scholarly journals Hydraulic Responses and Flow Regulation in Multi-Demand Water Transfer Systems

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2402
Author(s):  
Liu ◽  
Tian ◽  
Lei ◽  
Fan ◽  
Wang
Keyword(s):  
Steady State ◽  
Transient Process ◽  
Working Conditions ◽  
Flow Regulation ◽  
Water Transfer ◽  
Time Interval ◽  
Process Energy ◽  
Demand Scenario ◽  
Maximum Allowable Time

It is of great significance for the practical operation scheduling to comprehensively analyze the influencing factors of the long-term steady-state operation state of different water demand scenarios and the coupled operation of hydraulic facilities when switching demand scenario as the demand changes. In the study, a case study is performed in the Daxing Branch project, the numerical model of which considered pipelines, pumps, valves, air valves, and regulating tanks is established using Method of Characteristics. The hydraulic responses and corresponding flow regulation of different demand scenarios and between changing demand scenarios are analyzed. The results show that steady-state working conditions can have important impacts on the transient process. Energy consumption and the amount of water transfer, as well as water hammer pressure and the allowable reaction time during the transient process should be taken into account in the selection of long-term steady-state working conditions of different demand scenarios. The sequence and maximum allowable time interval of the coupled operation of pumps and valves should be considered when switching demand scenario. Finally, the optimal steady-state working conditions of different demand scenarios, the coupled operation sequence of pumps and valves, the maximum allowable time interval of the Daxing Branch project are proposed, which can provide some insights into the safe operation of the project and other similar complex water transfer projects.

Steady-State Parametric Optimization and Transient Characterization of Heat Flow Regulation With Binary Diffusion

2020 ◽  
Vol 10 (12) ◽  
pp. 1996-2007
Author(s):  
Tanya Liu ◽  
James W. Palko ◽  
Joseph S. Katz ◽  
Feng Zhou ◽  
Ercan M. Dede ◽  
...  
Keyword(s):  
Steady State ◽  
Heat Flow ◽  
Parametric Optimization ◽  
Flow Regulation ◽  
Binary Diffusion

scholarly journals A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows

2019 ◽  
Vol 9 (22) ◽  
pp. 4813 ◽  
Author(s):  
Hanbo Yang ◽  
Fei Zhao ◽  
Gedong Jiang ◽  
Zheng Sun ◽  
Xuesong Mei
Keyword(s):  
Time Windows ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Time Interval ◽  
Kernel Module ◽  
Initial Stage ◽  
Research Task ◽  
Consecutive Time ◽  
Useful Life

Remaining useful life (RUL) prediction is a challenging research task in prognostics and receives extensive attention from academia to industry. This paper proposes a novel deep convolutional neural network (CNN) for RUL prediction. Unlike health indicator-based methods which require the long-term tracking of sensor data from the initial stage, the proposed network aims to utilize data from consecutive time samples at any time interval for RUL prediction. Additionally, a new kernel module for prognostics is designed where the kernels are selected automatically, which can further enhance the feature extraction ability of the network. The effectiveness of the proposed network is validated using the C-MAPSS dataset for aircraft engines provided by NASA. Compared with the state-of-the-art results on the same dataset, the prediction results demonstrate the superiority of the proposed network.

scholarly journals Time Interval to Biochemical Failure as a Surrogate End Point in Locally Advanced Prostate Cancer: Analysis of Randomized Trial NRG/RTOG 9202

2019 ◽  
Vol 37 (3) ◽  
pp. 213-221 ◽  
Author(s):  
James J. Dignam ◽  
Daniel A. Hamstra ◽  
Herbert Lepor ◽  
David Grignon ◽  
Harmar Brereton ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Randomized Trial ◽  
Locally Advanced ◽  
Biochemical Failure ◽  
Mediating Effect ◽  
Time Interval ◽  
Short Term ◽  
End Points ◽  
End Point

Background In prostate cancer, end points that reliably portend prognosis and treatment benefit (surrogate end points) can accelerate therapy development. Although surrogate end point candidates have been evaluated in the context of radiotherapy and short-term androgen deprivation (AD), potential surrogates under long-term (24 month) AD, a proven therapy in high-risk localized disease, have not been investigated. Materials and Methods In the NRG/RTOG 9202 randomized trial (N = 1,520) of short-term AD (4 months) versus long-term AD (LTAD; 28 months), the time interval free of biochemical failure (IBF) was evaluated in relation to clinical end points of prostate cancer–specific survival (PCSS) and overall survival (OS). Survival modeling and landmark analysis methods were applied to evaluate LTAD benefit on IBF and clinical end points, association between IBF and clinical end points, and the mediating effect of IBF on LTAD clinical end point benefits. Results LTAD was superior to short-term AD for both biochemical failure (BF) and the clinical end points. Men remaining free of BF for 3 years had relative risk reductions of 39% for OS and 73% for PCSS. Accounting for 3-year IBF status reduced the LTAD OS benefit from 12% (hazard ratio [HR], 0.88; 95% CI, 0.79 to 0.98) to 6% (HR, 0.94; 95% CI, 0.83 to 1.07). For PCSS, the LTAD benefit was reduced from 30% (HR, 0.70; 95% CI, 0.52 to 0.82) to 6% (HR, 0.94; 95% CI, 0.72 to 1.22). Among men with BF, by 3 years, 50% of subsequent deaths were attributed to prostate cancer, compared with 19% among men free of BF through 3 years. Conclusion The IBF satisfied surrogacy criteria and identified the benefit of LTAD on disease-specific survival and OS. The IBF may serve as a valid end point in clinical trials and may also aid in risk monitoring after initial treatment.

