Delhi Technological University

Abstract The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy , which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project.

<h3>Importance</h3> Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. <h3>Objective</h3> To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. <h3>Evidence Review</h3> We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. <h3>Findings</h3> In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572 000 deaths and 15.2 million DALYs), and stomach cancer (542 000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819 000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601 000 deaths and 17.4 million DALYs), TBL cancer (596 000 deaths and 12.6 million DALYs), and colorectal cancer (414 000 deaths and 8.3 million DALYs). <h3>Conclusions and Relevance</h3> The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care.

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become a popular symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select submodules. The supplementary material provide additional examples and further outline details of the architecture and features of SymPy.

The rewards of using plants and plant metabolites over other biological methods for nanoparticle synthesis have fascinated researchers to investigate mechanisms of metal ions uptake and bio-reduction by plants. Here, green chemistry were employed for the synthesis of silver nanoparticles (AgNPs) using leaf extracts of Ocimum Sanctum (Tulsi) and its derivative quercetin (flavonoid present in Tulsi) separately as precursors to investigate the role of biomolecules present in Tulsi in the formation of AgNPs from cationic silver under different physicochemical conditions such as pH, temperature, reaction time and reactants concentration. The size, shape, morphology, and stability of resultant AgNPs were investigated by optical spectroscopy (absorption, photoluminescence (PL), PL-lifetime and Fourier transform infrared), X-ray diffraction (XRD) analysis, and transmission electron microscopy (TEM). The enhanced antibacterial activity of AgNPs against E-Coli gram-negative bacterial strains was analyzed based on the zone of inhibition and minimal inhibitory concentration (MIC) indices. The results of different characterization techniques showed that AgNPs synthesized using both leaf extract and neat quercetin separately followed the same optical, morphological, and antibacterial characteristics, demonstrating that biomolecules (quercetin) present in Tulsi are mainly responsible for the reduction of metal ions to metal nanoparticles.

Machine learning (ML) is a new-age thriving technology, which facilitates computers to read and interpret from the previously present data automatically. It makes use of multiple algorithms to build models, mathematical in nature, and then makes predictions for the new data using the past data and knowledge. Lately, it has been adopted for text detection, hate speech detection, recommender system, face detection, and more. In this paper, majorly all the aspects concerning five machine learning algorithms namely-K-Nearest Neighbor (KNN), Genetic Algorithm (GA), Support Vector Machine (SVM), Decision Tree (DT) , and Long Short Term Memory (LSTM) network have been discussed in great detail which is a prerequisite for venturing into the field of ML. This paper throws light on various new results and conclusions related to these algorithms via research and review of recently published papers that carried out quantitative and qualitative research on real-time problems, mainly predictive analytics in multidisciplinary fields. This paper also talks about the circumstantial origin of these algorithms, which although has been rarely talked about in previous publications, is a preeminent point of discussion for ML enthusiasts and amateurs, both. To explain and understand the accuracy, robustness, and reliability of the algorithms, they were exhaustively reviewed and researched in all aspects qualitatively and quantitatively, wherein the LSTM network and SVM algorithm have projected a superior behavior over the rest. The paper answers all relevant questions that may arise during the study of these algorithms ranging from their origin, to their definition, methodologies of execution, real-time applications attached with sufficient novel evidence, followed by the advantages and major trade-offs; lastly an elaborate comparison of their performances on quantitative and qualitative grounds has been presented. To conclude, the paper also highlights the future scope of ML algorithms and artificial intelligence in the coming times and their roles in automation and holistic development, not just in technology-related aspects but also, the humanitarian aspects, finally followed by reliable and relevant conclusions derived from this exhaustive research.

The current SMAC (Social, Mobile, Analytic, Cloud) technology trend paves the way to a future in which intelligent machines, networked processes and big data are brought together. This virtual world has generated vast amount of data which is accelerating the adoption of machine learning solutions & practices. Machine Learning enables computers to imitate and adapt human-like behaviour. Using machine learning, each interaction, each action performed, becomes something the system can learn and use as experience for the next time. This work is an overview of this data analytics method which enables computers to learn and do what comes naturally to humans, i.e. learn from experience. It includes the preliminaries of machine learning, the definition, nomenclature and applications' describing it's what, how and why. The technology roadmap of machine learning is discussed to understand and verify its potential as a market & industry practice. The primary intent of this work is to give insight into why machine learning is the future.

