Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey

The most important theme in this study is to obtain equations based on economic indicators (gross national product—GNP and gross domestic product—GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years (1968–2005) are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R² values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections.     

Hardness and Wear Behaviours of Al Matrix Composites and Hybrid Composites Reinforced with B4C and SiC

Powder Metallurgy and Metal Ceramics - The conversion into the desired shape of the metal powders using Powder Metallurgy (PM) method enables economically mass productions. This case allows...     

Effects of annealing on the structural and magnetic properties of flame spray pyrolyzed MnFe2O4 nanoparticles

In this study, manganese ferrite (MnFe₂O₄) nanoparticles were produced through flame spray pyrolysis (FSP). To investigate the effects of heat treatment, the nanoparticles were annealed between 400 and 650°C for 4 h in air in a comparative manner. The structural, chemical, morphological, and magnetic properties of the nanoparticles were evaluated using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), dynamic light scattering (DLS), and vibrating sample magnetometry (VSM), respectively. The XRD results showed that the nanoparticles synthesized by the FSP method exhibited the MnFe₂O₄ spinel ferrite structure. The annealing process led to the decomposition of MnFe₂O₄ into various phases. According to the morphological analysis, the as-synthesized particles were hemispherical–cubic in shape and had an average particle size of less than 100 nm. In addition, the chemical bond structures of the nanoparticles were confirmed in detail by XPS elemental analysis. The highest saturation magnetization was recorded as 33.50 emu/g for the as-produced nanoparticles. The saturation magnetization of the nanoparticles decreased with increasing annealing temperature, while coercivity increased.     

Fuzzy FMEA in risk assessment for test and calibration laboratories

In this study, it is aimed to compare traditional and fuzzy FMEA in identifying areas that may pose risks and need improvement in Test and Calibration Laboratories. Within this scope, FMEA is used in ranking the possible risks. One hundred ninety-nine failures are detected in 91 inspections, carried out in the Test and Calibration Laboratories. Since FMEA uses experts’ evaluations, which are considered subjective, fuzzy logic is implemented to the approach where the evaluations are presented with linguistic variables. The comparison of FMEA and fuzzy FMEA showed that there exists a high correlation between these two analyses and the order of priority based on the Fuzzy Risk Priority Number calculation is overlapping with the Risk Priority Number sequence. Fuzzy FMEA can also be considered when the evaluations are not trustworthy or incomplete. Therefore, this study can be addressed as an example of how fuzzy implementation to FMEA substantially be used instead of traditional FMEA when there exist qualitative, subjective or incomplete evaluations, or in cases where traditional FMEA has troubles in practice.     

MPI8 is Potent against SARS-CoV-2 by Inhibiting Dually and Selectively the SARS-CoV-2 Main Protease and the Host Cathepsin L**

A number of inhibitors have been developed for the SARS-CoV-2 main protease (M^Pro) as potential COVID-19 medications but little is known about their selectivity. Using enzymatic assays, we characterized inhibition of TMPRSS2, furin, and cathepsins B/K/L by more than a dozen of previously developed M^Pro inhibitors including MPI1-9, GC376, 11a, 10–1, 10–2, and 10–3. MPI1-9, GC376 and 11a all contain an aldehyde for the formation of a reversible covalent hemiacetal adduct with the M^Pro active site cysteine and 10–1, 10–2 and 10–3 contain a labile ester to exchange with the M^Pro active site cysteine for the formation of a thioester. Our data revealed that all these inhibitors are inert toward TMPRSS2 and furin. Diaryl esters also showed low inhibition of cathepsins. However, all aldehyde inhibitors displayed high potency in inhibiting three cathepsins. Their determined IC₅₀ values vary from 4.1 to 380 nM for cathepsin B, 0.079 to 2.3 nM for cathepsin L, and 0.35 to 180 nM for cathepsin K. All aldehyde inhibitors showed similar inhibition levels toward cathepsin L. A cellular analysis indicated high potency of MPI5 and MPI8 in inhibiting lysosomal activity, which is probably attributed to their inhibition of cathepsins. Among all aldehyde inhibitors, MPI8 shows the best selectivity toward cathepsin L. With respect to cathepsins B and K, the selective indices are 192 and 150, respectively. MPI8 is the most potent compound among all aldehyde inhibitors in cellular M^Pro inhibition potency and anti-SARS-CoV-2 activity in Vero E6 cells. Cathepsin L has been demonstrated to play a critical role in the SARS-CoV-2 cell entry. By selectively inhibiting both SARS-CoV-2 M^Pro and the host cathepsin L, MPI8 potentiates dual inhibition effects to synergize its overall antiviral potency and efficacy. Due to its high selectivity toward cathepsin L that reduces potential toxicity toward host cells and high cellular and antiviral potency, we urge serious consideration of MPI8 for preclinical and clinical investigations for treating COVID-19.     

