scholarly journals Consistent Initial Conditions for the Saint-Venant Equations in River Network Modeling

2017 ◽  
Author(s):  
Cheng-Wei Yu ◽  
Frank Liu ◽  
Ben R. Hodges
Keyword(s):  
Steady State ◽  
Initial Conditions ◽  
River Network ◽  
Initial Time ◽  
Initial Condition ◽  
Cross Sectional ◽  
Data Set ◽  
Initial Flow ◽  
Saint Venant Equations ◽  
Time Marching

Abstract. Initial conditions for flows and depths (cross-sectional areas) throughout a river network are required for any time-marching (unsteady) solution of the one-dimensional (1D) hydrodynamic Saint-Venant equations. For a river network modeled with several Strahler orders of tributaries, comprehensive and consistent synoptic data are typically lacking and synthetic starting conditions are needed. Because of underlying nonlinearity, poorly-defined or inconsistent initial conditions can lead to convergence problems and long spin-up times in an unsteady solver. Two new approaches are defined and demonstrated herein for computing flows and cross-sectional areas (or depths). These methods can produce an initial condition data set that is consistent with modeled landscape runoff and river geometry boundary conditions at the initial time. These new methods are: (1) the Pseudo-Time-Marching Method (PTM) that iterates toward a steady-state initial condition using an unsteady Saint-Venant solver, and (2) the Steady-Solution Method (SSM) that makes use of graph theory for initial flow rates and solution of a steady-state 1D momentum equation for the channel cross-sectional areas. The PTM is shown to be adequate for short river reaches, but is significantly slower and has occasional non-convergent behavior for large river networks. The SSM approach is shown to provide rapid solution of consistent initial conditions for both small and large networks, albeit with the requirement that additional code must be written rather than applying an existing unsteady Saint-Venant solver.

scholarly journals Consistent initial conditions for the Saint-Venant equations in river network modeling

2017 ◽  
Vol 21 (9) ◽  
pp. 4959-4972 ◽  
Author(s):  
Cheng-Wei Yu ◽  
Frank Liu ◽  
Ben R. Hodges
Keyword(s):  
Steady State ◽  
Initial Conditions ◽  
River Network ◽  
Initial Time ◽  
Initial Condition ◽  
Cross Sectional ◽  
Data Set ◽  
Initial Flow ◽  
Saint Venant Equations ◽  
Time Marching

Abstract. Initial conditions for flows and depths (cross-sectional areas) throughout a river network are required for any time-marching (unsteady) solution of the one-dimensional (1-D) hydrodynamic Saint-Venant equations. For a river network modeled with several Strahler orders of tributaries, comprehensive and consistent synoptic data are typically lacking and synthetic starting conditions are needed. Because of underlying nonlinearity, poorly defined or inconsistent initial conditions can lead to convergence problems and long spin-up times in an unsteady solver. Two new approaches are defined and demonstrated herein for computing flows and cross-sectional areas (or depths). These methods can produce an initial condition data set that is consistent with modeled landscape runoff and river geometry boundary conditions at the initial time. These new methods are (1) the pseudo time-marching method (PTM) that iterates toward a steady-state initial condition using an unsteady Saint-Venant solver and (2) the steady-solution method (SSM) that makes use of graph theory for initial flow rates and solution of a steady-state 1-D momentum equation for the channel cross-sectional areas. The PTM is shown to be adequate for short river reaches but is significantly slower and has occasional non-convergent behavior for large river networks. The SSM approach is shown to provide a rapid solution of consistent initial conditions for both small and large networks, albeit with the requirement that additional code must be written rather than applying an existing unsteady Saint-Venant solver.

scholarly journals A New Mechanism for Generating Particle Number Asymmetry through Interactions

2019 ◽  
Vol 2019 ◽  
pp. 1-28
Author(s):  
Takuya Morozumi ◽  
Keiko I. Nagao ◽  
Apriadi Salim Adam ◽  
Hiroyuki Takata
Keyword(s):  
Scalar Field ◽  
Particle Number ◽  
Initial Conditions ◽  
Particle Energy ◽  
Expansion Rate ◽  
Initial Time ◽  
Initial Condition ◽  
Complex Scalar ◽  
Neutral Scalar ◽  
The Universe

