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Economic values have dominated water policy discourse over the last four decades. Very little has been written on social, cultural, and ecological values and their roles in enhancing water security. The primary objective of this paper is to provide a comprehensive analysis of diverse water values with a case study of the National Capital Territory of Delhi, India. To achieve this, a review of the existing scholarship on water values was conducted to develop a set of water values. Field surveys and interviews were conducted to comprehend the water values held by various stakeholders in Delhi. This paper is an attempt to show that viewing water security through the lens of diverse water values (social, cultural, economic and ecological) provides a better understanding of water policies, and enhanced comprehension could potentially result in better policies to promote water security. In the case of Delhi, we additionally found that the claimed predominance of water values such as efficiency, equity, equality, religiosity, and purity does not mean that these values are also actualized in water practices. Another major finding is that all four sets of values are integrated with one another, and policies underpinned by the identified values would be relatively better than policies solely based on economic values. [ABSTRACT FROM AUTHOR]

Eviction could hold a different meaning if a home's immediate surroundings contribute to its residents' livelihood, especially for informal laborers. This paper explores the notion of the fragility of a home within an expanded space—the space on which a home stands and its surroundings when turned into a contested area. It specifically looks at the slum of Yamuna Pushta in Delhi, which was demolished in 2004. The act uprooted thousands of low-income families who were blamed for polluting the river. The demolition was fueled by new urban visions and planning strategies, political and capitalist ambitions, projections of national pride, and an event-driven approach camouflaged under an environmentalist concern attempting to "clean" the river. Using the photographic works of artist, curator, and activist Ravi Agarwal as a case study, this paper argues the presence of a counternarrative in the works, challenging the projected environmentalist discourse around the river, the slum dwellers. This study further states the dual marginalization of the Pushta residents and the Yamuna by critiquing the economic format of majoritarianism through the growing normalcy and agreeability of the slum demolitions by the urban non-poor disguised as the "greater good". [ABSTRACT FROM AUTHOR]

While the problem of Delhi's air pollution is well recognized, as is the class bias of the judiciary in targeting certain sources of air pollution over others, what is less will understood are the unintended consequences of air pollution interventions on indoor and outdoor air quality. This paper shows how the National Green Tribunal has targeted one source of air pollution in recommending waste-to-energy technologies that deprive waste pickers of their livelihoods. The pandemic-related lockdown provides an opportunity to assess the impacts of livelihood deprivation on fuel use. Using data collected from surveys of waste pickers, this paper shows that decreases in income are related to an increase in reliance on solid fuels. Therefore, we argue that air pollution interventions designed to control air pollution from poor waste management have the unintended consequence of increasing air pollution from a different source (biomass burning for cooking) that specifically impacts waste pickers by increasing their exposure to indoor air pollution, while also undermining the government's urban air pollution mitigation efforts overall. [ABSTRACT FROM AUTHOR]

Near-fault ground motions (NFGM) may have a significant component of the long period velocity pulse (LPVP) due to the directivity effect. The structures subjected to such ground motions are prone to experience large displacements. In this paper, the damage potential of such motions is compared with NFGM (without LPVP) and far-field motions in terms of seismic demand (inter-storey drift and inelastic displacement). Due to the lack of site-specific recorded NFGM, the synthetic ground motions are generated and used in this study. For this purpose, first the bedrock motions are obtained through stochastic simulation approach in which the Specific Barrier Model (SBM) is used as source spectrum and then these motions are transferred to the ground surface using one-dimensional ground response analysis. In order to obtain the NFGM with pulse, the bedrock motions are superimposed with the LPVPs and then transferred to the ground surface. To illustrate, the case of a ten-storey reinforced concrete (RC) building is considered in Delhi region (India) which falls under the seismic zone-IV. The main finding of this study is the observation that the NFGM with LPVP impose almost double seismic demand compared to the NFGM without LPVP, specially for the structures lying in the velocity-region of the response spectrum. In this study, the effect of depth of bedrock on seismic demand is highlighted and recommended to be included in the design seismic demand of NFGM. [ABSTRACT FROM AUTHOR]

This paper delves into the sociocultural landscape portrayed in Chetan Bhagat's novel Five Point Someone to provide insights into the youth culture of metropolitan India. The narrative explores the lives of three protagonists as they navigate the demanding academic environment of an elite engineering institute in Delhi. Through a sociocultural lens, we examine how these characters grapple with societal expectations, peer pressure, and the impact of a rigid educational system on their identities and aspirations. The analysis focuses on key themes such as identity formation, rebellion against tradition, and the clash between individual desires and societal norms. We also investigate the characters' responses to familial pressures, the pursuit of personal goals, and the challenges they face while striving for academic success. This examination of Five Point Someone offers a deeper understanding of the sociocultural dynamics that shape the lives of metropolitan youth in contemporary India. By shedding light on the complexities of their experiences, we contribute to a broader conversation about the evolving youth culture in urban settings. [ABSTRACT FROM AUTHOR]

This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–2022 shows high accuracy (up to 80 %) and a very low false alarm ratio (∼ 20 %) from day 1 to day 5 of the forecasts, especially when the ambient air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) could reach 65 %–69 %. According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios. [ABSTRACT FROM AUTHOR]

This research paper delves into the landmark case of Amway India Enterprises vs. Union of India, adjudicated by the Delhi High Court in July 2019. The case pertained to alleged interference by e-commerce giants such as Amazon and Flipkart in the contractual relationships between direct selling companies and their independent sales representatives. The paper dissects the intricate legal facets of this case, encompassing constitutional matters, intellectual property rights, information technology laws, consumer protection, and contractual and tort laws. A central issue examined was whether e-commerce platforms were bound by the Direct Selling Guidelines, 2016, and if their actions constituted trademark violations. The case also probed the applicability of Section 79 of the Information Technology Act and intermediary responsibilities. The Delhi High Court's judgment wielded substantial influence over the Indian business landscape. E-commerce platforms were mandated to cease commercial activity on plaintiffs' products and furnish detailed seller data, necessitating operational adjustments. In conclusion, the Amway India Enterprises case signifies the evolving commerce dynamics in the digital era and underscores the pivotal role of the legal system in ensuring fair competition, consumer safety, and intellectual property preservation. As business landscapes evolve, this case illuminates the path toward a responsible, transparent, and secure online marketplace in India. [ABSTRACT FROM AUTHOR]