Object detection has seen many changes in algorithms to improve performance both on speed and accuracy. By the continuous effort of so many researchers, deep learning algorithms are growing rapidly with an improved object detection performance. Various popular applications like pedestrian detection, medical imaging, robotics, self-driving cars, face detection, etc. reduces the efforts of humans in many areas. Due to the vast field and various state-of-the-art algorithms, it is a tedious task to cover all at once. This paper presents the fundamental overview of object detection methods by including two classes of object detectors. In two stage detector covered algorithms are RCNN, Fast RCNN, and Faster RCNN, whereas in one stage detector YOLO v1, v2, v3, and SSD are covered. Two stage detectors focus more on accuracy, whereas the primary concern of one stage detectors is speed. We will explain an improved YOLO version called YOLO v3-Tiny, and then its comparison with previous methods for detection and recognition of object is described graphically.

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Silver nanoparticles (AgNPs) were synthesized using aqueous extract of Neem (Azadirachta indica) leaves and silver salt. XRD, SEM, FTIR, optical absorption and photoluminescence (PL) were measured and analysed. The synthesized AgNPs exhibits lowest energy absorption band at 400 nm. The effects of various parameters i.e., extract concentration, reaction pH, reactants ratio, temperature and interaction time on the synthesis of AgNPs were studied. It was found that the formation of AgNPs enhanced with time at higher temperature and alkaline pH. The AgNPs formed were found to have enhanced antimicrobial properties and showed zone of inhibition against isolated bacteria (Escherichia coli) from garden soil sample. Based on the results obtained, it can be concluded that the resources obtained from plants can be efficiently used in the production of AgNPs and could be utilized in various fields such as biomedical, nanotechnology etc.

&lt;p&gt;Background&lt;/p&gt;\n&lt;p&gt;Colorectal cancer is the third leading cause of cancer deaths worldwide. Given the recent increasing trends in colorectal cancer incidence globally, up-to-date information on the colorectal cancer burden could guide screening, early detection, and treatment strategies, and help effectively allocate resources. We examined the temporal patterns of the global, regional, and national burden of colorectal cancer and its risk factors in 204 countries and territories across the past three decades.&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Methods&lt;/p&gt;\n&lt;p&gt;Estimates of incidence, mortality, and disability-adjusted life years (DALYs) for colorectal cancer were generated as a part of the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) 2019 by age, sex, and geographical location for the period 1990–2019. Mortality estimates were produced using the cause of death ensemble model. We also calculated DALYs attributable to risk factors that had evidence of causation with colorectal cancer.&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Findings&lt;/p&gt;\n&lt;p&gt;Globally, between 1990 and 2019, colorectal cancer incident cases more than doubled, from 842 098 (95% uncertainty interval [UI] 810 408–868 574) to 2·17 million (2·00–2·34), and deaths increased from 518 126 (493 682–537 877) to 1·09 million (1·02–1·15). The global age-standardised incidence rate increased from 22·2 (95% UI 21·3–23·0) per 100 000 to 26·7 (24·6–28·9) per 100 000, whereas the age-standardised mortality rate decreased from 14·3 (13·5–14·9) per 100 000 to 13·7 (12·6–14·5) per 100 000 and the age-standardised DALY rate decreased from 308·5 (294·7–320·7) per 100 000 to 295·5 (275·2–313·0) per 100 000 from 1990 through 2019. Taiwan (province of China; 62·0 [48·9–80·0] per 100 000), Monaco (60·7 [48·5–73·6] per 100 000), and Andorra (56·6 [42·8–71·9] per 100 000) had the highest age-standardised incidence rates, while Greenland (31·4 [26·0–37·1] per 100 000), Brunei (30·3 [26·6–34·1] per 100 000), and Hungary (28·6 [23·6–34·0] per 100 000) had the highest age-standardised mortality rates. From 1990 through 2019, a substantial rise in incidence rates was observed in younger adults (age &lt;50 years), particularly in high Socio-demographic Index (SDI) countries. Globally, a diet low in milk (15·6%), smoking (13·3%), a diet low in calcium (12·9%), and alcohol use (9·9%) were the main contributors to colorectal cancer DALYs in 2019.&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Interpretation&lt;/p&gt;\n&lt;p&gt;The increase in incidence rates in people younger than 50 years requires vigilance from researchers, clinicians, and policy makers and a possible reconsideration of screening guidelines. The fast-rising burden in low SDI and middle SDI countries in Asia and Africa calls for colorectal cancer prevention approaches, greater awareness, and cost-effective screening and therapeutic options in these regions.&lt;/p&gt;

