Advance technique rectangular microstrip patch antenna with EBGS

EEE/201

AN INNOVATIVE METHOD DEVELOPMENT AND FORCED DEGRADATION STUDIES FOR SIMULTANEOUS ESTIMATION OF SOFOSBUVIR AND LEDIPASVIR BY RP HPLC

Objective: To develop an innovative, rapid, simple, cost-effective, stability indicating reverse phase-high performance liquid chromatography (RP-HPLC) method for simultaneous estimation of ledipasvir (LP) and sofosbuvir (SB) in combination pill dosage form. Methods: The method was developed using C8 column, 250 mm x 4.6 mm, 5 mm using mobile section comprising of 0.1% (v/v) orthophosphoric acid buffer at pH 2.2 and acetonitrile in the ratio of 45:55 that was pumped through the column at a flow rate of 0.8 ml/min. Temperature was maintained at 30 °C, the effluents were monitored at 260 nm with the help of usage of PDA detector. Results: The retention time of LP and SB were found to be 2.246 min and 3.502 min. The approach was found to be linear with the variety of 9-36 µg/ml and 40-240 μg/ml for LP and SB respectively, the assay of estimated compounds were found to be 99.65% and 99.73% w/v for LP and SB respectively. Conclusion: The pressured samples changed into analyzed and this proposed a technique turned into determined to be particular and stability indicating as no interfering peaks of decay compound and excipients were observed. Hence, the approach was easy and economical that may be efficiently applied for simultaneous estimation of both LP and SB in bulk and combination tablet system

Varietal Performance and Effect of Planting Method on Yield and Yield Contributing Characteristics of Rice

The experiment was carried out to study the performance of two aman rice varieties (BRRI dhan31 and BRRI dhan41) under different planting methods (line sowing with sprouted seeds by drum seeder, haphazard transplanting and transplanting in line). Both the variety and planting method had significant effect on crop characters plant height, number of total tillers m-2, effective tillers m-2, grains panicle-1, sterile spikelet's panicle-1, total spikelet's panicle-1, grain yield except panicle length and 1000-grain weight. BRRI dhan41 produced the highest grain yield (4.06 t ha-1). Line sowing method with sprouted seeds by drum seeder showed better performance in respect of no. total tillers m-2 (415.81), effective tillers m-2 (401.85) and grain yield (4.80 t ha-1). The highest no. of total tillers m-2 (421.12), effective tillers m-2 (410.65) and grain yield (5.08 t ha-1) were recorded due to effect of the interaction of line sowing method with sprouted seeds by drum seeder and the variety BRRI dhan41

Role of health hazardous ethephone on nutritive values of selected pineapple, banana and tomato

An experimental study of selected pineapple (Ananas sativus), banana (Musa acuminata) and tomato (Lycopersicon esculentum) was investigated on the basis of their biochemical and nutritional properties by the treatment of some doses of ethephone. It was found that the chemically treated samples ripened rapidly than untreated ones. The nutritional properties of chemically ripened fruits as well as market samples (ripe) were shown different from untreated. The chemically ripened samples showed shorter shelf life than non-treated samples. The highest vitamin C content of the selected non-treated fruits (17.5 mg/100 g in pineapple, 13 mg/100 g in banana and 20.2 mg/100 g tomato) and the lowest contentwas found in the market samples (10 mg/100 g in pineapple, 7 mg/100 g in banana and 12.3 mg/100 g tomato), whereas ethephone-treated groups contained the ascorbic acid 14.5 mg/100 g in pineapple, 9 mg/100 g in banana and 19.4 mg/100 g in tomato). Similarly the β-carotene content of ethephone-treated samples (63 μg/100 g in pineapple, 47 μg/100 g in banana and 757 μg/100 g in tomato) and market samples (31 μg/100 g in pineapple, 38 μg/100 g in banana and 512 μg/100 g in tomato) were less than that of control groups (78 μg/100 g in pineapple, 54 μg/100 g in banana and 807 μg/100 g in tomato). The mineral contents of samples in three groups showed ethephone-treated samples indicated less nutritional quality than untreated samples. Higher amount of lead and arsenic were found in all fruits and vegetables in both ethephone-treated and market samples but the concentrations were within acceptable limits

