The study examined the Nigerian perspective of sex selection visa -vis India's Pre-Conception and Pre-Natal Diagnostic Technique Act, 1994. Sex selection is rampant worldwide despite being prohibited by policies or laws of numerous countries. However, is there a need for prohibition of sex-selection in Nigeria when compared with Indian perspective? The paper adopted both primary and secondary data sources as a means of information. A semi-structured interview of One Hundred (100) participants; fifty (50) Nigerian and fifty (50) Indian by convenience sampling method was conducted. The research reveals that there is sex-imbalance in both countries, as there are more female adults than male adults in Nigeria. In contrast, there are more males than females in India. It's finally concluded that: the focus should be on abolition of barbaric cultures, all forms of discrimination and self-empowerment as a way of education to the both gender especially females.

This article contains the experimental and statistical data related to decolourization of Amido Black 10B using photocatalytic process. Box-Behnken experimental design (BBD) has been used to study the influence of operational parameters on photocatalytic oxidation of Amido Black 10B by using zinc oxide (ZnO) as a catalyst. This data set presents a concise description of experimental conditions for variable factors such as initial dye concentration of 100 ppm, oxidant dosage 20 mMol/L and catalyst dose 1g/L at natural pH for 4 hr of reaction time in presence of 12W intensity ultra-violet radiation were optimized for over a response parameter, decolorization efficiency of Amido Black 10B. The effects of decolorization on process variables were investigated by developing a mathematical model, results indicated that ZnO can be used as an efficient catalyst for the decolorization of Amido Black 10B. Analysis of variance (ANOVA) showed a high coefficient of statistical measure value (R 2 = 0.9915) and prediction of the driven regression model was found to be satisfactory.

The recommendations list that appears after watching a show on Netflix, the ads that come up on Instagram similar to other liked posts, these phenomenons are due to algorithmic decision making. Algorithms are able to help people do simple pattern-based tasks in day-to-day life but they also have implications on our society. Algorithms often make mistakes due to the human error in data they analyze. There are many different sources of algorithmic error in decision making, but the most significant cause by far is error in the data which algorithms are trained from. These errors affect advertisements, jobs, and also technological products. It is hard to get rid of these biases as many times it leads to underrepresentation, another cause of algorithmic error. The way an algorithm is trained has a large impact on the future decisions it makes.

In today’s world, technology has become much more prevalent in the world of medicine. Growing fields like biotechnology and artificial intelligence are helping save and improve lives in ways that we couldn’t have imagined just 40, 50 years ago. Some of the most common examples of this today are prosthetics and using artificial intelligence in radiology. In the past few years, artificial intelligence technology has been advancing and scientists have begun to research whether deep learning algorithms like convolutional neural networks can be used to help detect signs of and diagnose cancer. Specifically, a growing research field refers to using deep learning and CNN models to detect pediatric cancer, one of the hardest cancers to detect based on symptoms. In this paper, it will be discussed whether deep learning algorithms are effective in use for the detection or diagnosis of pediatric cancers.