scholarly journals Knowledge sharing about deep-sea ecosystems to inform conservation and research decisions

FACETS ◽  
2017 ◽  
Vol 2 (2) ◽  
pp. 984-997 ◽  
Author(s):  
Stephanie R. Januchowski-Hartley ◽  
Kimberly A. Selkoe ◽  
Natalya D. Gallo ◽  
Christopher E. Bird ◽  
J. Derek Hogan
Keyword(s):  
Decision Making ◽  
Deep Sea ◽  
Time Series Data ◽  
Current Knowledge ◽  
Series Data ◽  
Monitoring Networks ◽  
Drivers Of Change ◽  
Knowledge Gaps ◽  
Depth Interviews ◽  
Anthropogenic Drivers

The Marianas Trench Marine National Monument (MNM) currently extends policy-based protection to deep-sea ecosystems contained within it, but managers require better understanding of the current knowledge and knowledge gaps about these ecosystems to guide decision-making. To address this need, we present a case study of the Marianas Trench MNM using in-depth interviews to determine scientists’ (1) current understanding of anthropogenic drivers of change and system vulnerability in deep-sea ecosystems; and (2) perceptions of the least understood deep-sea ecosystems and processes in the Marianas Trench MNM, and which of these, if any, should be research priorities to fill knowledge gaps about these systems and the impacts from anthropogenic drivers of change. Interview respondents shared similar views on the current knowledge of deep-sea ecosystems and potential anthropogenic drivers of change in the Marianas Trench MNM. Respondents also identified trench and deep pelagic (bathyal, abyssal, and hadal zones) ecosystems as the least understood, and highlighted climate change, litter and waste, mining and fishing, and interactions between these drivers of change as critical knowledge gaps. To fill key knowledge gaps and inform conservation decision-making, respondents identified the need for monitoring networks and time-series data. Our approach demonstrates how in-depth interviews can be used to elicit knowledge to inform decision-making in data-limited situations.

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 2024-2030
Author(s):  
Malcolm J. Beynonm
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Complex Decision Making ◽  
Fuzzy Decision Trees

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.

scholarly journals Ship-Collision Avoidance Decision-Making Learning of Unmanned Surface Vehicles with Automatic Identification System Data Based on Encoder—Decoder Automatic-Response Neural Networks

2020 ◽  
Vol 8 (10) ◽  
pp. 754
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Time Series ◽  
Collision Avoidance ◽  
Time Series Data ◽  
Series Data ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Response ◽  
Ship Collision

Intelligent unmanned surface vehicle (USV) collision avoidance is a complex inference problem based on current navigation status. This requires simultaneous processing of the input sequences and generation of the response sequences. The automatic identification system (AIS) encounter data mainly include the time-series data of two AIS sets, which exhibit a one-to-one mapping relation. Herein, an encoder–decoder automatic-response neural network is designed and implemented based on the sequence-to-sequence (Seq2Seq) structure to simultaneously process the two AIS encounter trajectory sequences. Furthermore, this model is combined with the bidirectional long short-term memory recurrent neural networks (Bi-LSTM RNN) to obtain a network framework for processing the time-series data to obtain ship-collision avoidance decisions based on big data. The encoder–decoder neural networks were trained based on the AIS data obtained in 2018 from Zhoushan Port to achieve ship collision avoidance decision-making learning. The results indicated that the encoder–decoder neural networks can be used to effectively formulate the sequence of the collision avoidance decision of the USV. Thus, this study significantly contributes to the increased efficiency and safety of maritime transportation. The proposed method can potentially be applied to the USV technology and intelligent collision-avoidance systems.

scholarly journals Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 279 ◽  
Author(s):  
Tongle Zhou ◽  
Mou Chen ◽  
Yuhui Wang ◽  
Jianliang He ◽  
Chenguang Yang
Keyword(s):  
Decision Making ◽  
Decision Tree ◽  
Incomplete Information ◽  
Time Series Data ◽  
Short Term Memory ◽  
Prediction Method ◽  
Series Data ◽  
Air Combat ◽  
Solid Foundation ◽  
Long Short Term Memory

To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

scholarly journals Suicide Research and Adolescent Suicide Trends in New Zealand

2008 ◽  
Vol 8 ◽  
pp. 287-302 ◽  
Author(s):  
Said Shahtahmasebi
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Adolescent Suicide ◽  
Time Series Data ◽  
Current Knowledge ◽  
Medical Model ◽  
Well Being ◽  
Young Person ◽  
Series Data ◽  
International Experts

