scholarly journals COMP-02. MINING-GUIDED MACHINE LEARNING ANALYSES SUPPORTS GRASPING THE LATEST TRENDS ON NEURO-ONCOLOGY

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi61-vi61
Author(s):  
Taijun Hana ◽  
Shota Tanaka ◽  
Takahide Nejo ◽  
Yosuke Kitagawa ◽  
Satoshi Takahashi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Prediction ◽  
Research Field ◽  
Future Trends ◽  
Research Fields ◽  
The Future ◽  
Versatile Tool ◽  
Recent Trends ◽  
Novel Concept ◽  
The Impact

Abstract The systems that can objectively predict the future trends of a particular research field are always anticipated while conducting medical research. Such systems also provide a considerable aid to researchers while determining and acquiring appropriate research budgets. This study intended to establish a novel and versatile algorithm that can predict the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selected using computational sorting methods, such as text-mining analyses, along with 30 journals that represent the recent trends in the neuro-oncology field. Further, the annual impact (AI) for each year with respect to each journal and field (number of articles published in the journal × the impact factor of the journal) was calculated as a novel concept. Subsequently, the AI index (AII) for the year was defined as the sum of the AIs for the aforementioned 30 journals. With respect to the aforementioned neuro-oncological research fields, the AII trends from 2008 to 2017 were subjected to machine learning predicting analyses. The prediction accuracy of the latest trends in neuro-oncology was validated using actual data obtained from previous studies. In particular, the linear prediction model achieved a relatively good accuracy. The most notable and latest predicted fields in neuro-oncology included some interesting emerging fields, such as microenvironment and anti-mitosis, as well as the already renowned fields, such as immunology and epigenetics. Furthermore, we retrospectively attempted an analysis of the fields different from neuro-oncology. Interestingly, as of 2008, the future emergence of the CRISPR-Cas9 gene editing system has been predicted using this system. Overall, the presented algorithm displays potential to be an effective and versatile tool for the prediction of future trends in a particular medical field.

scholarly journals Mining-Guided Machine Learning Analyses Revealed the Latest Trends in Neuro-Oncology

Cancers ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 178 ◽  
Author(s):  
Taijun Hana ◽  
Shota Tanaka ◽  
Takahide Nejo ◽  
Satoshi Takahashi ◽  
Yosuke Kitagawa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Prediction ◽  
Research Field ◽  
Future Trends ◽  
Research Fields ◽  
Versatile Tool ◽  
Recent Trends ◽  
Past Data ◽  
Novel Concept ◽  
The Given

In conducting medical research, a system which can objectively predict the future trends of the given research field is awaited. This study aims to establish a novel and versatile algorithm that predicts the latest trends in neuro-oncology. Seventy-nine neuro-oncological research fields were selected with computational sorting methods such as text-mining analyses. Thirty journals that represent the recent trends in neuro-oncology were also selected. As a novel concept, the annual impact (AI) of each year was calculated for each journal and field (number of articles published in the journal × impact factor of the journal). The AI index (AII) for the year was defined as the sum of the AIs of the 30 journals. The AII trends of the 79 fields from 2008 to 2017 were subjected to machine learning predicting analyses. The accuracy of the predictions was validated using actual past data. With this algorithm, the latest trends in neuro-oncology were predicted. As a result, the linear prediction model achieved relatively good accuracy. The predicted hottest fields in recent neuro-oncology included some interesting emerging fields such as microenvironment and anti-mitosis. This algorithm may be an effective and versatile tool for prediction of future trends in a particular medical field.

scholarly journals Mandatory annuitization and money's worth: evidence from Singapore

2021 ◽  
pp. 1-20
Author(s):  
Joelle H. Fong ◽  
Jackie Li
Keyword(s):  
Expected Value ◽  
Longevity Risk ◽  
Future Trends ◽  
The Past ◽  
The Future ◽  
Population Mortality ◽  
Mortality Improvement ◽  
Life Annuities ◽  
Compulsory Purchase ◽  
The Impact

Abstract This paper examines the impact of uncertainties in the future trends of mortality on annuity values in Singapore's compulsory purchase market. We document persistent population mortality improvement trends over the past few decades, which underscores the importance of longevity risk in this market. Using the money's worth framework, we find that the life annuities delivered expected payouts valued at 1.019–1.185 (0.973–1.170) per dollar of annuity premium for males (females). Even in a low mortality improvement scenario, the annuities provide an expected value exceeding 0.950. This suggests that participants in the national annuity pool have access to attractively priced annuities, regardless of sex, product, and premium invested.

scholarly journals Nuclear Resonance Vibrational Spectroscopy: A Modern Tool to Pinpoint Site-Specific Cooperative Processes

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 909
Author(s):  
Hongxin Wang ◽  
Artur Braun ◽  
Stephen P. Cramer ◽  
Leland B. Gee ◽  
Yoshitaka Yoda
Keyword(s):  
Vibrational Spectroscopy ◽  
Nuclear Inelastic Scattering ◽  
Research Areas ◽  
Research Fields ◽  
Data Process ◽  
The Future ◽  
Vibration Spectroscopy ◽  
The Impact ◽  
Theoretical Issues ◽  
Modern Tool

