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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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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 2021-22 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 AQI is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the NCR 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 PM2.5 load in Delhi. During peak pollution events (stubble-burning periods), however, the contribution from sources within Delhi (farm fires in Punjab-Haryana) 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]

Indian capital megacity 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 (~0.4 km × 0.4 km) 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 100s 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 emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emission for PM2.5, PM10, CO, NOx, VOC, SO2, BC and OC over Delhi-NCR are estimated to be 123.8 Gg/yr, 243.6 Gg/yr, 799.0 Gg/yr, 488.9 Gg/yr, 730.0 Gg/yr, 425.8 Gg/yr, 33.6 Gg/yr, and 20.3 Gg/yr respectively for the year 2020. The decadal growth (2010–2020) in PM2.5 and PM10 are found to be marginal 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 pollutants BC, OC and NOx is 57 %, 34 % and 91 % respectively. The decadal shift of sectorial emissions with changing policies is examined. [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]

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]

The Citizenship Amendment Act (CAA), 2019, in India was widely contested by both political parties and various civil society formations. Shaheen Bagh, a sit-in protest demonstration which continued for over a hundred days in the nation's capital from mid-December 2019 to late March 2020, occupied the central position within the corpus of these protest demonstrations. The protests at Shaheen Bagh were led by poor, Muslim working-class women, who had come out on the streets protesting and asserting their rights amidst the dominant ruling-class communal politics. The Shaheen Bagh protests were a potent force of the working-class and oppressed minorities of the country. The paper brings in Marxist and Gramscian perspectives to explain how Shaheen Bagh has contributed to Indian left-wing politics. The paper argues that the women in Shaheen Bagh have been successful in bridging the gap between the civil society and political society in the country and has to an extent, altered the very nature of Indian politics. Moreover, the assertive nature of the Muslim women regarding their religion and the support which they garnered from the Indian left, widely accused by many of being Islamophobic in nature, has wide repercussions as far as political and social alliances between the left and Muslim politics in India is concerned. The present paper locates the protest within the notion of subaltern unity and tries to analyse the possible impacts of the support for the protests, within and beyond the anti-CAA protest movement, from the Indian left. [ABSTRACT FROM AUTHOR]

Sushila Aggarwal v State (NCT of Delhi) forms an important part of the law on anticipatory Bail in India. Prior to Sushila Aggarwal judgment, the law on anticipatory bail in India was ambiguous due to the varying interpretations of section 438 of the Criminal Procedure Code 1973 (anticipatory bail) by the Supreme Court. It was only in the year 2020 that the law on the matter was settled by the Supreme Court in its Sushila Aggarwal judgment. With this paper, the authors aim to trace the evolution of the law on anticipatory bail in India. It focusses on the landmark judgments of the Supreme Court and meanders its way through conflicting opinions of the Court. The paper concludes by welcoming the Sushila Aggarwal judgment for settling the long ambiguous law on anticipatory bail in India. However, it also highlights the concerns that Constitution Bench failed to appreciate, which if addressed would have made the law free of the loopholes presently plaguing the law on anticipatory bail. [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]

Two palliative cancer-care models are being pioneered in India. The first has been developed by CanSupport, a cancer-care NGO based in Delhi. The CanSupport model of care emphasizes expertise and focuses on a relatively small number of patients. The second model is the Neighbourhood Network of Palliative Care, advanced by a group of physicians in the southern state of Kerala. The NNPC model emphasizes reach over expertise. It provides short-term training to community workers and civic-minded citizens, mobilizing numbers to treat a wider patient pool. This paper compares the strengths and drawbacks of both models in order to understand the generalizability of each for providing care to lower-income communities in lower- and middle-income countries. [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]

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]

Background: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds.Methods: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range).Results: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4-100.0) were completed by DCC, compared with 75.1% (66.7-86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3-27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1-3]), compared with the paper checklist (2 days [1-4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement.Conclusions: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU.Clinical Trial Registration: NCT03599856. [ABSTRACT FROM AUTHOR]

The paper deals with the Indian physics literature published in resources as reflected in Web of Science (WOS) during the period 2004-2018. The study analyses India's performance based on its publication output in physics, few Scientometric indices, such as the number of publications, number of citations, activity index, relative growth rate, doubling time, patterns of communication in national and international journals, are considered for portraying the different dimensions, Most prolific scientists and Institutions, and national & international collaboration from India were also considered. It was examined that during 2004-2018 there was a substantial increase in yearly output of Indian physics literature, compared to the global output ratio. The study reveals that the literature in the field of physics is gradually increasing and there is an upward trend, however there is some fluctuation in trends of publications with mean growth rate of 0.220 and average doubling time is 3.714. Among the subfields Materials Science has the largest number of publications amounting to 31.30% of the overall publications. Journal of Applied Physics has published highest number of papers i.e. 3097 with 12.5 average citations per paper. Among the Research Institutes, Indian Institute of Technology (IIT), Delhi, has published highest number of publications i.e. 19931 among the physics research institutes in India. However the publication trends indicates that there is greater scope for higher research in physics in India. [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]

