scholarly journals Evaluating the consequences of common assumptions in run reconstructions on Pacific-salmon biological status assessments

2019 ◽  
Author(s):  
Stephanie J. Peacock ◽  
Eric Hertz ◽  
Carrie A. Holt ◽  
Brendan Connors ◽  
Cameron Freshwater ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stochastic Simulation ◽  
Incomplete Data ◽  
Pacific Salmon ◽  
Management Strategies ◽  
Simulation Approach ◽  
The Face

AbstractInformation on biological status is essential for designing, implementing, and evaluating management strategies and recovery plans for threatened or exploited species. However, the data required to quantify status are often limited, and it is important to understand how assessments of status may be biased by assumptions in data analysis. For Pacific salmon, biological status assessments based on spawner abundances and spawner-recruitment (SR) analyses often involve “run reconstructions” that impute missing spawner data, expand observed spawner abundance to account for unmonitored streams, assign catch to individual stocks, and quantify age-at-return. Using a stochastic simulation approach, we quantified how common assumptions in run reconstructions biased assessments of biological status based on spawner abundance. We found that status assessments were robust to most common assumptions in run reconstructions, even in the face of declining monitoring coverage, but that overestimating catch tended to increase rates of status misclassification. Our results lend confidence to biological status assessments based on spawner abundances and SR analyses, even in the face of incomplete data.

scholarly journals Evaluating the consequences of common assumptions in run reconstructions on Pacific salmon biological status assessments

2020 ◽  
Vol 77 (12) ◽  
pp. 1904-1920
Author(s):  
Stephanie J. Peacock ◽  
Eric Hertz ◽  
Carrie A. Holt ◽  
Brendan Connors ◽  
Cameron Freshwater ◽  
...  
Keyword(s):  
Data Analysis ◽  
Stochastic Simulation ◽  
Incomplete Data ◽  
Pacific Salmon ◽  
Management Strategies ◽  
Simulation Approach ◽  
The Face

Information on biological status is essential for designing, implementing, and evaluating management strategies and recovery plans for threatened or exploited species. However, the data required to quantify status are often limited, and it is important to understand how assessments of status may be biased by assumptions in data analysis. For Pacific salmon, biological status assessments based on spawner abundances and spawner–recruitment (SR) analyses often involve “run reconstructions” that impute missing spawner data, expand observed spawner abundance to account for unmonitored streams, assign catch to individual stocks, and quantify age-at-return. Using a stochastic simulation approach, we quantified how common assumptions in run reconstructions biased assessments of biological status based on spawner abundance. We found that status assessments were robust to most common assumptions in run reconstructions, even in the face of declining monitoring coverage, but that overestimating catch tended to increase rates of status misclassification. Our results lend confidence to biological status assessments based on spawner abundances and SR analyses, even in the face of incomplete data.

Stochastic simulation for determining the design flood of cascade reservoir systems

2012 ◽  
Vol 43 (1-2) ◽  
pp. 54-63 ◽  
Author(s):  
Baohong Lu ◽  
Huanghe Gu ◽  
Ziyin Xie ◽  
Jiufu Liu ◽  
Lejun Ma ◽  
...  
Keyword(s):  
Simulation Model ◽  
Stochastic Simulation ◽  
Southern China ◽  
Flow Simulation ◽  
Simulation Method ◽  
Simulation Approach ◽  
Design Flood ◽  
Design Values ◽  
Stochastic Simulation Model ◽  
The Impact

Stochastic simulation is widely applied for estimating the design flood of various hydrosystems. The design flood at a reservoir site should consider the impact of upstream reservoirs, along with any development of hydropower. This paper investigates and applies a stochastic simulation approach for determining the design flood of a complex cascade of reservoirs in the Longtan watershed, southern China. The magnitude of the design flood when the impact of the upstream reservoirs is considered is less than that without considering them. In particular, the stochastic simulation model takes into account both systematic and historical flood records. As the reliability of the frequency analysis increases with more representative samples, it is desirable to incorporate historical flood records, if available, into the stochastic simulation model. This study shows that the design values from the stochastic simulation method with historical flood records are higher than those without historical flood records. The paper demonstrates the advantages of adopting a stochastic flow simulation approach to address design-flood-related issues for a complex cascade reservoir system.