The Yamuna khadar or the floodplains of the river Yamuna in Delhi is a deeply contested agrarian space. While thousands have been involved in farming these floodplains for decades, contestations over their legality, compensation, land use, displacement, and impact on the river, soil, and city have been significant public concerns over the last few decades. In this paper, I focus on one aspect of this agrarian landscape--the toxicity associated with river water due to the pesticides and fertilizers used in agriculture. I examine how toxicity is perceived, discursively constructed, and dealt with by differently located stakeholders in this landscape. The differently located stakeholders associated with the toxic agrarian landscape of Yamuna khadar are not experiencing pollution as a homogenous community, i.e., neither all of them see themselves as suffering from pollution nor do they see themselves as contributing to the toxicity of the landscape in a similar way. [ABSTRACT FROM AUTHOR]

Academic research and publishing are facing a crisis. The importance of access to academic literature in an interconnected world, the ever-growing cost of subscriptions to this literature, different revenue models of journals, and reduced or stagnant library budgets are pushing the academic community to find alternatives for research publications. In its 25 years of existence, the Open Access Movement and models which sought to contain the crisis have become the subject of considerable criticism. At the same time, a significant portion of academic literature remains locked behind steep paywalls. This has led to the growth of pirate websites and shadow libraries, which have been met with forceful legal retribution by publishers using copyright laws. Using the Sci-Hub case, a current copyright infringement case brought by a group of publishers before the Delhi High Court, this Paper evaluates the Open Access Movement, fair dealing in copyright law, academic piracy, and court cases in the United States, India, and other countries within the broad meaning of the right to research. The Paper concludes that a purposive interpretation of copyright law may have an answer enabling a just outcome.† [ABSTRACT FROM AUTHOR]

Living and working for a month at the Sanskriti Foundation in Delhi, the artist's life was watched and observed by a group of resident monkeys. This paper is based on notes begun during that studio residency and represents the critical reflections emerging alongside the hands-on sculptural practice. It is illustrated with close-up photographs of the artist's sculpture that asks how encounters with fabled animals in densely populated 21st century urban areas can alter our understanding of the gaze as an inter-species gaze. The sculpture and paper begin to ask broader questions, including how can sculpture provide a different, and perhaps more tacit and empathetic, encounter with the other to enable a physical, mental or spiritual experience of cultural entanglement between the various onlookers? In how far is modelling the other's gaze a form of embodiment and mimicry? Do the fast-changing camera angles and soundtracks of natural history programmes hinder an empathic inter-species encounter? Or, does the slow animation of the artist's sculpted surface heighten a sense of being alongside equally curious, cunning and adaptable others such as crows, foxes and monkeys? [ABSTRACT FROM AUTHOR]

This research paper explores the economic, environmental, and socio-cultural sustainability of Delhi heritage tourism development from the perspective of tourists. Primary research was conductedamong the touristsbased on a structured questionnaire survey executed at various tourist places across Delhi. This research paper used exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modelling (SEM) for exploring the sustainability of Delhi heritage tourism. The research findingson environmental pressure confirm that tourism activities are inflicting pressure on the environment. The environment management mechanisms implemented by the government have notbeen successfulin mitigating the adverse impact of tourism and accomplishing environmental sustainability. The findings on economic and cultural empowerment demonstrate that tourism ensures economic sustainability. However, the research findings on socio-cultural pressure reveal that tourism is exerting adverse impact on society and culture. Thus, the heritage tourism development inDelhi has not been encouragingeconomic and social sustainability. Therefore, the government of Delhi should initiate appropriate policies and programmes to mitigate the adverse environmental and social impacts. [ABSTRACT FROM AUTHOR]

There was a time when it was said that the commanding height of Indian economy will be with the public sector. But with gradual deterioration in the functioning of government owned public utilities, increasing public chaos and demand for better public utility services necessitated revolutionary change in state owned enterprises and boards. This paper throws light upon the exigency to undertake revolutionary change in DJB (Delhi Jal Board) to circumvent public misery and outcry. It expresses the need to commence a major change that encompasses altering internal policies, procedures, structure, mindset as well as the mission and vision of the organisation. Accordingly, this paper attempts to substantiate the obligatory high level change through research conducted on TPDDL (Tata Power Delhi Distribution Limited) that was transformed on PPP (public-privatepartnership) basis. It also exhibits the various forces operating in the environment that incessantly drive towards a comprehensive change, through Kurt Lewin model of change. Consequently, it addresses the problems of water scarcity and mismanagement. Besides, it also updates about the small projects or initiatives by DJB to deal with the growing needs of water in Delhi. [ABSTRACT FROM AUTHOR]

The increasing trend of multi-channel urban consumption, driven by the rise of E-commerce, has led to the emergence of new freight facilities. The COVID-19 pandemic further amplified this shift, with consumers relying more heavily on online purchases for regular needs, resulting in the advent of Q-commerce as a distinct subvertical within the E-commerce industry. However, there is a significant lack of understanding regarding the characteristics, requirements, and impacts of this sector in both existing literature and planning documents. Therefore, the aim of this research is to investigate the freight patterns and associated externalities within urban areas for this emerging trend in E-commerce, while also assessing the adequacy of planning documents in addressing these issues. The paper presents a case study-based examination of various Q-commerce grocery companies operating in Delhi, delving into their operations, freight patterns, and neighborhood-level externalities of last-mile delivery. By highlighting the externalities and deficiencies related to E-commerce, this study provides valuable insights into the planning implications of Q-commerce and its impact on urban areas. [ABSTRACT FROM AUTHOR]

