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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]

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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]

Introduction: Women empowerment in society and family are closely linked to decision-making. Empowerment of women is essentially the process of upliftment of economic, social, and political status of women, the traditionally underprivileged ones, in society. It involves the building up of a society wherein women can breathe without the fear of oppression, exploitation, apprehension, discrimination, and the general feeling of persecution which goes with being a woman in a traditionally male dominated society. The present study was conducted to assess women's empowerment status and factors affecting it. Material and Method: Community-based cross-sectional study was carried out among 175 women residing in urban field practice area of Dr. M. K. Shah Medical College and Research Centre, Ahmedabad. A pre-designed questionnaire was used for data collection by interviewing the women through house to house survey. Data were entered in Microsoft Excel and analyzed using (Statistical Package for the Social Sciences (SPSS). Result: Around 85.71% of women were involved in decisions regarding trivial issues. A total of 17.72% of the women reported that they had suffered some form of domestic violence in their life. Around 23% of the women were not involved in any financial decisions of the family. Association between education of the women and involvement in decision-making was found statistically significant. Employment increases the participation of women in financial decision-making. 88.89% of employed women participated in financial decisions, while 71.42% of housewives were participating in these decisions. Conclusion: Education and employment were found to have impact on decision-making. So, education of girls should be given the priority. [ABSTRACT FROM AUTHOR]

A naturally occurring component called pectin is present in many fruits, including berries and apples. Almost all plants contain it, where it adds to the structure of the cell. Pectinase is an enzyme product that is used professionally to break down pectin. Pectinases are widely used in the wine and citrus juice industries. It is used in the fruit juice business for clarification because it lowers viscosity, which results in the creation of clear juice. In the current research, soil samples were taken from six different regions in the Ahmedabad district in an effort to isolate bacteria that produce pectinase from those samples. Primary screening produced a total of 41 strains, of which 14 showed pectinase activity. Screening was done by using Vincent's agar medium containing pectin. Best pectinolytic activity was determined by clear zone of hydrolysis on selective media. Among 14 isolates, Isolate 1 was showing highest zone of utilization which was selected for further study. Pectinase was produced by submerge fermentation technique and physicochemical parameters were optimized in which isolate 1 showed highest activity at pH 7.4, temperature 37°C, incubation period 48hrs., inoculation size, substrate, carbon source, nitrogen source. Isolate 1 was characterized by its cultural, morphological, biochemical and molecular basis by 16S rRNA sequencing and designated as Bacillus subtilis. This isolate was applied for fruit juice clarification and demucilization. [ABSTRACT FROM AUTHOR]

Introduction: Medical education in India is evolving/changing in view of implementation of new curriculum in undergraduate schools. Dundee Ready Educational Environment Measure (DREEM) is one of the tools developed specifically to assess the educational environment of medical institutions as perceived by the students. DREEM-12, an abridged version of DREEM-50 had been developed which retains its factors structure, validity, reliability and at the same time avoids respondent fatigue. We used the DREEM-12 questionnaire to assess final M.B.B.S students' perception about their learning environment at our institute. Materials and Methods: A cross-sectional study was done including 215 undergraduate students from the final MBBS. The DREEM-12 questionnaire was shared with all the participants as Google form. The mean score for each variable was calculated. Variable scores were summed up to get each domain score and interpreted. Results: We received responses from 60% of students. The highest score was given to faculty's encouragement towards learning with a mean score of 3.1. Least score - 2.1 was given to the stress support system for students. In the cumulative score, students' perception of teacher (SPOT) was 8.8 which is interpreted as "model course organizers," while student's social self-perceptions was 4.7 which infers "not too bad." The overall DREEM-12 score of our study is 33.1, which is indicative of a "More Positive than Negative" educational environment. Conclusion: The stress support system has the lowest score which suggests that efforts should be made to provide continuous psychological support to students may be in the form of periodical assessment by psychologists to find vulnerable ones early and provide them timely help. Items in SPOT are scored highest, pertaining to faculty's knowledge about the subject and their encouragement to students about their learning. As this was the first-ever study done at this institute using DREEM-12 it can serve as a baseline to monitor the effects of changes made over a period of time. [ABSTRACT FROM AUTHOR]

Objective: The study was conducted to understand health-seeking behavior (HSB) of people with diabetes during the coronavirus disease 2019 (COVID-19) pandemic. Materials and methods: A hospital-based cross-sectional study was conducted at All India Institute of Diabetes and Research (AIIDR), Swasthya Diabetes Care in Ahmedabad, India. Data were collected with the help of a structured questionnaire. Response rate was 97%. One hundred thirty-eight participants who visited the hospital during the month of May 2022 were included and interviewed after obtaining informed consent. Results: Of the study participants (n = 138), 43.5% were female, while 56.5% were male. The mean age of study participants was 51.22. Out of 138 participants, 18.1% (n = 25) had type 1 diabetes mellitus (T1D) and 81.9% (n = 113) had type 2 diabetes mellitus (T2D). It was found that 55.7% faced delay in regular checkups, 39.8% used telemedicine to avoid travelling to hospital in fear of getting COVID infection, 7.8% faced delay in consuming medicines due to unavailability of medicines. Regular intake of medicines/insulin altered for 40.5% of study participants. 54.3% of our study participants felt fear while visiting the laboratory to measure their blood glucose level. Conclusions: Uncertainties created by COVID-19 pandemic have affected HSB of people with diabetes in terms of access to healthcare facilities, medicine adherence, laboratory testing, self-management habits. Knowledge shared here can help program planners to identify influencing factors and implement appropriate interventions. This understanding also helps in setting the stage for the formulation of effective diabetesrelated educational programs which might help for future pandemic. [ABSTRACT FROM AUTHOR]

The article discusses the challenges posed by the COVID-19 pandemic to educational institutions worldwide, leading to the closure of traditional teaching methods. To address this, virtual education was introduced, impacting library usage. It highlights the need for educational libraries to adapt to virtual education.