scholarly journals Long-Term Observations of Beach Variability at Hasaki, Japan

2020 ◽  
Vol 8 (11) ◽  
pp. 871
Author(s):  
Masayuki Banno ◽  
Satoshi Nakamura ◽  
Taichi Kosako ◽  
Yasuyuki Nakagawa ◽  
Shin-ichi Yanagishima ◽  
...  
Keyword(s):  
Morphological Change ◽  
High Frequency ◽  
Climate Changes ◽  
Wave Climate ◽  
Time Interval ◽  
Observation Data ◽  
Beach Profile ◽  
Water Level Variations ◽  
Monthly Variations

Long-term beach observation data for several decades are essential to validate beach morphodynamic models that are used to predict coastal responses to sea-level rise and wave climate changes. At the Hasaki coast, Japan, the beach profile has been measured for 34 years at a daily to weekly time interval. This beach morphological dataset is one of the longest and most high-frequency measurements of the beach morphological change worldwide. The profile data, with more than 6800 records, reflect short- to long-term beach morphological change, showing coastal dune development, foreshore morphological change and longshore bar movement. We investigated the temporal beach variability from the decadal and monthly variations in elevation. Extremely high waves and tidal anomalies from an extratropical cyclone caused a significant change in the long-term bar behavior and foreshore slope. The berm and bar variability were also affected by seasonal wave and water level variations. The variabilities identified here from the long-term observations contribute to our understanding of various coastal phenomena.

scholarly journals Long-term repopulating hematopoietic stem cells and “side population” in human steady state peripheral blood

2013 ◽  
Vol 11 (1) ◽  
pp. 625-633 ◽  
Author(s):  
Philippe Brunet de la Grange ◽  
Marija Vlaski ◽  
Pascale Duchez ◽  
Jean Chevaleyre ◽  
Veronique Lapostolle ◽  
...  
Keyword(s):  
Stem Cells ◽  
Steady State ◽  
Hematopoietic Stem Cells ◽  
Peripheral Blood ◽  
Side Population ◽  
Hematopoietic Stem

scholarly journals Application of the Monte Carlo Method for the Assessment of Long-term Success in Keratoprosthesis Surgery

2002 ◽  
Vol 4 (3) ◽  
pp. 183-190 ◽  
Author(s):  
W. Hitzl ◽  
G. Grabner
Keyword(s):  
Monte Carlo ◽  
Visual Acuity ◽  
Survival Analysis ◽  
Monte Carlo Method ◽  
Surgical Techniques ◽  
Distribution Functions ◽  
Time Interval ◽  
Specific Method ◽  
The Monte Carlo Method

The comparison of different methods of keratoprosthesis (KP) regarding their long-term success, as far as visual acuity is concerned, is difficult: this is the case both as a standardized reporting method agreed upon by all research groups has not been reported and far less accepted, and as the quality of life for the patient not only depends on the level of visual acuity, but also quite significantly on the “survival time” of the implant. Therefore, an analysis of a single series of patients with Osteo–Odonto–Keratoprosthesis (OOKP) was performed. Statistical analysis methods used by others in similar groups of surgical procedures have included descriptive statistics, survival analysis and ANOVA. These methods comprised comparisons of empirical densities or distribution functions and empirical survival curves. It is the objective of this paper to provide an inductive statistical method to avoid the problems with descriptive techniques and survival analysis. This statistical model meets four important standards: (1) the efficiency of a surgical technique can be assessed within an arbitrary time interval by a new index (VAT-index), (2) possible autocorrelations of the data are taken into consideration and (3) the efficiency is not only stated by a point estimator, but also 95% point-wise confidence limits are computed based on the Monte Carlo method, and finally, (4) the efficiency of a specific method is illustrated by line and range plots for quick illustration and can also be used for the comparison of different other surgical techniques such as refractive techniques, glaucoma and retinal surgery.

scholarly journals Temporal Evolution of Immunity Distributions in a Population with Waning and Boosting

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
M. V. Barbarossa ◽  
M. Polner ◽  
G. Röst
Keyword(s):  
Mathematical Model ◽  
Steady State ◽  
Temporal Evolution ◽  
Disease Outbreaks ◽  
Population Level ◽  
Coupled System ◽  
Disease Outbreak ◽  
Periodic Disease ◽  
Evolution Of Immunity

We investigate the temporal evolution of the distribution of immunities in a population, which is determined by various epidemiological, immunological, and demographical phenomena: after a disease outbreak, recovered individuals constitute a large immune population; however, their immunity is waning in the long term and they may become susceptible again. Meanwhile, their immunity can be boosted by repeated exposure to the pathogen, which is linked to the density of infected individuals present in the population. This prolongs the length of their immunity. We consider a mathematical model formulated as a coupled system of ordinary and partial differential equations that connects all these processes and systematically compare a number of boosting assumptions proposed in the literature, showing that different boosting mechanisms lead to very different stationary distributions of the immunity at the endemic steady state. In the situation of periodic disease outbreaks, the waveforms of immunity distributions are studied and visualized. Our results show that there is a possibility to infer the boosting mechanism from the population level immune dynamics.

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