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Industrial developments and consumption of massive amount of fossil fuels, vehicle pollution, and other calamities upsurges the AQI (Air Quality Index) of major cities in a drastic manner. Owing to these factors, it is important to take proactive measures for reducing the air pollution in order to avoid life- threatening consequence. Therefore, prediction of air quality is significant for improving the health of living beings as highly polluted regions have a higher concentration of pollutants mixed in the air, affecting the respiratory system and reducing the lifetime. To control pollution, AQI is used as a measure for estimating the pollutant content in the air. Even though many existing techniques have predicted AQI, enhancement is required in prediction algorithms with minimized loss. To address the challenges in traditional algorithms, the proposed smart cities-based AQI prediction intends to utilize the proposed regression algorithm in the dataset, namely Air- Quality-Data, which collected harmful pollutants on an hourly and daily basis from multiple cities in India between 2015 to 2020. To achieve prediction efficiency with reduced loss, pre-processing of input data is being performed using Deep GAN (Generative Adversarial Network). It performs the imputation of data in place of missing values to improve accurate prediction. Additionally, feature scaling normalizes independent real-data features to a fixed scale. With the processed data, regression is done through modified Stacked Attention GRU with KL divergence, which predicts Ernakulam, Chennai and Ahmedabad cities with higher, medium, and low levels of AQI in India. The performance of the proposed regression algorithm is measured using metrics such as MAE (Mean Absolute Error), MSE (Mean Square Error), R2 (Coefficient of determination), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Square Error) and better MAE, MSE, R2, MAPE and RMSE obtained by the model is 0.1013, 0.0134, 0.9479, 0.1152 and 0.1156. Internal assessment and comparative analysis performed with existing regression algorithms exhibit lower loss values obtained from the present research, which determines the efficacy of the proposed model. [ABSTRACT FROM AUTHOR]

Introduction/Purpose: The outbreak of methanol poisoning described in this paper occurred in Ahmedabad, Gujarat, India in July 2009. Our intention is to share the experience of clinical features, laboratory investigations and their relation during this tragedy. Materials and Methods: Single center, retrospective study of clinical features and laboratory parameters of 178 cases of methanol toxicity treated at tertiary care hospital in Ahmedabad, Gujarat. Results: Maximum patients (39.8%, n = 45) were received in 48 h; Mean age of presentation was 41.9 ± 10.2 years. Most of them were men (175 out of 178). On presentation, 83% patients had gastro-intestinal symptoms, 46% had neurological symptoms, 73% had visual symptoms and 32% had dyspnoea. 62% had blurred vision, 10.5% had blindness. Patients with visual symptoms had high mean level of methanol (120.12 ± 23.12 vs. 55.43 ± 29.24, P = 0.014). On fundus examination 52.8% (n = 62) had bilateral hyperaemia of discs, 8.4% (n = 12) had bilateral disc pallor and 4.5% had papilledema (n = 5). Patients with hyperaemia of discs, discs pallor or papilledema, had higher mean methanol level (121.1 ± 32.2 mg% v/s 70.1 ± 23.2 mg%, P = 0.032). Mean of pH values was 7.17 ± 0.22 and bicarbonate was 12.3 ± 7.3 mmol/L. Both pH and bicarbonate levels correlated well with mortality and serum methanol level. Mean serum methanol level was 87.1 mg/dL, and correlated significantly with the mortality (53.1 ± 41 mg/dL v/s 121 ± 92 mg/dL, P value < 0.05). Conclusion: GI symptoms, neurological symptoms and breathlessness are important clue to ED physician for diagnose methanol poisoning. Visual symptoms and fundus findings correlate well with the methanol level. Arterial Blood Gas derived pH and bicarbonate levels correlate significantly with the methanol level and mortality. [ABSTRACT FROM AUTHOR]

Urban expansion of the Indian metropolitan cities has reached the rural peripheries. There have been social, economic, and environmental consequences of this process of peri-urbanization on villages surrounding the big cities. In such situations, it becomes crucial to understand whether the peri-urban areas present opportunities for women emerging out of the processes of development or they expose rural women to previously unavailable challenges or not observed in other regions. This study analyses Female Work Participation (FWP) with related parameters in 712 villages surrounding the Ahmedabad city of Gujarat state. The analysis is based on village-level secondary data derived from the Office of the Registrar General, Census of India for 2001 and 2011. FWP is calculated using the standard formula provided by the Census of India. For the graphical presentation and urban proximity analysis, a geospatial mapping of all villages is performed with the help of Geographic Information System. To understand the association of urban proximity and other covariates with FWP, Ordinary Least Square regression is applied for 2001 and 2011. FWP reached 23 percent in 2011 from 38 percent in 2001. FWP has an inverse relationship with urban proximity, which means as the distance from the main city increases, there is an increase in FWP of villages. More than 40 percent of women are still working as marginal workers. Moreover, within the main worker category, a huge proportion of female is engaged as agricultural laborers. The findings suggest a pressing need for women-centric policies in the region. [ABSTRACT FROM AUTHOR]