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-hour 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 PME (Exhaust 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 NOx (Oxide of Nitrogen), 64 Gg for VOC (Volatile Organic Carbon), 0.28 Gg for NH3 (Ammonia), 0.26 Gg for N2O (Nitrous Oxide) and 11.38 Gg for CH4 (Methane) for 2018. 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 PME, BC and NOx, and within them the 2W (Two-wheeler motorcycles) are the major contributors. Diesel fuelled vehicles contribute most of the PME emission. Diesel and CNG vehicles have a substantial contribution in NOx emission. This study provides very detailed spatio-temporal 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]

Victim compensation has become a symbolic part of the Indian Criminal Justice System (CJS). With the growth and development of the modern CJS, the States were given the role to provide protection to the people from crimes unlike the ancient and medieval practice. The rights of the victim have been given special recognition by providing them assistance and compensation to make good for the losses incurred due to the commission of the crime. The concept of Victim Compensation Schemes (VCS) for providing compensation to the victims of crime has been accepted widely. India has imbedded it in its CJS under section 357A of the criminal procedure. Under this provision the States are required to frame their own VCS. It is here that the question regarding its effective implementation arises. This paper aims to provide a comparative analysis of the VCS of the States of Gujarat, Delhi, Telangana and Kerala to point out the areas where it is lacking in terms of ambiguity and non-uniformity. [ABSTRACT FROM AUTHOR]

This paper examines the portfolio carbon footprint of Indian banking system. To this end, we combine the emissions data from India's Biennial Update Reports to UNFCCC and Reserve Bank of India's bank database from 2010 to 2016; apply Network for Greening the Financial System's backward-looking methodology and Partnership for Carbon Accounting Financials Standards in conjunction with Leontief's Input-Output technique to estimate the weighted average carbon intensity (WACI) of bank's loans portfolio at three levels – System level, Bank group level and Spatial level. Our major findings include-first, at system level, the carbon footprint of Indian banks shows a rising trend over the study period with the aggregate WACI of bank loans increasing by 9.4% from 332.5 tCO 2 e/US$ million in 2010 to 363.7 tCO 2 e/US$ million in 2016. Second, at the group level, while the financed emissions of public sector banks have plateaued, for private sector banks they have nearly doubled during the study period, suggesting the increased exposure to carbon intensive assets. Third, on the spatial level, top five regions by WACI include Delhi, Maharashtra, Gujarat, Tamil Nadu, and Telangana in descending order. • First study to estimate carbon footprint of bank loans for India. • Combines environment input-output analysis with PCAF standard. • Analyses carbon footprint at three levels – system level, bank group level and spatial level. • Carbon footprint of Indian banks show a rising trend over the study period. • Financed emissions of public sector banks plateaued, of private sector banks doubled. [ABSTRACT FROM AUTHOR]

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]

Purpose - This study aims to evaluate the library websites of selected state and deemed universities in Delhi, capital city of India to ascertain how effectively libraries are leveraging their respective websites to provide library related information on their websites. Design/Methodology - The study adopted the checklist technique to evaluate the content of library's websites. A comprehensive checklist was designed on the basis of previous studies. It consists of 6 main aspects which are further divided into 66 elements. Findings -The study reveals that library related information such as general library information, about collection, links to e-resources, library services, currency and authority of websites are presented on the university library websites but not to the full extent. Web 2.0 tools are used by least libraries where only 2 libraries have library Facebook page, and only one has a library blog. DTU scored the highest, 45 out of 66 and Indira Gandhi Delhi Technical University for Women (IGDTUW) scored the least 13 out of 66 in overall ranking of websites based on elements presented on their websites. Originality/Value - The study suggested that libraries need to incorporate more information on their websites to promote the use of library resources and services. Libraries need to have a dedicated website and provide all the valuable and relevant information related to the library on their websites. This paper will guide librarians to effectively utilise and improve the quality of library websites. [ABSTRACT FROM AUTHOR]

Background: Despite the Indian government's Universal Immunization Program (UIP), the progress of full immunization coverage is plodding. The cost of delivering routine immunization varies widely across facilities within country and across country. However, the cost an individual bears on child immunization has not been focussed. In this context, this study tries to estimate the expenditure on immunization which an individual bears and the factors affecting immunization coverage at the regional level.Methods: Using the 75th round of National Sample Survey Organization data, the present paper attempts to check the individual expenditure on immunization and the factors affecting immunization coverage at the regional level. Descriptive statistics and multivariate regression analysis were used to fulfil the study objectives. The two-part model has been employed to inspect the determinants of expenditure on immunization.Results: The overall prevalence of full immunization was 59.3 % in India. Full immunization was highest in Manipur (75.2 %) and lowest in Nagaland (12.8 %). The mean expenditure incurred on immunization varies from as low as Rs. 32.7 in Tripura to as high as Rs. 1008 in Delhi. Children belonging to the urban area [OR: 1.04; CI: 1.035, 1.037] and richer wealth quintile [OR: 1.14; CI: 1.134-1.137] had higher odds of getting immunization. Moreover, expenditure on immunization was high among children from the urban area [Rs. 273], rich wealth quintile [Rs. 297] and who got immunized in a private facility [Rs. 1656].Conclusions: There exists regional inequality in immunization coverage as well as in expenditure incurred on immunization. Based on the findings, we suggest looking for the supply through follow-up and demand through spreading awareness through mass media for immunization. [ABSTRACT FROM AUTHOR]

