Localized corrosion evaluation of newly developed stainless-steel alloys in chloride medium through dynamic and localized micro electrochemical techniques

Novel Fe–Cr SS alloys were fabricated by alloying with 0, 0.10 and 0.20 wt-% Sn respectively to investigate the effect of the Sn addition on the microstructure, passive characteristics and localized corrosion behavior in chloride medium at room temperature. The advanced scanning micro-electrochemical techniques along with the traditional electrochemical methods were used for in-depth investigations. Potentiodynamic and potentiostatic test results exhibited that the addition of Sn in SS alloys increased the resistance against pitting corrosion in NaCl medium. The acquired dynamic impedance data confirmed the effective electrochemical stability of the alloy SS3 (SS with 0.20 wt-% Sn) at applied anodic potential, suggesting the presence of a compact and stable passive film in Sn containing alloys, which contributed to their improved localized corrosion resistance in chloride solution. The scanning electrochemical microscopic results corroborated that the alloy SS3 remained stable even after 24 h of exposure and displayed no noticeable surface heterogeneity. A reduced anodic current density in scanning vibrative electrode technique mapping images was observed even after 24-h of exposure in alloy SS3 compared to other samples, indicating that the anodic metal dissolution was successfully obstructed by forming a compact and dense passive film on this alloy

Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models

Emotions play a crucial role in human interaction and healthcare. This study introduces an automatic emotion recognition system based on deep learning using electroencephalogram signals. A lightweight pyramidal one-dimensional convolutional neural network model is proposed that involves a small number of learnable parameters. Using the model, a two-level ensemble classifier is designed. Each channel is scanned incrementally in the first level to generate predictions, which are fused using the majority vote. The second level fuses the predictions of all the channels of a signal using a majority vote to predict the emotional state. The method was validated using the public domain challenging benchmark DEAP dataset. The electroencephalogram signals over five brain regions were analyzed. The results indicate that the frontal brain region plays a dominant role, achieving accuracies of 98.43% and 97.65% for two emotion recognition problems (distinguishing high valence vs. low valence and high arousal vs. low arousal states)

Pulmonary Nodule Classification Using Feature and Ensemble Learning-Based Fusion Techniques

The Pulmonary nodule indicates the presence of lung cancer. The deep convolutional neural networks (DCNNs) have been widely used to classify the pulmonary nodule as benign or malignant. However, an individual learner usually performs unsatisfactorily due to limited response space, incorrect selection of hypothesis space, or falling into local minimums. To investigate these issues, we propose ensemble learners fusion techniques based on averaging of prediction score and maximum vote score (MAX-VOTE). First, the support vector machine (SVM) and AdaBoostM2 machine learning algorithms are trained on the deep features from DCNNs. The results of both classifiers are fused separately based on averaging of the prediction score. Secondly, the feature fusion technique is developed by fusing the feature of three DCNNs (AlexNet, VGG-16 and VGG-19) through predefined rules. After that, the SVM and AdaBoostM2 are trained on fused features independently to build ensemble learners by fusing the multiple DCNN learners. The predictions of all DCNN learners are fused based on the MAX-VOTE. The results show that the ensemble learners based MAX-VOTE technique yields better performance out of twelve single learners for binary class classification of pulmonary nodules. The proposed fusion techniques are also tested for multi-class classification problem. The SVM based feature fusion technique performs better as compared to all the implemented and the state-of-the-art techniques. The achieved maximum accuracy, AUC and specificity scores are 96.89%±0.25, 99.21%±0.10 and 97.70%±0.21, respectively

Insight into Structural Changes and Electrochemical Properties of Spark Plasma Sintered Nanostructured Ferritic and Austenitic Stainless Steels

Nanostructured ferritic (Fe(82−x)-Cr18-Six, x = 0–3 wt %) and austenitic (Fe(73−x)-Cr18-Ni9-Six, x = 0–3 wt %) stainless steel (SS) alloys were developed by mechanical alloying (MA) and spark plasma sintering (SPS). The unit cell parameter estimated from X-ray diffraction spectra exhibited a decreasing trend with an increase in wt % of Si content in both alloy systems. The particle size of powders estimated using bright field transmission electron microscopy images for ferritic (3 wt % Si) and austenitic (3 wt % Si) SS powders was found to be 65 ± 5 nm and 18 ± 3 nm, respectively. In case of the ferritic system, 3 wt % Si exhibited the highest densification (~98%) and micro-hardness of about 350.6 ± 11.2 HV, respectively. Similarly, for the austenitic system (3 wt % Si), maximum densification and micro-hardness values were about 99% and 476.6 ± 15.2 HV, respectively. Comparative analysis of potentiodynamic polarization, linear polarization, and electrochemical impedance spectroscopy results indicates an increase in electrochemical performance of both alloy systems as the wt % Si was increased. The increase in electrochemical performance is directly related to the increase in densification owing to Si addition in these alloys

Comparison of classical and transgenic genetic sexing strains of Mediterranean fruit fly (Diptera: Tephritidae) for application of the sterile insect technique.

