Search

India houses the third biggest Muslim population in the world but over the years it has experienced massive segregation of people based on their religious identities. This large-scale forced and systemic 'ghettoization' of Muslims is a recent trend largely ascribed to the frequent communal' violence and state's negligence towards minorities. In this backdrop, the paper discusses the communal ghettoization of Muslims and uses the Juhapura area of Ahmedabad as illustration to explain the same. This study is essentially based on field observations and literature to comprehend communal tone of ghettoization process. Muslims in India are a minority (15 percent of total population) and suffer most in case riots occur. In the last few decades, following the 1985, 1992 and 2002 riots, Gujarat emerged as a hotbed of communal tension, forcing the Muslim population to migrate from their original locations and seek refuge in other parts of the state. Residents of these newly populated ghettos such as Juhapura are forced to endure these conditions due to fear, lack of choice and state apathy. These localities are largely ignored and lack all forms of socio-economic and physical infrastructure for a decent living. [ABSTRACT FROM AUTHOR]

At the start of the year 2020, World Health Organization declared a public health emergency of international concern about "The COVID-19 pandemic". During this pandemic, when the entire world was facing lockdown, and all industries were getting a setback, there was a spurt observed in the insurance industry. With the rise in COVID-19 cases in India, it became a wake-up call for people towards health insurance. Health Insurance is one of the critical elements for sustainable and inclusive development. A paradigm shift has been noticed among individuals towards health insurance during this pandemic time, especially to protect their families from unforeseen medical expenses. This paper identifies the impact of COVID-19 as a fear that leads to a change in behaviour towards health insurance products. This study is based on the primary data collected from Ahmedabad city showing behaviour towards purchasing an insurance product during the pandemic situation. A sample of 200 individuals from Ahmedabad city of Gujarat State in India has been surveyed. The Binary logistic model is used to analyse the significant factors for the purchase of health insurance and also to check whether fear has a significant impact on a purchase decision. [ABSTRACT FROM AUTHOR]

Water is very crucial liquid which has the potential to fluidly various materials along with biological and mineral substances. The condition of water usually indicates to the substance of water exist at the peak level for sufficient rise of plants and animals. The Bibi talav, Ahmedabad, Gujarat, India is situated at favela location. People discharge their domiciliary ravage unswervingly into the talav water forging environmental imperilment. The motive of this research paper is to establish the water quality index. Numerous physical and chemical parameters such as pH(BPH), Electrical Conductivity (BEC), Turbidity (BTD), Total dissolved solids (BTDS), Alkalinity (BAL), Total Hardness (BTH), Calcium(BCA), Chloride(BCL), Magnesium (BMG), Total dissolved Oxygen (BDO), Sodium (BSOD), Nitrates (BN) and Biochemical Oxygen demand (BBOD) of talav water is examined during three sessions such as monsoon, winter and summer. After calculating water quality index it shows that Bibi talav water is dangerous for drinking and survival of water borne species because the water quality index is above 75. This shows that water is unsuitable for drinking throughout the year. [ABSTRACT FROM AUTHOR]

Head and neck cancer is a significant public health concern in India and globally, with substantial social and economic consequences for affected individuals and their families. The study aimed to assess the socioeconomic impact of head and neck cancer. This paper presents the results of a questionnaire-based study involving 178 head and neck cancer patients who were evaluated at least two years post-completion of their treatment. The questionnaire data collected data on various factors, including site of cancer, treatment modality, speech and diet impairment, changes in earning capacity, occupation, and salary. This was collected from all patients visiting our tertiary cancer care center outpatient department in Ahmedabad, India, between January 2023 and August 2023. The findings highlight the diverse and profound socioeconomic consequences of head and neck cancer. The findings emphasize the need for comprehensive support systems for affected individuals and their families. [ABSTRACT FROM AUTHOR]