As the world struggles to cope with the pressing matters of global warming, rising temperature, and environmental degradation, the need for sustainable practices has become more urgent than ever for individuals and other establishments. Organizations across industries recognize the importance of adopting sustainable practices, not only to meet societal expectations but also to achieve long-term success. This research paper studies the influence of sustainable marketing on customer loyalty, focusing on the consumer electronics sector in India. A mixed-methods approach incorporating descriptive and causal research methods was used; with data collected from 302 participants from India's capital, Delhi and its nearby regions. Structural equation modeling was employed to establish multivariate relationships and Smart PLS 4 was used to analyze the data. The results suggest that sustainable marketing practices positively influence brand image and loyalty. Additionally, brand image acts as an intermediary between sustainable marketing and customer loyalty. These results provide valuable insights for managers aimed at fostering customer loyalty through sustainable practices, particularly in the consumer electronics sector. [ABSTRACT FROM AUTHOR]

This paper studies a collection centre location problem for e-waste acquisition from households. The proposed location-allocation model is a bi-objective mixed-integer non-linear model that maximises waste collection and minimises the cost of collection. The model considers that both drop-off and pick-up policies are in place, i.e., people can either drop their e-waste at a collection centre or request doorstep pick-up of their e-waste. The model considers that people's decision to drop off their e-waste at the collection centres depends on the financial incentive offered by the firm and the distance of the collection centre. However, in the case of pick-up, no incentive is provided, and it is considered that the households' willingness to return depends on people's concern for environmental degradation. The proposed model decides the location of the collection centre, the assignment of customer nodes, the incentive offered, and the e-waste collected using both pick-up and drop-off methods. Instances generated using a real case of Delhi, India, demonstrate the feasibility and validity of the proposed model, and the epsilon constraint method is employed to handle its multi-objectivity. Furthermore, sensitivity analysis is used to assess the relative importance of the parameters on the model output. The study offers valuable theoretical and practical implications. • A bi-objective mixed-integer non-linear program model is proposed. • Both pick-up and drop off strategy are simultaneously considered. • Drop off is modelled as a function of incentive and distance. • The ε -constraint method is employed to solve the proposed bi-objective mixed-integer non-linear program model. [ABSTRACT FROM AUTHOR]

Several cities in the developing world, of which the capital city of India, New Delhi, is an example, often experience air quality in which pollutant levels go way above the levels considered hazardous for human health. To bring down the air quality to within permissible limits quickly, the measures typically taken involve shutting down certain high-polluting activities for some time to enable the air quality to recover temporarily. This paper presents a first-ever model based on artificial neural networks to forecast the extent of reduction in air quality parameters that can be achieved and the time period within which a change can be experienced when the source of the emissions is cut off temporarily. The model is based on the extensive data on the extent of reduction in air quality parameters that occurred during the lockdown that was imposed during the COVID-19 pandemic. The non-linear autoregressive exogenous network-based model chosen for the purpose employs the hour since stopping of emissions, relative humidity, wind speed, wind direction, and ambient temperature as input parameters to predict the rate of change of PM 2.5 with respect to the concentration at the start of the stopping of the emissions. Air quality data from a key monitoring station in New Delhi was used to develop the model. The model predicted the rate of drop in PM 2.5 with an R and MSE of 0.0044 and 0.9736, respectively, while training and 0.0095 and 0.9583 while testing. The model was then tested with data from 19 other stations in New Delhi, and accuracy of the model was found to be exceptionally accurate, with the correlation between the measured and the predicted PM 2.5 levels ranging from 0.74 to 0.94 and the MSE ranging from 0.0110 to 1.0746. Thus, the model can be employed to determine the number of hours of temporary stoppage of emissions required for the PM 2.5 concentration to reach safe levels. The methodology of development of the model can be extrapolated to construct models tailored for use in other parts of the world as well. • A novel ANN -based model is reported to predict rate of drop of PM 2.5 • Model has been trained on extensive dataset from the covid lockdown in Delhi. • Duration and efficacy of emergency measures to reduce emissions can be estimated. • The model uses easy-to-measure meteorological parameters as inputs. • Model has been validated for 19 stations of various land-use types in Delhi. [ABSTRACT FROM AUTHOR]

