scholarly journals Effects of fertilization and stand age on N<sub>2</sub>O and NO emissions from tea plantations: A site-scale study in a subtropical region using a modified biogeochemical model

2020 ◽  
Author(s):  
Wei Zhang ◽  
Zhisheng Yao ◽  
Xunhua Zheng ◽  
Chunyan Liu ◽  
Rui Wang ◽  
...  
Keyword(s):  
Soil Ph ◽  
Process Model ◽  
Early Stage ◽  
Stand Age ◽  
Biogeochemical Model ◽  
N2o Emissions ◽  
Model Based ◽  
Ph Reduction ◽  
Tea Plantations ◽  
No Emissions

Abstract. To meet increasing demands, tea plantations are rapidly expanding in China. Although the emissions of nitrous oxide (N2O) and nitric oxide (NO) from tea plantations may be substantially influenced by soil pH reduction and intensive nitrogen fertilization, process model-based studies on this issue are still rare. In this study, the process-oriented biogeochemical model, Catchment Nutrient Management Model – DeNitrification-DeComposition (CNMM-DNDC), was modified by adding tea growth-related processes that may induce a soil pH reduction. Using a dataset for intensively managed tea plantations at a subtropical site, the performances of the original and modified models for simulating the emissions of both gases subject to different fertilization alternatives and stand ages were evaluated. Compared with the observations in early stage of a tea plantation, the original and modified models showed comparable performances for simulating the daily gas fluxes (with Nash-Sutcliffe index (NSI) of 0.10 versus 0.18 for N2O and 0.32 versus 0.33 for NO), annual emissions (with NSI of 0.81 versus 0.94 for N2O and 0.92 versus 0.94 for NO) and annual direct emission factors (EFds). The observations and simulations consistently demonstrated that short-term replacement of urea with oilcake stimulated N2O emissions by ~ 62 % and ~ 36 % and mitigated NO emissions by ~ 25 % and ~ 14 %, respectively. The model simulations resulted in a positive dependence of EFd of either gas against nitrogen doses, implicating the importance of model-based quantification of this key parameter for inventory. In addition, the modified model with pH-related scientific processes showed overall inhibitory effects on the gases emissions in the mid to later stages during a full tea lifetime. In conclusion, the modified CNMM-DNDC exhibits the potential for quantifying N2O and NO emissions from tea plantations under various conditions. Nevertheless, wider validation is still required for simulation of long-term soil pH variations and emissions of both gases from tea plantations.

scholarly journals Effects of fertilization and stand age on N<sub>2</sub>O and NO emissions from tea plantations: a site-scale study in a subtropical region using a modified biogeochemical model

2020 ◽  
Vol 20 (11) ◽  
pp. 6903-6919
Author(s):  
Wei Zhang ◽  
Zhisheng Yao ◽  
Xunhua Zheng ◽  
Chunyan Liu ◽  
Rui Wang ◽  
...  
Keyword(s):  
Soil Ph ◽  
Process Model ◽  
Stand Age ◽  
Biogeochemical Model ◽  
N2o Emissions ◽  
Modified Model ◽  
Model Based ◽  
Ph Reduction ◽  
Tea Plantations ◽  
No Emissions

Abstract. To meet increasing demands, tea plantations are rapidly expanding in China. Although the emissions of nitrous oxide (N2O) and nitric oxide (NO) from tea plantations may be substantially influenced by soil pH reduction and intensive nitrogen fertilization, process model-based studies on this issue are still rare. In this study, the process-oriented biogeochemical model, Catchment Nutrient Management Model – DeNitrification-DeComposition (CNMM-DNDC), was modified by adding tea-growth-related processes that may induce a soil pH reduction. Using a dataset for intensively managed tea plantations at a subtropical site, the performances of the original and modified models for simulating the emissions of both gases subject to different fertilization alternatives and stand ages were evaluated. Compared with the observations in the early stage of a tea plantation, the original and modified models showed comparable performances for simulating the daily gas fluxes (with a Nash–Sutcliffe index (NSI) of 0.10 versus 0.18 for N2O and 0.32 versus 0.33 for NO), annual emissions (with an NSI of 0.81 versus 0.94 for N2O and 0.92 versus 0.94 for NO) and annual direct emission factors (EFds). For the modified model, the observations and simulations demonstrated that the short-term replacement of urea with oil cake stimulated N2O emissions by ∼62 % and ∼36 % and mitigated NO emissions by ∼25 % and ∼14 %, respectively. The model simulations resulted in a positive dependence of EFds of either gas on nitrogen doses, implicating the importance of model-based quantification of this key parameter for inventory purposes. In addition, the modified model with pH-related scientific processes showed overall inhibitory effects on the gases' emissions in the middle to late stages during a full tea plant lifetime. In conclusion, the modified CNMM-DNDC exhibits the potential for quantifying N2O and NO emissions from tea plantations under various conditions. Nevertheless, wider validation is still required for simulation of long-term soil pH variations and emissions of both gases from tea plantations.

