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Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States.

Physical therapy

Landry MD, Hack LM, Coulson E, Freburger J, Johnson MP, Katz R, Kerwin J, Smith MH, Wessman HC, Venskus DG, Sinnott PL, Goldstein M.
PMID: 26472298
Phys Ther. 2016 Jan;96(1):71-80. doi: 10.2522/ptj.20150010. Epub 2015 Oct 15.

BACKGROUND: Health human resources continue to emerge as a critical health policy issue across the United States.OBJECTIVE: The purpose of this study was to develop a strategy for modeling future workforce projections to serve as a basis for analyzing...

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Landry MD, Hack LM, Coulson E, et al. Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States. Phys Ther. 2015;96(1):71-80doi: 10.2522/ptj.20150010.
Landry, M. D., Hack, L. M., Coulson, E., Freburger, J., Johnson, M. P., Katz, R., Kerwin, J., Smith, M. H., Wessman, H. C., Venskus, D. G., Sinnott, P. L., & Goldstein, M. (2016). Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States. Physical therapy, 96(1), 71-80. https://doi.org/10.2522/ptj.20150010
Landry, Michel D, et al. "Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States." Physical therapy vol. 96,1 (2016): 71-80. doi: https://doi.org/10.2522/ptj.20150010
Landry MD, Hack LM, Coulson E, Freburger J, Johnson MP, Katz R, Kerwin J, Smith MH, Wessman HC, Venskus DG, Sinnott PL, Goldstein M. Workforce Projections 2010-2020: Annual Supply and Demand Forecasting Models for Physical Therapists Across the United States. Phys Ther. 2016 Jan;96(1):71-80. doi: 10.2522/ptj.20150010. Epub 2015 Oct 15. PMID: 26472298.
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Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding.

Sensors (Basel, Switzerland)

Venskus J, Treigys P, Bernatavičienė J, Tamulevičius G, Medvedev V.
PMID: 31480449
Sensors (Basel). 2019 Aug 31;19(17). doi: 10.3390/s19173782.

The automated identification system of vessel movements receives a huge amount of multivariate, heterogeneous sensor data, which should be analyzed to make a proper and timely decision on vessel movements. The large number of vessels makes it difficult and...

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Venskus J, Treigys P, Bernatavičienė J, et al. Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding. Sensors (Basel). 2019;19(17)doi: 10.3390/s19173782.
Venskus, J., Treigys, P., Bernatavičienė, J., Tamulevičius, G., & Medvedev, V. (2019). Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding. Sensors (Basel, Switzerland), 19(17), . https://doi.org/10.3390/s19173782
Venskus, Julius, et al. "Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding." Sensors (Basel, Switzerland) vol. 19,17 (2019). doi: https://doi.org/10.3390/s19173782
Venskus J, Treigys P, Bernatavičienė J, Tamulevičius G, Medvedev V. Real-Time Maritime Traffic Anomaly Detection Based on Sensors and History Data Embedding. Sensors (Basel). 2019 Aug 31;19(17). doi: 10.3390/s19173782. PMID: 31480449; PMCID: PMC6749247.
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