scholarly journals Recent advances of quantitative modeling to support invasive species eradication on islands

2020 ◽  
Author(s):  
Christopher Baker ◽  
Michael Bode
Keyword(s):  
Decision Making ◽  
Invasive Species ◽  
Large Scale ◽  
Quantitative Modelling ◽  
Critical Research ◽  
Research Gaps ◽  
The Past ◽  
Complex Projects ◽  
Uncertainty Analyses ◽  
Project Level

The eradication of invasive species from islands is an important part of managing these ecologically unique and at-risk regions. Island eradications are complex projects and mathematical models play an important role in supporting efficient and transparent decision-making. In this review we cover the past applications of modelling to island eradications, which range from large-scale prioritisations across groups of islands, to project-level decision-making tools. While quantitative models have been formulated and parameterised for a range of important problems, there are also critical research gaps. Many applications of quantitative modelling lack uncertainty analyses, and are therefore over-confident. Forecasting the ecosystem-wide impacts of species eradications is still extremely challenging, despite recent progress in the field. Overall, the field of quantitative modelling is well-developed for island eradication planning. Multiple practical modelling tools are available for, and are being applied to, a diverse suite of important decisions, and quantitative modelling is well-placed to address pressing issues in the field.

Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

scholarly journals What influences PhD graduate trajectories during the degree: a research-based policy agenda

2020 ◽  
Vol 80 (6) ◽  
pp. 1011-1043
Author(s):  
Lynn McAlpine ◽  
Montserrat Castello ◽  
Kirsi Pyhaltö
Keyword(s):  
Research Evidence ◽  
Doctoral Programs ◽  
Decision Makers ◽  
Critical Research ◽  
Research Gaps ◽  
The Past ◽  
Timely Completion ◽  
Systematized Review ◽  
Institutional Contexts ◽  
Doctoral Experience

AbstractDuring the past two decades, PhD graduate numbers have increased dramatically with graduates viewed by governments as a means to advance the knowledge economy and international competitiveness. Concurrently, universities have also invested in policies to monitor satisfaction, retention, and timely completion—and researchers have expanded the study of PhD experience. We, as such researchers, have increasingly received invitations from university decision-makers to present research evidence which might guide their doctoral programs. Their interest provoked us to do a qualitative systematized review of research on doctoral experience—seeking evidence of practices that influenced retention, satisfaction, and completion. The result contributes a synthesis of the critical research evidence that could be used to inform doctoral education policy. We also demonstrate the possibilities of such evidence by suggesting some potential recommendations, while recognizing that there is no direct relationship between research results and their transformation into particular institutional contexts in ways that enhance doctoral experience. We hope our initiative will be taken up and extended by other researchers, particularly the research gaps we note, so we can collectively support the use of research evidence to influence both doctoral policies and practices—with the goal to better prepare PhD researchers for their futures and better support their supervisors.

Cold chains, interrupted

2018 ◽  
Vol 8 (1) ◽  
pp. 49-69 ◽  
Author(s):  
Tina Comes ◽  
Kristin Bergtora Sandvik ◽  
Bartel Van de Walle
Keyword(s):  
Decision Making ◽  
Research Agenda ◽  
Basic Structure ◽  
Cold Chain ◽  
Critical Research ◽  
Research Gaps ◽  
Content Type ◽  
Vaccination Campaigns ◽  
Uncertainty Information ◽  
Chain Problem

Purpose The purpose of this paper is to analyze how far technology and information enable, facilitate or support the planning and implementation decisions in humanitarian vaccine cold chains for vaccination campaigns. The authors specifically focus on three emerging technologies that have the potential to create more flexible conditions in the field, and identify the need to further explore the link between uncertainty, information and irreversibility. Design/methodology/approach The authors present a basic structure for the analysis of cold chain disruptions in terms of three distinct yet connected layers of deficient infrastructure and capacity, information gaps and failures in decision making. The authors then review three humanitarian technologies and their impact on vaccine campaigns along these layers. From there, a research agenda is developed to address research gaps this review brought forward. Findings Three critical research gaps in the areas of technology innovation for humanitarian vaccine cold chain management are presented. The authors argue that technology to improve capacity, information and decisions need to be aligned, and that the areas of uncertainty, information and irreversibility require further investigation to achieve this alignment. In this way, the paper contributes to setting the research agenda on vaccine cold chains and connects humanitarian logistics to technology, information management and decision making. Originality/value This paper presents the humanitarian vaccine cold chain problem from an original angle by illuminating the implications of technology and information on the decisions made during the planning and implementation phases of a vaccine campaign. The authors develop an agenda to provide researchers and humanitarians with a perspective to improve cold chain planning and implementation at the intersection of technology, information and decisions.