The development of genetic sexing strains (GSSs) based on classical genetic approaches has revolutionized the application of the sterile insect technique (SIT) against the Mediterranean fruit fly Ceratitis capitata (Wiedemann) (Diptera: Tephritidae). The global use of Mediterranean fruit fly GSS for SIT applications as part of area-wide integrated pest management (AW-IPM) programmes is testimony to their effectiveness. During recent years, transgenic sexing strains (TSSs) have been developed through genetic engineering techniques offering the possibility to produce male-only progeny by introducing female embryonic lethal genes and to increase the efficacy to identify released sterile males by means of the expression of fluorescent transgene markers. Here, we present a comparative analysis of two Mediterranean fruit fly strains: the classical GSS VIENNA 8D53-/Toliman and the transgenic FSEL#32. The strains were compared for production efficiency and quality control indices under semi mass-rearing conditions, response to sterilizing irradiation doses, male mating performance in walk-in field cages, and production cost of male-only pupae. The results showed that, the FSEL #32 TSS had a similar fecundity but a higher production of male-only pupae than the VIENNA 8D53-/Toliman GSS. For some of the quality control parameters tested, such as pupal weight and survival under starvation conditions, the FSEL #32 TSS was inferior to the VIENNA 8D53-/Toliman GSS. Both the transgenic and the classical genetic sexing strains have shown acceptable and similar mating competitiveness when compared with wild males for mating with wild females. The cost production for both strains is similar but the FSEL#32 TSS may potentially be more cost effective at higher production levels. The results are discussed in the context of incorporating the transgenic strain for SIT application

Association of virulence genes with antibiotic resistance in Pakistani uropathogenic E. coli isolates

Background: Escherichia coli various strains can cause alarmingly serious infections. Countries like Pakistan harbour the class of bacteria with one of the highest rates of resistance, but very little has been done to explore their genetic pool. Objectives: This study was designed to find out the frequency of virulence genes of Uropathogenic E. coli and their association with antibiotic resistance along with the evolutionary adaptation of the selected gene through the phylogenetic tree. Methods: Isolates from 120 urinary tract infected patients were collected. Antibiotic sensitivity was detected by the disk diffusion method and DNA extraction was done by the boiling lysis method followed by PCR-based detection of virulence genes. The final results were analysed using the chi-square test. Results: The isolates were found to be least susceptible to nalidixic acid, followed by ampicillin, cotrimoxazole, cefotaxime, ciprofloxacin, aztreonam, amoxicillin, gentamycin, nitrofurantoin and imipenem. The iucC was the most common virulence gene among the resistant isolates. About 86% of the collected samples were found to be multi-drug resistant. Statistical analysis revealed a significant association between the iucC gene and resistance to ampicillin (P=0.03) and amoxicillin (P=0.04), and also between fimH and resistance to aztreonam (P=0.03). Conclusion: This study unravels the uncharted virulence genes of UPEC in our community for the very first time. We report a high frequency of the iucC and fimH virulence genes. This, along with their positive association with resistance to beta-lactam antibiotics in the studied community, indicates their important role in the development of complicated UTIs

Wavelet based de-noising using logarithmic shrinkage function

Noise in signals and images can be removed through different de-noising techniques such as mean filtering, median filtering, total variation and filtered variation techniques etc. Wavelet based de-noising is one of the major techniques used for noise removal. In the first part of our work, wavelet transform based logarithmic shrinkage technique is used for de-noising of images, corrupted by noise (during under-sampling in the frequency domain). The logarithmic shrinkage technique is applied to under-sampled Shepp–Logan Phantom image. Experimental results show that the logarithmic shrinkage technique is 7–10% better in PSNR values than the existing classical techniques. In the second part of our work we de-noise the noisy, under-sampled phantom image, having salt and pepper, Gaussian, speckle and Poisson noises through the four thresholding techniques and compute their correlations with the original image. They give the correlation values close to the noisy image. By applying median or wiener filter in parallel with the thresholding techniques, we get 30–35% better results than only applying the thresholding techniques individually. So, in the second part we recover and de-noise the sparse under-sampled images by the combination of shrinkage functions and median filtering or wiener filtering

Biogenic Synthesis of Zinc Oxide Nanoparticles Using <i>Citrullus colocynthis</i> for Potential Biomedical Applications

Green nanoparticle synthesis is considered the most efficient and safe nanoparticle synthesis method, both economically and environmentally. The current research was focused on synthesizing zinc oxide nanoparticles (ZnONPs) from fruit and leaf extracts of Citrullus colocynthis. Four solvents (n-hexane, methanol, ethyl acetate, and aqueous) were used to prepare the extracts from both plant parts by maceration and extraction. Zinc acetate was used to synthesize the nanoparticles (NPs), and color change indicated the synthesis of ZnONPs. X-ray diffraction, UV spectroscopy, and scanning electron microscopy were used to study the ZnONPs. UV–visible spectroscopy revealed an absorbance peak in the 350–400 nm range. XRD patterns revealed the face-centered cubic structure of the ZnONPs. SEM confirmed a spherical morphology and a size range between 64 and 82 nm. Phytochemical assays confirmed that the complete flavonoid, phenolic, and alkaloid concentrations were higher in unrefined solvent extracts than in nanoparticles. Nanoparticles of C. colocynthis fruit aqueous extracts showed stronger antioxidant activity compared with the crude extracts. Strong antifungal activity was exhibited by the leaves, crude extracts, and nanoparticles of the n-hexane solvent. In a protein kinase inhibition assay, the maximum bald zone was revealed by nanoparticles of ethyl acetate extracts from leaves. The crude extracts and nanoparticles of leaves showed high cytotoxic activities of the n-hexane solvent, with LC50 values of 42.08 and 46.35, respectively. Potential antidiabetic activity was shown by the n-hexane (93.42%) and aqueous (82.54%) nanoparticles of the fruit. The bioactivity of the plant showed that it is a good candidate for therapeutic use. The biosynthesized ZnONPs showed promising antimicrobial, cytotoxic, antidiabetic, and antioxidant properties. Additionally, the in vivo assessment of a nano-directed drug delivery system for future therapeutic use can be conducted based on this study