In recent years, there have been a number of claims and counterclaims from suicide research using time series and longitudinal data; in particular, the linkage of increased antidepressant prescriptions to a decrease in suicide rates. Suicide time series appear to have a memory compounded with seasonal and cyclic effects. Failure to take into account these properties may lead to misleading conclusions, e.g., a downward blip is interpreted as the result of current knowledge and public health policies, while an upward blip is explained as suicide being complex depending on many variables requiring further research. In previous publications, I argued that this misuse of time series data is the result of an uncritical acceptance of a medical model that links mental ill-health to suicide. The consequences of such research behaviour are further increases in antidepressant prescriptions and medications to those who should not be prescribed them, with adverse effects showing across the population, e.g., the prescription of antidepressants to very young children (some under 1 year of age) in New Zealand. Moreover, the New Zealand Evidence-based Health Care Bulletin recommends an authoritarian approach for every interaction with a young person to check their psychosocial well-being. When viewed holistically, this kind of human behaviour makes researchers, policy makers (politicians), treatment, and practitioners, and society in general part of the problem rather than the solution. This paper explores some dynamic aspects of suicide, using only official data with particular reference to youth suicide, and suggests that the medical model of suicide is only an attempt to treat depression without addressing suicide, and recommends the creation of a unified database through understanding the society that individuals live in. It is hoped that this paper will stimulate debate and the collaboration of international experts regardless of their school of thought.

scholarly journals Judicial impact on bureaucratic decision-making : the case of public procurement and contracting

2015 ◽  
Author(s):  
◽  
Yuan Gao
Keyword(s):  
Decision Making ◽  
Affirmative Action ◽  
Goal Attainment ◽  
Time Series Data ◽  
Public Procurement ◽  
Executive Branch ◽  
Series Data ◽  
Cross Sectional ◽  
Public Contracting ◽  
Judicial Impact

This study examines whether and how much the bureaucracy responds to the judiciary. Specifically, I utilize cross-sectional, time-series data to analyze the extent to which variation in bureaucratic decision-making regarding affirmative action programs in public contracting across time and U.S. states is explained by the shifting legal environment. Federal agencies are found more likely to adjust minority contract amounts in response to the executive branch. State agencies appear to be somewhat responsive to courts during affirmative action goal-setting, but not in goal attainment. Overall, I did not find enough evidence that indicates significant bureaucratic responsiveness to judicial review. The lack of judicial impact may be further understood from utilitarian, communications and organizational theoretical perspectives.

scholarly journals Managing human impacts across diverse marine environments

2018 ◽  
Author(s):  
Laura M Robson ◽  
Anita J Carter ◽  
Ellen Last ◽  
Frances J Peckett ◽  
Elly Hill
Keyword(s):  
Deep Sea ◽  
Oil And Gas ◽  
Time Series Data ◽  
Human Impacts ◽  
Series Data ◽  
Marine Biodiversity ◽  
Management Measures ◽  
Joint Nature Conservation Committee ◽  
The Uk

As an island nation, the UK is surrounded by water, spanning from the coast and intertidal, to the circalittoral and deep-sea. Understanding the changing condition and resilience of marine biodiversity within these vastly different water masses is of key importance to understanding both the impacts of, and how to best manage, human activities whilst enabling continued sustainable development. One of the biggest challenges to understanding biodiversity state is the lack of time-series data, particularly in areas where long-term monitoring has not yet been implemented around our offshore (>12nm) and deep-sea waters. To manage this, the UK’s Joint Nature Conservation Committee are further developing spatial mapping proxy methods, gathering data on human activity presence, pressures caused by these activities, and the associated sensitivity of biodiversity to these pressures, to understand key areas of risk. Whilst evidence for these assessments is becoming more widely available for offshore waters, there is a large evidence gap on deep-sea biodiversity sensitivity, and understanding how to manage this little-studied environment. With ongoing pressures from fishing and oil and gas activity, and future threats from deep-sea mining, this is a key area of research which is urgently needed to help develop effective and sustainable management measures.

scholarly journals Analysis of a long-term Forest Accountancy Network to Support Management and Controlling

