International Research Journal on Advanced Science Hub

In the past decades, magnesium niobate materials have been extensively inves- tigated due to their exceptional dielectric characteristics at microwave fre- quencies and are widely employed in microwave dielectric resonators. In present research, the nanocrystalline MgNb₂O₆ having an orthorhombic crys- tal structure with P b c n space group was successfully synthesized at 1000oC using a chemical route. X-ray diffraction (XRD), Raman spectroscopy, FESEM, impedance analyzer, and diffuse reflectance spectroscopy (DRS) were used to characterize the prepared phase. The average crystallite size, unit cell volume and the X-ray density of the prepared material were evaluated to be 52.55 nm,
407.65A˚ 3  and4.9865g/cm3,respectively.Themolecularbendingandstretch- ing vibrations of metal oxide bonds were examined by Raman spectroscopy, which ranged from 232 cm−1 to 1007 cm−1. FESEM analysis of the prepared ceramics revealed uniformly distributed grains with clear grain boundaries bearing the average grain size of 0.78 µm.  A high direct band gap of 2.97  eV was investigated from DRS. The impedance analysis of the prepared phase revealed a decrease in the capacitance and dielectric constant between 40 Hz to 10 MHz. At 10 MHz frequency, the dielectric constant of the material was found to be 13.15. The loss tangent also displayed a systematic decrease with the increase in frequency from 40 Hz to 10 MHz.

Digital data including photographs, audio, and video are now readily available because to the development of the Internet. The ease of access to multime-  dia raises concerns about ownership identification, authentication of content, security, and copyright protection. Here, we talk about the idea of digital pic- ture watermarking with an emphasis on the method utilized for embedding and extracting the watermark from images. This paper also presents a complete classification of digital watermarking along with its fundamental properties, such as visual imperceptibility, resilience, capacity, and security. Additionally, we have covered the most recent uses of digital watermarking in the fields of healthcare, distance learning, electronic voting, and the military. The robust- ness is assessed by looking at how image processing assaults affect the content that is signed and the recoverability of the watermark. The thorough survey that is offered in this study, in the opinion of the authors, will aid brand-new scholars in learning more about this area. Additionally, the comparative study can spark suggestions for how to enhance the methods already outlined.

Heart disease is one of the leading killers that are widely recognized through- out the globe. Large volumes of clinical data are stored in a variety of sys- tems and biological equipment at hospitals. It is essential to grasp the facts of heart disease in order to improve forecast accuracy. In this paper, experimental evaluations have been conducted to assess the effectiveness of models created utilizing classification algorithms and relevant attributes selected using Extra Tree feature selection procedures. Several people suffer originated at heart disease globally. It is necessary to use data mining and machine learning techniques to extract new insights originated at this data. Analyzing medical data sets and diagnostic issues, including heart disease, involved the use of a number of categorization approaches. However, these methods were only per- formed on small, balanced data; then, the features must be derived originated at trial and error. Additionally, several sectors have made substantial use of feature selection techniques to enhance classification performance. This paper aims to propose a comprehensive approach to enhance the prediction of heart disease using several machine learning methods such as Bagging, Support Vec- tor Machine, Multilayer Perception and Gradient Boost with feature selection methods such as extra tree. The experimental results showed improvements of prediction. Bagging received scores in training model on 80% data sample as 99.08, 73.19, 67.20, 69.20 and 80.66 of accuracy, precision, recall, F1-score and roc respectively. In the experiment,  we have tested on 20% data sam- ple for each classifier algorithms and find Bagging classifier model perform higher score for accuracy, precision, recall, F1-score and roc 92.62, 48.44, 39.63, 41.89, 66.82 respectively.

TiO₂ and Y₂O₃ nanoparticles’ molecular, mechanical, and energy absorption, as well as their manufacture and applications, were evaluated. Consider- ations include their dimensions, structure, friction factor, and propagation. Matrix composites nanoparticles are fascinating substances with a great deal of promise for usage in many different sectors of the economy. Recent stud- ies imply that optimising the dispersion of the particles can create composites with truly intriguing material characteristics. It was possible to attain incred- ible results for hardness, robustness and encroaching behaviour. The conclu- sion that varying ratios of nano-TiO₂ particles have successfully strengthened composite materials may be drawn primarily from the study’s findings. The endurance of the hybrid composites was greatly increased by increasing the Nano – TiO₂ concentration. The surface roughness behaviour clearly shows that the fracture toughness is inversely connected with the ultimate force but directly related with the amount of TiO₂ nanoparticles present.

This study seeks to: (1) assess students’ vocabulary mastery levels before and after utilizing the Secret Door approach; and (2) ascertain whether employ- ing the Secret Door method enhances students’ vocabulary. This supports the idea that using the Secret Door approach improves pupils’ vocabularies. Pre- experimentalOneGroupPretest-PosttestDesignwithaSampleof16Students from Class X IPS 2 and Cluster Random Sampling Technique is the design employed in this study.   The test served as an instrument for the researcher   to gather the data. Data research revealed that applying the Secret Door approach increased students’ vocabulary mastery. It can be proven by the t- testvalueof-27.547whichislessthanthet-tablevalueof2.602,whichmeans thatH0ofthisresearchwasrejectedandH1wasaccepted.Therefore,itcanbe concludedthattheuseoftheSecretDoormethodimprovestudents’vocabulary at the first year students of Vocational Secondary School in SouthSulawesi.