Background: In India, many cities struggle with extreme levels of air pollution and noise. Delhi, in particular, has the notorious reputation of being one of the most polluted cities in the world. Cyclists constitute a particularly exposed population, since they cycle among motor vehicles without any protection. This paper modeled the cyclists' exposure to nitrogen dioxide (NO2) and noise in Delhi, India. Methods: Using primary data collected on 1,229 kms of roads in Delhi, Generalized Additive Mixed Models with Auto-Regressive terms (GAMMAR) are constructed for noise exposure, NO2 exposure and NO2 inhalation doses. Results: Results show that cyclists are exposed to 47 µg/m3 of NO2 and 3.3 dB(A) more when cycling on a primary road than on a residential street. Using WHO guideline values for noise and air pollution, we assessed how many minutes of inhaling doses and noise doses become potentially harmful to cyclists' health in Delhi. Such thresholds are quickly exceeded: after cycling one hour in an area with moderate predicted values of noise and air pollution, the noise dose and inhaled dose of NO2 will reach 212% and 403 µg on residential streets, and 459% and 482 µg on primary roads, respectively. Conclusion: Policy makers should take these results into account to minimize cyclists' exposure, especially for the most deprived people. [ABSTRACT FROM AUTHOR]

Background: Diagnosis, notification and timely initiation of treatment is an important cornerstone for the elimination of tuberculosis (TB). The referral and feedback mechanism under National Tuberculosis Programme of India has been changed from paper-based to web-based electronic system (Nikshay) since 2018. The current study was carried out to assess the effect of Nikshay in referral and receipt of feedback on treatment initiation and to understand the early implementation challenges. Methods: A mixed-methods study was conducted in a medical college referral unit (MCRU) of Delhi, India. The electronic TB notification data for July 2018–March 2019 were abstracted from Nikshay portal and analysed. Unadjusted and adjusted relative risk (aRR) was calculated to assess the factors associated with the receipt of feedback. Themes and subthemes were generated from qualitative data obtained through key-informant interviews of healthcare providers. Results: Of the total 4395 patients handled by MCRU during the study period, 3315 (75.4%) were referred out within and outside Delhi for treatment. Feedback was received among 797 (24.0%) of the patients who were referred out. Patients with extrapulmonary TB (aRR: 1.3, confidence interval (CI): 1.1–1.8), previously treated (aRR: 1.2, CI: 1.2–1.3) and registered for drug-resistant TB care (aRR: 1.4, CI: 1.1–1.8), had high chance of receiving feedback. Four broad themes emerged, namely, (a) awareness of programme and Nikshay; (b) tracking of patients; (c) user-friendly portal and (d) workload. Conclusion: The low feedback on treatment initiation of patients with TB needs further research after health system-level quality improvement interventions. Real-time tracking of patients is the need of the hour towards the path for TB elimination. [ABSTRACT FROM AUTHOR]

Using a mainly ethnographic methodology, this paper discusses why the voice of women and their agency is still scarce in the history of India and especially in Sikh history. It then describes the 1984 Delhi pogroms as voiced by these women. The text analyzes the complex ways in which widows open spaces for decision making, advocacy, activism, and organization. Violence destroyed the world as they knew it, but it opened up other possibilities, such as the ability to move from the private to the public sphere, work for the first time, and decide on their marital status. They also formed leaderships and consolidated a political movement. [ABSTRACT FROM AUTHOR]

The first Mohalla or Community clinic was set up in July 2015 in Delhi, India. Four hundred and eighty such clinics were set up in Delhi, since then. This review was conducted to synthesize evidence on access, utilization, functioning, and performance of Mohalla clinics. A desk review of secondary data from published research papers and reports was conducted initially from February–May 2020 and updated in August 2020. Eleven studies were included in the final analysis. Studies have documented that more than half to two-third of beneficiaries at these clinics were women, elderly, poor, and with school education up to primary level. One-third to two-third of all beneficiaries had come to the government primary care facility for the first time. A majority who attended clinics lived within 10 min of walking distances. There was high rate of satisfaction (around 90%) with overall services, doctor–patient interaction time and the people were willing to return for future health needs. Most beneficiaries received consultations, medicines, and diagnostics at no cost. A few challenges such as dispensing of medicines for shorter duration, lack of awareness about the exact location of the clinics, and services available among target beneficiaries, and the incomplete records maintenance and reporting system at facilities were identified. Mohalla Clinics of Delhi ensured continuity of primary care and laboratory services during COVID-19 pandemic in 2020. In summary, Mohalla Clinics have made primary care accessible and affordable to under-served population (thus, addressed inequities) and brought attention of policy makers on strengthening and investing on health services. The external evaluations and assessments on the performance of these clinics, with robust methodology are needed. The services through these clinics should be expanded to deliver comprehensive package of primary healthcare with inclusion of preventive, promotive, community outreach, and other public health services. [ABSTRACT FROM AUTHOR]

