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Forecasting renewable energy for environmental resilience through computational intelligence.

PloS one

Khan M, Al-Ammar EA, Naeem MR, Ko W, Choi HJ, Kang HK.
PMID: 34415924
PLoS One. 2021 Aug 20;16(8):e0256381. doi: 10.1371/journal.pone.0256381. eCollection 2021.

Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh...

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Khan M, Al-Ammar EA, Naeem MR, et al. Forecasting renewable energy for environmental resilience through computational intelligence. PLoS One. 2021;16(8):e0256381doi: 10.1371/journal.pone.0256381.
Khan, M., Al-Ammar, E. A., Naeem, M. R., Ko, W., Choi, H. J., & Kang, H. K. (2021). Forecasting renewable energy for environmental resilience through computational intelligence. PloS one, 16(8), e0256381. https://doi.org/10.1371/journal.pone.0256381
Khan, Mansoor, et al. "Forecasting renewable energy for environmental resilience through computational intelligence." PloS one vol. 16,8 (2021): e0256381. doi: https://doi.org/10.1371/journal.pone.0256381
Khan M, Al-Ammar EA, Naeem MR, Ko W, Choi HJ, Kang HK. Forecasting renewable energy for environmental resilience through computational intelligence. PLoS One. 2021 Aug 20;16(8):e0256381. doi: 10.1371/journal.pone.0256381. eCollection 2021. PMID: 34415924; PMCID: PMC8378711.
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Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.

BMJ health & care informatics

Warde PR, Patel S, Ferreira T, Gershengorn H, Bhatia MC, Parekh D, Manni K, Shukla B.
PMID: 33972270
BMJ Health Care Inform. 2021 May;28(1). doi: 10.1136/bmjhci-2020-100248.

OBJECTIVES: We describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic.METHODS: We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system...

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Warde PR, Patel S, Ferreira T, et al. Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ Health Care Inform. 2021;28(1)doi: 10.1136/bmjhci-2020-100248.
Warde, P. R., Patel, S., Ferreira, T., Gershengorn, H., Bhatia, M. C., Parekh, D., Manni, K., & Shukla, B. (2021). Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ health & care informatics, 28(1), . https://doi.org/10.1136/bmjhci-2020-100248
Warde, Prem Rajendra, et al. "Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic." BMJ health & care informatics vol. 28,1 (2021). doi: https://doi.org/10.1136/bmjhci-2020-100248
Warde PR, Patel S, Ferreira T, Gershengorn H, Bhatia MC, Parekh D, Manni K, Shukla B. Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic. BMJ Health Care Inform. 2021 May;28(1). doi: 10.1136/bmjhci-2020-100248. PMID: 33972270; PMCID: PMC8111872.
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Time dynamics of COVID-19.

Scientific reports

Carroll C, Bhattacharjee S, Chen Y, Dubey P, Fan J, Gajardo Á, Zhou X, Müller HG, Wang JL.
PMID: 33273598
Sci Rep. 2020 Dec 03;10(1):21040. doi: 10.1038/s41598-020-77709-4.

We apply tools from functional data analysis to model cumulative trajectories of COVID-19 cases across countries, establishing a framework for quantifying and comparing cases and deaths across countries longitudinally. It emerges that a country's trajectory during an initial first...

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Carroll C, Bhattacharjee S, Chen Y, et al. Time dynamics of COVID-19. Sci Rep. 2020;10(1):21040doi: 10.1038/s41598-020-77709-4.
Carroll, C., Bhattacharjee, S., Chen, Y., Dubey, P., Fan, J., Gajardo, �. �., Zhou, X., Müller, H. G., & Wang, J. L. (2020). Time dynamics of COVID-19. Scientific reports, 10(1), 21040. https://doi.org/10.1038/s41598-020-77709-4
Carroll, Cody, et al. "Time dynamics of COVID-19." Scientific reports vol. 10,1 (2020): 21040. doi: https://doi.org/10.1038/s41598-020-77709-4
Carroll C, Bhattacharjee S, Chen Y, Dubey P, Fan J, Gajardo Á, Zhou X, Müller HG, Wang JL. Time dynamics of COVID-19. Sci Rep. 2020 Dec 03;10(1):21040. doi: 10.1038/s41598-020-77709-4. PMID: 33273598; PMCID: PMC7712909.
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The COVID-19 pandemic: prediction study based on machine learning models.

Environmental science and pollution research international

Malki Z, Atlam ES, Ewis A, Dagnew G, Ghoneim OA, Mohamed AA, Abdel-Daim MM, Gad I.
PMID: 33840016
Environ Sci Pollut Res Int. 2021 Aug;28(30):40496-40506. doi: 10.1007/s11356-021-13824-7. Epub 2021 Apr 10.

COVID-19 was first discovered in Wuhan, China in December 2019. It is one of the worst pandemics in human history. Recent studies reported that COVID-19 is transmitted among humans by droplet infection or direct contact. COVID-19 pandemic has invaded...

