the presence of only one double bond. Their polymerization rates follow the order 1 ～ 2 > 3 ～ triethylene glycol dimethacrylate (TEGDMA) > 2-hydroxyethyl methacrylate (HEMA). A tendency toward high crosslinking density during thermal bulk polymerizations, low oxygen sensitivity and high conversions with benzophenone during photopolymerization indicated the importance of hydrogen abstraction/chain transfer reactions. It was found that the addition of the monomers to HEMA significantly increased its polymerization rate, proving their utility as replacements for TEGDMA as reactive diluents for 2,2-bis[4-(2-hydroxy-3-methacryloyloxy propyloxy) phenyl] propane (Bis-GMA). Copolymer systems containing 2 and 3 showed improved T_g values compared to Bis-GMA/TEGDMA systems.     

Symmetric positive solutions of fourth order integral BVP for an increasing homeomorphism and homomorphism with sign-changing nonlinearity on time scales

In this paper, we consider the eigenvalue problems for fourth order integral boundary value problems on time scales for an increasing homeomorphism and homomorphism with sign changing nonlinearities. By using a fixed point index theorem, we give the existence of eigenvalue intervals in which there exist one symmetric positive solution to the problem. An example is also given to demonstrate the main results.     

Estimation of thermal and flow fields due to natural convection using support vector machines (SVM) in a porous cavity with discrete heat sources

Support vector machines (SVM), a soft programming technique, has been used to estimate the temperature distribution and flow fields in a square porous enclosure heated discretely by three isothermal heaters from the left vertical wall. Right vertical wall of the cavity was isothermal but it has colder temperature than the heaters while remaining walls were adiabatic. A database was prepared by solving the governing equations which were written using Darcy flow model. Using finite difference method to discretize the equation, a computational fluid dynamics (CFD) code was written. A correlation was developed between Nusselt and Rayleigh numbers. Using obtained database, further values of temperature and velocities were estimated by SVM technique at different Rayleigh numbers and locations of heater. It was observed that SVM was a useful technique on estimation of streamlines and isotherms. Thus, SVM reduces the computational time and helps to solve some cases when CFD fails to solve due to numerical instability.