Effects of Light, Oxygen, and pH on the Absorbance of 2,2-Diphenyl-1-picrylhydrazyl

ABSTRACT: The absorbance of 2,2-diphenyl-1-picrylhydrazyl (DPPH) at 517 nm in methanol and acetone decreased by 20 and 35% for 120 min at 25 °C under light, respectively; in the dark it did not change significantly for 150 min. Decomposition of DPPH under 21% oxygen after 90 min under light was significantly higher than that under 1% oxygen. Absorbance of DPPH in pH 4 buffer solution in methanol, and in pH 10 buffer solution in acetone, decreased by 55 and 80%, respectively, under light for 120 min. The evaluation of antioxidant activity by the changes of DPPH absorbance should be carefully interpreted since the absorbance of DPPH at 517 nm is decreased by light, oxygen, pH, and type of solvent in addition to the antioxidant.     

The statistical modeling of surface roughness in high-speed flat end milling

Surface roughness is one of the most important requirements in machining process. The surface roughness value is a result of the tool wear. When tool wear increase, the surface roughness also increases. The determination of the sufficient cutting parameters is a very important process obtained by means of both minimum surface roughness values and long tool life. The statistical models were developed to predict the surface roughness.This paper presents the development of a statistical model for surface roughness estimation in a high-speed flat end milling process under wet cutting conditions, using machining variables such as spindle speed, feed rate, depth of cut, and step over. First- and second-order models were developed using experimental results of a rotatable central composite design, and assessed by means of various statistical tests. The highest coefficient of correlation (R_adj²) (88%) was obtained with a 10-parameter second-order model. Meanwhile, a time trend was observed in residual values between model predictions and experimental data, reflecting the probable effect of the tool wear on surface roughness. Thus, in order to enhance the estimation capability of the model, another independent variable was included into the model to account for the effect of the tool wear, and the total operating time of the tool was selected as the most suitable variable for this purpose. By inserting this new variable as a linear term into the model, R_adj² was increased to 94% and a good fit was observed between the model predictions and supplementary experimental data.In this study, it was observed that, the order of significance of the main variables is as X₅>X₃>X₄>X₁>X₂ (total machining time, depth of cut, step over, spindle speed and feed rate, respectively).     

Self-sufficient Cytochrome P450s and their potential applications in biotechnology

Cytochrome P450s(CYPs) are ubiquitously found in all kingdoms of life, playing important role in various biosynthetic pathways as well as degradative pathways; accordingly find applications in a vast variety of areas from organic synthesis and drug metabolite production to modification of biomaterials and bioremediation.Significantly, CYPs catalyze chemically challenging C—H and C—C activation reactions using a reactive high-valent iron-oxo intermediate generated upon dioxygen activation at their heme center,while the other oxygen atom is reduced to the level of water by electrons provided through a reductase partner protein.Self-sufficient CYPs, encoding their heme domain and reductase protein in a single polypeptide, facilitate increased catalytic efficiency and render a less complicated system to work with.The self-sufficient CYP enzyme from CYP102A family(CYP102A1, BM3) is among the earliest and most-investigated model enzymes for mechanistic and structural studies as well as for biotechnological applications.An increasing number of self-sufficient CYPs from the same CYP102 family and from other families have also been reported in last decade.In this review, we introduce chemistry and biology of CYPs, followed by an overview of the characteristics of self-sufficient CYPs and representative reactions.Enzyme engineering efforts leading to novel self-sufficient CYP variants that can catalyze synthetically useful natural and non-natural(nature-mimicking) reactions are highlighted.Lastly, the strategy and efforts that aim to circumvent the challenges for improved thermostability, regio-and enantioselectivity,and total turnover number; associated with practical use of self-sufficient CYPs are reviewed.     

Influence of the cross‐sectional shape on the structure and properties of polyester fibers

The effects of the fiber cross‐sectional shape on the structure and properties of polyester fibers were investigated. Fully drawn yarn (FDY) polyester fibers (167 dtex and 48 filaments) were produced under the same spinning conditions used in a spinning plant. The only difference between the fibers was their cross‐sectional shapes. Four different cross‐sectional shapes were chosen for the experimental work: round, hollow‐round, trilobal, and hollow‐trilobal. The crystallinity and values of the maximum stress, maximum strain, modulus, yield stress, shrinkage in boiling water, and unevenness of the fibers were determined. The difference in the cross‐sectional shapes influenced the modulus, maximum strain, yield stress, and shrinkage in boiling water. No effects on the crystallinity and maximum stress were observed. The results suggested that the hollow fibers had higher amorphous orientation than the full fibers. The hollow‐round fiber had the highest unevenness value. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 2615–2621, 2007     

Effect of the use of mineral filler on the properties of concrete

Engineers of the concrete technology are increasingly concerned with the material passing through a sieve of the size under 0.149 mm. Materials called very fine aggregate or mineral filler may affect the performance of concrete in an either positive or a negative way. Discussions on aggregate containing very fine material are vitally important. Washing the aggregate residue has been the sole way to solve this matter to date. This is mainly based on the debatable opinion that materials of this kind are regarded as clay material. The goal of the study was to determine how the content of mineral filler might affect properties of concrete. Two types of aggregates with different amounts of cement and mineral filler were used. Basically, mineral filler replaced sand. The effect of applying different amounts of mineral filler on concrete was then determined. The addition of 7-10% of mineral filler to fine aggregate (0-2 mm) was found to considerably improve the properties of concrete.     

Fusion of smartphone sensor data for classification of daily user activities

Multimedia Tools and Applications - New mobile applications need to estimate user activities by using sensor data provided by smart wearable devices and deliver context-aware solutions to users...     

(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

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.     

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

Abstract: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.     

Micro-milling performance of AISI 304 stainless steel using Taguchi method and fuzzy logic modelling

In this study, micro-milling of AISI 304 stainless steel with ball nose end mill was conducted using Taguchi method. The influences of spindle speed, feed rate and depth of cut on tool wear, cutting forces and surface roughness were examined. Taguchi’s signal to noise ratio was utilized to optimize the output responses. The influence of control parameters on output responses was determined by analysis of variance. In this study, the models describing the relationship between the independent variables and the dependent variables were also established by using regression and fuzzy logic. Efficiency of both models was determined by analyzing correlation coefficients and by comparing with experimental values. The results showed that both regression and fuzzy logic modelling could be efficiently utilized for the prediction of tool wear, cutting forces and surface roughness in micro-milling of AISI 304 stainless steel.