Volume 14 | Issue 5 |

 Abstract

An adverse effect of changing titanium dioxide nanoparticle shapes and concentrations on the amount of friction and Nusselt number ratio of improvement for thermal solar collectors (TSCs) has been examined. Several nanoparticle shapes (PD = 20-50 nm) and varying concentrations of TiO2 nanoparticles (NC = 1-4%) have been used for preparation of nanofluid via stirrer mechanism. Both pure water and water/TiO2 flow under diverse Reynolds numbers, spanning from 5,000 to 30,000. The permitted amount Nusselt number is found in the water/TiO2 mixture with nanoparticle concentration (NC = 4%) and nanoparticle diameter (PD = 20 nm). With water/TiO2 nanofluid flow at Re = 5,000 and 15% flow at Re = 30,000, the average Nusselt numbers climb by 23% and 13%, respectively. It has been observed that for both fluids, the skin friction factor diminishes as the Reynolds number rises. The skin friction factor of a water/TiO2 nanofluid is higher than that of pure water. Employing water/TiO2 nanofluid as the working fluid and adjusting the concentration of TiO2 nanoparticles from (NC = 1%) to (NC = 4%), the average Nusselt numbers improved by 29% at the lowest Reynolds number and by 39% at the highest Reynolds number of 30,000. Across all Reynolds principles, the Nusselt number increased as the nanoparticle diameter shrank. The maximum Nusselt number is synthesized by TiO2 nanoparticles with the smallest dimension (PD = 20 nm). The most important ratio of improvement for the Nusselt number in TSC is offered by the smallest size TiO2 nanoparticles (PD = 20 nm). This investigation showed that infusing 5% Al2O3 nanoparticles to water/TiO2 (diameter of 20 nm) leads to significant heat transfer development, which raises TSC's thermal performance.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Solar Collector; Nanoparticle; Nanofluids; Parameters; Performance.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

HIND-PRASHANT KSHETRA ME SANSKRITIK KUTNEETI OR SOFT POWER KA YOGDAN

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

HIND-PRASHANT KSHETRA ME SANSKRITIK KUTNEETI OR SOFT POWER KA YOGDAN

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Influence Of Family, Social And Economic Factors On Professional "Choice Of Teaching Career

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

The Influence Of Family, Social And Economic Factors On Professional "Choice Of Teaching Career

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Information literacy is the ability to find, understand, evaluate, and use information properly. In engineering education, students must work with many types of information such as textbooks, research papers, technical reports, standards, and online resources. Simply having access to information is not enough. Students need skills to select correct and reliable information and use it in an ethical way. This article explains the meaning of information literacy, its importance for engineering students, common challenges faced by students, and methods to improve these skills. It also discusses the role of libraries and teachers in helping students develop strong information literacy abilities. Developing these skills helps engineering students achieve academic success and prepares them for professional careers.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Information Literacy, Engineering Students, Academic Success, Research Skills, Digital Learning

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A Comparative Study of the Attitude of Teacher trainee of Central and State Universities Towards Inclusive Education

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

A Comparative Study of the Attitude of Teacher trainee of Central and State Universities Towards Inclusive Education

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Managing heart health often requires a strategic combination of medications, and Nebivolol hydrochloride and Ramipril are two of the most effective tools in that kit. Nebivolol works as a selective beta-blocker, essentially helping the heart beat more calmly and efficiently to lower blood pressure. Ramipril, on the other hand, is an ACE inhibitor that does double duty: it treats high blood pressure and heart failure while providing a vital layer of protection for the kidneys, particularly for those living with diabetes. Research suggests that finding the "sweet spot" for a patient's dosage can significantly reduce the risk of major cardiovascular events and slow the progression of kidney disease. Because these medications are so frequently used together, scientists rely on advanced analytical techniques like UV spectrometry, HPLC, and HPTLC to ensure each dose is precise and pure. These methods allow researchers to detect and measure both drugs simultaneously, even when they are mixed with other substances. By refining these detection methods, the medical community ensures that these life-saving treatments remain consistent, safe, and effective for the patients who depend on them.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Nebivolol hydrochloride, Ramipril, UV spectrometric, HPLC, HPTLC

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mental health disorders such as depression, anxiety, and stress are increasing rapidly due to modern lifestyle changes, academic pressure, and workplace stress. Early identification of mental health conditions is essential for proper treatment and support. This paper presents a Mental Health Prediction System using Machine Learning techniques to predict mental health conditions effectively. The proposed system uses preprocessing techniques, feature extraction, and classification algorithms such as Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine for prediction. The experimental analysis shows that Random Forest achieved the highest prediction accuracy. The developed system can help healthcare professionals and institutions in early mental health assessment.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

