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Women and girls constitute half of India's population. However, gender inequities continue to be a severe problem in India, affecting women's and girls' access to school, health care, and employment. According to the data, girls and boys are largely at par until adolescence. However, as girls enter puberty, their results start to differ, and their freedom of movement and mobility rights are further constrained. Even though India has over 355 million menstruating women and girls, many still face significant barriers to enjoying a respectable and comfortable period (MHM). In India, 70% of women say their families have trouble affording sanitary napkins. In 2012, 40% of all government schools lacked a standard functional toilet, not to speak of separate toilets for girls [Menstrual Health in India | Country Landscape Analysis, 2016]. Donors, the government, and businesses have all increased their efforts to address problems with menstruation health. Products and, more recently, boosting awareness, particularly among ladies, have received the majority of emphasis. In the present study, the Researcher tried to identify and assess the vulnerability status of menstrual hygiene behaviour and address the core issues prevalent in the Tribal areas of Ramgarh and Ranchi Districts of Jharkhand. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to evaluate the prevalence of edentulousness, the present prosthetic status, and the need for prosthetic treatment in the population of Garhwa, Jharkhand. Materials and Methods: A descriptive cross-sectional study was conducted among the population of Garhwa, Jharkhand. The sample size included 460 subjects belonging to an age group of 25 years and above. Detailed case history and thorough dental examination was done and all the findings were recorded and compiled. Each subject was interrogated with the help of a predesigned closed ended questionnaire. Results: A total of six patients were completely edentulous, while 55.04% of males and 42.80% of females were partially edentulous. All patients who were completely edentulous were denture wearer, while among partially edentulous patients, 6.31% of males and 3.15% of females had RPD and 13.68% and 6.31% of males and females, respectively, had FPD as prosthesis. Prosthetic status of females was better than males In a comparison of prosthetic status between male and female, out of 460 subjects, 195 subjects opted for prosthesis, in which 117 were male and 78 were female. Conclusion: Community-based oral health education programs should be conducted to improve patients' oral health knowledge. Educating patients regarding the limitations of prosthesis as mechanical substitutes for natural teeth must be a continuing process from the initial patient contact until adjustments are completed. [ABSTRACT FROM AUTHOR]

Nature is a complex collection of physiological, chemical, and abiotic factors that affect living things and ultimately determine their structure and survival. In today's world conditions are often affected by various factors, such as pollution, population, and many other factors. And it destroys native plants and animals. Increased levels of pollution can lead to various diseases such as heart disease and skin diseases. Jharkhand is a very beautiful nature. Coal mining and profitability may be one of the most polluting tests. Pollution is the main reason why Global is heating its impact on our environment. Environmental change harms humans, wildlife, and the environment. Environmental changes can cause climate change, global climate change, and the destruction of natural resources. It also disrupts normal social and economic processes. Humans are cruelly ruining the environment. The mountains are barren. Due to the constant deforestation, this results in frequent flooding. The environment can cause a pattern of low rainfall, drought, and increased mortality rates. It greatly affects agriculture due to low rainfall and increased pollution can make the land less suitable for growing crops. Jharkhand province is already suffering from over-reliance on mineral resources. In addition, the government's water and water resources are under threat due to industrial and urban growth and inequality in temporary and spatial distribution. So,this research work based on the ecological changes in Jharkhand with a holistic view, focusing on other major regions that are experiencing the most environmental changes in that region. [ABSTRACT FROM AUTHOR]

