Elaboration and Characterization of Amorphous Silicon Carbide Thin Films (a-SiC) by Sputerring Magnetron Technique for Photoelectrochemical CO2 Conversion

Silicon - Semiconductors as photoelectric catalysis is recognized as a promising strategy for simultaneous face energy crisis and environmental pollution. In this study, amorphous silicon carbide...     

Effects on Escherichia coli O157:H7 and meat starter cultures of bovine lactoferrin in broth and microencapsulated lactoferrin in dry sausage batters

The effects of lactoferrin (LF) alone or with various chelating agents on the growth of 5 strains of Escherichia coli O157:H7 and 7 meat starter cultures were evaluated. E.coli O157:H7 and starter cultures were grown at 13 or 26 degrees C in Lauria (LB) or All Purpose Tween (APT) broths, respectively, with both broths being supplemented with 2.9% NaCl. LF alone prevented the growth of E. coli O157:H7 strains 0627 and 0628 but other strains grew. The antimicrobial effectiveness of LF was enhanced by EDTA but LF alone did not affect the growth of meat starter cultures in broth. However, when LF plus EDTA and sodium bicarbonate (SB) were used the growth of all meat starter cultures except Lactobacillus curvatus was reduced. During dry sausage manufacture with L. curvatus and Staphylococcus carnosus starter cultures the effects of LF, unencapsulated or microencapsulated in paste-like and dried powder forms, in sausage batters with or without EDTA and SB, on the viability of E. coli O157:H7 were examined. The reduction of E. coli O157:H7 during sausage manufacture was significantly enhanced (p<0.05) by all LF treatments. The largest reduction (4.2 log units) was obtained with unencapsulated LF. However, some of the apparent reduction in E.coli O157:H7 numbers with all treatments was due to cell injury rather than lethality, since significantly greater numbers were recovered on APT agar overlaid with the selective medium cefixime-tellurite Sorbitol McConkey agar (ct-SMAC) than on ct-SMAC alone. The narrow spectrum of LF activity and induction of injury rather than inactivation of E. coli O157:H7 limit the effectiveness of this agent against the pathogen in fermented meats.     

Efficacy of the thin agar layer method for the recovery of stressed Cronobacter spp. (Enterobacter sakazakii)

Cronobacter spp. (Enterobacter sakazakii) are emerging opportunistic pathogens for all age groups, and are of particular concern when it comes to infants. Prior to contaminating food, the organism may be exposed to a variety of stresses, leading to a generation of sublethally injured cells that may not be detected by selective media unless a protracted recovery period is included in the isolation procedure. This study evaluated the efficacy of the thin agar layer (TAL) method for the recovery of Cronobacter cells that had been exposed to various stress conditions. Five strains of C. sakazakii and C. muytjensii were exposed to starvation, heat, cold, acid, alkaline, chlorine, or ethanol, with or without further exposure to desiccation stress. The recovery of the stressed cells was determined on tryptone soy agar (TSA; nonselective control medium), violet red bile glucose agar (VRBGA; selective agar), Druggan-Forsythe-Iversen (DFI; selective agar), and TAL media (viz., VRBGA overlaid with TSA, and DFI overlaid with TSA). Regardless of stress type, there were no significant differences among the recoveries of stressed desiccated Cronobacter spp. cultures on TSA, DFI+TSA, and VRBGA+TSA, but there was significantly less recovery on VRBGA. The recovery of prestressed desiccated Cronobacter spp. on DFI+TSA was similar to that on TSA, whereas the recovery on VRBGA+TSA was lower. DFI+TSA performed better than VRBGA+TSA did in differentiating Cronobacter spp. within mixed bacterial cultures. The results of this study suggest the use of the TAL method DFI+TSA as an improved method for the direct recovery of stressed Cronobacter spp.     

Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: between simplistic scenarios and complex dynamics

Toxic cyanobacterial blooms represent a serious hazard to environmental and human health, and the management and restoration of affected waterbodies can be challenging. While cyanobacterial blooms are already a frequent occurrence, in the future their incidence and severity are predicted to increase due to climate change. Climate change is predicted to lead to increased temperature and changes in rainfall patterns, which will both have a significant impact on inland water resources. While many studies indicate that a higher temperature will favour cyanobacterial bloom occurrences, the impact of changed rainfall patterns is widely under-researched and therefore less understood.This review synthesizes the predicted changes in rainfall patterns and their potential impact on inland waterbodies, and identifies mechanisms that influence the occurrence and severity of toxic cyanobacterial blooms.It is predicted that there will be a higher frequency and intensity of rainfall events with longer drought periods in between. Such changes in the rainfall patterns will lead to favourable conditions for cyanobacterial growth due to a greater nutrient input into waterbodies during heavy rainfall events, combined with potentially longer periods of high evaporation and stratification. These conditions are likely to lead to an acceleration of the eutrophication process and prolonged warm periods without mixing of the water column. However, the frequent occurrence of heavy rain events can also lead to a temporary disruption of cyanobacterial blooms due to flushing and de-stratification, and large storm events have been shown to have a long-term negative effect on cyanobacterial blooms. In contrast, a higher number of small rainfall events or wet days can lead to proliferation of cyanobacteria, as they can rapidly use nutrients that are added during rainfall events, especially if stratification remains unchanged.With rainfall patterns changing, cyanobacterial toxin concentration in waterbodies is expected to increase. Firstly, this is due to accelerated eutrophication which supports higher cyanobacterial biomass. Secondly, predicted changes in rainfall patterns produce more favourable growth conditions for cyanobacteria, which is likely to increase the toxin production rate. However, the toxin concentration in inland waterbodies will also depend on the effect of rainfall events on cyanobacterial strain succession, a process that is still little understood. Low light conditions after heavy rainfall events might favour non-toxic strains, whilst inorganic nutrient input might promote the dominance of toxic strains in blooms. This review emphasizes that the impact of changes in rainfall patterns is very complex and will strongly depend on the site-specific dynamics, cyanobacterial species composition and cyanobacterial strain succession. More effort is needed to understand the relationship between rainfall patterns and cyanobacterial bloom dynamics, and in particular toxin production, to be able to assess and mediate the significant threat cyanobacterial blooms pose to our water resources.     