The rise of the modern commercial political-economic system is influencing rural to urban migration in ad-dition to urban morphology. Slum settlements are one of the major results of rapid and often unregulated rural to urban migration. Slum settlements create a major problem for developing cities. The slum settlements do not fit the leadership’s vision for a modern city, one that can be marketed to outside businesses and organizations for investment. As a result they are often seen as a nuisance by the leadership and targeted for destruction, especially when the land of the slum is valuable. The process of slum removal is therefore a political process.This is especially relevant in India, where slums are growing at an almost unimaginable rate. The Delhi Development Agency views slums as the type of settlement that negatively impacts their vision of creating a “world class city.” In the Kathputli colony, a slum settlement in Delhi, development has been occurring for almost 50 years as the region is a vibrant mix of local production and circus performers. In recent years, as Delhi develops outward, the Delhi Development Agency has looked to redevelop Kathputli. This redevelopment is being presented as an improvement for the residents of the slum, but in interviews with the residents, they are skeptical of the end result. The goal of this paper is to employ Kathputli as a case study to view the life cycle of a typical slum community, using Wolfel, Richmond and Grazaitis’ (2017) framework of urban development to demonstrate how the complexity of slum development, slum dynamics and slum destruction. As more people continue to migrate to a city, understanding the dynamics of slum communities becomes more essential if one is to successfully operate in a large urban area. [ABSTRACT FROM AUTHOR]

Housing quality is crucially linked to health and sustainability goals, yet there is limited research on informal housing and settlements where housing quality is poor, and the health risks are expected to be greatest. This paper describes the investigation of housing conditions in a low-income resettlement colony in Delhi. A novel transdisciplinary methodology to evaluate multiple housing health hazards and establish intervention priorities in participation with the community was developed. Findings from housing surveys and indoor environmental monitoring were contrasted with a participatory self-assessment—revealing the widespread prevalence of hazards and suboptimal housing conditions as well as substantial differences in priorities, and thus perspectives, between participants and researchers. Focus group discussions explored the findings and built consensus on priorities. Our findings uncovered how poor housing conditions affect daily practices and thus are likely to adversely affect socio-economic development and gender equality. We highlight limitations in current frameworks to assess housing hazards and argue that a transdisciplinary approach is vital to provide a holistic understanding and to develop effective interventions. These insights are crucial to inform inclusive solutions for adequate housing and human settlements that can support improved health and help achieve the sustainable development goals. [ABSTRACT FROM AUTHOR]

Rising awareness of the health risks posed by elevated levels of ground-level O3 and NO2 have led to an increased demand for affordable and dense spatio-temporal air quality monitoring networks. Low-cost sensors used as a part of Internet of Things (IoT) platforms offer an attractive solution with greater mobility and lower maintenance costs, and can supplement compliance regulatory monitoring stations. These commodity low-cost sensors have reasonably high accuracy but require in-field calibration to improve precision. In this paper, we report the results of a deployment and calibration study on a network of seven air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed at sites situated within two mega-cities with diverse geographical, meteorological and air quality parameters - Faridabad (Delhi National Capital Region) and Mumbai, India. The deployment was done in two phases over a period of three months. A unique feature of our deployment is a swap-out experiment wherein four of these sensors were relocated to different sites in the two deployment phases. Such a diverse deployment with sensors switching places gives us a unique opportunity to ablate the effect of seasonal, as well as geographical variations on calibration performance. We perform an extensive study of more than a dozen parametric as well as non-parametric calibration algorithms and find local calibration methods to offer the best performance. We propose a novel local calibration algorithm based on metric-learning that offers, across deployment sites and phases, an average R2 coefficient of 0.873 with respect to reference values for O3 calibration and 0.886 for NO2 calibration. This represents an upto 9 % increase over R2 values offered by classical local calibration methods. In particular, our proposed model far outperforms the default calibration models offered by the gas sensor manufacturer. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature as free parameters (or features) into all calibration models, (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature weighing or metric learning technique, (3) the use of local (or even hyper-local) calibration techniques such as k-NN that seem to offer the best performance in high variability conditions such as those encountered in field deployments, (4) performing temporal smoothing over raw time series data, say by averaging sensor signals over small windows, but being careful to not do so too aggressively, and (5) making all efforts at ensuring that data with enough diversity is demonstrated to the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors, and offer an encouraging opportunity at using them to supplement and densify compliance regulatory monitoring networks. [ABSTRACT FROM AUTHOR]