Emphysematous pyelonephritis

Emphysematous pyelonephritis is an acute necrotizing infection of the renal parenchyma, prompt diagnosis and early treatment is crucial because of the high rate of mortality. We report a case of a 55-year-old female patient with ten-year history of diabetes mellitus presented with pain in right flank, fever and malaise. She was diagnosed as a case of emphyse­matous pyelonephritis, and was successfully treated in our department. The case is presented along with a literature review

Numerical Investigation of the Thermo-Hydraulic Performance of Water-Based Nanofluids in a Dimpled Channel Flow using Al₂O₃, CuO, and Hybrid Al₂O₃-CuO as Nanoparticles

In this study, the authors study the impact of spherical dimple surfaces and nanofluid coolants on heat transfer and pressure drop. The main objective of this paper is to evaluate the thermal performance of nanofluids with respect to different Reynolds numbers (Re) and nanoparticle compositions in dimpled channel flow. Water-based nanofluids with Al2O3, CuO, and Al2O3-CuO nanoparticles are considered for this investigation with 1%, 2%, and 4% volume fraction for each nanofluid. The simulations are conducted at low Reynolds numbers varying from 500 to 1250, assuming constant and uniform heat flux. The effective properties of nanofluids are estimated using models proposed in the literature and are combined with the computational fluid dynamics solver ANSYS Fluent for the analysis. The results are discussed in terms of heat transfer coefficient, temperature distributions, pressure drop, Nusselt number, friction factors, and performance criterion for all the cases. For all cases of different nanoparticle compositions, the heat transfer coefficient was seen as 35%-46% higher for the dimpled channel in comparison with the smooth channel. Besides, it was observed that with increasing volume fraction, the values of heat transfer and pressure drop were increased. With a maximum of 25.18% increase in the thermal performance, the 1% Al2O3/water was found to be the best performing nanofluid at Re = 500 in the dimpled channel flow

A Numerical Thermal Analysis of a Battery Pack in an Electric Motorbike Application

Today, electric driven motorbikes (e-motorbikes) are facing multiple safety, functionality and operating challenges, particularly in hot climatic conditions. One of them is the increasing demand for efficient battery cooling to avoid the potential thermal stability concerns due to extreme temperatures and the conventional plastic enclosure of the battery pack. A reliable and efficient thermal design can be formulated by accommodating the battery within an appropriate battery housing supported by a cooling configuration. The proposed design includes a battery pack housing made of high conductive materials, such as copper (Cu) and aluminum (Al), with an adequate liquid cooling system. This study first proposes a potted cooling structure for the e-motorbike battery and numerical studies are carried out for a 72 V, 42 Ah battery pack for different ambient temperatures, casing materials, discharge rates, coolant types, and coolant temperatures. Results reveal that up to 53 °C is achievable with only the Cu battery housing material. Further temperature reduction is possible with the help of a liquid cooling system, and in this case, with the use of coolant temperature of 20◦ C, the battery temperature can be maintained within 28 °C. The analysis also suggests that the proposed cooling system can keep a safe battery temperature up to a 5C rate. The design was also validated for different accelerated driving scenarios. The proposed conceptual design could be exploited in future e-motorbike battery cooling for optimum thermal stability

Digital Applications Using Real-Time Vehicle Exhaust Information

Vehicle emission is a major source of air pollution that causes a significant number of deaths globally. It has a profound impact on energy and the environment as well. The existing vehicle emission monitoring system is unable to help mitigating the pollution properly and therefore, requires precise real-time pollution measurement. The purpose of this paper is to discuss novel applications using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. Today, it is possible to measure such emission due to the emergence of affordable digital technologies such as the Internet of Things (IoT), wireless connectivity, cloud platform, and artificial intelligence. This paper discusses how digital technologies can be used for real-time monitoring of NOx gas as a measure of vehicle emission and predictive analytics applications. A description of data collection and pre-processing methodologies, actual collected data, and an approach to identify patterns between inputs such as vehicle speed and altitude and output such as NOx emission are included. Applying a simple neural network has produced promising results and is a first step towards developing predictive applications

An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple parties can collaborate in training a machine-learning model without exchanging raw data but using the learned or fused features. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method's effectiveness through a case study on pituitary tumor classification, achieving a significant level of accuracy. However, the primary focus of the study is on the development and evaluation of federated learning and partially homomorphic encryption as tools for secure medical image analysis. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning