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The Epidemiologic Transition can help us understand a fundamental puzzle about aging. The puzzle stems from two seemingly contradictory facts. The first fact is that death rates from noninfectious degenerative maladies – the so-called diseases of aging – increase as people age. It seems to be at odds with the historical fact that for nearly a century in which people were aging more than ever before, the aggregate rates of such diseases have been decreasing. In what sense can both be true? Crucial to resolving the puzzle are the age-profiles of such diseases in cohorts that grew up in the different regimes of the Transition. For each cohort, noninfectious diseases had increased with age, resulting in an upward-sloping age profile, which affirms the first fact. As the regimes were transitioning from the Malthusian to the modern one, however, the profiles of successive cohorts had been shifting downward: death rates from noninfectious diseases were shrinking at each age, signifying the newer cohorts’ greater aging potentials. The shifting profiles had been renewing the cohort mix of the population, shaping the century-long descent of such diseases in aggregate, giving rise to the historical fact. The profiles had shifted early in the cohorts’ adult years, associating closely with the newer epidemiologic conditions in childhood. Those conditions appear to be a circumstance under which aging potentials of cohorts could be misgauged, including in one troubling episode in the first half of the nineteenth century when the potentials had reversed.

Measures are important to healthcare outcomes. Outcome changes result from deliberate selective intervention introduction on a measure. If measures can be characterized and categorized, then the resulting schema may be generalized and utilized as a framework for uniquely identifying, packaging and comparing different interventions and probing target systems to facilitate selecting the most appropriate intervention for maximum desired outcomes. Measure characterization was accomplished with multi-axial statistical analysis and measure categorization by logical tabulation. The measure of interest is a key provider productivity index: “patient visits per hour,” while the specific intervention is “patient schedule manipulation by overbooking.” The paper aims to discuss these issues.

For statistical analysis, interrupted time series (ITS), robust-ITS and outlier detection models were applied to an 18-month data set that included patient visits per hour and intervention introduction time. A statistically significant change-point was determined, resulting in pre-intervention, transitional and post-effect segmentation. Linear regression modeling was used to analyze pre-intervention and post-effect mean change while a triangle was used to analyze the transitional state. For categorization, an “intervention moments” table was constructed from the analysis results with: time-to-effect, pre- and post-mean change magnitude and velocity; pre- and post-correlation and variance; and effect decay/doubling time. The table included transitional parameters such as transition velocity and transition footprint visualization represented as a triangle.

The intervention produced a significant change. The pre-intervention and post-effect means for patient visits per hour were statistically different (0.38, p=0.0001). The pre- and post-variance change (0.23, p=0.01) was statistically significant (variance was higher post-intervention, which was undesirable). Post-intervention correlation was higher (desirable). Decay time for the effect was calculated as 11 months post-effect. Time-to-effect was four months; mean change velocity was +0.094 visits per h/month. A transition triangular footprint was produced, yielding 0.35 visits per hr/month transition velocity. Using these results, the intervention was fully profiled and thereby categorized as an intervention moments table.

One limitation is sample size for this time series, 18 monthly cycles’ analysis. However, interventions on measures in healthcare demand short time cycles (hence necessarily yielding fewer data points) for practicality, meaningfulness and usefulness. Despite this shortcoming, the statistical processes applied such as outliers detection, t-test for mean difference, F-test for variances and modeling, all consider the small sample sizes. Seasonality, which usually affects time series, was not detected and even if present, was also considered by modeling.

Obtaining an intervention profile, made possible by multidimensional analysis, allows interventions to be uniquely classified and categorized, enabling informed, comparative and appropriate selective deployment against health measures, thus potentially contributing to outcomes optimization.

The inevitable direction for healthcare is heavy investment in measures outcomes optimization to improve: patient experience; population health; and reduce costs. Interventions are the tools that change outcomes. Creative modeling and applying novel methods for intervention analysis are necessary if healthcare is to achieve this goal. Analytical methods should categorize and rank interventions; probe the measures to improve future selection and adoption; reveal the organic systems’ strengths and shortcomings implementing the interventions for fine-tuning for better performance.

An “intervention moments table” is proposed, created from a multi-axial statistical intervention analysis for organizing, classifying and categorizing interventions. The analysis-set was expanded with additional parameters such as time-to-effect, mean change velocity and effect decay time/doubling time, including transition zone analysis, which produced a unique transitional footprint; and transition velocity. The “intervention moments” should facilitate intervention cross-comparisons, intervention selection and optimal intervention deployment for best outcomes optimization.

In the current study, two grey prediction models, Even GM (1, 1) and Non-homogeneous discrete grey model (NDGM), and ARIMA models are deployed to forecast cocoa bean production of the six major cocoa-producing countries. Furthermore, relying on Relative Growth Rate (RGR) and Doubling Time (D_t), production growth is analyzed.