A Winter Fog Experiment (WiFEX) was conducted to study the genesis of fog formation between winters 2016–17 and 2017–18 at Indira Gandhi International Airport (IGIA), Delhi, India. To support the WiFEX field campaign, the Weather Research and Forecasting (WRF) Model was used to produce real-time forecasts at 2-km horizontal grid spacing. This paper summarizes the performance of the model forecasts for 43 very dense fog episodes (visibility < 200 m) and preliminary evaluation of the model against the observations. Similarly, near-surface liquid water content (LWC) from models and continuous visibility observations are used as a metric for model evaluation. Results show that the skill score is relatively promising for the hit rate with a value of 0.78, whereas the false alarm rate (0.19) and missing rate (0.32) are quite low. This indicates that the model has reasonable predictive accuracy, and the performance of the real-time forecast is better for both dense fog events and no-fog events. For success cases, the model accurately captured the near-surface meteorological conditions, particularly the low-level moisture, wind fields, and temperature inversion. In contrast, for failed cases, the WRF Model shows large error in near-surface relative humidity and temperature compared to the observations, although it captures temperature inversions reasonably well. Our results also suggest that the model is able to capture the variability in fog onset for consecutive fog events. Errors in near-surface variables during failed cases are found to be affected by the errors in the initial conditions taken from the Indian Institute of Tropical Meteorology Global Forecasting System (IITM-GFS) spectral model forecast. Further evaluation of the operational forecasts for dense fog cases indicates that the error in predicting fog onset stage is relatively large (mean error of 4 h) compared to the dissipation stage. [ABSTRACT FROM AUTHOR]

Recently there has been a growing interest in the applicability of reversible circuits. Reversible circuits are designed using reversible gates, which can efficiently reconstruct the previous state of the computation from the current state. These circuits may find potential applications to the future generation of optical and quantum computers. To ensure the reliability of these circuits, testing is a mandatory phase of the design cycle. Several fault models have been introduced for reversible circuits among which some of them have been taken from the conventional circuits. In this paper, we consider the problem of testing bridging faults (such as single and multiple input bridging faults, single and multiple intra-level bridging faults) in a reversible circuit designed with the NOT, CNOT, Toffoli gates (NCT library) and generalized (n-bit) Toffoli gates (GT library). We propose an Automatic Test Pattern Generation (ATPG) method using the Path-Level expression for generating the minimal complete test set to detect the faults mentioned above. The analysis of the experimental results shows that the proposed method has 100% fault coverage, and test set size is smaller than the existing methods. [ABSTRACT FROM AUTHOR]

This paper deals specifically with the electrical resistivity method, which was applied for the exploration of groundwater potential and water-bearing strata at Jamia Millia Islamia (JMI) campus, so as to deal with the problems of water scarcity in the National Capital Territory of Delhi. Accordingly, four sites were identified at JMI campus for electrical soundings; one for ground truthing, one for estimation of groundwater table and water-bearing strata and the other two to investigate the impact of the injection well located at JMI campus. Moreover, the soundings were carried out by a terrameter using Schlumberger and Wenner configurations. Additionally, the variation of apparent resistivity versus electrode spacing as well as the expected lithology for all the sites were also obtained. As a result, the resistivity method indicated that the depth of groundwater table was available at about 18.3 m and goes to a depth of 47.6 m near pumping well (VES2). At the time of installation of the pump, the groundwater table was found at a depth of about 15.5 m. The groundwater table at VES1 was estimated at a depth of 11 m, also possessing sufficient water-bearing strata. The study revealed that sufficient water was available near VES1 and VES2 as compared to other sites. The objective of the study was accomplished with substantial findings which further corroborate the effectiveness of the approach adopted for groundwater exploration. [ABSTRACT FROM AUTHOR]

Accessibility of the built environment plays a significant role in the lives of persons with disabilities, as lack of accessibility results in exclusion from mainstream society. This paper aims to highlight the barriers faced by women with locomotor disabilities to access the built environment in the Delhi-National Capital Region (NCR) of India. Building upon the author's M. Phil research, the paper engages with aspects of the lives of study participants that have hindered their access to the built environment. In-depth interviews with 12 women with different locomotor disabilities were conducted and the transcripts were analysed using the intersectionality paradigm. The analysis highlights problems faced by the women while accessing education, employment, healthcare and public transportation. These problems include negative attitudes of society which restrict the women's mobility outside their homes. The findings demonstrate that accessibility of the built environment is determined by a myriad of factors which encompass components elaborated by the International Classification of Functioning, Disability and Health (ICF) including health condition, personal factors and environmental factors. The paper discusses these findings in light of the Accessible India Campaign, recently introduced by the Government of India. [ABSTRACT FROM AUTHOR]