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Malki Z, Atlam ES, Ewis A, et al. The COVID-19 pandemic: prediction study based on machine learning models. Environ Sci Pollut Res Int. 2021;28(30):40496-40506doi: 10.1007/s11356-021-13824-7.
Malki, Z., Atlam, E. S., Ewis, A., Dagnew, G., Ghoneim, O. A., Mohamed, A. A., Abdel-Daim, M. M., & Gad, I. (2021). The COVID-19 pandemic: prediction study based on machine learning models. Environmental science and pollution research international, 28(30), 40496-40506. https://doi.org/10.1007/s11356-021-13824-7
Malki, Zohair, et al. "The COVID-19 pandemic: prediction study based on machine learning models." Environmental science and pollution research international vol. 28,30 (2021): 40496-40506. doi: https://doi.org/10.1007/s11356-021-13824-7
Malki Z, Atlam ES, Ewis A, Dagnew G, Ghoneim OA, Mohamed AA, Abdel-Daim MM, Gad I. The COVID-19 pandemic: prediction study based on machine learning models. Environ Sci Pollut Res Int. 2021 Aug;28(30):40496-40506. doi: 10.1007/s11356-021-13824-7. Epub 2021 Apr 10. PMID: 33840016; PMCID: PMC8035887.
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Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France.

Anaesthesia, critical care & pain medicine

Sofonea MT, Alizon S.
PMID: 34479681
Anaesth Crit Care Pain Med. 2021 Aug;40(4):100943. doi: 10.1016/j.accpm.2021.100943.

No abstract available.

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Sofonea MT, Alizon S. Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France. Anaesth Crit Care Pain Med. 2021;40(4):100943doi: 10.1016/j.accpm.2021.100943.
Sofonea, M. T., & Alizon, S. (2021). Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France. Anaesthesia, critical care & pain medicine, 40(4), 100943. https://doi.org/10.1016/j.accpm.2021.100943
Sofonea, Mircea T, and Alizon, Samuel. "Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France." Anaesthesia, critical care & pain medicine vol. 40,4 (2021): 100943. doi: https://doi.org/10.1016/j.accpm.2021.100943
Sofonea MT, Alizon S. Anticipating COVID-19 intensive care unit capacity strain: A look back at epidemiological projections in France. Anaesth Crit Care Pain Med. 2021 Aug;40(4):100943. doi: 10.1016/j.accpm.2021.100943. PMID: 34479681; PMCID: PMC8407772.
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The NHS: the next 50 years.

Journal of the Royal College of Physicians of London

Langlands A.
PMID: 9762620
J R Coll Physicians Lond. 1998 Jul-Aug;32(4):302-5.

No abstract available.

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Langlands A. The NHS: the next 50 years. J R Coll Physicians Lond. 1998;32(4):302-5
Langlands, A. (1998). The NHS: the next 50 years. Journal of the Royal College of Physicians of London, 32(4), 302-5.
Langlands, A. "The NHS: the next 50 years." Journal of the Royal College of Physicians of London vol. 32,4 (1998): 302-5.
Langlands A. The NHS: the next 50 years. J R Coll Physicians Lond. 1998 Jul-Aug;32(4):302-5. PMID: 9762620.
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Training for careers in academic surgical oncology: the future is bright.

Annals of surgical oncology

Pollock RE, Curley S.
PMID: 10493614
Ann Surg Oncol. 1999 Sep;6(6):517-8. doi: 10.1007/s10434-999-0517-2.

No abstract available.

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Pollock RE, Curley S. Training for careers in academic surgical oncology: the future is bright. Ann Surg Oncol. 1999;6(6):517-8doi: 10.1007/s10434-999-0517-2.
Pollock, R. E., & Curley, S. (1999). Training for careers in academic surgical oncology: the future is bright. Annals of surgical oncology, 6(6), 517-8. https://doi.org/10.1007/s10434-999-0517-2
Pollock, R E, and Curley, S. "Training for careers in academic surgical oncology: the future is bright." Annals of surgical oncology vol. 6,6 (1999): 517-8. doi: https://doi.org/10.1007/s10434-999-0517-2
Pollock RE, Curley S. Training for careers in academic surgical oncology: the future is bright. Ann Surg Oncol. 1999 Sep;6(6):517-8. doi: 10.1007/s10434-999-0517-2. PMID: 10493614.
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[Surgical school--an anachronism?].

Langenbecks Archiv fur Chirurgie. Supplement. Kongressband. Deutsche Gesellschaft fur Chirurgie. Kongress

Borst HG.
PMID: 9101977
Langenbecks Arch Chir Suppl Kongressbd. 1996;113:723-6.

No abstract available.

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Borst HG. Die chirurgische Schule--Ein Anachronismus? [Surgical school--an anachronism?]. Langenbecks Arch Chir Suppl Kongressbd. 1996;113:723-6
Borst, H. G. (1996). Die chirurgische Schule--Ein Anachronismus? [Surgical school--an anachronism?]. Langenbecks Archiv fur Chirurgie. Supplement. Kongressband. Deutsche Gesellschaft fur Chirurgie. Kongress, 113723-6.
Borst, H G. "Die chirurgische Schule--Ein Anachronismus?" [Surgical school--an anachronism?]. Langenbecks Archiv fur Chirurgie. Supplement. Kongressband. Deutsche Gesellschaft fur Chirurgie. Kongress vol. 113 (1996): 723-6.
Borst HG. Die chirurgische Schule--Ein Anachronismus? [Surgical school--an anachronism?]. Langenbecks Arch Chir Suppl Kongressbd. 1996;113:723-6. German. PMID: 9101977.
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Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?.