The secondary data were extracted from the United Nations Food and Agricultural Organization (FAO) database. Grey forecasting models are applied using the data covering 2008 to 2017 as their performance on the small sample size is well-recognized. The models' performance was estimated through MAPE, MAE and RMSE.

Results show the two grey models fell below 10% of MAPE confirming their high accuracy and forecasting performance against that of the ARIMA. Therefore, the suitability of grey models for the cocoa production forecast is established. Findings also revealed that cocoa production in Côte d'Ivoire, Cameroon, Ghana and Brazil is likely to experience a rise with a growth rate of 2.52, 2.49, 2.45 and 2.72% by 2030, respectively. However, Nigeria and Indonesia are likely to experience a decrease with a growth rate of 2.25 and 2.21%, respectively.

For a sustainable cocoa industry, stakeholders should investigate the decline in production despite the implementation of advanced agricultural mechanization in cocoa farming, which goes further to put food security at risk.

The study presents a pioneering attempt of using grey forecasting models to predict cocoa production.

The present investigation aims to present the status of planetary science research in India using different scientometric indicators, as reflected in the Web of Science Core Collection database.

The researcher adopted systematic approaches to retrieve the data from the Web of Science Core Collection database for 20 years by using AAS Astronomical subject keywords. A total of 1,504 Indian publications and 55,572 World's publications were considered for analysis. The data were analyzed using the biblioshiny application of bibliometrix to investigate the most productive countries/territories, institutions, authors, research fields, journals, keywords, and h, g-index. The VOSviewer program is used to construct and visualize scientometric networks and analyze the co-occurrence of terms. “Webometric Analyst 2.0” is used to retrieve the Altmetric attention scores for the articles.

The results revealed that the publications on planetary science research has increased over time, with an annual growth rate of 9.66%. The study also revealed the prolific authors and institutions, productive journals and most frequently cited journals. The USA was the major collaborating partner of India. The results also provided valuable information on the citations made to these papers on planetary science, including a total number of citations, average citations per item, cited rate and h-index. There were 28,086 citations to 1,504 papers. The top 67 citation papers were the h-core papers on planetary science in India. Altmetric score for planetary science articles ranged from 1 to 2,418. Twitter (69%), news outlets (16%), blogs (6%), and Facebook (6%) were the most popular Altmetric data resources.

This investigation is the first attempt to employ scientometrics and visualization techniques to planetary science research in India.

This study aims to measure the global research landscape of the National Institute of Pharmaceutical Education and Research (NIPER) of India on a set of quantitative and qualitative metrics in terms of research output toward exploring research trends and give an overview of collaborative practices by researchers of NIPERs.

The present study has selected the Scopus database as a tool to retrieve potential publications of studied NIPERs during the last 12 years (2010–2021). NIPER-Mohali, NIPER-Hyderabad, NIPER-Ahmedabad, NIPER-Guwahati and NIPER-Kolkata have been selected for the study. The study has adopted a comprehensive search strategy to extract 3,926 publications data. VOS viewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The global scientific research output of NIPERs accrued 3,926 publications with an average of 327 publications per year. The retrieved publications fetched a total of 67,772 citations with an average citation impact of 17.26. There observed a steady growth of publications from 168 to 509 registered with an average growth rate of 18.44%. The mean relative growth rate and doubling time of research output are 0.26 and 2.94. The authorship patterns explore collaborative trends as most of the publications were published by multiple authors (99.39%). NIPERs have expanded their outreach to collaborate with the USA, Malaysia, Saudi Arabia, Australia and the UK to collaborate on research and regulatory reforms exhibits in the USA as a major contributor.

The present study is the first effort to evaluate the global research productivity of NIPERs and assess the current research trends on a set of quantitative and qualitative metrics to provide some insights into the complex dynamics of research productivity. The study’s outcome may help to identify the current research progress of NIPERs at the global level.

The aim of this paper is to present the first diffusion analysis of ISO/IEC 27001, the fourth most popular ISO certification at global level and the most important standard for information security.

To achieve the purposes, the authors applied Grey Models (GM) – Even GM (1,1), Even GM (1,1,α,θ), Discrete GM (1,1), Discrete GM (1,1,α) – complemented by the relative growth rate and the doubling time indexes on the six most important countries in terms of issued certificates.

Results show that a growing trend is likely to be expected in the years to come and that China will lead at country level.

The study contributes to the scientific debate by presenting the first diffusive analysis of ISO/IEC 27001 and by proposing a forecasting approach that to date has found little application in the field of international standards.

The present study aims to measure the global research landscape on coronavirus indexed in the Web of Science from 1989 to 2020. The study examines growth rates, authorship trends, institutional productivity, collaborative networks and prominent authors, institutions and countries.