Objectives: 1. To assess the efficacy of Mesenchymal Stromal Cells (MSC) versus a control arm as described in the primary endpoint. 2. To evaluate the effects of MSC on the secondary efficacy endpoints. 3. To evaluate the safety and tolerability profiles of MSC. 4. To study soluble and cellular biomarkers that might be involved in the course of the disease and the response to the investigational product.Trial Design: A double-blind, randomized, controlled, trial to evaluate the efficacy and safety of MSC intravenous administration in patients with COVID-induced Acute Respiratory Distress Syndrome (ARDS) compared to a control arm.Participants: The trial is being conducted at a third level hospital, Hospital Universitario Puerta de Hierro, in Majadahonda, Madrid (Spain). Inclusion criteria 1. Informed consent prior to performing study procedures (witnessed oral consent with written consent by representatives will be accepted to avoid paper handling). Written consent by patient or representatives will be obtained whenever possible. 2. Adult patients ≥18 years of age at the time of enrolment. 3. Laboratory-confirmed SARS-CoV-2 infection as determined by Polymerase Chain Reaction (PCR), in oropharyngeal swabs or any other relevant specimen obtained during the course of the disease. Alternative tests (e.g., rapid antigen tests) are also acceptable as laboratory confirmation if their specificity has been accepted by the Sponsor. 4. Moderate to severe ARDS (PaO2/FiO2 ratio equal or less than 200 mmHg) for less than 96 hours at the time of randomization. 5. Patients requiring invasive ventilation are eligible within 72 hours from intubation. 6. Eligible for ICU admission, according to the clinical team. Exclusion criteria 1. Imminent and unavoidable progression to death within 24 hours, irrespective of the provision of treatments (in the opinion of the clinical team). 2. "Do Not Attempt Resuscitation" order in place. 3. Any end-stage organ disease or condition, which in the investigator's opinion, makes the patient an unsuitable candidate for treatment. 4. History of a moderate/severe lung disorder requiring home-based oxygen therapy. 5. Patient requiring Extracorporeal Membrane Oxygenation (ECMO), haemodialysis or hemofiltration at the time of treatment administration. 6. Current diagnosis of pulmonary embolism. 7. Active neoplasm, except carcinoma in situ or basalioma. 8. Known allergy to the products involved in the allogeneic MSC production process. 9. Current pregnancy or lactation (women with childbearing potential should have a negative pregnancy test result at the time of study enrolment). 10. Current participation in a clinical trial with an experimental treatment for COVID-19 (the use of any off-label medicine according to local treatment protocols is not an exclusion criteria). 11. Any circumstances that in the investigator's opinion compromises the patient's ability to participate in the clinical trial. INTERVENTION AND COMPARATOR: - Experimental treatment arm: Allogeneic MSC (approximately 1 x 106 cells/kg). - Control arm: placebo solution (same composition as the experimental treatment, without the MSC). One single intravenous dose of the assigned treatment will be administered on Day 0 of the study. All trial participants will receive standard of care (SOC). In the context of the current worldwide pandemic, SOC can include medicines that are being used in clinical practice (e.g. lopinavir/ritonavir; hydroxy/chloroquine, tocilizumab, etc.), as well as those authorised for COVID (e.g., remdesivir).Main Outcomes: Primary endpoint: Change in the PaO2/FiO2 ratio from baseline to day 7 of treatment administration, or to the last available PaO2/FiO2 ratio if death occurs before day 7. Secondary endpoints: - All-cause mortality on days 7, 14, and 28 after treatment. - PaO2/FiO2 ratio at baseline and days 2, 4, 7, 14 and 28 after treatment. - Oxygen saturation (by standardized measurement) at baseline, daily until day 14, and on day 28 after treatment. - Time to PaO2/FiO2 ratio greater than 200 mmHg. - Subjects' clinical status on the WHO 7-point ordinal scale at baseline, daily until day 14, and on day 28 after treatment. - Time to an improvement of one category from admission on the WHO 7-point ordinal scale. - Percentage of patients that worsen at least one category on the WHO 7-point ordinal scale. - Percentage of patients that improve at least one category (maintained 48h) on the WHO 7-point ordinal scale. - Sequential Organ Failure Assessment (SOFA) scale at baseline and days 2, 4, 7, 14 and 28 after treatment. - Duration of hospitalization (days). - Duration of ICU stay (days). - Oxygen therapy-free days in the first 28 days after treatment. - Duration of supplemental oxygen. - Incidence of and duration of non-invasive and invasive mechanical ventilation in the first 28 days after treatment. - Mechanical ventilation-free days in the first 28 days after treatment. - Ventilation parameters. - Incidence of new onset pulmonary fibrosis at 3 and 12 months after treatment, based on CT scan and pulmonary function tests. - Survival at 3 and 12 months. - Cumulative incidence of Serious Adverse events (SAEs) and Grade 3 and 4 Adverse Events (AEs). - Cumulative incidence of Adverse Drug Reactions (ADR) in the experimental treatment arm. - Cumulative incidence of AEs of special interest. - Levels of analytical markers (C-Reactive Protein, lymphocyte and neutrophil counts, lymphocyte subpopulations, LDH, ferritin, D-dimer, coagulation tests and cytokines...) at baseline and days 2, 4, 7, 14 and 28 after treatment. - Other soluble and cellular biomarkers that might be involved in the course of the disease and the response to MSC.Randomisation: The assignment to treatment will be carried out randomly and blinded, with a 1:1 allocation. Randomization will be done through a centralized system embedded in the electronic Case Report Form (CRF).Blinding (masking): To ensure blinding, treatments will be prepared for administration at the Cell Production Unit and the administration of the treatment will be masked, not allowing the identification of the Investigational Medicinal Product (IMP).Numbers To Be Randomised (sample Size): A total of 20 participants are planned to be randomized, 10 to each treatment group.Trial Status: Protocol version: 1.2, dated October 14th, 2020 Start of recruitment: 01/10/2020 End of recruitment (estimated): December 2020.Trial Registration: EudraCT Number: 2020-002193-27 , registered on July 14th, 2020. NCT number: NCT04615429 , registered on November 4th, 2020.Full Protocol: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to examine the extent to which the capital city of Delhi has gained financial autonomy over the years. In order to better understand its progress, the paper compares the periods before and after the submission of the Third State Finance Commission Report of Delhi. The main findings suggest there have been some efforts to reduce reliance on transfers from upper tiers of government and to strengthen 'own revenues' at the Municipal Corporation of Delhi (MCD). A greater diversification of tax and non-tax revenue sources is responsible for this improvement. In the second period, other tax sources such as corporation tax and electricity tax gained in importance. Non-tax revenues were also strengthened by higher collection of certain components such as conversion charges. However, own revenues have been inadequate to meet growing expenditure requirements, resulting in high revenue expenditure gaps. Further, the growth in Gross State Domestic Product (GSDP has not led to a rise in own revenues for MCD. Rather, the paper finds that higher GSDP and its tertiary sector components are associated with higher expenditures in MCD. As far as local revenues are concerned, higher GSDP is associated with higher transfers, but has no discernible impact on own revenues. [ABSTRACT FROM AUTHOR]

