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Mg0.5−xZnxCo0.5Fe2O4 with ferrites were substituted with Zn2+ with concentrations x = 0.0, 0.05, 0.1, 0.15, 0.2, and 0.25 prepared from a sol–gel auto-combustion method. The impact of Zn2+ ion substitution on morphological, structural, DC electrical resistivity, and magnetic characteristics was examined. The diffraction analysis with X-ray reveals that the prepared ferrites are spinel with a single-phase face-centred cubic structure. The cubic crystal structure samples’ values for the crystallite size (42 to 25 nm) and lattice constant (8.386 to 8.425 Å) increased as the concentration increased. Field-effect scanning electron microscopy revealed the ferrite samples’ polycrystalline structures and spherical morphologies. Using FESEM micrographs, the average grain size was between 56.4 and 85.7 nm. Fourier-transform infrared spectroscopy was used to identify two different peaks that emerged at around 585–592 cm−1 and 401–405 cm−1. These peaks revealed information on the functional groups of the sample chemicals. Temperature-dependent DC electrical resistivity shows the semiconductors’ nature samples. The vibrating sample magnetometer analysis shows that the ferrites are soft ferrimagnetic. The saturation magnetisation was increased (60.45 to 85.04 emu/g), and coercivity was decreased (356.46 to 620.59 Oe) when the concentration was increased. The study unveiled tuned values of the physical characteristics, thereby highlighting potential applications in recent technologies. [ABSTRACT FROM AUTHOR]

Unhealthy behaviors including smoking, drinking, and drug use often begin during adolescence and are closely related to morbidity and mortality. This study examines the prevalence and factors associated with substance use among adolescent boys in Bihar, India. Data from the Understanding the Lives of Adolescents and Young Adults (UDAYA), conducted in 2015–16 in two Indian states, was used for analysis (n = 2004 adolescent boys aged 14–19 years). A multivariate logistic regression model was applied to estimate the effect of various correlates on substance use. Prevalence of tobacco consumption (19%) is high, followed by consumption of alcohol (8%) and drug use (1.2%) among adolescent boys in Bihar. One-fifth have ever used at least one substance. Age, education, current schooling status, wealth quintile, household head's educational attainment, and mother's education level are found to be important factors influencing substance use. It is essential to adopt more pointed programs which can effectively reduce substance use among adolescents in this and similar states. Programs related to prevention of substances should be expanded effectively through health education programs, media messages at the grassroots level, especially in the socio-economically backward states in India. [ABSTRACT FROM AUTHOR]

Aim: The aim of the proposed work is to predict the performance of an Artificial Neural Network (ANN) algorithm in detection of vehicle quality performance based on Features customization and differentiators by comparing it with the Digraph algorithm. Materials and Methods:The proposed ANN is trained and tested with a "Novel Car Evaluation Database" created by Marko Bohanec. With correct quality 826 samples and inaccurate quality 826 samples in two groups with a total sample size of 1652. Training data [75% of dataset] and testing data set [25% of data set] are separated from the obtained samples. The samples are calculated using G power analysis using clincalc, which includes two groups: alpha (0.05), power (80%). There is a statistically significant difference between the groups with p=0.04 Results: The proposed ANN algorithm provided better results in predicting the quality of novel car evaluation compared to the Digraph approach. Conclusion: For the given data set, from the results it is found that the ANN algorithm is significantly more suitable for recognition of better car quality. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the burden and correlates of morbidity and other dimensions (perceived health and physical mobility) of the health status of older adults (defined as those aged 60 and above). We used data from the National Sample Survey (NSS) 71st round for understanding the health status of older adults in India. The NSS 71st round collected data from 65,932 households (36,480 in rural and 29,452 in urban areas). The present analysis is based on data related to 27,245 persons aged 60 and above living in the sample households. Overall, 30% of those aged 60 and above reported that they have morbidity, either chronic or acute. More than one-fifth (22%) rated their health status as poor while 8% are physically immobile. Forty-three percent of older adults have overall poor health status. Multivariate analysis reveals that age, religion, expenditure quintile are significantly associated with poor overall health of the older adults. Given the high proportion of poor overall health status among older adults and their increasing population size, this study indicates the need to strengthen healthcare services as well as the economic and social security programs directed at the welfare of older adults in the country. [ABSTRACT FROM AUTHOR]