A new mechanism for generating particle number asymmetry (PNA) has been developed. This mechanism is realized with a Lagrangian including a complex scalar field and a neutral scalar field. The complex scalar carries U(1) charge which is associated with the PNA. It is written in terms of the condensation and Green’s function, which is obtained with two-particle irreducible (2PI) closed time path (CTP) effective action (EA). In the spatially flat universe with a time-dependent scale factor, the time evolution of the PNA is computed. We start with an initial condition where only the condensation of the neutral scalar is nonzero. The initial condition for the fields is specified by a density operator parameterized by the temperature of the universe. With the above initial conditions, the PNA vanishes at the initial time and later it is generated through the interaction between the complex scalar and the condensation of the neutral scalar. We investigate the case that both the interaction and the expansion rate of the universe are small and include their effects up to the first order of the perturbation. The expanding universe causes the effects of the dilution of the PNA, freezing interaction, and the redshift of the particle energy. As for the time dependence of the PNA, we found that PNA oscillates at the early time and it begins to dump at the later time. The period and the amplitude of the oscillation depend on the mass spectrum of the model, the temperature, and the expansion rate of the universe.

scholarly journals Diagnosing Snowband Predictability Using a Multimodel Ensemble System

2012 ◽  
Vol 27 (3) ◽  
pp. 565-585 ◽  
Author(s):  
David R. Novak ◽  
Brian A. Colle
Keyword(s):  
Initial Conditions ◽  
Initial Condition ◽  
Multimodel Ensemble ◽  
Band Formation ◽  
Initial Flow ◽  
Considerable Uncertainty ◽  
Feature Based ◽  
Formation And Evolution ◽  
Upper Level ◽  
Uncertainty Information

Abstract The forecast uncertainty of mesoscale snowband formation and evolution is compared using predictions from a 16-member multimodel ensemble at 12-km grid spacing for the 25 December 2002, 12 February 2006, and 14 February 2007 northeast U.S. snowstorms. Using these predictions, the case-to-case variability in the predictability of band formation and evolution is demonstrated. Feature-based uncertainty information is also presented as an example of what may be operationally feasible from postprocessing information from future short-range ensemble forecast systems. Additionally, the initial condition sensitivity of band location in each case is explored by contrasting the forecast evolutions of initial condition members with large differences in snowband positions. Considerable uncertainty in the occurrence, and especially timing and location, of band formation and subsequent evolution was found, even at forecast projections <24 h. The ensemble provided quantitative mesoscale band uncertainty information, and differentiated between high-predictability (14 February 2007) and low-predictability (12 February 2006) cases. Among the three cases, large (small) initial differences in the upper-level PV distribution and surface mean sea level pressure of the incipient cyclone were associated with large (small) differences in forecast snowband locations, suggesting that case-to-case differences in predictability may be related to the quality of the initial conditions. The complexity of the initial flow may also be a discriminator. Error growth was evident in each case, consistent with previous mesoscale predictability research, but predictability differences were not correlated to the degree of convection. Discussion of these results and future extensions of the work are presented.

Suicide Ideation and Attempt in a Community Cohort of Urban Aboriginal Youth: A Cross-Sectional Study

Crisis ◽  
2013 ◽  
Vol 34 (4) ◽  
pp. 251-261 ◽  
Author(s):  
Joanne N. Luke ◽  
Ian P. Anderson ◽  
Graham J. Gee ◽  
Reg Thorpe ◽  
Kevin G. Rowley ◽  
...  
Keyword(s):  
Health Service ◽  
Indigenous People ◽  
Suicide Ideation ◽  
Aboriginal Health ◽  
Cross Sectional ◽  
Data Set ◽  
Indigenous Youth ◽  
Aboriginal Youth ◽  
Social Distress ◽  
Suicide Behavior

