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Nikolay Vladimirov Conev - One of the best experts on this subject based on the ideXlab platform.

  • One-Minute Time interval estimation as a novel potent indicator of emotional concerns in cancer patients prior to starting chemotherapy
    Current Psychology, 2019
    Co-Authors: Ivan Donev, Dragomir Svetozarov Stoyanov, Teodorika Vitalinova Panayotova, Martina Stoyanova Ivanova, Yavor Kostadinov Kashlov, Merlin Erol Efraim, Nikolay Vladimirov Conev
    Abstract:

    Our study explored the potential relationship between Time estimation and issues that lead to distress in cancer patients prior to starting chemotherapy. Time estimation was assessed in 375 chemonaïve patients with solid tumors by evaluating each subject’s prospective estimation of how quickly one Minute passed compared to the actual Time. The median estimated value (40 s) was used to stratify the patients into the two categories of fast and slow Time estimation. The National Comprehensive Cancer Network Distress Thermometer (DT) and Problem List (PL) were used at the beginning of treatment to evaluate levels of distress and patient concerns. A fast Time estimation correlated significantly with gender and items reported in the emotional domain on the PL. Females exhibited significantly faster Time estimation than males. Patients who reported fear, worry and loss of interest in usual activities estimated the one-Minute interval significantly faster than patients who did not report such items. In the multivariate logistic regression model, patients who experienced fast Time estimation had a higher risk of reporting items in the emotional domain. Time estimation is a novel potent indicator of emotional concerns on the PL. This test is an easily performed, Time-saving, nonintrusive, ultrashort screening tool that is suitable even for patients who are not willing to reveal their emotional status via direct questionnaires.

  • one Minute Time interval estimation as a novel ultrashort tool for distress screening
    Supportive Care in Cancer, 2019
    Co-Authors: Nikolay Vladimirov Conev, Ivan Donev, Dragomir Stoyanov
    Abstract:

    Purpose Our study explores the potential relationship between Time estimation and level of distress in cancer patients prior to starting chemotherapy. Methods Time estimation was assessed in 262 chemonaive patients with solid tumors by evaluating each subject's prospective estimation of how fast 1 min passed compared to the actual Time. The median value (40 s) of Time estimation was used to stratify the patients into two categories of fast and slow Time estimation. The National Comprehensive Cancer Network Distress Thermometer was used at the beginning of treatment to evaluate levels of distress. Patients scoring 4 or above (51.9%) were regarded as having high levels of distress. Results The pattern of the Time estimation distributions significantly changed according to the level of distress. Patients with a fast Time estimation had significantly higher levels of distress (4.55 ± 3.1) than patients with a slow Time estimation (3.3 ± 2.9) (p = 0.001). ROC analysis revealed that at the optimal cutoff value of Time estimation, patients with low and high distress levels can be discriminated with an AUC = 0.60 (95% CI: 0.53-0.67, p = 0.005) and with a sensitivity of 62.5% and specificity of 53.2%. Moreover, in a multivariate logistic regression model, fast Time estimation was an independent predictor of high levels of distress. Conclusion Time estimation is a novel potent indicator of high levels of distress in cancer patients. This test is an easily performed, Time-saving, and nonintrusive ultrashort screening tool that is even suitable for patients who are not willing to reveal their level of distress via direct questionnaires.

  • one Minute Time interval estimation as a novel potent indicator of need for help in cancer patients prior to starting chemotherapy
    Journal of Clinical Oncology, 2019
    Co-Authors: Dragomir Stoyanov, Nikolay Vladimirov Conev, Teodorika Vitalinova Panayotova, Ivan Donev, Martina Ivanova
    Abstract:

    e23161Background: Perception of Time strongly correlates with people’s current emotional state. Our study explored the potential relationship between the Time estimation and the need of help in can...

