Generation of millimeter wave Gaussian beams and their propagation in a dielectric waveguide

Linearly polarized fundamental mode Gaussian beams were generated and coupled to a hollow circular oversized dielectric waveguide by placing the waist of the beam at the guide entrance. The transmission properties of the waveguide were characterized as a function of frequency for a variety of coupling conditions. These conditions included changes in the input beam waist radius, angle of incidence, and displacement perpendicular and parallel to the guide axis. It has been found that: 1.) power transmission is maximized when the waist of the input beam is centered at the guide input, injected normally, and has a radius of 0.43 times the waveguide radius, 2.) power transmission decreases rapidly with increasing angle of incidence and the rate of that loss increases with frequency, 3.) the waveguide preserves the linear polarization of the input beam, 4.) power transmission in the fundamental waveguide mode is not greatly affected by moderate displacements in the input beam position, and 5.) upon exit from the waveguide the launched EH₁₁ mode propagates as a fundamental mode Gaussian beam in the quasi-far field. The results compare favorably to the transmission theory of Belland and Crenn and approximately to the near and far field mode pattern theory of Degnan.     

Application of three unsupervised neural network models to singular nonlinear BVP of transformed 2D Bratu equation

In this paper, numerical techniques are developed for solving two-dimensional Bratu equations using different neural network models optimized with the sequential quadratic programming technique. The original two-dimensional problem is transformed into an equivalent singular, nonlinear boundary value problem of ordinary differential equations. Three neural network models are developed for the transformed problem based on unsupervised error using log-sigmoid, radial basis and tan-sigmoid functions. Optimal weights for each model are trained with the help of the sequential quadratic programming algorithm. Three test cases of the equation are solved using the proposed schemes. Statistical analysis based on a large number of independent runs is carried out to validate the models in terms of accuracy, convergence and computational complexity.     

SARS-CoV-2 Viral Entry Proteins in Hyperandrogenemic Female Mice: Implications for Women with PCOS and COVID-19

SARS-CoV-2, the causative agent of COVID-19, infects host cells using the angiotensin I converting enzyme 2 (ACE2) as its receptor after priming by host proteases, including TMPRSS2. COVID-19 affects multiple organ systems, and male patients suffer increased severity and mortality. Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in reproductive-age women and is characterized by hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology. PCOS is associated with obesity and cardiometabolic comorbidities, both being risk factors associated with severe COVID-19 pathology. We hypothesize that elevated androgens in PCOS regulate SARS-CoV-2 entry proteins in multiple tissues increasing the risk for this population. Female mice were treated with dihydrotestosterone (DHT) for 90 days. Body composition was measured by EchoMRI. Fasting glucose was determined by an enzymatic method. mRNA and protein levels of ACE2, Tmprss2, Cathepsin L, Furin, Tmprss4, and Adam17 were quantified by RT-qPCR, Western-blot, or ELISA in tissues, serum, and urine. DHT treatment increased body weight, fat and lean mass, and fasting glucose. Ace2 mRNA was upregulated in the lung, cecum, heart, and kidney, while downregulated in the brain by DHT. ACE2 protein was upregulated by DHT in the small intestine, heart, and kidney. The SARS-CoV-2 priming proteases Tmprss2, Cathepsin L, and Furin mRNA were upregulated by DHT in the kidney. ACE2 sheddase Adam17 mRNA was upregulated by DHT in the kidney, which corresponded with increased urinary ACE2 in DHT treated mice. Our results highlight the potential for increased cardiac, renal, and gastrointestinal dysfunction in PCOS women with COVID-19.     

Metal nanoparticle doped coloured coatings on glasses and plastics through tuning of surface plasmon band position

Several noble metal nanoparticles doped sol-gel derived thin coloured films have been synthesized and characterized. These are pure (Ag, Au, Cu and Pt), mixed/alloy (Ag-Cu, Au-Cu, Au-Ag and Au-Pt) nanoparticles in SiO₂, Au in mixed SiO₂-TiO₂ and SiO₂-ZrO₂, Au and Ag nanoparticles in inorganic-organic hybrid film matrices etc. This investigation leads to the development of tailor-made coloured coatings by tuning the surface plasmon resonance (SPR) band positions originating from the embedded nanometals by controlling mainly (i) refractive index of the film matrices and (ii) nanoalloy composition. In the later case a new layer-by-layer (two-layer) synthetic protocol has been developed to prepare binary nanoalloy particles with controlled atomic ratios.     