Frontiers in public health

Zhang Y, Zhu P, Xu Y.
PMID: 34386475
Front Public Health. 2021 Jul 27;9:704900. doi: 10.3389/fpubh.2021.704900. eCollection 2021.

The Bitcoin market has become a research hotspot after the outbreak of Covid-19. In this paper, we focus on the relationships between the Bitcoin spot and futures. Specifically, we adopt the vector autoregression-dynamic correlation coefficient-generalized autoregressive conditional heteroskedasticity (VAR-DCC-GARCH)...

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Zhang Y, Zhu P, Xu Y. Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?. Front Public Health. 2021;9:704900doi: 10.3389/fpubh.2021.704900.
Zhang, Y., Zhu, P., & Xu, Y. (2021). Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?. Frontiers in public health, 9704900. https://doi.org/10.3389/fpubh.2021.704900
Zhang, Yinpeng, et al. "Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?." Frontiers in public health vol. 9 (2021): 704900. doi: https://doi.org/10.3389/fpubh.2021.704900
Zhang Y, Zhu P, Xu Y. Has COVID-19 Changed the Hedge Effectiveness of Bitcoin?. Front Public Health. 2021 Jul 27;9:704900. doi: 10.3389/fpubh.2021.704900. eCollection 2021. PMID: 34386475; PMCID: PMC8353111.
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Rise and demise of bioinformatics? Promise and progress.

PLoS computational biology

Ouzounis CA.
PMID: 22570600
PLoS Comput Biol. 2012;8(4):e1002487. doi: 10.1371/journal.pcbi.1002487. Epub 2012 Apr 26.

The field of bioinformatics and computational biology has gone through a number of transformations during the past 15 years, establishing itself as a key component of new biology. This spectacular growth has been challenged by a number of disruptive...

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Ouzounis CA. Rise and demise of bioinformatics? Promise and progress. PLoS Comput Biol. 2012;8(4):e1002487doi: 10.1371/journal.pcbi.1002487.
Ouzounis, C. A. (2012). Rise and demise of bioinformatics? Promise and progress. PLoS computational biology, 8(4), e1002487. https://doi.org/10.1371/journal.pcbi.1002487
Ouzounis, Christos A. "Rise and demise of bioinformatics? Promise and progress." PLoS computational biology vol. 8,4 (2012): e1002487. doi: https://doi.org/10.1371/journal.pcbi.1002487
Ouzounis CA. Rise and demise of bioinformatics? Promise and progress. PLoS Comput Biol. 2012;8(4):e1002487. doi: 10.1371/journal.pcbi.1002487. Epub 2012 Apr 26. PMID: 22570600; PMCID: PMC3343106.
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Economics of chronic diseases protocol: cost-effectiveness modelling and the future burden of non-communicable disease in Europe.

BMC public health

Divajeva D, Marsh T, Logstrup S, Kestens M, Vemer P, Kriaucioniene V, Peresson S, O'Kelly S, Rito A, Webber L.
PMID: 24886110
BMC Public Health. 2014 May 16;14:456. doi: 10.1186/1471-2458-14-456.

BACKGROUND: The majority of chronic disease is caused by risk factors which are mostly preventable. Effective interventions to reduce these risks are known and proven to be applicable to a variety of settings. Chronic disease is generally developed long...

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Divajeva D, Marsh T, Logstrup S, et al. Economics of chronic diseases protocol: cost-effectiveness modelling and the future burden of non-communicable disease in Europe. BMC Public Health. 2014;14:456doi: 10.1186/1471-2458-14-456.
Divajeva, D., Marsh, T., Logstrup, S., Kestens, M., Vemer, P., Kriaucioniene, V., Peresson, S., O'Kelly, S., Rito, A., & Webber, L. (2014). Economics of chronic diseases protocol: cost-effectiveness modelling and the future burden of non-communicable disease in Europe. BMC public health, 14456. https://doi.org/10.1186/1471-2458-14-456
Divajeva, Diana, et al. "Economics of chronic diseases protocol: cost-effectiveness modelling and the future burden of non-communicable disease in Europe." BMC public health vol. 14 (2014): 456. doi: https://doi.org/10.1186/1471-2458-14-456
Divajeva D, Marsh T, Logstrup S, Kestens M, Vemer P, Kriaucioniene V, Peresson S, O'Kelly S, Rito A, Webber L. Economics of chronic diseases protocol: cost-effectiveness modelling and the future burden of non-communicable disease in Europe. BMC Public Health. 2014 May 16;14:456. doi: 10.1186/1471-2458-14-456. PMID: 24886110; PMCID: PMC4047783.
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Showing 88681 to 4019 of 4019 entries