Characterization of Valuable Compounds from Winter Melon (<Emphasis Type="Italic">Benincasa hispida</Emphasis> (Thunb.) Cogn.) Seeds Using Supercritical Carbon Dioxide Extraction Combined with Pressure Swing Technique

In this study, we describe the extraction of different valuable compounds from winter melon seeds using supercritical carbon dioxide extraction combined with pressure swing technique (SCE-PST). The effects of the extraction variables, namely pressure, holding time (HT), and continuous extraction time (CT), were optimized by response surface methodology (RSM) to maximize the crude extraction yield (CEY). The optimal conditions were at pressure of 181.35 bar, HT of 9.93 min, and CT of 50.14 min. Under these conditions, the experimental CEY was 235.70?±?0.11 mg g^?1 with a relatively strong antioxidant activity (64.42?±?0.21 % inhibition of DPPH^· radicals, 67.36?±?0.34 % inhibition of ABTS^·+ radicals) and considerable amount of phenolic compounds (42.77?±?0.40 mg gallic acid equivalent/g extract). The high-performance liquid chromatography (HPLC) analysis revealed that the bioactive phenolic compounds increased significantly using PST (p?<?0.05), where gallic acid had the highest concentration (0.688?±?0.34 mg g^?1). The extract obtained using optimal SCE-PST conditions contained more than 83.65 % total unsaturated fatty acids (UFAs) and linoleic acid accounted for 67.33?±?0.22 % in the total extract. From the results, the SCE efficiency in terms of extract quantity and quality has been enhanced significantly applying PST. Finally, the results were compared with previous published findings using supercritical carbon dioxide, ultrasound-assisted, and Soxhlet extraction. It was found that higher CEY could be achieved using Soxhlet extraction even through the quality of SCE-PST extracts in terms of antioxidant activity and phenolic compounds was better.     

New mode of vapor-liquid-solid nanowire growth

We report on the new mode of the vapor-liquid-solid nanowire growth with a droplet wetting the sidewalls and surrounding the nanowire rather than resting on its top. It is shown theoretically that such an unusual configuration happens when the growth is catalyzed by a lower surface energy metal. A model of a nonspherical elongated droplet shape in the wetting case is developed. Theoretical predictions are compared to the experimental data on the Ga-catalyzed growth of GaAs nanowires by molecular beam epitaxy. In particular, it is demonstrated that the experimentally observed droplet shape is indeed nonspherical. The new VLS mode has a major impact on the crystal structure of GaAs nanowires, helping to avoid the uncontrolled zinc blende-wurtzite polytylism under optimized growth conditions. Since the triple phase line nucleation is suppressed on surface energetic grounds, all nanowires acquire pure zinc blende phase along the entire length, as demonstrated by the structural studies of our GaAs nanowires.     

An Adaptive Multitaper-SVD Spectrum Sensing Method for OFDM-Based Cognitive Radio Systems

Cognitive radio (CR) has been proposed and widely investigated as an approach for increasing the spectrum efficiency. CR devices exploit so called white spaces in the spectrum allocated to the primary users (PU) by a process commonly referred to as the spectrum sensing. In this work, we use the singular value decomposition (SVD) for spectrum sensing in orthogonal frequency division multiplexing-based (OFDM) CR systems. A single input multiple output channel is assumed between the PU and the secondary user equipped with multiple antennas. At the CR side, the multitaper method (MTM) is used for the spectrum sensing in each antenna. As a first contribution, we aim at reducing the time necessary to perform spectrum sensing. To this end, we propose an adaptive MTM–SVD spectrum sensing method that decreases the sensing time. As a second contribution, we formulate a three dimensional SVD (referred to as 3-D SVD) scheme that efficiently processes signals and quantities related to multiple antenna traffic, OFDM multiple blocks and different tapers, simultaneously. Simulation results indicate that the proposed adaptive MTM–SVD decreases the sensing time by about 61–69 % for various proposed adaptive algorithms, compared to the conventional MTM–SVD method. Besides, performance improvement in probability of detection is achieved from 2–13 % for a predefined probability of false alarm by using adaptive MTM–SVD. In addition to further reduction of the sensing time, the proposed 3D-MTM–SVD outperforms conventional methods for the low probability of the false alarm.     