Recent progresses in the field of Induced Pluripotent Stem Cells (iPSCs) have opened up many gateways for the research in therapeutics. iPSCs are the cells which are reprogrammed from somatic cells using different transcription factors. iPSCs possess unique properties of self renewal and differentiation to many types of cell lineage. Hence could replace the use of embryonic stem cells (ESC), and may overcome the various ethical issues regarding the use of embryos in research and clinics. Overwhelming responses prompted worldwide by a large number of researchers about the use of iPSCs evoked a large number of peple to establish more authentic methods for iPSC generation. This would require understanding the underlying mechanism in a detailed manner. There have been a large number of reports showing potential role of different molecules as putative regulators of iPSC generating methods. The molecular mechanisms that play role in reprogramming to generate iPSCs from different types of somatic cell sources involves a plethora of molecules including miRNAs, DNA modifying agents (viz. DNA methyl transferases), NANOG, etc. While promising a number of important roles in various clinical/research studies, iPSCs could also be of great use in studying molecular mechanism of many diseases. There are various diseases that have been modeled by uing iPSCs for better understanding of their etiology which maybe further utilized for developing putative treatments for these diseases. In addition, iPSCs are used for the production of patient-specific cells which can be transplanted to the site of injury or the site of tissue degeneration due to various disease conditions. The use of iPSCs may eliminate the chances of immune rejection as patient specific cells may be used for transplantation in various engraftment processes. Moreover, iPSC technology has been employed in various diseases for disease modeling and gene therapy. The technique offers benefits over other similar techniques such as animal models. Many toxic compounds (different chemical compounds, pharmaceutical drugs, other hazardous chemicals, or environmental conditions) which are encountered by humans and newly designed drugs may be evaluated for toxicity and effects by using iPSCs. Thus, the applications of iPSCs in regenerative medicine, disease modeling, and drug discovery are enormous and should be explored in a more comprehensive manner.

Purpose SMEs are considered as engine for economic growth all over the world. After the globalization of market, SMEs have got many opportunities to work in integration with large‐scale organizations. They cannot exploit these opportunities and sustain their competitiveness if they focus only on certain aspects of their functioning and work in isolation. This paper tries to identify the major areas of strategy development by SMEs for improving competitiveness of SMEs in globalised market. Design/methodology/approach About 134 research papers, mainly from referred international journals are reviewed to identify thrust areas of research. On the basis of review, gaps are identified and research agenda is proposed. Findings SMEs have not given due attention for developing their effective strategies in the past. They are localized in functioning. On export fronts SMEs face many constraints due to lack of resources and poor innovative capabilities. For sustaining their competitiveness, they have to benchmark their assets, processes and performance with respect to the best in industry. There is also need for developing a framework for quantifying the competitiveness by adopting a holistic approach. Originality/value This paper explores major areas for research on SMEs. It will be of great value for researchers and professionals involved on SMEs management.