Few studies have examined how students' entrepreneurial inclinations are influenced by entrepreneurship education. These studies' contentious findings drew academics' interest and prompted additional inquiry. This paper investigates the moderating impact of entrepreneurship education on attitude, subjective norms, and self- efficacy-based determinants of entrepreneurial desires and aspirations. Entrepreneurial education is taken as a moderating variable for the study. True experimental design is chosen to analyze the respective relationship between some independent variables and a single dependent variable, such as EINT. From various higher education institutions in India, data were gathered from two groups, those who finished an entrepreneurship course (treatment group) and those who did not have the entrepreneurship course (control group). The period of the study is from 2020-2022. Out of the 500 respondents, 324 completed questionnaires were received, and 307 qualified as valid answers. Structural equation modeling was used to test the hypothesis. Using the partial least squares method, the statistical relationship between the modeled variables was estimated. The study found that the most important predictors of EINT are attitude, self-efficacy, and subjective norms. SEM showed that the association of entrepreneurial attitude and self-efficacy with entrepreneurial intentions was found to be moderated by entrepreneurship education. However, EE weakened the influence of subjective norms on professional objectives and intentions to start their own business. Education influences strong intentions to be an entrepreneur successfully. This study is limited based on the information available in Delhi and NCR when the research was carried out. The result might be affected when taken from different regions or states. This research is essential to policymakers because India has cultural barriers, a lack of entrepreneurship education, and very few support programs that prevent new firms from beginning. They should encourage entrepreneurship, R&D, and technology. Robust, inclusive measurements could aid researchers and practitioners. Financial educators help youth manage their financial issues and succeed in this sector. Behavior management requires financial researchers to understand behavior formation and change, which applies the implications of the true experimental design of the study. The study contributes empirically and theoretically by focusing on an area of research that has received less attention, especially in the context of the environment. [ABSTRACT FROM AUTHOR]

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Regression Tree (RT) and K-Nearest Neighbor (KNN) models play significant roles in machine learning. RT facilitates interpretable decision-making, aiding in the comprehension of complex data relationships, while KNN is valued for its simplicity, adaptability to non-linear data, and robustness to noise, making it a versatile tool across various applications. The primary drawback of Regression Tree is its tendency to assign the same predicted value (average value) to all tuples satisfying the same corresponding splitting criterion. K-Nearest Neighbors (KNN) is sensitive to irrelevant or redundant features since all features contribute to similarity. This paper proposes a hybrid regression model based on Regression Tree (RT) and KNN, addressing the aforementioned issues. The model's performance is compared with KNN using 10 types of distance measures and further assessed against RT, KNearest Neighbor regression (KNN), and Support Vector Regression (SVR) through a Monte Carlo simulation study. Simulation results indicate that the hybrid model outperforms all other regression models, regardless of sample size, when observations follow normal distributions or t-distributions. The proposed model's effectiveness is demonstrated through a real-life application using data on global warming in Delhi. [ABSTRACT FROM AUTHOR]

This paper analyzed the price dynamics and volatility among tomato-producing (Solan and Ludhiana) and consuming (Delhi and Bangalore) markets using monthly price data from April 2005 to March 2023. The Cudda Della-Valle index and seasonality index were used to assess the price dynamics of the tomato market. Correlation analysis, Regression analysis, Vector Error Correction Model, and Granger causality were used to analyze the market integration. The GARCH model is used to measure the price volatility in the selected markets. Results showed that the maximum instability in tomato prices was in May (53 to 77 %), and the seasonality index showed that farmers received more than average prices between July and November. The significance of the Engle-Granger tau-statistic revealed that all the selected markets were well integrated into the long run. The speed of adjustment was highest in the Delhi market (56 %) followed by Bangalore (55 %) and Solan (27 %). The Bangalore market was the key market that influenced the price of all other markets by the Granger Causality test. Further, GARCH (1,1) model is used to estimate price volatility in the selected market. The price volatility was high in the Solan market and low in all the selected markets. [ABSTRACT FROM AUTHOR]

Seasonal production, weather abnormalities, and high perishability introduce a high degree of volatility to potato prices. Price volatility is said to be asymmetric when positive and negative shocks of the same magnitude affect it in a dissimilar way. GARCH is a symmetric model, and it cannot capture asymmetric price volatility. EGARCH, APARCH, and GJR-GARCH models are popularly used to capture asymmetric price volatility. In this paper, an attempt is made to model the price volatility of the weekly wholesale modal price of potatoes for the Agra, Ahmedabad, Bengaluru, Delhi, Kolkata, and Mumbai markets using the above-mentioned models. The News Impact Curves (NICs) are derived from the fitted models, which confirmed the presence of asymmetry in the price volatility. To this end, NICs are used to describe the degree of asymmetry in volatility present in different markets. [ABSTRACT FROM AUTHOR]

The Indian capital megacity of Delhi is reeling under deteriorating air quality, and control measures are not yielding any significant changes, mainly due to a poor understanding of sources of emissions; hence, priority option in mitigation planning is lacking. In this paper, we have made an attempt to develop a spatially resolved technological high-resolution gridded (∼0.4km×0.4km) emission inventory for eight major pollutants of the Delhi region where high-resolution activity data of all possible major and unattended minor sources are generated by organizing a mega-campaign involving hundreds of volunteers. It is for the first time that we are able to estimate the decadal growth in emissions of various pollutants by comparing newly developed 2020 emissions with SAFAR (System of Air Quality and Weather Forecasting and Research) emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emissions for PM 2.5 , PM 10 , CO, NOx , VOC, SO2 , BC, and OC over the Delhi National Capital Region (NCR) are estimated to be 123.8, 243.6, 799.0, 488.9, 730.0, 425.8, 33.6, and 20.3 Ggyr-1 , respectively, for the year 2020. The decadal growth (2010–2020) in PM 2.5 and PM 10 is found to be marginal at 31 % and 3 %, respectively. The maximum growth is found to be in the transport sector followed by the industrial and other sectors. Maximum decadal growth found for the pollutants BC, OC, and NOx is 57 %, 34 %, and 91 %, respectively. The decadal shift of sectorial emissions with changing policies is examined. The complete dataset is available on Zenodo at 10.5281/zenodo.7715595 (Sahu et al., 2023). [ABSTRACT FROM AUTHOR]