A Novel Strategy as a Potential Rapid Therapy Modality in the Treatment of Corneal Ulcers: Fluconazole/Vancomycin Dual Drug-Loaded Nanofibrous Patches

Corneal ulcer, which is brought on by a breach in the epithelial barrier, is a dangerous infection of the avascular corneal stroma. New treatment strategies are needed, suppressing the aggressive nature of the disease and including a combination of different drugs. In this study, vancomycin (VAN) and fluconazole (FLU) dual-drug loaded dual-layered polyvinyl alcohol and gelatin (PVA/GEL) nanofibrous patches are produced by electrospinning. Scanning electron microscopy (SEM) images show smooth surfaces are obtained for both pure and drug-loaded nanofibrous patches. The tensile test results report that loading the FLU and VAN separately into the PVA/GEL patches decrease both the tensile strength and elongation at break and it is further reduced when combining two drug-loaded layers in one patch. According to drug release results, the FLU and VAN-loaded nanofibrous patches show a controlled release profile extending up to 96 h. Moreover, PVA/GEL/FLU, PVA/GEL/VAN, and PVA/GEL/FLU/VAN nanofibrous patches display significant antimicrobial activity against Candida albicans and Staphylococcus aureus. SEM, 4'-6diamidynofenyloindol (DAPI) staining, and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay show that PVA/FLU and PVA/GEL/FLU/VAN nanofibrous patches have a superior effect on NIH3T3 cell spreading and proliferation. The novelty of this study lays in the development of a potential dual drug rapid treatment for corneal ulcers of aggressive nature.     

The effect of zinc oxide nanoparticles on the weathering performance of wood-plastic composites

In recent years, wood-plastic composites (WPCs) have become among the most popular engineering materials. Most of their usage areas are outdoors, where they encounter various damaging factors. The weathering conditions cause significant deterioration to WPC surfaces, which negatively influences their service life. In this study, zinc oxide nanoparticles at different concentrations (1%, 3%, 5%, 10%) were added to a high-density polyethylene-based WPC matrix. The effect of zinc oxide nanoparticles on the weathering performance of WPC was evaluated after 840 hours of an artificial weathering test. The highest colour changes (∆E*) were monitored with control samples exposed for 840 hours. Adding zinc oxide nanoparticles improved the ultraviolet (UV) resistance and decreased the colour changes. The wood flour content also affected the colour changes on the WPC surface. A combination of 10% zinc oxide nanoparticles and 50% wood flour content provided the lowest colour changes. The barrier effect of nanoparticles protected the WPC surfaces from UV light. Zinc oxide nanoparticles also positively affected the load transfer, which restricted the reduction in mechanical properties after the weathering test. The degradation on the surface of WPCs was also investigated using attenuated total reflectance-Fourier Transform–infrared analysis. The changes in the characteristic bands of polymer and wood indicated that surface degradation was inevitable. Light and scanning electron microscopy images also demonstrated micro-cracks and roughness on the surface of WPCs. It is concluded that UV degradation is unavoidable, but zinc oxide nanoparticles can improve surface resistance against weathering conditions.