Correction: Novel Surface Topography and Microhardness Characterization of Laser Clad Layer on TC4 Titanium Alloy Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Metallurgical and Materials Transactions A -     

Investigations for the optimal combination of zinc oxide nanoparticle-diesel fuel with optimal compression ratio for improving performance and reducing the emission features of variable compression ratio diesel engine

Clean Technologies and Environmental Policy - The continual hike in environmental pollution from diesel engines has raised the concern of researchers to identify certain clean fuels. For this...     

Screening of arsenic in tubewell water with field test kits: evaluation of the method from public health perspective

There is an urgent need for Bangladesh to identify the arsenic (As) contaminated tubewells (TWs) in order to assess the health risks and initiate appropriate mitigation measures. This will involve testing water in millions of TWs and raising community awareness about the health problems related to chronic As exposure from drinking water. Field test kits offer the only practical tool within the time frame and financial resources available for screening and assessment of the As contaminated TWs as well as their monitoring than that of the laboratory measurement. A comparison of field test kit and laboratory measurements by AAS as "gold standard" for As in water of 12,532 TWs in Matlab Upazila in Bangladesh, indicates that the field kit correctly determined the status of 91% of the As levels compared to the Bangladesh Drinking Water Standard (BDWS) of 50 microg/L, and 87% of the WHO guideline value of 10 microg/L. Nevertheless, due to analytical and human errors during the determination of As by the field test kits, some misclassification of wells is inevitable. Cross-checking of the field test kit results, both by Field Supervisor and by the laboratory analyses reveal considerable discrepancies in the correct screening mainly at As concentration ranges of 10-24.9 microg/L and 50-99.9 microg/L, critical from a public health point of view. The uncertainties of misclassification of these two groups of TWs have severe public health implications due to As exposure from drinking water sources. This can be reduced through proper training of the field personnel, cross verification of the field test kit results with laboratory analyses and further development of the field test kits to determine As at low concentrations.     

Histochemical study of magellanic penguin (Spheniscus magellanicus) minor salivary glands during postnatal growth

The histological and histochemical features of the minor salivary glands during postnatal development have been generally associated with the type of food ingested. However, recent studies support the fact that these salivary glands develop independently of the diet; in fact, minor salivary glands have similar morphological and histochemical characteristics in adult individuals of species with different diet regimens. Thus, the aim of this study was to characterize the developmental morphology of the penguin minor salivary glands and to contrast them with minor salivary glands of other species. The tongue, palatine, and mouth cavity (bottom) minor salivary glands of newborn, 1- to 20-day-old, and adult magellanic penguins were studied with hematoxylin-eosin, periodic acid-Schiff, alcian blue, toluidine blue, and lectin histochemistry. Minor salivary glands were present at all ages, although they were only moderately developed in animals less than 15 days old. After this age, glands were abundant in all age groups; in addition, cells from the glandular epithelium were functionally mature and secreted mucins. Nevertheless, in newborn to 15-day-old penguins, mucins were located only at the apical cytoplasm of mucous cells. In all ages, mucous cells displayed periodic acid-Schiff-positive, alcianophilic, and metachromatic reactions; among mucous cells, other orthochromatic cells appeared interspersed. From 15 days on, histochemical reactions became more intense until adulthood, and the cytoplasm of secretory cells was filled with glycoproteins and sulfomucins. Moreover, lectins bound to different oligosaccharides in mucous cells, depending on the stage of maturation of the glands. In conclusion, penguin minor salivary glands are already present at birth, and show progressive and quantitative increases in mucous secretion during postnatal development. These changes are necessary not only for nutrient ingestion, but also for nonimmune protection of the buccal cavity.     

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the average of the feature engineering approaches includes: Recursive Feature Eliminator (RFE), Extreme Gradient Boosting (XGboost), Random Forest (RF), and are upgraded to extract the most relevant and significant features. To this end, we have proposed the DensetNet-121 network and Support Vector Machine (SVM) ensemble with Aquila Optimizer (AO) to ensure adaptability and handle the complexity of data in the classification. Further, the AO method helps to tune the parameters of DensNet (121 layers) and SVM, which achieves less training loss, computational time, minimized overfitting problems and more training/test accuracy. Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes. Our proposed method has achieved a minimal value of the Mean Average Percentage Error (MAPE) rate i.e., 8% by DenseNet-AO and 6% by SVM-AO and the maximum accurateness rate of 92% and 95%, respectively.