1,423 research outputs found

    Supplementary Tables - Supplemental material for Chronotypes and circadian timing in migraine

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    Supplemental material, Supplementary Tables for Chronotypes and circadian timing in migraine by WPJ van Oosterhout, EJW van Someren, GG Schoonman, MA Louter, GJ Lammers, MD Ferrari and GM Terwindt in Cephalalgia</p

    Supplementary Text - Supplemental material for Chronotypes and circadian timing in migraine

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    Supplemental material, Supplementary Text for Chronotypes and circadian timing in migraine by WPJ van Oosterhout, EJW van Someren, GG Schoonman, MA Louter, GJ Lammers, MD Ferrari and GM Terwindt in Cephalalgia</p

    The Optimization of the Performance Management for Operators in GJ Company Xiamen

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    本文以绩效管理的基本理论和GJ厦门厂的实际情况为基础,分析了GJ厦门厂一线作业员绩效管理的问题点,并结合公司目前的实际情况提出一线作业员的绩效管理改善方案。该方案以平衡计分卡的绩效管理体系为逻辑,将公司的战略目标分解到制造处,再分解至一线作业员,让作业员的绩效也能从财务面、客户面、内部运营面、学习成长面四个维度上对公司的战略发展起到支撑作用,以促进组织战略目标的实现。 在绩效管理改进方案中,作者特别强调沟通在绩效管理过程中的重要作用,它贯穿了绩效管理的整个过程。沟通有助于促进员工个人绩效的提升和员工个人的成长,从而提升组织的整体效益。 根据GJ厦门厂的实际情况,将一线作业员的绩效考核结...In this paper, operators’ performance management problems in GJ Company are analyzed based on the basic theory of performance management and the real situation of the company. The author propose to break down the company’s strategic targets to lower levels with balanced scorecard system by four levels of financial, customer, internal operation, learning and developing. So that operators’ performa...学位:管理学硕士院系专业:管理学院_工商管理硕士(工商管理硕士)学号:1792012115103

    Exergy comparison of lunar propellant manufacturing and insertion into LEO using a fully reusable refueling rocket

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    Quantifying the exergy requirement of propellant insertion into LEO can lead to insight into the feasibility of a lunar propellant-generating architecture. Spacecraft leaving from Earth can greatly reduce their lift-off mass if in-space refueling would possible. Exergy analyses quantify the available energy of a system and show where a reduction in usable energy occurs. Insight into the exergy destruction and input provides a key parameter into the scaling and design of processes and corresponding power systems. The present study aims to define an exergy environment in the lunar PSRs and then to analyze the exergy destruction related to the production of oxygen, ALICE, and hydrolox, in terms of both manufacturing and transportation using a two-stage fully reusable rocket. Extraction processes for ALICE and hydrolox were selected and analyzed w.r.t. the lunar environment to get an understanding of the exergy input. The behavior and exergy requirements of an LEO propellant depot was described. Two fully reusable two-stage rockets using ALICE and hydrolox were designed and compared based on their payload-to-propellant ratio for the oxygen, ALICE, and hydrolox payloads. The study found that the exergetic cost for the insertion of oxygen, hydrolox, and ALICE in LEO were 1.32 GJ/kg , 1.64 GJ/kg, and GJ/kg and 23.3 GJ/kg, 23.4 GJ/kg and 26.9 GJ/kg for the hydrolox and ALICE rocket, respectively.Aerospace Engineerin

    Development of the ProPal-COPD tool to identify patients with COPD for proactive palliative care

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    RG Duenk,1 C Verhagen,1 EM Bronkhorst,2 RS Djamin,3 GJ Bosman,4 E Lammers,5 PNR Dekhuijzen,6 KCP Vissers,1 Y Engels,1,* Y Heijdra6,* 1Department of Anesthesiology, Pain and Palliative Medicine, 2Department of Health Evidence, Radboud University Medical Center, Nijmegen, 3Department of Respiratory Medicine, Amphia Hospital, Breda, 4Department of Respiratory Medicine, Slingeland Hospital, Doetinchem, 5Department of Respiratory Medicine, Gelre Hospitals, Zutphen, 6Department of Pulmonary Diseases, Radboud University Medical Center, Nijmegen, the Netherlands *These authors contributed equally to&nbsp;this work Background: Our objective was to develop a tool to identify patients with COPD for proactive palliative care. Since palliative care needs increase during the disease course of COPD, the prediction of mortality within 1 year, measured during hospitalizations for acute exacerbation COPD (AECOPD), was used as a proxy for the need of proactive palliative care.Patients and methods: Patients were recruited from three general hospitals in the Netherlands in 2014. Data of 11 potential predictors, a priori selected based on literature, were collected during hospitalization for AECOPD. After 1 year, the medical files were explored for the date of death. An optimal prediction model was assessed by Lasso logistic regression, with 20-fold cross-validation for optimal shrinkage. Missing data were handled using complete case analysis.Results: Of 174 patients, 155 patients were included; of those 30 (19.4%) died within 1 year. The optimal prediction model was internally validated and had good discriminating power (AUC =0.82, 95% CI 0.81&ndash;0.82). This model relied on the following seven predictors: the surprise question, Medical Research Council dyspnea questionnaire (MRC dyspnea), Clinical COPD Questionnaire (CCQ), FEV1% of predicted value, body mass index, previous hospitalizations for AECOPD and specific comorbidities. To ensure minimal miss out of patients in need of proactive palliative care, we proposed a cutoff in the model that prioritized sensitivity over specificity (0.90 over 0.73, respectively). Our model (ProPal-COPD tool) was a stronger predictor of mortality within 1 year than the CODEX (comorbidity, age, obstruction, dyspnea, and previous severe exacerbations) index.Conclusion: The ProPal-COPD tool is a promising multivariable prediction tool to identify patients with COPD for proactive palliative care. Keywords: COPD, exacerbation, proactive palliative care, prognosis, mortalit

    Diagnosing Intermittent Faults

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    In this working report we outline how to determine the intermittency parameters gj from the activity matrix A (context: DX’08 paper Abreu, Zoeteweij, Van Gemund). We start with the single fault (SF) case and show that averaging over the error vector e is the exact way. We also show that in this way the probability of obtaining exactly this e vector in A is optimal. This is the key insight that allows us to determine g in the general multiple-fault (MF) case. We formulate the gj problem as a (probability) maximization problem, which we solve using a simple gradient ascent technique.Software Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc

    Testing contributor & translator roles - harvest

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    GJ author, edited to add dc.contributor and dc.contributor.translator; cc-by-nc-n
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