Art collectives come into existence for many reasons, whether to collaborate on art making or to generate a space for contemporary art outside of the established channels of exhibition and the art market. These efforts have been captured in recent exhibitions such as The Ungovernables, organized by the New Museum in 2012; Six Lines of Flight, which was launched at SFMOMA in 2013; and Cosmopolis I, organized by the Centre Pompidou in 2017. Artist collectives have received some scholarly attention, primarily as producers of artworks, but their exhibition-making practices have not been explored. Some of the collectives included in these exhibitions have also been very involved in exhibition making themselves. The Indonesian art collective ruangrupa was selected to curate the 2022 edition of documenta. This selection emerges not only from their participation in international biennials and their own exhibition practice in Jakarta—including the organization of regular exhibitions, workshops and film screenings at their compound—but also more ambitions events such as Jakarta 32 °C, a festival of contemporary art and media (2004–2014), or O.K. Video (2006–2018). Another group, the Raqs Media Collective, based in Delhi, curated the Shanghai Bienniale in 2016 and the Yokohama Trienniale in 2020. This paper will connect the local and the global through an examination of art collectives' community-based work in their own cities, and the way it translates into global art events. [ABSTRACT FROM AUTHOR]

Forecasting of PM2.5 concentration is a global concern. Evidence has shown that the ambient PM2.5 concentrations are harmful to human health, climate change, plant species mortality, etc. PM2.5 concentrations are caused by natural and anthropogenic activities, and it is challenging to predict them due to many uncertain factors. Current research has focused on developing a new model while overlooking the fact that every single model for PM2.5 prediction has its own strengths and weaknesses. This paper proposes an ensemble framework which combines four diverse learning models for PM2.5 forecasting in Puli, Taiwan. It explores the synergy between parametric and non-parametric learning, and short-term and long-term learning. The feature set covers periodic, meteorological, and autoregression variables which are selected by a spiral validation process. The experimental dataset, spanning from 1 January 2008 to 31 December 2019, from Puli Township in central Taiwan, is used in this study. The experimental results show the proposed multi-model framework can synergize the advantages of the embedded models and obtain an improved forecasting result. Further, the benefit obtained by blending short-term learning with long-term learning is validated, in surpassing the performance obtained by using just single type of learning. Our multi-model framework compares favorably with deep-learning models on Puli dataset. It also shows high adaptivity, such that our multi-model framework is comparable to the leading methods for PM2.5 forecasting in Delhi, India. [ABSTRACT FROM AUTHOR]

Acid attack is a gender based problem. In fact the patriarchal mindset of Indian society is that women’s beauty and body only controlled by men, Men have a set concept about women’s body are essential meant to please men. Women invest more time in beauty, fashion, cosmetics, dressing patterns, fitness and other stuffs, to just impress men, rather men do less, have focused on career and money making process. This deceit illustration making society to except it like this way, however it is largely bogus. Several types of Crime against women are already deeply rotten in Indian society, presently a horrifying menace of acid attacks on women has been disturbing social order. The large number of acid attacks on women in India is growing in every passing year. The latest data released by ‘India Today Data Intelligence Unit’ has exposed that between 2014 and 2018 there are approx 1,483 victims of acid in the country. It also reveals that the huge number of reported cases are; due to relationship breakups, one side love, romance refusal, rejection of marriage proposal and physical intimacy, jealousy, family property disputes, and marital disengagement. This paper will try to explore scenario of acid attacks on women in Delhi. This paper will try to make an attempt to highlight prime causes and consequences of attacks and supporting and benefitting agencies to Acid burn survivors in Delhi in contemporary times. [ABSTRACT FROM AUTHOR]

Delhi's need for pure water is growing with its growing population. Along with the need for drinking water, useful sewage treatment is much needed. Another immense problem that the city faces is air pollution that causes health problems to the local community mainly caused by energy traditional plants. The patent Natural Biological System (NBS) developed by Ayala Water and Ecology offered Delhi and many other locations around the globe a practical, on-site method for the simultaneous purification of soil, water, and air with a negative carbon footprint. The current paper describes how those systems were applied to the on-ground situation after an extensive environmental survey that was conducted to identify the needs and difficulties characterizing the city. The solutions provided to Delhi are an example of how urban eras can benefit from the NBS system improving their ecosystem and supporting better and healthier life for their residents. [ABSTRACT FROM AUTHOR]

The present paper investigates the location pattern of co-working spaces in Delhi which is absent in the existing body of knowledge. Delhi is a political, administrative, educational, scientific and innovation capital that accommodates many co-working spaces in India. We developed Ordinary least squares (OLS) and geographically weighted regression (GWR) models to understand the associations of co-working spaces of digital labourers with other urban socio-economic, services and lifestyle variables in Delhi using secondary data for 117 coworking locations in 280 municipal wards of NCT-Delhi. Model diagnostic suggested that the GWR model provides additional information regarding geographical distribution of coworking spaces, and density of bars, median house rent, fitness centres, metro train stations, restaurants, cinemas, cafés, and creative enterprises are statistically significant parameters to estimate them. The importance of coworking spaces has increased in the post-disaster period, so this study informs public policies to benefit people and companies who choose coworking routes, and recommends urban planners, developers, and real-estate professionals to consider the proximity of creative industries in planning and developing coworking spaces in the future. Also, in the post COVID-19 period, to increase local jobs and long-term place sustainability, a localised policy intervention for coworking spaces in Delhi is highly recommended. [ABSTRACT FROM AUTHOR]