  • One-Minute Time interval estimation as a novel ultrashort tool for distress screening.
    Supportive Care in Cancer, 2018
    Co-Authors: Nikolay Vladimirov Conev, Ivan Donev, Dragomir Stoyanov
    Abstract:

    PURPOSE: Our study explores the potential relationship between Time estimation and level of distress in cancer patients prior to starting chemotherapy. METHODS: Time estimation was assessed in 262 chemonaive patients with solid tumors by evaluating each subject's prospective estimation of how fast 1 min passed compared to the actual Time. The median value (40 s) of Time estimation was used to stratify the patients into two categories of fast and slow Time estimation. The National Comprehensive Cancer Network Distress Thermometer was used at the beginning of treatment to evaluate levels of distress. Patients scoring 4 or above (51.9%) were regarded as having high levels of distress. RESULTS: The pattern of the Time estimation distributions significantly changed according to the level of distress. Patients with a fast Time estimation had significantly higher levels of distress (4.55 ± 3.1) than patients with a slow Time estimation (3.3 ± 2.9) (p = 0.001). ROC analysis revealed that at the optimal cutoff value of Time estimation, patients with low and high distress levels can be discriminated with an AUC = 0.60 (95% CI: 0.53-0.67, p = 0.005) and with a sensitivity of 62.5% and specificity of 53.2%. Moreover, in a multivariate logistic regression model, fast Time estimation was an independent predictor of high levels of distress. CONCLUSION: Time estimation is a novel potent indicator of high levels of distress in cancer patients. This test is an easily performed, Time-saving, and nonintrusive ultrashort screening tool that is even suitable for patients who are not willing to reveal their level of distress via direct questionnaires.

Dragomir Stoyanov - One of the best experts on this subject based on the ideXlab platform.

  • one Minute Time interval estimation as a novel ultrashort tool for distress screening
    Supportive Care in Cancer, 2019
    Co-Authors: Nikolay Vladimirov Conev, Ivan Donev, Dragomir Stoyanov
    Abstract:

    Purpose Our study explores the potential relationship between Time estimation and level of distress in cancer patients prior to starting chemotherapy. Methods Time estimation was assessed in 262 chemonaive patients with solid tumors by evaluating each subject's prospective estimation of how fast 1 min passed compared to the actual Time. The median value (40 s) of Time estimation was used to stratify the patients into two categories of fast and slow Time estimation. The National Comprehensive Cancer Network Distress Thermometer was used at the beginning of treatment to evaluate levels of distress. Patients scoring 4 or above (51.9%) were regarded as having high levels of distress. Results The pattern of the Time estimation distributions significantly changed according to the level of distress. Patients with a fast Time estimation had significantly higher levels of distress (4.55 ± 3.1) than patients with a slow Time estimation (3.3 ± 2.9) (p = 0.001). ROC analysis revealed that at the optimal cutoff value of Time estimation, patients with low and high distress levels can be discriminated with an AUC = 0.60 (95% CI: 0.53-0.67, p = 0.005) and with a sensitivity of 62.5% and specificity of 53.2%. Moreover, in a multivariate logistic regression model, fast Time estimation was an independent predictor of high levels of distress. Conclusion Time estimation is a novel potent indicator of high levels of distress in cancer patients. This test is an easily performed, Time-saving, and nonintrusive ultrashort screening tool that is even suitable for patients who are not willing to reveal their level of distress via direct questionnaires.

  • one Minute Time interval estimation as a novel potent indicator of need for help in cancer patients prior to starting chemotherapy
    Journal of Clinical Oncology, 2019
    Co-Authors: Dragomir Stoyanov, Nikolay Vladimirov Conev, Teodorika Vitalinova Panayotova, Ivan Donev, Martina Ivanova
    Abstract:

    e23161Background: Perception of Time strongly correlates with people’s current emotional state. Our study explored the potential relationship between the Time estimation and the need of help in can...

  • One-Minute Time interval estimation as a novel ultrashort tool for distress screening.
    Supportive Care in Cancer, 2018
    Co-Authors: Nikolay Vladimirov Conev, Ivan Donev, Dragomir Stoyanov
    Abstract:

    PURPOSE: Our study explores the potential relationship between Time estimation and level of distress in cancer patients prior to starting chemotherapy. METHODS: Time estimation was assessed in 262 chemonaive patients with solid tumors by evaluating each subject's prospective estimation of how fast 1 min passed compared to the actual Time. The median value (40 s) of Time estimation was used to stratify the patients into two categories of fast and slow Time estimation. The National Comprehensive Cancer Network Distress Thermometer was used at the beginning of treatment to evaluate levels of distress. Patients scoring 4 or above (51.9%) were regarded as having high levels of distress. RESULTS: The pattern of the Time estimation distributions significantly changed according to the level of distress. Patients with a fast Time estimation had significantly higher levels of distress (4.55 ± 3.1) than patients with a slow Time estimation (3.3 ± 2.9) (p = 0.001). ROC analysis revealed that at the optimal cutoff value of Time estimation, patients with low and high distress levels can be discriminated with an AUC = 0.60 (95% CI: 0.53-0.67, p = 0.005) and with a sensitivity of 62.5% and specificity of 53.2%. Moreover, in a multivariate logistic regression model, fast Time estimation was an independent predictor of high levels of distress. CONCLUSION: Time estimation is a novel potent indicator of high levels of distress in cancer patients. This test is an easily performed, Time-saving, and nonintrusive ultrashort screening tool that is even suitable for patients who are not willing to reveal their level of distress via direct questionnaires.