- Mental Health, Machine Learning, Depression Prediction, Anxiety Detection, Artificial Intelligence

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

????????? ???????? ?????? ?????? ??? ?? ???? ?????? ????????? ??? ?? ?? ?? ??? ??? ???? ??, ?? ???? ?? ??????? ?? ???? ?? ??? ?? ?? ????????? ??????? ?? ?????? ?? ???? ??? ??? ??? ??? ?? ??? ???? ????????? ???????? ?? ??????? ?? ??????? ?? ??? ????? ??, ??? ?? ???? ???? ?? ?? ??????? ?? ???? ??, ?????? ?? ?????? ?? ???????? ??? ?????? ?????? ??? ??? ????? ????????? ???????? ?? ?????? ???????? --?????????? ?? ???????? ?? ??????? ????? ??? ?????? ?? ???? ????? ?? ??????? ???? ?? ?????? ??-- ?? ????? ?? ?? ??, ?????? ?? ???????? ?? ????? ?? ?? ?????? ???? ?? ????????? ??????? ??? ??? ?? ??? ??? ????? ??? ???? ?? ???? ?????, ????? ?? ??????-????? ???? ????? ???? ???????? ????????? ??????? ?? ?? ??? ?? ????? ???? ??, ?? ?? ??? ?? ?????? ??? ?????? ??? ?? ????? ?? ????? ??? ????????, ???????????, ??? ?? ???? ???? ?? ???? ?? ?? ????????????? ????? ???????, ???? ???????? ?? ????? ???????? ???? ???????? (???????) ?? ?????? ???? ???? ?? ?????? ????? ?? ???????? ????, ?????? ??? ????? ?? ???????? ?? ?????? ??????? ?? ?????? ????? ??? ?????? ?????? ?? ???, ??? ???????? ?? ????? ?????? ????? ?? ??? ??? ????????? ??????? ???????, ????? ??????? ?? ?????? ??????? ?? ????? ?? ??? ???? ??? ???????? ????? ??? ?? ??????? ??????? ?????? ???, ????? ???????? ???????? ???????? ?? ???????? ?? ?????? ???? ??? ????? ??--???? ??????????, ???????? ?? ????? ?????????? ????????? ???? ?? ?????? ??? ?? ???? ??? ?????, ?? ???? ??? ??????????, ?????? ?? ???? ?????? ??????? ?????? ?? ?????? ???? ??? ??, ???? ????????? ???????? ?? ???? ?? ????? ????????? ?? ????? ??????? ?? ????? ?????????? ?? ?????? ????? ?? ??? ?

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

????????? ????????, ??? ?????, ??????, ??????? ????? ?

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This project presents an automated waste segregation system that integrates Arduino technology with machine learning for classifying waste into three categories: metal, plastic, and glass. The system uses inductive sensors, ultrasonic sensors, and a camera module for detection and classification. Metallic objects are identified using the inductive sensor, while non-metallic items are analyzed through image processing and classified using a trained machine learning model. The Arduino microcontroller controls servo motors to sort the items into the appropriate bins. This combination of automation and artificial intelligence improves waste segregation accuracy, reduces manual labor, and promotes sustainable recycling practices.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

o To develop an automated system for segregating waste into metal, plastic, and glass categories. o To integrate sensors (inductive, IR, vibration/piezo) with an Arduino microcontroller for material detection. o To use machine learning algorithms to improve the accuracy of waste classification

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study was undertaken to examine the effect of phenthoate (50% EC), an organophosphate pesticide, on the respiratory metabolism of the freshwater fish Ctenopharyngodon idella. Oxygen consumption was used as an indicator to evaluate the physiological stress caused by sublethal and lethal exposure over a period of 24 hours. At the beginning of the experiment (0 h), no noticeable variation in oxygen consumption was observed between the control and treated groups. In the sublethal exposure group, oxygen consumption showed an initial increase and reached a peak at 4 h (17.92%), followed by a gradual decline during the later stages of exposure. In the lethal exposure group, oxygen consumption decreased continuously throughout the experimental period, with the maximum reduction observed at 22 h (37.98%). The decline in oxygen consumption suggests respiratory stress and disturbance in normal metabolic activity due to phenthoate exposure. The findings of the present study indicate that phenthoate can significantly affect the respiratory physiology of Ctenopharyngodon idella, particularly under lethal exposure conditions. The study also suggests that changes in oxygen consumption may serve as a useful biomarker for assessing pesticide-induced stress in aquatic organisms.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Phenthoate (50% EC), Ctenopharyngodon idella, oxygen consumption, respiratory metabolism, organophosphate pesticide, aquatic toxicology

  License

Creative Commons Attribution 4.0 and The Open Definition