Drilling Data Quality Management: Case Study With a Laboratory Scale Drilling Rig

2018 ◽  
Author(s):  
Suranga C. H. Geekiyanage ◽  
Dan Sui ◽  
Bernt S. Aadnoy
Keyword(s):  
Decision Making ◽  
Quality Improvement ◽  
Control System ◽  
Data Analysis ◽  
Quality Management ◽  
Data Quality ◽  
Incomplete Data ◽  
Laboratory Scale ◽  
Data Quality Management ◽  
Drilling Rig

Drilling industry operations heavily depend on digital information. Data analysis is a process of acquiring, transforming, interpreting, modelling, displaying and storing data with an aim of extracting useful information, so that the decision-making, actions executing, events detecting and incident managing of a system can be handled in an efficient and certain manner. This paper aims to provide an approach to understand, cleanse, improve and interpret the post-well or realtime data to preserve or enhance data features, like accuracy, consistency, reliability and validity. Data quality management is a process with three major phases. Phase I is an evaluation of pre-data quality to identify data issues such as missing or incomplete data, non-standard or invalid data and redundant data etc. Phase II is an implementation of different data quality managing practices such as filtering, data assimilation, and data reconciliation to improve data accuracy and discover useful information. The third and final phase is a post-data quality evaluation, which is conducted to assure data quality and enhance the system performance. In this study, a laboratory-scale drilling rig with a control system capable of drilling is utilized for data acquisition and quality improvement. Safe and efficient performance of such control system heavily relies on quality of the data obtained while drilling and its sufficient availability. Pump pressure, top-drive rotational speed, weight on bit, drill string torque and bit depth are available measurements. The data analysis is challenged by issues such as corruption of data due to noises, time delays, missing or incomplete data and external disturbances. In order to solve such issues, different data quality improvement practices are applied for the testing. These techniques help the intelligent system to achieve better decision-making and quicker fault detection. The study from the laboratory-scale drilling rig clearly demonstrates the need for a proper data quality management process and clear understanding of signal processing methods to carry out an intelligent digitalization in oil and gas industry.

scholarly journals Change Management Strategy for Organization in the Era of Industry 4.0

2020 ◽  
Vol 8 (5) ◽  
pp. 3629-3634
Keyword(s):  
Change Management ◽  
Industry 4.0 ◽  
Management Strategy ◽  
Management Strategies ◽  
Literature Study ◽  
The Face ◽  
Management Concept ◽  
Changing Times ◽  
Sense Of Power ◽  
Future Condition

The Changes have survivalbenefits for an organization. And with out any change, it can be ascertained that the age of the organization will not last long. The Change sint end to make the organization notastatic but remained dynamic in the face of changing times. A leadershouldhave a vision and a change in the strategy based on assumptions about future condition sthatare expected to occur. The only leader who have the personality, be havior, and the sense of power that are able to deal with change. This paper analyzed the several literature study from national journals and books in Indonesia and international journals to see the change management concept from two perspective. And the results showed the similarity research funding from the researcher in Indonesia and the other countries about change management strategies and challenges.

scholarly journals Keeping Conversation Rolling by Iraqi EFL University Learners

2019 ◽  
Vol 9 (2) ◽  
pp. 84
Author(s):  
Muayad Mingher Al-Shemmery ◽  
Hisham Adnan AlMumar ◽  
Dheyaa Al-Fatlawi
Keyword(s):  
Data Analysis ◽  
Diagnostic Test ◽  
College Of Education ◽  
Production Level ◽  
Test Administration ◽  
Efl Learners ◽  
Optimal Form ◽  
The Face

The present study sheds some light on the conversation as an optimal form of communication. Also, it tries to illustrate Iraqi EFL learners’ aptitude for producing conversation and specifying the errors types committed by them in its progression. To accomplish these purposes, it is hypothesized that the learners may face formidable problems in sharing their experiences with each other and are unable to keep a conversation going. In the face of such problems, a sample of (50) learners is selected to a diagnostic test administration. The subjects are at the Fourth year, Department of English, College of Education for Humanities, University of Babylon. Data analysis has proved that the leaners encounter more difficulty on the production level than the recognition one. This manifestation is manifested itself in the number of the learners’ correct responses as compared with their incorrect ones on both levels.