Cultural heritage is under tremendous pressure in the rapidly growing and transforming cities of the global south. Historic cities and towns are often faced with the dilemma of having to preserve old monuments while responding to the pressure of adapting itself to a modern lifestyle, which often results in the loss of cultural heritage. Indian cities such as Delhi possess a rich legacy of tangible heritage, traditions, and arts, which are reflected in their present urban form. The creation of temporal 3D models of such cities not only provides a platform with which one can experience the past, but also helps to understand, examine, and improve its present deteriorating state. However, gaining access to historical data to support the development of city-scale 3D models is a challenge. While data gaps can be bridged by combining multiple data sources, this process also presents considerable technical challenges. This paper provides a framework to generate LoD-2 (level-of-detail) 3D models of the present (the 2020s) and the past (the 1970s) of a heritage mosque surrounded by a dense and complex urban settlement in Shahjahanabad (Old Delhi) by combining multiple VHR (very high resolution) satellite images. The images used are those of Pleiades and Worldview-1 and -3 (for the present) and HEXAGON KH-9 declassified spy images (for the past). The chronological steps are used to extract the DSMs and DTMs that provide a base for the 3D models. The models are rendered, and the past and present are visualized using graphics and videos. The results reveal an average increase of 80% in the heights of the built structures around the main monument (mosque), leading to a loss in the visibility of this central mosque. [ABSTRACT FROM AUTHOR]

Purpose: Employee turnover, a reality that Indian retail organizations cannot ignore, is the central theme of this paper. The authors have aimed to empirically establish corporate social responsibility initiatives (CSRI) and transformational leadership (TL) as rather unconventional predictors that can potentially influence retail employees' intention to stay (ITS) through sequential mediation by employer branding (EB) and organizational identification (OI). Design/methodology/approach: Data collected using a structured questionnaire from three hundred and five frontline employees working with twenty-nine Indian retail outlets in the Delhi-National Capital Region (NCR) region was tested using structural equation modelling. Findings: Findings confirmed the impact of both CSRI and TL on ITS, with sequential mediation by EB and OI. While OI partially mediated the effect of EB on ITS, TL exerted more influence than CSRI in enhancing EB. Originality/value: This study enhances retail literature by empirically testing a unique fusion of organization and individual-level predictors that influence ITS as an individual-level outcome. Having TL and a firm corporate philosophy of CSR spending can enhance a retailer's image as a preferred employer brand and generate OI to successfully address employee turnover [ABSTRACT FROM AUTHOR]

This systematic review and meta-analysis sought to evaluate the acceptability levels for COVID vaccine(s) in various states in India. Published articles in PubMed/Scopus/Cochrane/DOAJ/the Web of Science that focused on assessing COVID-19 vaccine hesitation/vaccine acceptance using a survey/questionnaire were included. After extensive research, 524 records were found, and after screening on the basis of eligibility criteria, only 23 papers were added to this review. Increased vaccine assumption percentage (>70%) among the population was found in two surveys nationwide (92.8%) and in Delhi (79.5%). For pooled estimates of COVID-19 vaccine acceptance and heterogeneity, twenty-three studies (23) consisting of 39,567 individuals reported for acceptance of COVID 19 vaccine in India.. Out of these, 26,028 individuals accepted the COVID-19 vaccine, giving a pooled estimate of 62.6% (95% CI: 55.6–69.4) with considerable heterogeneity (χ2 = 3397.3, p < 0.0001; I2 = 99.40%). The results of this study give a brief insight into the percentage acceptance and hesitancy among the Indian population regarding COVID-19 vaccine immunisation. Future research and vaccine education initiatives can be steered by the findings of this work as a starting point. [ABSTRACT FROM AUTHOR]

India is home to some of the most polluted cities on the planet. The worsening air quality in most of the cities has gone to an extent of causing severe impact on human health and life expectancy. An early warning system where people are alerted well before an adverse air quality episode can go a long way in preventing exposure to harmful air conditions. Having such system can also help the government to take better mitigation and preventive measures. Forecasting systems based on machine learning are gaining importance due to their cost-effectiveness and applicability to small towns and villages, where most complex models are not feasible due to resource constraints and limited data availability. This paper presents a study of air quality forecasting by application of statistical models. Three statistical models based on autoregression (AR), moving average (MA), and autoregressive integrated moving average (ARIMA) models were applied to the datasets of PM2.5 concentrations of Delhi and Bengaluru, and forecasting was done for 1-day-ahead and 7-day-ahead time frames. All three models forecasted the PM2.5 reasonably well for Bengaluru, but the model performance deteriorated for the Delhi region. The AR, MA, and ARIMA models achieved mean absolute percentage error (MAPE) of 10.82%, 7.94%, and 8.17% respectively for forecast of 7 days and MAPE of 7.35%, 5.62%, and 5.87% for 1-day-ahead forecasts for Bengaluru. For the Delhi region, the model gave an MAPE of 27.82%, 24.62%, and 27.32% for the AR, MA, and ARIMA models respectively in the 7-day-ahead forecast, and 24.48%, 23.53%, and 23.72% respectively for 1-day-ahead forecast. The analysis showed that ARIMA model performs better in comparison to the other models but performance varies with varying concentration regimes. Study indicates that other topographical and meteorological parameters need to be incorporated to develop better models and account for the effects of these parameters in the study. [ABSTRACT FROM AUTHOR]

This paper studies the process of urban development and expansion by using a theoretical lens of conceptualisation of waste. Applying from the literature on the political economy of land as a resource, urban planning and labour geography, I analyse the ways in which transformation of the value of land from 'waste' to 'use' or vice versa; as conceived by state agencies and planners; have implications on human lives, particularly working-class. The paper deals with the qualitative question(s) of how the planning policies transform urban landscapes by imagining the potential of space and in turn marginalises people, hence creating a huge pool of labour reserve to achieve greater surplus accumulation. In such conditions, I show that how this marginalised section, particularly working class, built their living environment. The research further reflects upon the labouring practices in the peripheral town of Delhi metropolis, which is largely built by gentrification of labour from the core of the city. The exploratory study is ethnographically inspired and deploys qualitative methods such as in-depth interviews and Focussed Group Discussions to capture nuances of the field. [ABSTRACT FROM AUTHOR]