Nuclear resonant vibrational spectroscopy (NRVS) is a synchrotron radiation (SR)-based nuclear inelastic scattering spectroscopy that measures the phonons (i.e., vibrational modes) associated with the nuclear transition. It has distinct advantages over traditional vibration spectroscopy and has wide applications in physics, chemistry, bioinorganic chemistry, materials sciences, and geology, as well as many other research areas. In this article, we present a scientific and figurative description of this yet modern tool for the potential users in various research fields in the future. In addition to short discussions on its development history, principles, and other theoretical issues, the focus of this article is on the experimental aspects, such as the instruments, the practical measurement issues, the data process, and a few examples of its applications. The article concludes with introduction to non-57Fe NRVS and an outlook on the impact from the future upgrade of SR rings.

Scientific Web Intelligence

2011 ◽  
pp. 995-999
Author(s):  
Mike Thelwall
Keyword(s):  
Data Mining ◽  
End Users ◽  
Research Field ◽  
Data Sources ◽  
Web Pages ◽  
Web Intelligence ◽  
Research Fields ◽  
Patterns Of Communication ◽  
The Impact ◽  
Citation Databases

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, Web intelligence, and scientometrics to extract useful information from the links and text of academic-related Web pages using various clustering, visualization, and counting techniques. Its origins lie in previous scientometric research into mining off-line academic data sources such as journal citation databases. Typical scientometric objectives are either evaluative (assessing the impact of research) or relational (identifying patterns of communication within and among research fields). From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end users, and the causes of the results can be found and explained.

scholarly journals Assessing the Impact of Increasing Antimicrobial Resistance of Vibrio cholerae on the Future Trends of Cholera Epidemic

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Steady Mushayabasa ◽  
Claver P. Bhunu
Keyword(s):  
Antimicrobial Resistance ◽  
Vibrio Cholerae ◽  
Public Health Problem ◽  
Reproductive Number ◽  
Major Public Health Problem ◽  
Future Trends ◽  
Intestinal Infection ◽  
Cholera Epidemic ◽  
The Future ◽  
The Impact

Cholera, an acute intestinal infection caused by the bacterium Vibrio cholerae, remains a major public health problem in many parts of Africa, Asia, and Latin America. A mathematical model is developed, to assess the impact of increasing antimicrobial resistance of Vibrio cholerae on the future trends of the cholera epidemic. Equilibrium states of the model are determined and their stabilities have been examined. The impacts of increasing antimicrobial resistance of Vibrio cholerae on the future trends of cholera epidemic have been investigated through the reproductive number. Numerical results are provided to support analytical findings.

Shaping the future of hospitality and travel: trends in energy, environmental, and labor force and work

2020 ◽  
Vol 34 (2) ◽  
pp. 129-152
Author(s):  
Marvin J. Cetron ◽  
Owen Davies ◽  
Fred DeMicco ◽  
Mohan Song
Keyword(s):  
Labor Force ◽  
Design Methodology ◽  
Hospitality Industry ◽  
Future Trends ◽  
Content Type ◽  
The Future ◽  
Travel Industry ◽  
The Impact ◽  
Practical Implications

PurposeThe purpose of this study is to continue to forecast trends in the hospitality and travel industry with practical implications.Design/methodology/approachThis study is the updated version of our previous list of trends. The new edition updates the previous report on the implications for the hospitality industry of major trends now shaping the future. We focus mainly on energy, environmental and labor force and work trends and discuss sub-trends under each trend. We then implicate how the trends affect the Hospitality and Travel industry.FindingsWe shared implications under each sub-trends.Originality/valueThe value of this article is to analyze the impact of the environment on the Hospitality and Travel industry from a macro perspective. For each trend, we implicate an estimate for future trends. We hope this article sheds light on the prediction of the Hospitality and Travel industry.

The Future of Advertising

2021 ◽  
pp. 151-166
Author(s):  
Elissa Moses ◽  
Kimberly Rose Clark ◽  
Norman J. Jacknis
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Processing ◽  
Emotional Response ◽  
Optimum Placement ◽  
The Future ◽  
Effective Advertising ◽  
The Cost ◽  
Development Assessment ◽  
The Impact

This chapter summarizes the role that artificial intelligence and machine learning (AI/ML) are expected to play at every stage of advertising development, assessment, and execution. Together with advances in neuroscience for measuring attention, cognitive processing, emotional response, and memory, AI/ML have advanced to a point where analytics can be used to identify variables that drive more effective advertising and predict enhanced performance. In addition, the cost of computation has declined, making platforms to apply these tools much less expensive and within reach. The authors then offer recommendations for 1) understanding the clients/customers and users of the products and services that will be advertised, 2) aiding creativity in the process of designing advertisements, 3) testing the impact of advertisements, and 4) identifying the optimum placement of advertisements.

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