scholarly journals REVIEW:APPLICATION OF BIG DATA AND IOT PROTECTED MODE IN AGRICULTURE

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr.Anita K.Patil ◽  
Dr.A.R. Laware
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Climatic Factors ◽  
Cost Effective ◽  
Big Data Analysis ◽  
Modern Agriculture ◽  
Agriculture Industry ◽  
The Face ◽  
Smart Agriculture ◽  
Protected Mode

Advance researches in the field of Internet of Things (IoT) are helping to make water management smarter and also used for optimizing consumption in the smart agriculture industry. Now days the development and research in Intelligent Smart Farming IoT based devices is turning the face of agriculture production in enhancing as well making it cost-effective and reducing wastage. To create environmental conditions suitable for the growth of animals and plants, modern agriculture that uses artificial techniques to change climatic factors such as temperature a highly efficient protected agriculture mode is used. To handle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. Modern digital technology used for continuously monitoring the physical environment and producing large quantities of data in an unprecedented pace. For improving productivity the analysis of big data would enable farmers and companies to extract value from it. Moreover big data analysis is leading to advances in various industries; it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employs the recent practice of big data analysis, in order to solve various relevant problems.

Possibility Clustering Algorithm for Incomplete Data Based on a Deep Computing Model

2021 ◽  
pp. 2141012
Author(s):  
Dongping Li ◽  
Yingchun Yang ◽  
Qiang Yue ◽  
Liqi Cheng ◽  
Jie Song ◽  
...  
Keyword(s):  
Game Theory ◽  
Incomplete Data ◽  
Data Analytics ◽  
Clustering Algorithm ◽  
Fundamental Theory ◽  
Computing Model ◽  
The Game Theory ◽  
The Face ◽  
Experimental Findings ◽  
Rule Out

Clustering is an essential part of data analytics and in Wireless Sensor Networks (WSN). It becomes a problem for causes such as insufficient, unavailable, or compromised data in the face of uncertainties. A solution to tackle the instability of clusters due to missed values has been proposed. The fundamental theory determines whether to incorporate an entity into a group if it is not clear and probable. One of the main issues is identifying requirements for three forms of decision definition, including an entity in a cluster, removing an object from a group, or delaying a decision (defer) to involve or rule out a group. Current studies do not adequately discuss threshold identification and use their fixed values implicitly. This work explores using the game theory-based Possibility Clustering Algorithm for Incomplete Data (PCA-ID) framework to address this problem. In specific, a game theory will be described in which thresholds are determined based on a balance between the groups’ precision and generic characteristics. The points calculated are used to elicit judgments for the grouping of unknown objects. Experimental findings on the deep learning datasets show that the PCA-ID increases the overall quality considerably while maintaining comparable precision levels in competition with similar systems.

Enabling Knowledge Flow

2021 ◽  
pp. 157-175
Author(s):  
Dana Tessier
Keyword(s):  
Knowledge Management ◽  
Corporate Culture ◽  
New Technology ◽  
Management Strategies ◽  
Management Model ◽  
New Knowledge ◽  
The Face ◽  
Flow Through ◽  
Management Behaviors

Organizations are facing many challenges to remain relevant in the face of new technology, emerging markets, and changing consumer behaviors. Many organizations look to become learning organizations with knowledge management strategies to leverage their knowledge assets and continuously innovate their strategies and products. However, organizations struggle to achieve success with knowledge management because their organizational culture does not support knowledge-sharing and must be adapted for this new behavior. Knowledge must flow through the organization, and so, therefore, these necessary behaviors must work within the existing corporate culture. Observations from a case study at a software company are discussed, and a new knowledge management model, the Knowledge Management Triangle, is introduced. The Knowledge Management Triangle is a simple model to explain and implement knowledge management within organizations and is customizable to work within the organization's culture to ensure the new knowledge management behaviors are appropriately adopted.

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