Migrants preferring to settle in the villages abutting urban centers avoiding slums and ghettos in metropolitan cities influences and changes rural demographics as migrants outnumber the host denizens who shift to the cities to leverage urban amenities for quality life and convenience. In this context of new dynamics in the rural socioeconomic setup the focus of the paper is to highlight positive factors which entice migrants towards villages and local denizens shifting to the cities. The study elucidates contributions of migrants in making rural economy vibrant and thriving with myriad new avenues for the host communities to prosper. In spite of being the harbinger of dynamism in rural landscapes, migrants are deprived of socio economic amenities and denied roles in local governess by supremacist thinking dominant status and local hierarchy. The need is to address there denied rights for inclusive and harmonious rural societies. Keywords: Dynamics, Migration, Demographics, Prosper Friction. [ABSTRACT FROM AUTHOR]

Air quality information is not captured adequately due to limited numbers of air quality monitoring stations across many cities worldwide. Limited studies apply advanced spatial mapping techniques to predict pollutant concentrations in highly polluted regions with high spatial variability. This paper demonstrates an advanced detrending spatial mapping technique to assess the variations of particulate matter concentrations across different land use categories in a highly polluted city—Delhi—and estimate population-weighted average concentrations in the city. The "Detrended Kriging" method uses the city's monitored datasets and land use information to predict pollutant concentrations. Concentrations are detrended based on high-resolution local land use characteristics and then interpolated using ordinary kriging before retrending again. The model estimates population-weighted concentrations (more important for health exposures) of PM2.5 (113 µg/m3) and PM10 (248 µg/m3) for Delhi and finds them to be 21–36% higher than the monitored values in the crucial winter season of 2018. The model demonstrates satisfactory performance on both spatial and temporal scales in Delhi and shows high index of agreement (d = 0.86 for PM10 and 0.81 for PM2.5), low RMSE (27.3 µg/m3 for PM10 and 11.8 µg/m3 for PM2.5), and low bias (− 1.6 µg/m3 for PM10 and − 0.5 µg/m3 for PM2.5) for the detrended kriging model, in comparison to ordinary kriging (PM2.5 (d = 0.54, RMSE = 13.81, bias = − 0.86) and PM10 (d = 0.33, RMSE = 41.73, bias = − 4.7)) and inverse distance weighting method (PM2.5 (d = 0.65, RMSE = 16.08, bias = 2.93) and PM10 (d = 0.55, RMSE = 46.10, bias = 7.8)). Statistical measure "d" varies between 0 (no agreement) and 1 (perfect match). [ABSTRACT FROM AUTHOR]

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first, the traffic volume and congestion (travel time delay) relation is applied to model the 24 h traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning–evening rush hours. We estimated an annual emission of 1.82 Gg for PM (particulate matter), 0.94 Gg for BC (black carbon), 0.75 Gg for OM (organic matter), 221 Gg for CO (carbon monoxide), 56 Gg for NO x (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH 3 (ammonia), 0.26 Gg for N 2 O (nitrous oxide) and 11.38 Gg for CH 4 (methane) for 2018 with an uncertainty of 60 %–68 %. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (>50 %) across all pollutants except PM, BC and NO x , and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NO x emission. This study provides very detailed spatiotemporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic-related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022). [ABSTRACT FROM AUTHOR]

Water Sensitive Planning (WSP) recognizes the centrality of water in the urban built environment, something often overlooked in urban planning systems. WSP was primarily developed for western city contexts but many cities in the Global South are rapidly urbanizing, and informal settlements are the predominant form of development, with limited existing water provision. A new WSP framework is needed for these contexts. This paper envisions what a WSP theory would look like for a city in the Global South to enhance water security, using Delhi as a starting point. Firstly, the substantive components of WSP are identified from the literature. Secondly, this framework is critiqued with respect to the extent to which it is appropriate for planning rapidly urbanizing cities, especially in India. Finally, the key principles of WSP for cities in the Global South are suggested. Notable principles include integration of water planning and city planning; linking of drains with statute protected water bodies; green places as recharge areas; reuse of adequately treated wastewater for irrigation purposes; and building roads and drains as per hydrogeography of a place. [ABSTRACT FROM AUTHOR]

The present study intends to use machine learning (ML) and deep learning (DL) models to forecast PM2.5 concentration at a location in Delhi. For this purpose, multi-layer feed-forward neural network (MLFFNN), support vector machine (SVM), random forest (RF) and long short-term memory networks (LSTM) have been applied. The air pollutants, e.g., CO, Ozone, PM10, NO, NO2, NOx, NH3, SO2, benzene, toluene, as well as meteorological parameters (temperature, wind speed, wind direction, rainfall, evaporation, humidity, pressure, etc.), have been used as inputs in the present study. Moreover, this is one of the first papers that employ aerodynamic roughness coefficient as an input parameter for the prediction of PM2.5 concentration. The result of the study shows that the LSTM model with index of agreement (IA) 0.986, root mean square error (RMSE) 21.510, Nash–Sutcliffe efficiency index (NSE) 0.945, (coefficient of determination)R2 0.945, and (correlation coefficient)R 0.972 is the best performing technique for the prediction of PM2.5 followed by MLFFNN, SVM, and RF models. The sensitivity analysis for the LSTM model reported that PM10, wind speed, NH3, and benzene are the key influencing parameters for the estimation of PM2.5. The findings in this work suggest that the LSTM could advance in PM2.5 forecasting and thus would be useful for developing fine-scale, state-of-the-art air pollution forecasting models. [ABSTRACT FROM AUTHOR]