Jimmy Chih-hsien Peng - One of the best experts on this subject based on the ideXlab platform.

  • Robust Constrained Model Predictive Voltage Control in Active Distribution Networks
    IEEE Transactions on Sustainable Energy, 1
    Co-Authors: Salish Maharjan, Ashwin M Khambadkone, Jimmy Chih-hsien Peng
    Abstract:

    High penetration of renewables in the distribution network brings significant uncertainties, especially during volatile weather conditions. Hence, the network controllers should be designed to account for these uncertainties and respond to unpredictable events like voltage-dips for reliable voltage control. This paper proposes a control scheme where inverter-based Distributed Energy Resources (DERs) respond locally with Q(V) control and adapt to set-points assigned by the centralized controller (CC). The Robust Constrained Model Predictive Control (RCMPC) scheme is proposed for centralized voltage control. The RCMPC robustly deploys control resources from DERs and tap-changers to regulate the lower/upper bound of node voltages within the targeted limit. Moreover, it ensures minimum resource utilization by relaxing the targeted voltage limit whenever it anticipates significant uncertainties. The CC is implemented in Python, which communicates with the RMS model of the UKGDS network for measurements and dispatching set-points. The performance of RCMPC is compared with deterministic MPC (DMCP) at 5, 10, and 15-Minute Time-steps of CC. The proposed RCMPC was able to regulate the node voltage even at a higher degree of uncertainty seen at a 15-Minute Time-step. In contrast, the DMPC could not contain the node voltages under the targeted limit and worsened at a larger Time-step.

M Caleb - One of the best experts on this subject based on the ideXlab platform.

  • probing the extragalactic fast transient sky at Minute Time scales with decam
    Monthly Notices of the Royal Astronomical Society, 2020
    Co-Authors: Igor Andreoni, J Cooke, Sara Webb, A Rest, T Pritchard, M Caleb
    Abstract:

    Research support to IA is also provided by the GROWTH project, funded by the National Science Foundation under Grant No 1545949. GROWTH is a collaborative project between California Institute of Technology (USA), Pomona College (USA), San Diego State University (USA), Los Alamos National Laboratory (USA), University of Maryland College Park (USA), University of Wisconsin Milwaukee (USA), Tokyo Institute of Technology (Japan), National Central University (Taiwan), Indian Institute of Astrophysics (India), Inter-University Center for Astronomy and Astrophysics (India), Weizmann Institute of Science (Israel), The Oskar Klein Centre at Stockholm University (Sweden), Humboldt University (Germany). JC acknowledges the Australian Research Council Future Fellowship grant FT130101219. This work has made use of data from the European Space Agency (ESA) mission Gaia (https://www.cosmos.esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.cosmos.esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. The national facility capability for SkyMapper has been funded through ARC LIEF grant LE130100104 from the Australian Research Council, awarded to the University of Sydney, the Australian National University, Swinburne University of Technology, the University of Queensland, the University of Western Australia, the University of Melbourne, Curtin University of Technology, Monash University and the Australian Astronomical Observatory. SkyMapper is owned and operated by The Australian National University's Research School of Astronomy and Astrophysics. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Funda c~ao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cient co e Tecnol ogico and the Minist erio da Ci^encia, Tecnologia e Inovac~ao, the Deutsche Forschungsgemeinschaft, and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones En ergeticas, Medioambientales y Tecnol ogicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgen ossische Technische Hochschule (ETH) Z urich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ci encies de l'Espai (IEEC/CSIC), the Institut de F sica d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universit at M unchen and the associated Excellence Cluster Universe, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, the Ohio State University, the OzDES Membership Consortium the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University. Based on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.

Martina Ivanova - One of the best experts on this subject based on the ideXlab platform.