The diffusion-absorption refrigerator (DAR) is a cooling technology that can be driven entirely by thermal energy. With solar-thermal collectors as the heat source, this technology can provide a zero-emissions solution for essential cold-chain provision in the world's poorest regions. In the accompanying paper, a system model of a solar-driven DAR system was developed for simulating operation under variable solar conditions. In Part II of this study, we apply this model to assess the predicted performance of the system in its intended application: for on-farm refrigeration of harvested crops in rural India. The roles of the working-fluid charge pressure and collector array area for maximum cooling are investigated. A charge pressure of 16 bar is found to be optimal for ambient conditions during the October harvest season in Ahmedabad, with a collector-array area of 1.5 m2 found to be most appropriate for the nominal 70 W cooling-capacity system. However, the time taken to reach the required solar collector temperature for bubble pump activation results in a daily operating period of less than 5 h and a predicted overall solar-to-cooling efficiency of less than 1%. To address these limitations, modifications to various components are considered based on simple assumptions for improving performance. • An assessment is performed of a solar diffusion absorption refrigeration system. • Off-grid cooling operation is simulated for the target region of Ahmedabad, India. • A cooling output of 170 kJ/day is predicted with a solar collector area of 1.5 m 2. • An optimal charge pressure of 16 bar is determined for maximum cooling output. • Modifications to key system components are recommended based on the results. [ABSTRACT FROM AUTHOR]

The conventional standardized theoretical models (such as Webster, Alcelik, Indo-HCM) used for the delay estimation revolve around the mathematical hypothesis and assumptions, making them static with limitations in accommodating the dynamic traffic behaviors. Recent advances in artificial intelligence make machine learning techniques suitable for estimating vehicle control delay compared to conventional methods. This paper demonstrates the application of several machine learning models developed by focusing on the fluctuations of traffic observed at an intersection having heterogeneous traffic conditions in Ahmedabad city for delay estimation. Several parameters are extracted from on-field and video surveys. However, not all parameters are relevant for accurately estimating vehicle control delay. Hence, a feature selection process consisting of several feature-scoring techniques from filter, wrapper, and embedded methods is applied to all the parameters. This process gave insights into the most statistically relevant independent parameters, and out of all the parameters, cycle time was found to be insignificant, with a feature score of 0 from almost all techniques. Hence, it was removed, and then the prominent parameters were then used to build a vehicle control delay model using support vector regression (SVR), K-nearest neighbor (KNN), artificial neural network (ANN), random forest regression (RF), and decision tree regression (DT). Error distribution, standard deviation of errors, coefficient of Determination (R2), and Root Mean Squared Error (RMSE) are the parameters used for evaluating the performance of the machine learning models. RF outperformed them all with a standard deviation of errors, R2, and RMSE of 11.065, 0.926, and 11.081 on testing data. But ANN, KNN, and DT also performed satisfactorily. Compared with conventional standardized theoretical models, all the machine learning models except SVR performed better. [ABSTRACT FROM AUTHOR]

Urban redundancy, often observed in the core areas of cities, poses significant challenges to sustainable development and urban vitality. This study addresses urban redundancy by proposing a quantitative framework to both identify and mitigate its effects, using Ashram Road in Ahmedabad, India, as a case study. The research employs a Multi-Criteria Decision-Making Analysis (MCDMA) based on data collected through physical surveys and literature reviews. This analysis integrates approaches such as redevelopment, adaptive reuse, policy interventions, and economic strategies to counter redundancy effectively. The findings highlight that Ashram Road's core area, despite its historical and cultural significance, faces challenges like poor maintenance, high vacancy rates, and inadequate utilization of the available floor space index (FSI). The proposed framework aims to develop context-sensitive strategies that uphold the intrinsic value of core urban areas while promoting sustainable regeneration. The study demonstrates the need for a balanced approach that combines modern development with preservation, providing insights for urban policy-making that enhances the resilience and longevity of city cores. [ABSTRACT FROM AUTHOR]