A new image‐based unlevelness index

A new quantitative unlevelness index based on the Fourier transformation frequency component is introduced for evaluation of the degree of unlevelness of a set of dyed fabrics with different surface colour uniformities. A series of dyed denims with different degrees of unlevelness were prepared, and the degree of uneven appearance of fabrics was ranked by a group of observers. The surfaces of fabrics were imaged by a conventional scanner, and the Fourier transform was employed to compute the spectrum of desired images. It was found that the low‐frequency components of the computed matrix were stronger than the others, while its DC component, which related to the mean of the desired image, was too large. By this method, it was demonstrated that the fraction of the sum of the maximum of the second to sixth columns of the Fourier components of the captured image to the maximum of the first column component varied with the degree of unlevelness of the desired surfaces. The performance of the method was compared with five spectral and image based instrumental levelness–unlevelness indices, as well as those reported by visual ranking. Based on the results, the Fourier transformation method and the singular value decomposition technique show the best agreement with visual evaluation results, but the singular value decomposition method requires a longer computation time.     

Emulsion formulation optimization and characterization of spray-dried κ-carrageenan microparticles for the encapsulation of CoQ10

The present study is aimed to prepare κ-carrageenan microparticles for the encapsulation of model drug, coenzyme Q10 (CoQ10). A face-centered central composite design was employed to study the effects of three different formulation variables (κ-carrageenan, emulsifier, and oil). The powder yield was found inversely affected by the κ-carrageenan and oil concentration. The encapsulation efficiency was maximized in the region of the middle level κ-carrageenan concentration, the high level emulsifier concentration, and the low level oil concentration. The emulsifier concentration was the most influential variable on the particle size of powder. The optimal formulation was reported as 0.91% (w/v) κ-carrageenan concentration, 0.64% (w/v) emulsifier, and 1.0% (w/w) oil. Both differential scanning colorimeter and X-ray diffraction analyses proved that incorporation of CoQ10 into κ- carrageenan microcapsules resulted in amorphous powder with significantly (p<0.05) higher water solubility compared to pure CoQ10 and physical mixture in the crystalline form.     

Syngas production from dry reforming of methane over ni/perlite catalysts: Effect of zirconia and ceria impregnation

Ni catalysts with nominal loadings ranging between 2.5 and 20 wt% were synthesized over perlite by wet impregnation, then filtered, washed and calcined at 500 °C. Chemical analyses performed by MP-AES revealed that the maximum Ni content loaded over perlite corresponded to ～15 wt%. Therefore, for comparison reasons, a Ni(20 wt%)/perlite catalyst was prepared by wetness impregnation without performing any washing treatment. The so prepared catalysts were tested in methane dry reforming without performing any pre-treatment reduction. The catalytic performances were compared by increasing the temperature from 500 up to 800 °C under the reaction mixture composed of 15 vol% CH₄ + 15 vol% CO₂/N₂. Ni(15 wt%)/perlite was the most active catalyst among the series of monometallic samples. The effect of co-impregnating perlite with Ni and Zr or Ni and Ce precursors in order to obtain catalysts with final composition, Ni(15 wt%), 10 and 20 wt% as Zr or Ce, perlite (75 or 65 wt%) was, then, investigated. Characterizations performed by XRD, BET, DRS and H₂-TPR evidenced that the physico-chemical and reduction properties are influenced by the Ni content and by the presence of zirconia and ceria oxides. It is worth of noting the increased reducibility of NiO species promoted by zirconia and ceria addition. The catalytic activity in the dry reforming of methane was also affected by the presence of doping oxides, in terms of enhanced CH₄ and CO₂ conversions and higher H₂/CO atomic ratios. Runs tests at 700 °C for 12 h were carried out and the spent catalysts were analysed by TGA and TEM. Over Ni(15 wt%)/perlite large amount of amorphous carbon grows on the surface blocking the active centres, while zirconia and ceria doping improved the resistance to carbon poisoning favouring growing of filamentous carbon residues in small amount.     