The paper presents the temporal variations of surface ozone (O) and its precursors (oxides of nitrogen (NO), carbon monoxide (CO), methane (CH) and non-methane hydrocarbons (NMHCs)) along with particulate matter (PM and PM) and their relationship with meteorology during January 2012 to December 2014 at an urban site of Delhi, India. The mean mixing ratio of surface O, NO, CO, CH and NMHCs were 29.5 ± 7.3 ppb, 34.7 ± 11.2 ppb, 1.82 ± 0.52 ppm, 3.07 ± 0.37 ppm and 0.53 ± 0.17 ppm, respectively. This study also comprises an analysis of the relation between UV irradiance and surface O. A relationship between the total oxidant concentrations (OX) and NO has been used to identify the regional background O values and the local levels of primary pollution. An attempt has been made to identify the existence of NO or NMHC sensitive regime by charting out relationships between O, NO and NMHCs. The respective high pollution periods of surface O and PM differ on a seasonal timescale. Linear regression analysis has been used to quantify the negative influence of the chemical constituents of PM (elemental carbon, NO , SO ) on O values. Statistical validation using bivariate correlation analysis, multiple linear regression (MLR) analysis and principal component analysis (PCA) strongly describes the intricate relationships among the aforesaid variables and meteorology. Potential Source Contribution Function (PSCF) and Concentration Weighted Trajectory (CWT) analysis indicated upper Indo-Gangetic Plain (IGP) as a significant source region of O precursor gases contributing for O values at the study site. [ABSTRACT FROM AUTHOR]

The present paper reports chemistry and fluxes of dust-carbon mixed coarse particles. For the purpose of this study, different carbonaceous fractions i.e. organic carbon ((OC), elemental carbon (EC) and carbonate carbon (CC) of atmospheric dust and their respective local soils were quantified at three sites of National Capital Region (NCR) of Delhi viz. Jawaharlal Nehru University campus (JNU), Connaught Place (CP) and Vishali area of Ghaziabad (GB). It has been observed that the OC and EC levels were approximately five to nine times higher in urban atmospheric dust than their corresponding soils, whereas CC levels were about three times higher than the corresponding soils. Average dustfall fluxes were significantly different at all the sites due to their different land-use patterns. At urban background site (JNU), the dust flux was lowest (172 mg/m/day) followed by CP, a commercial site, (192 mg/m/day) and GB, an industrial/residential area, (302 mg/m/day). Similar to the dustfall pattern, the mean values of OC, EC and CC deposition fluxes were also observed to be lowest at JNU (9.2, 0.8 and 1.0 mg/m/day, respectively) as compared to CP (12.2, 1.2 and 1.3 mg/m/day, respectively) and GB sites (11.1, 1.1 and 1.4 mg/m/day, respectively). Interestingly, unlike fine mode, different correlation pattern of OC and EC in coarse mode dust aerosols at three sites has suggested their independent deposition processes and source contribution. Fluxes of major water soluble inorganic ions (Na, NH , K, Ca, Mg, F, Cl, NO and SO ) were also determined. Ca, Cl and SO were found to be the major ionic species of water soluble fraction of the urban dust at all the sites. These interactions are corroborated by the morphology of the mixed aerosols. High levels of measured chemical species and their spatial distribution revealed close correspondence with the local emissions from transport, industries, biomass burning, road dust and construction activities etc. [ABSTRACT FROM AUTHOR]