Selection of Wind Turbine Systems for the Sultanate of Oman

The Sultanate of Oman has been dealing with a severe renewable energy issue for the past few decades, and the government has struggled to find a solution. In addition, Oman’s strategy for converting power generation to sources of renewable energy includes a goal of 60 percent of national energy demands being met by renewables by 2040, including solar and wind turbines. Furthermore, the use of small-scale energy from wind devices has been on the rise in recent years. This upward trend is attributed to advancements in wind turbine technology, which have lowered the cost of energy from wind. To calculate the internal and external factors that affect the small-scale energy of wind technologies, the study used a fuzzy analytical hierarchy process technique for order of preference by similarity to an ideal solution. As a result, in the decision model, four criteria, seventeen sub-criteria, and three resources of renewable energy were calculated as options from the viewpoint of the Sultanate of Oman. This research is based on an examination of statistics on energy produced by wind turbines at various locations in the Sultanate of Oman. Further, six distinct miniature wind turbines were investigated for four different locations. The outcomes of this study indicate that the tiny wind turbine has a lot of potential in the Sultanate of Oman for applications such as homes, schools, college campuses, irrigation, greenhouses, communities, and small businesses. The government should also use renewable energy resources to help with the renewable energy issue and make sure that the country has enough renewable energy for its long-term growth.     

Adenine Modification at C7 as a Viable Strategy to Potentiate the Antimalarial Activity of Quinolones

Although many quinolones have shown promise as potent antimalarials, their clinical development has been slow due to poor performance in vivo. Insights into structural modifications that can improve their therapeutic potential will be very valuable in this vibrant area of research. Our studies involving a library of quinolones which vary in substitution pattern at N1, C3, C6 and C7 positions have shown that the presence of adenine moiety at C7 can bring a noticeable improvement in activity compared to other heterocyclic groups at this location. The most potent compound emerged from this study showed IC₅₀ values of 0.38 μM and 0.75 μM against chloroquine-sensitive and -resistant (W2) strains, respectively. Docking analysis in the Q_o site of cytochrome bc1 complex revealed the contribution of a key H-bonding interaction from the adenine unit in target binding. This corroborates with compound-induced loss of mitochondrial functions. These findings not only open avenues for further exploration of antimalarial potential of adenine-modified quinolones, but also suggests broader opportunities during lead-optimization against other antimalarial targets.     

Color Edge Detection Using Multidirectional Sobel Filter and Fuzzy Fusion

A new model is proposed in this paper on color edge detection that uses the second derivative operators and data fusion mechanism. The second-order neighborhood shows the connection between the current pixel and the surroundings of this pixel. This connection is for each RGB component color of the input image. Once the image edges are detected for the three primary colors: red, green, and blue, these colors are merged using the combination rule. Then, the final decision is applied to obtain the segmentation. This process allows different data sources to be combined, which is essential to improve the image information quality and have an optimal image segmentation. Finally, the segmentation results of the proposed model are validated. Moreover, the classification accuracy of the tested data is assessed, and a comparison with other current models is conducted. The comparison results show that the proposed model outperforms the existing models in image segmentation.     

A Highly Accurate Dysphonia Detection System Using Linear Discriminant Analysis

The recognition of pathological voice is considered a difficult task for speech analysis. Moreover, otolaryngologists needed to rely on oral communication with patients to discover traces of voice pathologies like dysphonia that are caused by voice alteration of vocal folds and their accuracy is between 60%–70%. To enhance detection accuracy and reduce processing speed of dysphonia detection, a novel approach is proposed in this paper. We have leveraged Linear Discriminant Analysis (LDA) to train multiple Machine Learning (ML) models for dysphonia detection. Several ML models are utilized like Support Vector Machine (SVM), Logistic Regression, and K-nearest neighbor (K-NN) to predict the voice pathologies based on features like Mel-Frequency Cepstral Coefficients (MFCC), Fundamental Frequency (F₀), Shimmer (%), Jitter (%), and Harmonic to Noise Ratio (HNR). The experiments were performed using Saarbrucken Voice Database (SVD) and a privately collected dataset. The K-fold cross-validation approach was incorporated to increase the robustness and stability of the ML models. According to the experimental results, our proposed approach has a 70% increase in processing speed over Principal Component Analysis (PCA) and performs remarkably well with a recognition accuracy of 95.24% on the SVD dataset surpassing the previous best accuracy of 82.37%. In the case of the private dataset, our proposed method achieved an accuracy rate of 93.37%. It can be an effective non-invasive method to detect dysphonia.