Purpose This paper aims to understand the factors that affect consumers’ adoption of mobile wallet as an alternative method of making payments to purchase goods and services. Design/methodology/approach A survey of over 210 mobile phone consumers was made. The study added two additional constructs – perceived regulatory support (PRS) and promotional benefits (PBs) – and proposed an integrated approach to understanding mobile wallet adoption. The hypothesized relationships were analysed via structural equation modelling. Findings The results indicated performance expectancy, social influence, facilitating conditions, perceived risk, perceived value, PRS, as well as PBs, to be significant factors in predicting behavioural intentions to adopt mobile wallet solutions. The impact of effort expectancy was found to be statistically insignificant. Research limitations/implications The small sample size and the possibility of including new variables such as personal innovativeness, which have not been addressed here, are some of the limitations of this study. Practical implications The findings of this paper would be useful for mobile wallet service providers, mobile app writers and institutions involved in the facilitation and regulation of such services to develop suitable strategic frameworks to encourage the adoption of mobile wallets. Originality/value The study is the first of its kind in India and has added a new dimension in the assessment of technology adoption by proposing two new variables.

Abstract In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models.

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

Landslides are one of the most devastating and recurring natural disasters and have affected several mountainous regions across the globe. The Indian Himalayan region is no exception to landslide incidences affecting key economic sectors such as transportation and agriculture and often leading to loss of lives. As reflected in the global landslide dataset, most of the landslides in this region are rainfall triggered. The region is prone to 15% of the global rainfall-induced landslides, and thereby a review of the studies in the region is inevitable. The high exposure to landslide risk has made the Indian Himalayas receive growing attention by the landslides community. A review of landslides studies conducted in this region is therefore important to provide a general picture of the state-of-the-art, a reference point for researchers and practitioners working in this region for the first time, and a summary of the improvements most urgently needed to better address landslide hazard research and management. This article focuses on various studies ranging from forecasting and monitoring to hazard and susceptibility analysis. The various factors used to analyze landslide are also studied for various landslide zones in the region. The analysis reveals that there are several avenues where significant research work is needed such as the inclusion of climate change factors or the acquisition of basic data of highest quality to be used as input data for computational models. In addition, the review reveals that, despite the entire region being highly landslide prone, most of the studies have focused on few regions and large areas have been neglected. The aim of the review is to provide a reference for stakeholders and researchers who are currently or looking to work in the Indian Himalayas, to highlight the shortcomings and the points of strength of the research being conducted, and to provide a contribution in addressing the future developments most urgently needed to obtain a consistent advance in landslide risk reduction of the area.

Improved surface properties with the retainment of bulk properties are necessary for a component for enhanced wear characteristics. Friction stir processing (FSP) is used to produce such surface composites. Fabrication of 5083 aluminum alloy with reinforced layers of boron carbide (B4C) through FSP was carried out. Micro and nano sized B4C particles were used as reinforcements. The friction processed surface composite layer was analyzed through optical and scanning electron microscopical studies. The number of passes and the size of reinforcement play a vital role in the development of surface composites by FSP. Mechanical properties of the friction stir processed surface composites were evaluated through micro hardness and universal tensile tests. The results were compared with the properties of the base metal. The role of reinforcement and number of passes on properties were also evaluated. Tribological performance of the surface composites is tested through pin on disk test. The surface composite layer resulted in three passes with nano particle reinforcement exhibited better properties in hardness, tensile behavior and wear resistance compared to the behavior of the base metal.

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

In this paper, a combination of a fuzzy multiobjective approach and ant colony optimization (ACO) as a metaheuristic algorithm is used to solve the simultaneous reconfiguration and optimal allocation (size and location) of photovoltaic (PV) arrays as a distributed generation (DG) and distribution static compensator (DSTATCOM) as a distribution flexible ac transmission system (DFACT) device in a distribution system. The purpose of this research includes loss reduction, voltage profile (VP) improvement, and increase in the feeder load balancing (LB). The proposed method is validated using the IEEE 33-bus test system and a Tai-Power 11.4-kV distribution system as a real distribution network. The results proved that simultaneous reconfiguration and optimal allocation of PV array and DSTATCOM unit leads to significantly reduced losses, improved VP, and increased LB. Obtained results have been compared with the base value and found that simultaneous placement of PV and DSTATCOM along with reconfiguration is more beneficial than separate single-objective optimization. Also, the proposed fuzzy-ACO approach is more accurate as compared to ACO and other intelligent techniques like fuzzy-genetic algorithm (GA) and fuzzy-particle swarm optimization (PSO).