scholarly journals Tenacity in Performance of Small and Mid-cap Mutual Funds in Indian Scenario- In support to Investors Decision

2020 ◽  
Vol 9 (1) ◽  
pp. 1088-1091
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Mutual Funds ◽  
Crucial Role ◽  
Large Scale ◽  
Small Scale ◽  
The Past ◽  
Market Risks ◽  
Support Decision Making ◽  
Indian Scenario

Mutual funds play a crucial role in financial sector for small-scale and large-scale investors. Within the Indian scenario, there is a need to define criteria to guide the investors in selection between small-caps and mid-caps mutual funds. Although small-caps provide there is always a question of higher market risks compared to mid-caps. So, the work emphasizes on analysis performances of Small caps in comparison with mid-caps that would certainly support decision-making. In the present work a comprehensive assessment of existing mutual funds that involves small and mid-cap with respect to Indian scenario is presented and their performance in the market for the past ten years is analyzed. The study analyses the fund’s performance by considering parameters like market risk, momentum, expenses, size and value. The persistence and decision-making of the investor are discussed with respect to the small and mid-cap funds. In this regard, we have considered the best and worst-performing small and mid-cap funds according to their returns in a overall span of more than 3 years. A comparative analysis between the decision making parameters that are performing and underperforming during this period are considered. In this study, small-caps funds like HDFC small-cap fund, Kotak, DSP small Cap fund and Franklin India Small MF and in parallel, mid-cap funds including Kota Emerging equity, DSP Midcap and Axis Midcap and Franklin India are considered

Technology and Decision Making: Some Aspects of the Development of OGAS

Slavic Review ◽  
1980 ◽  
Vol 39 (3) ◽  
pp. 426-445 ◽  
Author(s):  
William J. Conyngham
Keyword(s):  
Decision Making ◽  
Socioeconomic Development ◽  
Large Scale ◽  
Systems Approach ◽  
Rapid Expansion ◽  
Technical Program ◽  
Economic Management ◽  
The Past ◽  
Scientific Technical ◽  
And Control

During the past decade, the most important large-scale effort to expand the decision-making and control capabilities of the Soviet economic management system has been the scientific-technical program for creating an integrated nationwide management information system. Formally designated as the Obshchegosudarstvennaia sistema sbora i obrabotki informatsii (dannykh) dlia ncheta, planirovaniia i upravleniia narodnym khosiaistvom (All-State System for the Collection and Processing of Information for Reporting, Planning, and Management of the National Economy), the system is more happily described by its acronym OGAS.1 It is an outgrowth of the rapid expansion of the systems approach (particularly its cybernetic expression) to the rationalization of management, and has developed into the technological variant of reform and an evident alternative and successor to the ill-fated economic reforms of 1965. As a system, OGAS has been projected as a solution to many of the fundamental economic, social, and organizational problems resulting from Soviet socioeconomic development.

Truth Matters: Cognitive Integrity as an Intervention for Self-Deception

2017 ◽  
Author(s):  
Eugenia Isabel Gorlin ◽  
Michael W. Otto
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Autonomous Agents ◽  
Therapeutic Interventions ◽  
Empirical Validation ◽  
The Past ◽  
Adaptive Problem

To live well in the present, we take direction from the past. Yet, individuals may engage in a variety of behaviors that distort their past and current circumstances, reducing the likelihood of adaptive problem solving and decision making. In this article, we attend to self-deception as one such class of behaviors. Drawing upon research showing both the maladaptive consequences and self-perpetuating nature of self-deception, we propose that self-deception is an understudied risk and maintaining factor for psychopathology, and we introduce a “cognitive-integrity”-based approach that may hold promise for increasing the reach and effectiveness of our existing therapeutic interventions. Pending empirical validation of this theoretically-informed approach, we posit that patients may become more informed and autonomous agents in their own therapeutic growth by becoming more honest with themselves.