Affordable manufacturing along with high efficiency perovskite solar cell in photovoltaic technology has everyone's attention. Perovskite, which is in the lead role in solar cells, is full of characteristics such as high absorption coefficient, low exciton binding energy, charge carrier capable of having better mobility as well as more diffusion length and availability in suitable energy band. The application of machine learning technology is proving to be a boon to ensure optimum implementation with different properties in photovoltaic device, design, simple construction process and low-cost price. Machine learning is a branch of artificial intelligence which includes large data aggregation, precise structure property installation, demonstration and final model after model validation. The most of the source of database is the simulation and experimental results, calculations and related literature surveys which have a comprehensive compilation of the performance of hybrid perovskite device, collection of structures and properties of elements. Structure-property relationship installation comes under feature engineering which establishes a clear relationship between structure and the properties. In other demonstration process, proper algorithms are selected, data is generated and tested as well as pure estimated values are taken. This article contains a detailed discussion on the involvement of machine learning technology to build high-performance Perovskite solar cells. Proper selection as well as designing of active perovskite absorbent layer by machine learning successfully establishes results by including other parts such as non-toxic (lead free) and stability. Mature machine learning technology becomes a very essential method in determining the solvent combination of hybrid perovskite and in estimating design of the entire solar cell to ensure optimum implementation in the sector of perovskite solar technology. Finally, a phased concept has been briefly discussed to meet the challenges of machine learning and potential future compatibilities related to the prevalence. [ABSTRACT FROM AUTHOR]

In this paper, we used the one dimensional unitatry Lie algebraic model to compute the vibrational frequencies of nitrogen dioxide (NO2) molecule in the gas phase up to the sixth overtone. In this model, the Hamiltonian operator describes the stretching and bending vibrations with algebraic parameters. The calculated fundamental vibrational frequencies are compared with experimental values and results consistent with the referencevalues. [ABSTRACT FROM AUTHOR]

Background: Diabetic nephropathy (DN) is a chronic hyperglycemic manifestation of microvascular damage in the kidneys. Widespread research in this area suggests the involvement of perturbed redox homeostasis and autophagy in renal cells phrase- promote the progression of DN., Materials and Methods: Reframed sentences-The present study investigates the pharmacological effect of Syringic acid (SYA), in streptozotocin (STZ, 55 mg/kg, i.p) induced diabetic nephropathy model and in high glucose (30 mM) challenged rat renal epithelial cells (NRK 52E) cells with a focus on oxidative stress and autophagy mechanisms., Results: Both in vivo and in vitro experimental data revealed elevated oxidative stress markers along with compromised levels of nuclear factor erythroid 2-related factor 2 (Nrf2), a pivotal cellular redox-regulated transcription factor in renal cells upon glycemic stress. Elevated blood glucose also reduced the autophagy process as indicated by low expression of light chain (LC) 3-IIB in diabetic kidney and in NRK 52E cells subjected to excess glucose. SYA (25 and 50 mg/kg, p.o.) administration for 4 weeks to diabetic rats, Reframed sentence-preserved the renal function as evidenced by reduced serum creatinine levels as well as improved urine creatinine and urea levles as compared to non treated diabetic animals. At the molecular level, SYA improved renal expression of Nrf2 and autophagy-related proteins (Atg5, Atg3, and Atg7) in diabetic rats. Similarly, SYA (10 and 20 μM) co-treatment in high glucose-treated NRK 52E cells displayed increased levels of Nrf2 and autophagy induction., Conclusion: Results from this study signify the renoprotective effect of SYA and highlight the modulation of oxidative stress and autophagy mechanisms to mitigate diabetic kidney disease., Competing Interests: None

Background: Workplace hygiene is vital in controlling infectious diseases in industries. The factors like unhealthy environment, unhygienic workplace, and contact between healthy and disease-affected employees cause industrial hazards in workplaces., Objectives: This study assessed various components of workplace hygiene among industrial workers., Materials and Methods: A quantitative survey has been conducted in a steel, and power industry limited in Odisha. The study surveyed 425 male contractual workers for primary data on workplace hygiene. Probability proportion to sample technique adopted for justification of the sample. The univariate and bivariate analysis models have been done according to the study's objective., Results: Nearly 92% of respondents reported proper use of washing facilities, and 97% used toilet and urinal appropriately. One-fourth of respondents use personal disinfected clothing, and 92.2% have access to safe drinking water. The factors such as education, technical education, and occupation are significantly associated with respondents' use of washing facilities, dining hall, personal protective clothing, safe drinking water, proper cleaning, and sanitation at the workplace., Conclusion: Workplace hygiene strengthens the safety measures at the workplace and reduces the incidences of occupational hazards., Competing Interests: There are no conflicts of interest., (Copyright: © 2022 Indian Journal of Community Medicine.)