Accurate classification of the satellite image is always a challenging task, and hence uncertainty involves in the change detection analysis in the land use for mining regions. The present study attempts to demonstrate the development of an optimized deep convolutional neural network with stochastic gradient descent momentum (DCNN + SGDM) using Linear imaging self-scanning sensor- IV (LISS-IV) data for automatic classification of satellites image to assess the land-use pattern in a mining region. An image dataset was prepared by extracting the LISS-IV image samples of five common land-use types (coal mining region, built-up area, barren land, vegetation, and waterbody). A total of 6250 image samples (1250 for each class) of 6 × 6 patch size were generated for the model training and testing. The study area selected for analyzing the model performance and impact analysis is Jharia coalfield (JCF), Jharkhand. The study area consists of 5184 × 7794 pixels. The model performance results indicated that classification accuracies on the training and testing dataset are 99.04% and 83.50%, respectively. The model performance was also compared to the performance reported in the well-established deep learning algorithms, including Alex Net, Squeeze Net, Google Net, VGG Net 16, and VGG Net 19. It was observed that the DCNN + SGDM model performs better (99.04%) than the existing deep learning models (73.8% in Alex Net, 80.3 in Squeeze Net, 93.33 in Google Net, 93.20% in VGG Net 16, and 93.00 in VGG Net 19). Moreover, the DCNN + SGDM model's performance was compared with the two pixel-based classification algorithms (support vector machine (SVM) and deep neural network (DNN)) in terms of accuracy and processing time. The comparative values of accuracies are 70.40% for SVM, 69.10% for DNN, 99.04% for DCNN on the training dataset, and the respective elapsed time of the SVM, DNN, and DCNN models are 1.716 s, 3.59 s, and 1587 s. That is, the processing time of the DCNN model is high but offers higher accuracy in comparison to the pixel-based classification algorithms (SVM and DNN). • A new image database is generated for image classification using deep learning. • Image database can be used globally for land-use classification using satellite images. • Optimized DCNN algorithm is developed for the land-use study using satellite data. • The DCNN model was successfully applied for the land-use study of a mining region. • Accuracy of the developed DCNN model outperforms the other CNN algorithms. [ABSTRACT FROM AUTHOR]

Enhanced spatio-temporal and up-to-date information on vegetation dynamics at various spatial scales are imperative in understanding the human, biosphere, and atmosphere interactions. Thus, the present study attempts to derive the vegetation greenness trends with the medium spatial resolution (30 m) satellite data at the regional scale with the support of Google Earth Engine (GEE) cloud platform. The long-term Landsat series satellite dataset was employed to characterize vegetation greenness trends using the Mann-Kendall test over the mining-dominated regions of Eastern India (Jharkhand and Odisha states) for two study periods, viz. earlier (1988–2004) and later (2000−2020). The key findings revealed that ∼1285 km2 (2.97%) and 1688 km2 (3.91%) areas over Jharkhand state and ∼ 5213 km2 (5.68%) and 2940 km2 (3.20%) areas over Odisha state showed the negative vegetation greenness trend (indicative of decreasing vegetation activity) during 1988–2004 and 2000–2020, respectively. It was observed that the major anthropogenic activities, particularly opencast mining, are the major factor for vegetation degradation in Jharkhand and Odisha states, contributing to ∼3–5.7% vegetation degradation during the study periods. The negative vegetation greenness trend patches were mainly observed in mining sites, settlement encroachments, construction sites, roadways, logging sites, etc. The drastic rise in the intensity of mining activities in the last two decades (2000–2020) has led to massive vegetation destruction compared to the earlier period (1988–2004). Furthermore, the key climatic parameters (i.e., precipitation, temperature, downward radiation, and soil moisture) have less control over the long-term vegetation greenness trends in the mining-dominated regions (∼ 27%) in contrast to forest regions (∼ 47%). The findings of the study shall be helpful to the policy-makers, stakeholders, environmentalists, and government bodies to formulate and implement various sustainable development programs in the mining-dominated regions to ensure ecological conservation and enhance ecological services. [Display omitted] • Vegetation greenness trends over eastern India are depicted at 30 m spatial scale. • Vegetation degradation and regrowth in mining regions are portrayed. • Large-scale opencast mining activity is the key driver for ecosystem degradation. • Climatic parameters have less control on vegetation growth in mining areas. [ABSTRACT FROM AUTHOR]