The research literature on coronavirus published globally and indexed in the Web of Science core collection was retrieved using the term “Coronavirus” and its related and synonymous terms (e.g. COVID-19, SARS-COV, SARS-COV-2 and severe acute respiratory syndrome coronavirus) as per the Medical List of Subject Headings. A total of 5,625 publications were retrieved; however, the study was restricted to articles only (i.e. 4,471), and other document types were excluded. Quantitative and visualization techniques were used for data analysis and interpretation. VOSViewer software was employed to map collaborative networks of authors, institutions and countries.

A total of 4,471 articles have been published on coronavirus by 99 countries of the world with the maximum contribution from the USA, followed by the People's Republic of China. The United States, China, Canada, Netherlands and Germany are the front runners in the collaborative network and form strong sub-networks with other countries as well. More than 1,000 institutions collaborate in the field of coronavirus research among 99 contributing countries. The authorship pattern shows that 97.5% of publications are contributed by authors in collaboration in which 77.5% of publications are contributed by four or more than four authors. The range between degree of collaboration (DC) varies from 0.89 in 1993 to 1 in 2000 with an average of 0.96 from 1989 to 2020. The results confirm that the coronavirus research is carried out in teamwork at the individual, institutional and global levels with high magnitude and density of collaboration. The relative growth of the literature has shown inconsistency as a decreasing trend has been observed from 2007 onwards, thereby increasing the doubling time from 4.2 in the first ten years to 17.3 in the last ten years.

The study is limited to the publications indexed in the Web of Science; the findings cannot be generalized across other databases.

The results of the study may help medical scientists to identify the progress in COVID-19 research. Besdies, it will help to identify the prolific authors, institutions and countries in the development of research.

The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. It has not only attacked the health of the people but the economy of nations as well. Therefore, it is feasible to know the research landscape of the disease to conquer the problem.

The current COVID-19 pandemic poses urgent and prolonged threats to the health and well-being of the population worldwide. It has not only attacked the health of the people but also the economy of nations as well. Therefore, it is feasible to know the research landscape of the disease to conquer the problem.

Lung cancer is the leading cause of death worldwide. Physical and chemical agents such as tobacco smoke are the leading cause of various lung cancers. The intrinsic heterogeneity of normal lung tissue may be affected in different ways, giving rise to different types of lung cancers classified as either small‐cell lung cancer (SCLC) or non‐small cell lung cancer (NSCLC). Adenocarcinoma, a NSCLC, accounts for 40 percent of all lung cancer cases and the incidence is increasing worldwide, especially among women. The survival rate and prognosis is poorest for adenocarcinoma. Therefore, diagnosis at the earliest stage (Stage I, localized) is critical for increasing survival rates of those suffering from lung cancer. However, many factors affect early diagnosis including the variable natural growth of tumors plus technological and human factors associated with manipulation of tissue samples and interpretation of results. This article reviews potential problems associated with diagnosing lung cancer and considers future directions of diagnostic technology.

Whereas the Minister of Labour (hereafter in this Order referred to as “the Minister”) has received from the Rope, Twine and Net Wages Council (Great Britain) the wages regulation proposals set out in Schedules 1 and 2 hereof;

This study is an attempt to evaluating the growth of scientific literature in the domain of coronavirus and Covid-19 pandemic research based on scientometric indicators: prolific countries and relative citation impact (RCI); influential institutions; author analysis and network, h-index and citation; DC (degree of collaboration), CC (collaboration coefficient), MCI (modified collaboration index) in the subject domain of coronavirus and Covid-19 research.

The authors adopted approaches to obtain the literature data from Scopus database from 2000 to 2020 by conducting a systematic search using keywords related to the studied subject domain. In total, 15,297 numbers of records were considered for the literature analysis considering the real significant growth of this subject domain. This study presented the scientometric analysis of these publications. Furthermore, statistical correlations have been used to understand the collaboration pattern. Visualization tool VOSviewer is used to construct the co-author network.

The present study found that 53.57% (8,195) of the research documents published on the open-access platform. Journal of Virology was found to be most preferred journal by the researcher producing around 839(5.48%) articles. USA and China dominate in the research output, and the University of Hong Kong has produced the highest number of research paper 547(3.58%). A significant portion of the research documents are published in the subject domain of medicine (49.70%), followed by immunology and microbiology (35.72%), and biochemistry, genetics and molecular biology subject domains (22.32%). There has been an unparalleled proliferation of publications on COVID-19 since January 2020 and also a significant distribution of research funds across the globe.

The study exclusively examines 15,297 research outputs which have been indexed in the Scopus database from 2000 to 2020 (till 01 April 2020). Thus, documents published in any other different channels and sources which are not covered in Scopus are excluded from the purview of research.

It will be beneficial for researchers and practitioners worldwide for understanding the growth of scientific literature in the coronavirus and COVID-19 and identifying potential collaborator.

Considering the global impact and social distress due to the outbreak of COVID-19 pandemic, this study is significant in the present scenario for identifying the growth of scientific literature in this field and evolving of this domain of research around the globe. The research results are useful to identify valuable research patterns from publications and of developments in the field of coronavirus and COVID-19.