Evaluation of biogeochemical model simulations using World Ocean Atlas and satellite observations in the Arabian Sea

Model simulations of the Ocean General Circulation Model (OGCM; MOM4p1), coupled with a state-of-the-art biogeochemical model TOPAZ (Tracers of Phytoplankton with Allometric Zooplankton), which includes multi-nutrient limitations including iron limitation, are used to study the seasonal variations of mixed-layer properties and their influence on nutrients and chlorophyll in the Arabian Sea. The spatial variation of nitrate during the Northeast Monsoon (NEM) and Southwest Monsoon (SWM), in the northern and western parts of the Arabian Sea and coast of Somalia, are very well captured by the model and compare well with observations. Modelled chlorophyll and primary productivity are validated with satellite-derived maps for the Arabian Sea.     

A smart application to detect pupil for small dataset with low illumination

Biometric applications are very sensitive to the process because of its complexity in presenting unstructured input to the processing. The existing applications of image processing are based on the implementation of different programing segments such as image acquisition, segmentation, extraction, and final output. The proposed model is designed with 2 convolution layers and 3 dense layers. We examined the module with 5 datasets including 3 benchmark datasets, namely CASIA, UBIRIS, MMU, random dataset, and the live video. We calculated the FPR, FNR, Precision, Recall, and accuracy of each dataset. The calculated accuracy of CASIA using the proposed system is 82.8%, for UBIRIS is 86%, MMU is 84%, and the random dataset is 84%. On live video with low resolution, calculated accuracy is 72.4%. The proposed system achieved better accuracy compared to existing state-of-the-art systems.

     

Morphological exploration of chemical vapor–deposited P-doped ZnO nanorods for efficient photoelectrochemical water splitting

Photoelectrochemical (PEC) water splitting is beneficial and has received attractive attention due to a greater potential to generate hydrogen and oxygen from water by using plentiful solar light to solve the problem of energy crisis. Various active semiconductor materials are used in PEC water splitting applications. Nevertheless, in past decades, most of the researchers suggested that titanium oxide (TiO₂) is the best photoanode for this type of applications. Now, Zinc oxide (ZnO) is considered a perfect substitution to TiO₂ due to its comparable energy band structure and superior photogenerated electron transfer rate. In this study, bare and phosphorous-doped ZnO nanorods were successfully developed on fluorine-doped tin oxide-coated glass (FTO) substrate by chemical vapor deposition. X-ray diffraction (XRD) pattern authenticated hexagonal structure formation with strong diffraction peak of (101), which showed that ZnO nanorods were perfectly developed along c axis. The optical and morphological properties were analyzed by UV–Vis and scanning electron microscopy images. The energy-dispersive X-ray spectra demonstrated that doping agent phosphorous was present in ZnO nanorods. The PEC properties of the developed ZnO nanorods were further investigated and obtained results suggested that a small amount of phosphorous-doped ZnO nanorods enhances their PEC performance.     

Dynamic Embrittlement in Cu-Cr-Zr-Ti Alloy: Evidence of Intergranular Segregation of Sulphur

In the present investigation, Cu-0.6Cr-0.005Zr-0.0045Ti alloy was subjected to different heat treatment and thermomechanical treatment (TMT) to simulate the conditions experienced during brazing and forming, respectively. Grain coarsening was observed in the samples subjected to heat treatment, and grain refinement was observed in the samples subjected to TMT. Tensile tests conducted with these samples at room temperature and 600 °C have shown that Cu-Cr-Zr-Ti alloy was susceptible to dynamic embrittlement (DE). However, the observation was limited to coarse-grained samples (280-350 μm) at 600 °C. On the other hand, the fine-grained samples (20-40 μm) showed good ductility. Electron microscopy studies conducted on the tensile-tested specimens prone to DE indicated the presence of sulfur on the fractured surface and intergranular segregation of sulfur. Therefore, it can be inferred from the results that DE due to sulfur can occur in Cu-Cr-Zr-Ti alloy at elevated temperature for coarse-grained samples.     

Failure analysis of titanium alloy (Ti6Al4V) fastener used in aerospace application

Titanium alloy fasteners are being used in space programme. These fasteners are coated with MoS₂, which serves the purpose of solid lubricant. During the trial assembly of flight spin motor to the bracket mounted on subsystem, one of the two fasteners failed such that the head of the bolt had sheared off the shank. Metallographic analysis carried out on the failed fasteners revealed variations in the microstructures all along the shank axis. Microstructure consisted of equiaxed primary alpha in transformed beta matrix within lower portion of the shank, while it was elongated primary alpha with little bulging all along prior beta grain boundaries as well as acicular alpha at some other location towards the head side, features, typical of, as if worked above beta transus temperatures.This paper highlights the details of investigations carried out on the failed fasteners.     

In vivo Targeting of DNA Vaccines to Dendritic Cells via the Mannose Receptor Induces Long-Lasting Immunity against Melanoma

Herein, we report effective, C-type lectin mannose receptor (MR)-selective, in vivo dendritic cell (DC)-targeting lipid nanoparticles (LNPs) of a novel lipid-containing mannose-mimicking di-shikimoyl- and guanidine head group and two n-hexadecyl hydrophobic tails (DSG). Subcutaneous administration of LNPs of the DSG/p-CMV-GFP complex showed a significant expression of green fluorescence protein in the CD11c⁺ DCs of the neighboring lymph nodes compared to the control LNPs of the BBG/p-CMV-GFP complex. Mannose receptor-facilitated in vivo DC-targeted vaccination (s.c.) with the electrostatic complex of LNPs of DSG/pCMV-MART1 stimulated long-lasting (270 days post B16F10 tumor challenge) antimelanoma immunity under prophylactic conditions. Remarkably, under therapeutic settings, vaccination (s.c.) with LNPs of the DSG/pCMV-MART1 complex significantly delayed melanoma growth and improved the survival of mice with melanoma. These findings demonstrate that this nonviral delivery system offers a resilient and potential approach to deliver DNA vaccines encoding tumor antigens to DCs in vivo with high efficacy.     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

Incompressible deformation estimation algorithm (IDEA) from tagged MR images

Measuring the 3D motion of muscular tissues, e.g., the heart or the tongue, using magnetic resonance (MR) tagging is typically carried out by interpolating the 2D motion information measured on orthogonal stacks of images. The incompressibility of muscle tissue is an important constraint on the reconstructed motion field and can significantly help to counter the sparsity and incompleteness of the available motion information. Previous methods utilizing this fact produced incompressible motions with limited accuracy. In this paper, we present an incompressible deformation estimation algorithm (IDEA) that reconstructs a dense representation of the 3D displacement field from tagged MR images and the estimated motion field is incompressible to high precision. At each imaged time frame, the tagged images are first processed to determine components of the displacement vector at each pixel relative to the reference time. IDEA then applies a smoothing, divergence-free, vector spline to interpolate velocity fields at intermediate discrete times such that the collection of velocity fields integrate over time to match the observed displacement components. Through this process, IDEA yields a dense estimate of a 3D displacement field that matches our observations and also corresponds to an incompressible motion. The method was validated with both numerical simulation and in vivo human experiments on the heart and the tongue.