Optimization of Energy Aware Path Routing Protocol in Wireless Sensor Networks

Strength conservation is one of the biggest challenges to the successful WSNs since the tiny  very limited resource nodes  such as energy, memory space| as well as communication and computation capabilities. the sensors are unattended Implemented  and battery recharge is almost impossible. So  many investigations have be done in redirecting energy efficient algorithms or protocols for WSNs. Our reasons behinds the study of number is based on the following three aspects. Initially of all First, we see That immediate transmittal is employed under small scale while multi-hop network transmittal network is employed under mass. All of us want to find the Which factors influence the transmittal manner. Second, it is Commonly That multi-hop agree transmitting more energy efficient than Usually transmitting When the average solitary source to destination distance is large. Yet ,}how to look for the optimal hop number in order That the overall energy consumption is  nominal is not well  tackled. Third, the hot location phenomenon the networking lifetime influences directly. After that all of us recommend to Optimization of energy aware routing path (OEAPR) algorithm, Which incorporate the overall routing mechanism With hop-based direction-finding nature During process in WSNs

PREVALENCE OF SUBCLINICAL HYPOCALCAEMIA and subclinical ketosis in buffaloes

ABSTRACT The present study was undertaken to while buffaloes in fi rst parity were least affected by the two conditions. The prevalence of both conditions was higher in organised dairy farms than the unorganised dairy units. Of the diagnostic tests utilised for SCH, estimation of serum calcium levels was found superior to the Sulkowitch test while for SCK, estimation of blood ketones was found superior to Rothera's test and the urine dip stick test

Effect of Substrate Temperature on Structural and Optical Properties of Nanocrystalline CdTe Thin Films Deposited by Electron Beam Evaporation

Nanocrystalline Cadmium Telluride (CdTe) thin films were deposited onto glass substrates using electron beam evaporation technique. The effect of substrate temperature on the structural, morphological and optical properties of CdTe thin films has been investigated. All the CdTe films exhibited zinc blende structure with (111) preferential orientation. The crystallite size of the films increased from 35 nm to 116 nm with the increase of substrate temperature and the band gap of the films decreased from 2.87 eV to 2.05 eV with the increase of the crystallite size. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3195

Effect of Annealing on Structural and Optical Properties of Cu Doped In2O3 Thin Films

Cu-doped In2O3 thin films were prepared using flash evaporation method at different Cu-doping levels. The effect of annealing was studied on the structure, morphology and optical properties of the thin films. The films exhibited cubic structure and optical transmittance of the films increasing with annealing temperature. The highest optical transmittance of 78 % was observed with band gap of 4.09 eV. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3553

Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images

This research paper explores the classification of knee osteoarthritis (OA) severity levels using advanced computer vision models and augmentation techniques. The study investigates the effectiveness of data preprocessing, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data augmentation using diffusion models. Three experiments were conducted: training models on the original dataset, training models on the preprocessed dataset, and training models on the augmented dataset. The results show that data preprocessing and augmentation significantly improve the accuracy of the models. The EfficientNetB3 model achieved the highest accuracy of 84\% on the augmented dataset. Additionally, attention visualization techniques, such as Grad-CAM, are utilized to provide detailed attention maps, enhancing the understanding and trustworthiness of the models. These findings highlight the potential of combining advanced models with augmented data and attention visualization for accurate knee OA severity classification.Comment: Paper has been accepted to be presented at ICACECS 2023 and the final version will be published by Atlantis Highlights in Computer Science (AHCS) , Atlantis Press(part of Springer Nature

A Cross-Sectional Study of Investigating the Errors in Prescription and Drug Prescribing Pattern among General Community

The main purpose of this study is to access the awareness and knowledge of finding out errors in prescription among general public which include determination of the quality of prescription in hospital pharmacy and to address the errors identified in prescriptions. Implementation of teaching electronic medical record within didactic using drug info assignment. This survey emphasised the need for monitoring and evaluating the prescription handling conducted through google form. Therefore, the present study will help to understand the prescribing patterns and errors to the general community which will, in turn, improve the quality of the use of medicine and healthcare facilities. This following report has comprehensive studies that public have developed knowledge about prescription parts, drug information, self-evaluation, health care factors when compared to previous studie

Probabilistic Classification of Infrared-selected targets for SPHEREx mission: In search of YSOs

We apply machine learning algorithms to classify Infrared (IR)-selected targets for NASA's upcoming SPHEREx mission. In particular, we are interested in classifying Young Stellar Objects (YSOs), which are essential for understanding the star formation process. Our approach differs from previous work, which has relied heavily on broadband color criteria to classify IR-bright objects, and are typically implemented in color-color and color-magnitude diagrams. However, these methods do not state the confidence associated with the classification and the results from these methods are quite ambiguous due to the overlap of different source types in these diagrams. Here, we utilize photometric colors and magnitudes from seven near and mid-infrared bands simultaneously and employ machine and deep learning algorithms to carry out probabilistic classification of YSOs, Asymptotic Giant Branch (AGB) stars, Active Galactic Nuclei (AGN) and main-sequence (MS) stars. Our approach also sub-classifies YSOs into Class I, II, III and flat spectrum YSOs, and AGB stars into carbon-rich and oxygen-rich AGB stars. We apply our methods to infrared-selected targets compiled in preparation for SPHEREx which are likely to include YSOs and other classes of objects. Our classification indicates that out of $8,308,384$ sources, $1,966,340$ have class prediction with probability exceeding $90\%$, amongst which $\sim 1.7\%$ are YSOs, $\sim 58.2\%$ are AGB stars, $\sim 40\%$ are (reddened) MS stars, and $\sim 0.1\%$ are AGN whose red broadband colors mimic YSOs. We validate our classification using the spatial distributions of predicted YSOs towards the Cygnus-X star-forming complex, as well as AGB stars across the Galactic plane.Comment: 17 pages, 12 figures, Accepted for publication in MNRA