The extent of pollution in areas near industrial activities and Yamuna River has increased tremendously and to determine the overall water quality status in these locations is utmost important. This paper presents a comprehensive study on assessing the water quality and gain insights into the overall water quality status in the vicinity of industries and along the Yamuna River in Delhi. To achieve this, water samples were collected from Central Pollution Control Board (CPCB) for the last eight years (2013-2021) and was converted into a machine-readable format to facilitate further analysis. These samples were analysed for several water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient levels, heavy metals, and other relevant pollutants. By understanding these factors, a deeper understanding of the pollution sources and their impact on water quality is gained. Time series methods are applied to forecast future trends and values of the water quality parameters. This allows for predicting potential changes in water quality over time. A remarkable accuracy of 93.6% is attained in predicting water quality values up to the present time. The analysis reveals that water quality in the NCR region falls below acceptable standards. However, there has been a marginal enhancement in the water quality at Khajori Paltoon (Location L3) post-COVID. To achieve a more significant improvement in water quality, it is imperative to implement new policies. [ABSTRACT FROM AUTHOR]

Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield's model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion. [ABSTRACT FROM AUTHOR]

In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images' lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology—Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively. [ABSTRACT FROM AUTHOR]

To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India's economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere's natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World. [ABSTRACT FROM AUTHOR]

Delhi, India, experiences extremely high concentrations of primary organic aerosol (POA). Few prior source apportionment studies on Delhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol (COA) on POA. In a companion paper, we develop a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach "time-of-day PMF" and statistically demonstrate the improvements of this approach over traditional PMF. Here, we quantify the contributions of BBOA, COA, and hydrocarbon-like organic aerosol (HOA) by applying positive matrix factorization (PMF) resolved by time of day on two seasons (winter and monsoon seasons of 2017) using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. We also conduct detailed uncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition to HOA, COA is found to be a major constituent of POA in the monsoon, and BBOA is found to be a major constituent of POA in the winter. Neither COA nor the different types of BBOA were resolved in the seasonal (not time-resolved) analysis. The COA mass spectra (MS) profiles are consistent with mass spectral profiles from Delhi and around the world, particularly resembling MS of heated cooking oils with a high m/z 41. The BBOA MS have a very prominent m/z 29 in addition to the characteristic peak at m/z 60, consistent with previous MS observed in Delhi and from wood burning sources. In addition to separating the POA, our technique also captures changes in MS profiles with the time of day, a unique feature among source apportionment approaches available. In addition to the primary factors, we separate two to three oxygenated organic aerosol (OOA) components. When all factors are recombined to total POA and OOA, our results are consistent with seasonal PMF analysis conducted using EPA PMF. Results from this work can be used to better design policies that target relevant primary sources of organic aerosols in Delhi. [ABSTRACT FROM AUTHOR]

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities. [ABSTRACT FROM AUTHOR]

The pulse value chain played significant role in agriculture and other industry sector such as food, pharma industry. The pulse value chain consists of sequences of business activities that perform significant activities, from primary production transformation into the final product to marketing for final consumption to the consumer. Pulse value chain develops different framework structures for developing competitive business strategy development to identify the significant constraints in a compressive manner. However, pulse processing firms face many risks, i.e., production risk, operation risk, supply risk, etc., which hampers the growth of these firms. The paper aims to identify pulse processing firms' risk factors and measure the severity of the risk. The risk assessment of pulses processing firms creates the opportunity for continuous quality improvement of the pulse value chain, and their performance also develops quality up gradation of pulse processed food. The present study is based on 25 pulse processing firms selected firms from the states of Uttar Pradesh, Rajasthan, and Delhi/NCR. The study applied FMEA (Failure mode Effect Analysis) and Pareto Analysis to identify the potential risk and determine their severity index with the help of the Pareto analysis chart, and applied the ANOVA method to generate the frequency of F- the value of risk factors impact on pulse firms understand the level of significance of risk factors. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact of second wave of COVID-19 lockdown on environmental noise levels of 25 sites in Delhi city and compares the noise scenario during pre-lockdown, lockdown, and post-lockdown periods. The study utilized the noise monitoring data acquired from 25 real-time ambient noise monitoring stations, installed by the Delhi Pollution Control Committee, Delhi, at various sites throughout Delhi city. A significant reduction of up to 10 and 3 dB(A) in day and night equivalent noise levels, respectively, had been observed during the lockdown period as compared to the pre-lockdown and post-lockdown periods. The study also revealed that only nine sites, including four industrial and five commercial zone sites, complied with the ambient noise standards during lockdown period, and no silence or residential zone sites complied with the ambient noise standards even during the lockdown period. A roadmap for environmental noise management and control is suggested. The study also reports the community's perception toward the change in acoustic environment of Delhi city during the lockdown period by conducting an environmental noise perception survey. The present study should be helpful in devising noise control action plans and policy interventions for environmental noise management and control in the metropolitan city Delhi, India. [ABSTRACT FROM AUTHOR]

Atmospheric Boundary Layer (ABL) height is an important parameter in defining the Ventilation Coefficient (VC) of any area. In this work, SODAR (Sound Detection and Ranging) observed data of different echograms for the year 2020 has been used for structure identification. The interpretation of ecograms is often not straight-forward. In this paper, an interpretation of different echograms along with meteorological parameters over Delhi is described. The ecogram structures were interpreted along with VC analysis. It is observed from the structures that during foggy days, VC values vary from day to day in the winter, with daily maximum values occurring between 13:00 and 15:00 h. The daytime values varied significantly from sunrise to sunset, while the night-time values remained relatively stable (200 m²/s). However, due to fog diurnal variation from 16:00 h to 11:00 h, that is from evening to all night and then the next morning until 12:00 h, the atmospheric condition remained stable. At maximal wind speeds, a maximum VC of 1600 m²/s (at 12:00 h) was observed during extended fumigation. With minimal wind speeds and ABL height, a minimum VC of 100 m²/s (at 20:00 h) was observed. However, both VCs were smaller than the optimal VC for 6000 m²/s safe dispersion. [ABSTRACT FROM AUTHOR]