scholarly journals Early Stage Model Based System Design under Uncertainties

2019 ◽  
Vol 1 (1) ◽  
pp. 3621-3630
Author(s):  
Kazuya Oizumi ◽  
Akio Ito ◽  
Kazuhiro Aoyama
Keyword(s):  
Complex System ◽  
System Design ◽  
Systems Engineering ◽  
Process Model ◽  
Early Stage ◽  
Design Team ◽  
Support Design ◽  
Model Based ◽  
Design Process Model ◽  
High Level

AbstractSystem design at the early stage of design plays an important role in design process. Model based systems engineering is seen as a prominent approach for this challenge. System design can be explored by means of system simulation. However, as the system is a complex system, system model tends to have high level of abstraction. Therefore, the models cannot depict every details of the system, which makes optimization unreasonable.Furthermore, at the early stage of design, there are many uncertainties such as success of technological developments. By properly incorporating uncertain factors in system design, the system can be tolerant. Currently system design is conducted by experienced experts. However, for more complex system, it would be difficult to continue the current practice. Therefore, a method to support design team to make decision in system design is needed.This paper proposes a computational support for the system design. Design constraints, which seems the core information that design team wants at system design, are modeled. By visualizing constraints quantitatively and intuitively, the proposed method can support design team to conduct system design and design study.

Semantics of OWL-S process model based on temporal description logic

2013 ◽  
Vol 33 (1) ◽  
pp. 266-269 ◽  
Author(s):  
Ming LI ◽  
Shiyi LIU ◽  
Fuzhong NIAN
Keyword(s):  
Process Model ◽  
Description Logic ◽  
Model Based

The Evaluation of Efficient Process Model Based on Survey

2018 ◽  
Author(s):  
Thulasee Krishna Dr. S. ◽  
Sreekanth Dr.S. ◽  
Dharanidhar K. N.
Keyword(s):  
Process Model ◽  
Efficient Process ◽  
Model Based

scholarly journals DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks

Cancers ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 3308
Author(s):  
Won Sang Shim ◽  
Kwangil Yim ◽  
Tae-Jung Kim ◽  
Yeoun Eun Sung ◽  
Gyeongyun Lee ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Early Stage ◽  
External Validation ◽  
Area Under The Curve ◽  
The Cancer Genome Atlas ◽  
Deep Convolutional Neural Networks ◽  
Prognostic Features ◽  
Multi Scale ◽  
Model Based ◽  
Number Of Patients

The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.

On-line process-model-based control of a plasma etcher

1992 ◽  
Author(s):  
Michael E. Parten ◽  
R. R. Rhinehart ◽  
Vikram Singh
Keyword(s):  
Process Model ◽  
Model Based Control ◽  
Model Based ◽  
On Line ◽  
Line Process

An Approach to Generate Three Dimensional Machining Process Model According to Information from Design Model Based on Definition

2016 ◽  
Vol 693 ◽  
pp. 1684-1692 ◽  
Author(s):  
Hong Lei Zhang ◽  
Wen He Liao ◽  
Yu Guo ◽  
Wen An Yang
Keyword(s):  
Process Model ◽  
Three Dimensional ◽  
Design Model ◽  
Machining Process ◽  
Model Generation ◽  
Geometric Models ◽  
Model Based

Faced with the problem of generation for 3D machining process model, an approach to generate three dimensional machining process model according to information from design model based on definition is proposed. Compared with the existing methods, the approach utilizes multiple information of design model based on definition and takes many phases into consideration of 3D process model generation. The structure of 3D machining process model is defined and the course of 3D process model generation is researched, including multiple information acquirement, generation of procedure geometric models and annotation. Finally, the framework of system and application for 3D machining process model generation are presented for validation purposes.

Site controlling in infrastructure projects by digital methods

2021 ◽  
Author(s):  
Tino Walther ◽  
Marianne Pieper ◽  
Hans-Joachim Bargstädt
Keyword(s):  
Performance Measurement ◽  
Construction Industry ◽  
Process Model ◽  
Current Practice ◽  
Early Stage ◽  
Recommendations For Action ◽  
Digital Planning ◽  
The Status ◽  
Project Controlling ◽  
Digital Methods

<p>The construction industry is essentially determined by digital transformation and an increasingly complex market environment. Project controlling and monitoring is of high importance for construction site activities to achieve the project goals. Digital planning and recording methods make it possible to identify deviations at an early stage and to ensure the profitability of the project. To discuss the current practice of construction performance measurement as well as digital approaches in this domain, a qualitative study was carried out. The results of this empirical analysis examine the status quo of the construction performance measurement in civil engineering companies to illustrate the currently used methods and trends. Findings for the future use of digital planning and recording methods were obtained from the investigation. Based on empirical hypotheses, recommendations for action as well as for an improved process model are given.</p>

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