2018 ◽  
Vol 3 (1) ◽  
pp. 155-162 ◽  
Author(s):  
Markus Dög ◽  
Johannes Wildberg ◽  
Bernhard Möhring
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Management Strategies ◽  
Series Data ◽  
Forest Economics ◽  
Private Forest ◽  
Forest Landowners ◽  
Forest Enterprises

Abstract Multifunctional forestry in Germany is characterized by long production periods and complex biological-technical processes. Private forest enterprises are complex systems which are closely interwoven with the economic environment. To ensure their economic success, forest landowners need to take the economic development into consideration and adapt their management strategies. Management accounting is an important source for information needed to fulfil main tasks of accounting that help to manage forest enterprises: ‘description’, ‘explanation’ and ‘decision making’. To get general data, long time series data, taken from Forest Accountancy Networks (FAN), can be analysed. For more than 45 years, data from the FAN Westfalen-Lippe in Germany has been collected and analysed by the department of Forest Economics and Forest Management at the University of Göttingen. The long-term development and adaptation strategies of defined groups of private forest enterprises can be illustrated using this data. These valuable time series can support decision-making processes for private forest landowners and provide tools for forest policy. The data shows that private forest enterprises, with spruce as the dominating tree species, have performed above average in terms of operating revenues and profit margins, but are also more susceptible to calamities resulting in higher involuntary timber harvests.

scholarly journals Prediction of Interbank Offered Rate Based on Time Series Model

2021 ◽  
Vol 251 ◽  
pp. 01014
Author(s):  
Ding Huang ◽  
Ming Zhong ◽  
Xupeng Shi
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Time Series Data ◽  
Time Series Model ◽  
Grey Model ◽  
Actual Data ◽  
Series Data ◽  
Bank Management ◽  
Working Day ◽  
The Impact

This paper studies the prediction of interbank offered rate changes in each working day. Using the actual data of each working day of China’s interbank offered rate from 2007 to 2019, this paper sets up ARIMA, Prophet, grey model and MTGNN to study and verify the time series data, and make a comparison between these models. The limitation of this paper is that it does not consider the impact of macroeconomic characteristics but only considers the predict changes in time series. The results of this paper are expected to be helpful for bank management and interbank transaction decision making.

scholarly journals A hidden Markov model analysis of subject-specific seizure time-series data as a potential aid to clinical decision making

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2592
Author(s):  
Martin D. King ◽  
Suresh Pujar ◽  
Rod C. Scott
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Sojourn Time ◽  
Clinical Decision Making ◽  
Time Series Data ◽  
Seizure Activity ◽  
Hidden Markov ◽  
Clinical Decision ◽  
Series Data ◽  
Subject Specific

Background The seizure-count time series data acquired from three children with refractory epilepsy were used in a statistical modelling analysis designed to provide an explanation for the marked variation in seizure frequency that often occurs over time (over-dispersed Poisson behaviour). This was motivated by an expectation that a better understanding of the spontaneous shifts in seizure-activity that are observed in some cases should reduce the risk of over-treatment caused by inappropriate changes in medication. Methods The analyses were performed using Poisson hidden Markov models (HMMs), both Bayesian and non-Bayesian, implemented using Markov chain Monte Carlo and the expectation-maximisation algorithm, respectively. A defining feature of the models, as applied to epilepsy, is the assumed existence of two or more pathological states, with state-specific Poisson rates, and random transitions between the states. Posterior predictive simulation was used to assess the validity of the Bayesian HMMs. Results The results are presented in the form of state transition probability and Poisson rate estimates (i.e., the primary HMM parameters), together with information derived from these primary parameters. State-specific mean-duration (sojourn time) estimates and sojourn-time complementary cumulative probability distributions are the main focus. HMM analyses are presented for three children that differed markedly in their seizure behaviour. The first is characterised by an extreme seizure count on one occasion; the second underwent a spontaneous decrease in seizure activity during the observation period; the third seizure-count time trajectory is characterised by a gradual change in mean seizure activity. We show that, despite their considerable differences, each of the observed seizure-count trajectories can be treated adequately using an HMM. Conclusions The study demonstrates that clinically relevant information can be obtained using HM modelling in three cases with markedly different seizure behaviour. The resulting subject-specific statistics provide useful clinical insights which should aid those engaged in clinical decision making.

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