Background: There has been increasing attention over the last decade on the issue of indigenous youth suicide. A number of studies have documented the high prevalence of suicide behavior and mortality in Australia and internationally. However, no studies have focused on documenting the correlates of suicide behavior for indigenous youth in Australia. Aims: To examine the prevalence of suicide ideation and attempt and the associated factors for a community 1 The term ”community” refers specifically to Koori people affiliated with the Victorian Aboriginal Health Service. cohort of Koori 2 The term ”Koori” refers to indigenous people from the south-eastern region of Australia, including Melbourne. The term ”Aboriginal” has been used when referring to indigenous people from Australia. The term ”indigenous” has been used throughout this article when referring to the first people of a nation within an international context. (Aboriginal) youth. Method: Data were obtained from the Victorian Aboriginal Health Service (VAHS) Young People’s Project (YPP), a community initiated cross-sectional data set. In 1997/1998, self-reported data were collected for 172 Koori youth aged 12–26 years living in Melbourne, Australia. The data were analyzed to assess the prevalence of current suicide ideation and lifetime suicide attempt. Principal components analysis (PCA) was used to identify closely associated social, emotional, behavioral, and cultural variables at baseline and Cox regression modeling was then used to identify associations between PCA components and suicide ideation and attempt. Results: Ideation and attempt were reported at 23.3% and 24.4%, respectively. PCA yielded five components: (1) emotional distress, (2) social distress A, (3) social distress B, (4) cultural connection, (5) behavioral. All were positively and independently associated with suicide ideation and attempt, while cultural connection showed a negative association. Conclusions: Suicide ideation and attempt were common in this cross-section of indigenous youth with an unfavorable profile for the emotional, social, cultural, and behavioral factors.

Is it a Right Assumption That B and Ge are Distributed Randomly After Growing a Strained HBT-Structure?

2002 ◽  
Vol 716 ◽  
Author(s):  
Victor I. Kol'dyaev
Keyword(s):  
Surface Segregation ◽  
Initial Conditions ◽  
Initial Condition ◽  
Basic Assumptions ◽  
Transient Diffusion ◽  
Segregation Process ◽  
Main Observation

AbstractIt is accepted that surface Ge atoms are considered to be responsible for the surface B segregation process. A set of original experiments is carried out. A main observation from the B and Ge profiles grown at different conditions shows that at certain conditions B is taking initiative and determine the Ge surface segregation process. basic assumptions are suggested to self-consistently explain these original experimental features and what is observed in the literature. These results have a strong implication for modeling the B diffusion in Si1-xGex where the initial conditions should be formulated accounting for the correlation in B and Ge distribution. A new assumption for the initial condition to be “all B atoms are captured by Ge” is regarded as a right one implicating that there is no any transient diffusion representing the B capturing kinetics.

Association of CYP2D6*10 (c. 100 C>T) Genotype with Z-END Concentration in Patients with Breast Cancer Receiving Tamoxifen Therapy in Indonesian Population

2019 ◽  
Vol 19 (8) ◽  
pp. 1198-1206 ◽  
Author(s):  
Yenny ◽  
Sonar S. Panigoro ◽  
Denni J. Purwanto ◽  
Adi Hidayat ◽  
Melva Louisa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Steady State ◽  
Ultra Performance Liquid Chromatography ◽  
Cross Sectional Study ◽  
Threshold Concentration ◽  
Breast Cancer Patients ◽  
Cross Sectional ◽  
Significant Difference ◽  
Pcr Rflp ◽  
Liquid Chromatography Tandem Mass

Background: Tamoxifen (TAM) is a frequently used hormonal prodrug for patients with breast cancer that needs to be activated by cytochrome P450 2D6 (CYP2D6) into Zusammen-endoxifen (Z-END). Objective: The purpose of the study was to determine the association between CYP2D6*10 (c.100C>T) genotype and attainment of the plasma steady-state Z-END minimal threshold concentration (MTC) in Indonesian women with breast cancer. Methods: A cross-sectional study was performed in 125 ambulatory patients with breast cancer consuming TAM at 20 mg/day for at least 4 months. The frequency distribution of CYP2D6*10 (c.100C>T) genotypes (C/C: wild type; C/T: heterozygous mutant; T/T: homozygous mutant) was detected using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), the results of which were subsequently confirmed by sequencing. The genotypes were categorized into plasma Z- END concentrations of <5.9 ng/mL and ≥5.9 ng/mL, which were measured using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Results: Percentages of C/C, CT, and T/T genotypes were 22.4%, 29.6%, and 48.8%, respectively. Median (25-75%) Z-END concentrations in C/C, C/T, and T/T genotypes were 9.58 (0.7-6.0), 9.86 (0.7-26.6), and 3.76 (0.9-26.6) ng/mL, respectively. Statistical analysis showed a significant difference in median Z-END concentration between patients with T/T genotype and those with C/C or C/T genotypes (p<0.001). There was a significant association between CYP2D6*10 (c.100C>T) genotypes and attainment of plasma steady-state Z-END MTC (p<0.001). Conclusion: There was a significant association between CYP2D6*10 (c.100C>T) and attainment of plasma steady-state Z-END MTC in Indonesian breast cancer patients receiving TAM at a dose of 20 mg/day.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