The differential transmission of COVID-19 depending on the socio-economic status of a neighborhood is well established. For example, several studies have shown that COVID-19 transmission was higher in poorer and denser neighborhoods than in wealthier ones. However, what is less well known is how this varied rate of transmission interacted with established health measures, i.e., face masks and lockdowns, in the context of developing countries to reduce pandemic cases and hence resulted in fewer deaths. This study uses an Agent-Based Model (ABM) simulation to examine the context and impacts of COVID-19 mitigation efforts (i.e., lockdowns combined with masks) on the transmission of COVID-19 across a single neighborhood in Ahmedabad, a city in the state of Gujarat, India. The model is parameterized using real-world population data, which allows us to simulate the spread of COVID-19 to find conditions that most closely match the realities of COVID-19 in the spring of 2020. Consequently, the simulation can be used to understand the impact of nation-wide lockdown on the spread of COVID cases across Ahmedabad as a function of housing density. Thus, invaluable insight into the effectiveness of a lockdown as a mitigation measure can be derived. Further information about how the effectiveness of the lockdown varied by neighborhood, as well as other factors that impacted it, can be ascertained. [ABSTRACT FROM AUTHOR]

While most transliteration research is focused on single tokens such as named entities—for example, transliteration of from the Gujarati script to the Latin script "Ahmedabad" footnoteThe most populous city in the Indian state of Gujarat. the informal romanization prevalent in South Asia and elsewhere often requires transliteration of full sentences. The lack of large parallel text collections of full sentence (as opposed to single word) transliterations necessitates incorporation of contextual information into transliteration via non-parallel resources, such as via mono-script text collections. In this article, we present a number of methods for improving transliteration in context for such a use scenario. Some of these methods in fact improve performance without making use of sentential context, allowing for better quantification of the degree to which contextual information in particular is responsible for system improvements. Our final systems, which ultimately rely upon ensembles including large pretrained language models fine-tuned on simulated parallel data, yield substantial improvements over the best previously reported results for full sentence transliteration from Latin to native script on all 12 languages in the Dakshina dataset (Roark et al. 2020), with an overall 3.3% absolute (18.6% relative) mean word-error rate reduction. [ABSTRACT FROM AUTHOR]

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]

The transportation infrastructure sector significantly impacts a country's gross domestic product (GDP), particularly in developing nations striving to manage and maintain road networks as valuable assets. While asset generation is integral, the more intricate challenge lies in effective maintenance. Pavement monitoring, a crucial component of pavement maintenance and management systems (PMMS), evaluates defect severity, road maintenance prioritization, and maintenance types. To enhance road health monitoring, the present study introduces a hybrid machine learning (ML) method, integrating support vector machine (SVM) and convolutional neural network (CNN). The proposed semi-automated detection system aims to reduce human supervision in traditional surveys, thereby cutting down the cost of pavement distress maintenance The research utilizes data collected by the authors from Ahmedabad city, Gujarat, following Indian road congress (IRC) guidelines for defect selection. Training involves 1,000 images for each crack type, with testing on 100 images. Results indicate that the SVM-CNN model achieves 87% accuracy in training and 91% accuracy in testing for road defect classification, showcasing its efficiency in pavement maintenance and management. The system presents the potential to significantly enhance the efficiency of road maintenance processes, making it a valuable asset for developing nations striving for a more streamlined approach to road network preservation. [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The present study assessed the status of the financial literacy (FL) levels and examined its impact on the investment decisions of the investors from the cities of Ahmedabad and Vadodara in Gujarat state. We measured FL using a multi-dimensional construct of financial behaviour, attitude and knowledge. Investment decisions were analysed taking the six proxies – neutral information, personal financial need, macroeconomic indicators, accounting information, advocate information and firm image. The study revealed a high level of FL among the respondents with 62.50% of the respondents being highly financially literate and the remaining having a comparatively lower degree of FL. Though, respondents exhibited a higher level of financial literacy, they still lacked required degree of understanding of advanced financial and investment topics. Using simple linear regression, we found that investors’ investment decisions were significantly positively impacted by their levels of FL. This study has implications for employers, policymakers, regulatory authorities and financial education providers to achieve financial inclusion and well-being goals by devising appropriate strategies and interventions for individuals with varying level of FL. [ABSTRACT FROM AUTHOR]