Background: As countries like India improve access to maternal and infant care, the health systems need to develop services that enable all children to thrive. A key demographic which needs to be supported are children with disabilities, such as autism. With an estimated prevalence of one percent, there are over five million young children who need services to support their needs. However, the paucity of specialist care makes access to interventions diffcult. In this context a public health research not-for-profit is evaluating the effectiveness of the task-sharing approach to support the delivery of an evidenced social communication intervention for young children with autism. This paper describes the process of engaging and training the non-specialist frontline Accredited Social Health Activists (ASHAs), who are embedded within the Ministry of Health and Family Welfare under the Delhi State Health Mission, to deliver a complex intervention for autism to inform the future scalability of services for neurodevelopmental disorders. Methods: The present study describes the process which included (i) engagement meetings, (ii) recruitment, (iii) training, (iv) internship, and (v) competency evaluation. The shortlisted ASHAs received a 7-day classroom training followed by an internship period with practice cases. Finally, competency assessments, comprising of a test of knowledge and skills through role-plays, was administered. Results: Twenty three Primary Urban Health Centers across seven districts of Delhi were approached and 408 ASHAs were engaged in initial meetings. Telephonic screening with 127 ASHAs resulted in 72 ASHAs being selected for in-person interviews. Of the 45 ASHAs who attended, 33 were shortlisted for training and 18 completed it. Fifteen ASHAs entered the internship of which 7 ASHAs achieved competency. Discussion and conclusion: There was significant attrition along the pathway to having a competent non-specialist worker deliver a complex autism intervention. The lessons learnt from this process can inform the possibility of developing a cadre of disability specific frontline health workers who can deliver evidence-based interventions for neurodevelopmental disorders under supervision. [ABSTRACT FROM AUTHOR]

Background: Cancer of unknown primary (CUP) origin is cancer in which malignant cancer cells are in the body but the site of cancer where it began is unknown. Detailed incidence and time trends of these cancers, specific to various regions in India is needed. This paper aims to summarize and report the incidence of other and unknown (O&U) cancers across India in 27 population-based cancer registries (PBCRs) and to study the trends among these cancers using joinpoint regression analysis.Methods: Data on the incidence of CUP were obtained from the published reports on 27 PBCRs of the National Cancer Registry Programme (NCRP) of the Indian Council of Medical Research (ICMR). A joinpoint regression model was used to analyze the long-term trends of incidence related to CUP based on published data from PBCRs between 1986 and 2014. Annual Percent Change (APC) in incidence rate was estimated for various registries.Results: The northeast region had the highest age-adjusted rate (AAR) for both men (1.76-29.7) and women (1.99-14.68). Age-specific rate (ASR) for both men (39.8-855.7) and women (48.2-470.4) was highest in the northeast region. There is an increase in the incidence rate for all six major registries over the past decade with an exception of women in the Delhi Cancer Registry. There is a decline in incidence rate by 0.14 during 1990-2012 in the female population of the Delhi registry.Conclusion: The increasing incidence trends of CUP is a matter of concern for the healthcare professionals and researchers. There is a need for research and advanced and improved diagnostic tools for the improvement of the status of O&U cancers. [ABSTRACT FROM AUTHOR]

Introduction: Vitiligo is a common form of localized depigmentation and an important public health problem which affects around one percent of the global population and about two percent of the population in India. The present study aimed to document the results and side effects of non-cultured melanocyte transplant in patients with stable vitiligo without post-procedure use of NB UVB. Methods: The present study was a prospective interventional study conducted among patients with stable vitiligo who were attending the outpatient department (OPD) of dermatology of a tertiary center of Delhi. Patients with stable vitiligo aged >10 years who gave their consent for non-cultured epidermal suspension (NCES) were included in the study. Autologous skin graft of size 0.2 mm was taken from the donor site and a melanocyte cell suspension was prepared using standardized procedure, which was later transplanted to the recipient area. All patients were asked to come for follow-up at the OPD after seven days, 15 days, one month, followed by every month till six months post-transplantation, and repigmentation was assessed visually by both graph paper and photography. Data was analyzed using SPSS vs. 21. Results: In our study, the majority of patients had vitiligo vulgaris (27 subjects with 47 patches). A total number of 59 patches were transplanted, of which 38 (64.4%) achieved excellent repigmentation, 28.8% good repigmentation, 3.4% fair repigmentation and 3.39% poor repigmentation. A total of 1302.5 cm² of the depigmented area was operated by NCES and the repigmentation of 874.5 cm² (67.14%) was achieved by six months. Conclusion: The present study found that the majority of patients (93%) with vitiligo achieved excellent or good repigmentation after NCES and the rate of complication was very low. [ABSTRACT FROM AUTHOR]

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The rapid growth of electric vehicles in India necessitates more power to energize such vehicles. Furthermore, the transport industry emits greenhouse gases, particularly SO2, CO2. The national grid has to supply an enormous amount of power on a daily basis due to the surplus power required to charge these electric vehicles. This paper presents the various hybrid energy system configurations to meet the power requirements of the electric vehicle charging station (EVCS) situated in the northwest region of Delhi, India. The three configurations are: (a) solar photovoltaic/diesel generator/battery-based EVCS, (b) solar photovoltaic/battery-based EVCS, and (c) grid-and-solar photovoltaic-based EVCS. The meta-heuristic techniques are implemented to analyze the technological, financial, and environmental feasibility of the three possible configurations. The optimization algorithm intends to reduce the total net present cost and levelized cost of energy while keeping the value of lack of power supply probability within limits. To confirm the solution quality obtained using modified salp swarm algorithm (MSSA), the popularly used HOMER software, salp swarm algorithm (SSA), and the gray wolf optimization are applied to the same problem, and their outcomes are equated to those attained by the MSSA. MSSA exhibits superior accuracy and robustness based on simulation outcomes. The MSSA performs much better in terms of computation time followed by the SSA and gray wolf optimization. MSSA results in reduced levelized cost of energy values in all three configurations, i.e., USD 0.482/kWh, USD 0.684/kWh, and USD 0.119/kWh in configurations 1, 2, and 3, respectively. Our findings will be useful for researchers in determining the best method for the sizing of energy system components. [ABSTRACT FROM AUTHOR]