scholarly journals Machine Learning in Futures Markets

2021 ◽  
Vol 14 (3) ◽  
pp. 119
Author(s):  
Fabian Waldow ◽  
Matthias Schnaubelt ◽  
Christopher Krauss ◽  
Thomas Günter Fischer
Keyword(s):  
Machine Learning ◽  
Futures Markets ◽  
Learning Models ◽  
Cross Sectional ◽  
Data Set ◽  
Statistical Arbitrage ◽  
Out Of Sample ◽  
Sample Testing ◽  
Arbitrage Strategy ◽  
Machine Learning Models

In this paper, we demonstrate how a well-established machine learning-based statistical arbitrage strategy can be successfully transferred from equity to futures markets. First, we preprocess futures time series comprised of front months to render them suitable for our returns-based trading framework and compile a data set comprised of 60 futures covering nearly 10 trading years. Next, we train several machine learning models to predict whether the h-day-ahead return of each future out- or underperforms the corresponding cross-sectional median return. Finally, we enter long/short positions for the top/flop-k futures for a duration of h days and assess the financial performance of the resulting portfolio in an out-of-sample testing period. Thereby, we find the machine learning models to yield statistically significant out-of-sample break-even transaction costs of 6.3 bp—a clear challenge to the semi-strong form of market efficiency. Finally, we discuss sources of profitability and the robustness of our findings.

scholarly journals Symptoms of acute transfusion reactions at a general referral hospital in Kinshasa, Democratic Republic of Congo: a cross-sectional study

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e045081
Author(s):  
Patou Masika Musumari ◽  
Samclide Mutindu Mbikayi ◽  
Kriengkrai Srithanaviboonchai ◽  
Teeranee Techasrivichien ◽  
Arunrat Tangmunkongvorakul ◽  
...  
Keyword(s):  
Blood Transfusion ◽  
Respiratory Distress ◽  
Democratic Republic ◽  
Democratic Republic Of Congo ◽  
Referral Hospital ◽  
Sub Saharan Africa ◽  
Cross Sectional ◽  
Data Set ◽  
Republic Of Congo ◽  
Transfusion Reactions

ObjectivesBlood transfusion is a life-saving procedure and is also associated with a range of risks including the occurrence of symptoms of acute transfusion reactions (ATRs). Very few studies in sub-Saharan Africa have reported on ATRs. The present study addresses this gap in the literature by documenting the prevalence of and factors associated with ATRs in the Democratic Republic of Congo (DRC).DesignThis is a cross-sectional descriptive and analytical study using blood bank data from a general referral hospital.SettingCentre Hospitalier Mère-Enfant (CHME) Monkole, a general referral hospital in Kinshasa, DRC.ParticipantsGeneral population who have received blood transfusion in CHME Monkole between 2014 and 2019.ResultsThe data set included a total of 7166 patients; 3153 (44%) men and 4013 (56%) women. The overall prevalence of symptoms of ATRs was 2.6%; the lowest prevalence was in 2017 (2.34%) and highest in 2018 (2.95%) and 2019 (2.94%). The documented symptoms included 74 (39.6%) cases of dyspnoea/respiratory distress, 60 (32.1%) cases of fever, 36 (19.2%) cases of pruritus/urticaria and 17 (9.1%) cases of vomiting. None of the studied factors was associated with symptoms of ATRs.ConclusionSymptoms of ATRs were not uncommon in the studied population. Dyspnoea and respiratory distress, fever and pruritus/urticaria were the most common symptoms of ATRs. This study highlights the need for a clinical and biological surveillance to detect, prevent and manage ATRs in the context of the DRC.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

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