Introduction: To determine the periodontal status and treatment needs using CPITN in a group of adult diabetic patients attending OPD in a private dental clinic in Ahmedabad. Materials and Methods: One hundred and two diabetic patients were screened by a single examiner according to the WHO criteria using a mouth mirror and CPITN probe. The patients were selected from the routine OPD after obtaining verbal consent for the same, and about 76 patients were included in the study, which was carried out for 9 months. Results: Results showed that the highest number of patients had code 3. The majority of sextants had code 2 and 3. Amongst all, 42 patients had a TN score of 2 and 33 had TN score of 3. 56–65 age groups had the worst periodontal status among all groups and required the highest periodontal treatment. Conclusion: The periodontal health of our diabetic patients reinforces the need to establish a comprehensive oral health promotion programme for diabetic patients with a multidisciplinary approach. [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]

The rapid pace of urbanization makes it imperative that we better understand the influence of climate forcing on urban malaria transmission. Despite extensive study of temperature effects in vector-borne infections in general, consideration of relative humidity remains limited. With process-based dynamical models informed by almost two decades of monthly surveillance data, we address the role of relative humidity in the interannual variability of epidemic malaria in two semi-arid cities of India. We show a strong and significant effect of humidity during the pre-transmission season on malaria burden in coastal Surat and more arid inland Ahmedabad. Simulations of the climate-driven transmission model with the MLE (Maximum Likelihood Estimates) of the parameters retrospectively capture the observed variability of disease incidence, and also prospectively predict that of 'out-of-fit' cases in more recent years, with high accuracy. Our findings indicate that relative humidity is a critical factor in the spread of urban malaria and potentially other vector-borne epidemics, and that climate change and lack of hydrological planning in cities might jeopardize malaria elimination efforts. Climate conditions and urbanization can be major drivers of vector-borne infections. Here the authors demonstrate that an often-neglected climate variable, humidity, is an important factor for malaria epidemics in two urban areas in India. [ABSTRACT FROM AUTHOR]

Background: Seropositivity among health-care workers (HCWs) may help in better understanding of the immune response after COVID-19 infection. Objectives: To estimate seropositivity among HCWs and to compare available variables with seropositivity to understand the factors affecting seropositivity. Materials and Methods: A serosurveillance among HCWs was carried out using population proportion sampling during the second half of October 2020 in the city of Ahmedabad using the Covid-Kavach (immunoglobulin G [IgG] ELISA Antibody testing kit). Simple proportions and appropriate statistical tests were used as needed. Results: As on October' 2020, HCWs in Ahmedabad demonstrated a seropositivity of 20.84% (95% confidence interval [CI] 19.00–22.81%). Seropositivity among HCWs was lower than that of the general population (24.20%) which was estimated as part of the same study. Female HCWs had higher seropositivity 22.14% (95% CI 19.74–24.74%) as compared to 18.82% (95% CI 16.06–21.93%) among male HCWs and the difference was statistically not significant (Z = 1.66, P = 0.097). Age groups with increasing age show increasing trend in the seropositivity among HCWs. Conclusion: As on October 2020, with 20.84% seropositivity among HCWs in Ahmedabad, one in every five HCW already demonstrate IgG antibodies against severe acute respiratory syndrome– coronavirus-2. Further scientific studies on seropositivity and the factors affecting the seropositivity may be carried out to uncover more details of immune reaction after COVID-19 infection. [ABSTRACT FROM AUTHOR]

Many toxicological studies revealed the deleterious effects on human health induced by trace metals in ambient particulate matter (PM). This study reports the season-dependent water-soluble and total metal mass in PM10 collected simultaneously over five microenvironments in a semi-arid urban region, Ahmedabad, located in western India. The mineral dust fraction in PM10 over Bapunagar, Narol, Paldi, Income Tax, and Science City was estimated to be around 39, 45, 47, 44, and 31% during summer (May–June 2017) and 24, 55, 28, 27, and 28% during winter (December 2017–January 2018), respectively, corroborating mineral dust is perennial in the air over Ahmedabad. The PM2.5/PM10 mass ratios over all the sites were higher during winter (40–60%) as compared to those during summer (30–40%), indicating the contribution from the anthropogenic sources to PM mass. Among the metals monitored, the estimated considerable amount of high masses of Zn, Cu, Ni, Cd, and Sb during winter can be ascribed to the anthropogenic inputs based on the estimated enrichment factors (EF). In contrast to the crustal source, these metals might have been possibly emitted from several other man-made sources, which were found to be more water-soluble during both seasons. As per the standards of incremental excess lifetime cancer risk (IELCR), it is estimated that the atmospheric mass concentration of carcinogenic metals such as Cr, Co, and As was higher in all these sites, whereas the metals such as Pb, Ni, and Cd are also found over the industrial site (Narol) in addition to the above-said metals. Notably, people are highly susceptible to these metals, leading to the potential risk of cancer during both seasons. [ABSTRACT FROM AUTHOR]