Phyto‐fabrication,purification, characterisation,optimisation, and biological competence of nano‐silver

Published studies indicate that virtually any kind of botanical material can be exploited to make biocompatible, safe, and cost‐effective silver nanoparticles. This hypothesis is supported by the fact that plants possess active bio‐ingredients that function as powerful reducing and coating agents for Ag+. In this respect, a phytomediation method provides favourable monodisperse, crystalline, and spherical particles that can be easily purified by ultra‐centrifugation. However, the characteristics of the particles depend on the reaction conditions. Optimal reaction conditions observed in different experiments were 70–95 °C and pH 5.5–8.0. Green silver nanoparticles (AgNPs) have remarkable physical, chemical, optical, and biological properties. Research findings revealed the versatility of silver particles, ranging from exploitation in topical antimicrobial ointments to in vivo prosthetic/organ implants. Advances in research on biogenic silver nanoparticles have led to the development of sophisticated optical and electronic materials with improved efficiency in a compact configuration. So far, eco‐toxicity of these nanoparticles is a big challenge, and no reliable method to improve the toxicity has been reported. Therefore, there is a need for reliable models to evaluate the effect of these nanoparticles on living organisms.     

Effects of repeated frying on physicochemical characteristics,oxidative stress,and free radical scavenging potential of canola oil and ghee

The repeated use of cooking oils and ghee for the deep frying of food materials may affect their nutritional quality. The present study evaluated the effect of repeated frying on the physicochemical characteristics and antiradical potential of canola oil and ghee. The oil and ghee were used for frying of fish and chicken for 2, 4, 6, 8, and 10 frying cycles followed by the analysis of physicochemical, oxidative stress, and antiradical parameters. Regression analysis of the data showed a frying cycle-dependent significant linear increase in saponification (R² = 0.9507–0.9748), peroxide and acid values (R² = 0.956–0.9915), and malondialdehyde (MDA) production (R² = 0.9058–0.9557) of canola oil and ghee subjected to fish and chicken frying but exponential increase in saponification value (R² = 0.9778) and MDA production (R² = 0.7407) of canola oil and ghee used for fish frying. The increase in the number of frying cycles linearly decreased the iodine value (R² = 0.9781–0.9924), and 1, 1-diphenyl-2-picrylhydrazyl, hydroxyl, and 2, 2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) radical scavenging potential (R² = 0.9089–0.9979) of canola oil and ghee. Repeated frying in cooking oil and ghee increases oxidative stress and decreases their physicochemical and antioxidant qualities. Canola oil was comparatively more oxidative resistant than canola ghee. The regression equations derived from regression analysis will guide researchers to conduct similar types of univariate studies.     

MDEV Model: A Novel Ensemble-Based Transfer Learning Approach for Pneumonia Classification Using CXR Images

Pneumonia is a dangerous respiratory disease due to which breathing becomes incredibly difficult and painful; thus, catching it early is crucial. Medical physicians’ time is limited in outdoor situations due to many patients; therefore, automated systems can be a rescue. The input images from the X-ray equipment are also highly unpredictable due to variances in radiologists’ experience. Therefore, radiologists require an automated system that can swiftly and accurately detect pneumonic lungs from chest x-rays. In medical classifications, deep convolution neural networks are commonly used. This research aims to use deep pre-trained transfer learning models to accurately categorize CXR images into binary classes, i.e., Normal and Pneumonia. The MDEV is a proposed novel ensemble approach that concatenates four heterogeneous transfer learning models: MobileNet, DenseNet-201, EfficientNet-B0, and VGG-16, which have been finetuned and trained on 5,856 CXR images. The evaluation matrices used in this research to contrast different deep transfer learning architectures include precision, accuracy, recall, AUC-roc, and f1-score. The model effectively decreases training loss while increasing accuracy. The findings conclude that the proposed MDEV model outperformed cutting-edge deep transfer learning models and obtains an overall precision of 92.26%, an accuracy of 92.15%, a recall of 90.90%, an auc-roc score of 90.9%, and f-score of 91.49% with minimal data pre-processing, data augmentation, finetuning and hyperparameter adjustment in classifying Normal and Pneumonia chests.