Molecular Epidemiology and Biomarkers in Etiologic Cancer Research: The New in Light of the Old

2020 ◽  
Author(s):  
Lungwani Muungo
Keyword(s):  
Gene Expression ◽  
Cancer Research ◽  
Molecular Epidemiology ◽  
Exposure Assessment ◽  
Large Scale ◽  
First Generation ◽  
Cancer Epidemiology ◽  
Policy Changes ◽  
The Past ◽  
New Generation

The purpose of this review is to evaluate progress inmolecular epidemiology over the past 24 years in canceretiology and prevention to draw lessons for futureresearch incorporating the new generation of biomarkers.Molecular epidemiology was introduced inthe study of cancer in the early 1980s, with theexpectation that it would help overcome some majorlimitations of epidemiology and facilitate cancerprevention. The expectation was that biomarkerswould improve exposure assessment, document earlychanges preceding disease, and identify subgroupsin the population with greater susceptibility to cancer,thereby increasing the ability of epidemiologic studiesto identify causes and elucidate mechanisms incarcinogenesis. The first generation of biomarkers hasindeed contributed to our understanding of riskandsusceptibility related largely to genotoxic carcinogens.Consequently, interventions and policy changes havebeen mounted to reduce riskfrom several importantenvironmental carcinogens. Several new and promisingbiomarkers are now becoming available for epidemiologicstudies, thanks to the development of highthroughputtechnologies and theoretical advances inbiology. These include toxicogenomics, alterations ingene methylation and gene expression, proteomics, andmetabonomics, which allow large-scale studies, includingdiscovery-oriented as well as hypothesis-testinginvestigations. However, most of these newer biomarkershave not been adequately validated, and theirrole in the causal paradigm is not clear. There is a needfor their systematic validation using principles andcriteria established over the past several decades inmolecular cancer epidemiology.

An Assessment of a Low-Cost Wastewater Disposal System after Twenty-Five Years of Operation

1987 ◽  
Vol 19 (5-6) ◽  
pp. 701-710 ◽  
Author(s):  
B. L. Reidy ◽  
G. W. Samson
Keyword(s):  
Large Scale ◽  
Low Cost ◽  
Wastewater Disposal ◽  
Industrial Base ◽  
Industrial Wastewaters ◽  
The Past ◽  
Gasification Process ◽  
Appropriate Treatment ◽  
Environmentally Safe ◽  
Coal Resource

A low-cost wastewater disposal system was commissioned in 1959 to treat domestic and industrial wastewaters generated in the Latrobe River valley in the province of Gippsland, within the State of Victoria, Australia (Figure 1). The Latrobe Valley is the centre for large-scale generation of electricity and for the production of pulp and paper. In addition other industries have utilized the brown coal resource of the region e.g. gasification process and char production. Consequently, industrial wastewaters have been dominant in the disposal system for the past twenty-five years. The mixed industrial-domestic wastewaters were to be transported some eighty kilometres to be treated and disposed of by irrigation to land. Several important lessons have been learnt during twenty-five years of operating this system. Firstly the composition of the mixed waste stream has varied significantly with the passage of time and the development of the industrial base in the Valley, so that what was appropriate treatment in 1959 is not necessarily acceptable in 1985. Secondly the magnitude of adverse environmental impacts engendered by this low-cost disposal procedure was not imagined when the proposal was implemented. As a consequence, clean-up procedures which could remedy the adverse effects of twenty-five years of impact are likely to be costly. The question then may be asked - when the total costs including rehabilitation are considered, is there really a low-cost solution for environmentally safe disposal of complex wastewater streams?

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

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