scholarly journals Protective Pathways: Connecting Environmental and Human Security at Local and Landscape Level with NLP and Geospatial Analysis of a Novel Database of 1500 Project Evaluations

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 123
Author(s):  
Nathan Morrow ◽  
Nancy B. Mock ◽  
Andrea Gatto ◽  
Julia LeMense ◽  
Margaret Hudson
Keyword(s):  
Climate Change ◽  
Language Processing ◽  
Large Scale ◽  
Human Security ◽  
Geospatial Analysis ◽  
Unstructured Data ◽  
Future Research ◽  
Political Crisis ◽  
Data Set ◽  
Conceptual Frame

Localized actionable evidence for addressing threats to the environment and human security lacks a comprehensive conceptual frame that incorporates challenges associated with active conflicts. Protective pathways linking previously disciplinarily-divided literatures on environmental security, human security and resilience in a coherent conceptual frame that identifies key relationships is used to analyze a novel, unstructured data set of Global Environment Fund (GEF) programmatic documents. Sub-national geospatial analysis of GEF documentation relating to projects in Africa finds 73% of districts with GEF land degradation projects were co-located with active conflict events. This study utilizes Natural Language Processing on a unique data set of 1500 GEF evaluations to identify text entities associated with conflict. Additional project case studies explore the sequence and relationships of environmental and human security concepts that lead to project success or failure. Differences between biodiversity and climate change projects are discussed but political crisis, poverty and disaster emerged as the most frequently extracted entities associated with conflict in environmental protection projects. Insecurity weakened institutions and fractured communities leading both directly and indirectly to conflict-related damage to environmental programming and desired outcomes. Simple causal explanations found to be inconsistent in previous large-scale statistical associations also inadequately describe dynamics and relationships found in the extracted text entities or case summaries. Emergent protective pathways that emphasized poverty and conflict reduction facilitated by institutional strengthening and inclusion present promising possibilities. Future research with innovative machine learning and other techniques of working with unstructured data may provide additional evidence for implementing actions that address climate change and environmental degradation while strengthening resilience and human security. Resilient, participatory and polycentric governance is key to foster this process.

Climate change and security in Canada

2021 ◽  
Vol 76 (2) ◽  
pp. 183-203
Author(s):  
Wilfrid Greaves
Keyword(s):  
Climate Change ◽  
National Security ◽  
Human Security ◽  
Climate Change Impacts ◽  
Future Research ◽  
National Interests ◽  
Domestic Conflict ◽  
The Third ◽  
Current State ◽  
Humanitarian Crises

This article examines the implications of human-caused climate change for security in Canada. The first section outlines the current state of climate change, the second discusses climate change impacts on human security in Canada, and the third outlines four other areas of Canada’s national interests threatened by climate change: economic threats; Arctic threats; humanitarian crises at home and abroad; and the threat of domestic conflict. In the conclusion, I argue that climate change has clearly not been successfully “securitized” in Canada, despite the material threats it poses to human and national security, and outline directions for future research.

scholarly journals Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland

2016 ◽  
Vol 13 (4) ◽  
pp. 961-973 ◽  
Author(s):  
W. Simonson ◽  
P. Ruiz-Benito ◽  
F. Valladares ◽  
D. Coomes
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Critical Role ◽  
Carbon Fluxes ◽  
Carbon Dynamics ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Low Carbon ◽  
Data Set ◽  
Multi Temporal

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 yr−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha−1 yr−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 yr−1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.

scholarly journals Text analysis on health product reviews using r approach

2020 ◽  
Vol 18 (3) ◽  
pp. 1303
Author(s):  
Nasibah Husna Mohd Kadir ◽  
Sharifah Aliman
Keyword(s):  
Language Processing ◽  
Text Summarization ◽  
Unstructured Data ◽  
Product Reviews ◽  
Health Product ◽  
Text Analytics ◽  
Data Set ◽  
Web Based ◽  
The Social ◽  
Key Techniques

In the social media, product reviews contain of text, emoticon, numbers and symbols that hard to identify the text summarization. Text analytics is one of the key techniques in exploring the unstructured data. The purpose of this study is solving the unstructured data by sort and summarizes the review data through a Web-Based Text Analytics using R approach. According to the comparative table between studies in Natural Language Processing (NLP) features, it was observed that Web-Based Text Analytics using R approach can analyze the unstructured data by using the data processing package in R. It combines all the NLP features in the menu part of the text analytics process in steps and it is labeled to make it easier for users to view all the text summarization. This study uses health product review from Shaklee as the data set. The proposed approach shows the acceptable performance in terms of system features execution compared with the baseline model system.

scholarly journals Environmental Change Threatens Freshwater Insect Communities in Northwest Africa: A Meta-Analysis

2021 ◽  
Vol 9 ◽  
Author(s):  
Nils Kaczmarek ◽  
Ralf B. Schäfer ◽  
Elisabeth Berger
Keyword(s):  
Climate Change ◽  
Water Temperature ◽  
Negative Binomial ◽  
Small Body ◽  
Future Research ◽  
Insect Communities ◽  
Data Set ◽  
Temperature Conductivity ◽  
Northwest Africa ◽  
The Impact

A climatic shift from temperate to arid conditions is predicted for Northwest Africa. Water temperature, salinity, and river intermittency are likely to increase, which may impact freshwater communities, ecosystem functioning, and related ecosystem services. Quantitative data and information on the impact of climate change on insect communities (e.g., richness, taxonomic and trait composition) are still scarce for Northwest Africa. In this study, we extracted information on freshwater insect occurrence and environmental variables in Northwest Africa from the results of a literature search to study potential consequences of changing climatic conditions for these communities. Our data set covered 96 families in 165 sites in Morocco and Algeria. We quantified the impact of several explanatoryvariables (climate, altitude, water temperature, conductivity, intermittency, flow, aridity, dams, and land cover) on richness, taxonomic and functional trait composition using negative binomial regression models and constrained ordination. Family richness in arid sites was on average 37 % lower than in temperate sites in association with flow, river regulation, cropland extent, conductivity, altitude, and water temperature. With 36 % of the studied temperate sites predicted to turn arid by the end of the century, a loss of insect families can be predicted for Northwest Africa, mainly affecting species adapted to temperate environments. Resistance and resilience traits such as small body size, aerial dispersal, and air breathing promote survival in arid climates. Future research should report insect occurrences on species level to allow for better predictions on climate change effects.

scholarly journals Climate change impact on streamflow in large-scale river basins: projections and their uncertainties sourced from GCMs and RCP scenarios

2018 ◽  
Vol 379 ◽  
pp. 139-144 ◽  
Author(s):  
Olga N. Nasonova ◽  
Yeugeniy M. Gusev ◽  
Evgeny E. Kovalev ◽  
Georgy V. Ayzel
Keyword(s):  
Climate Change ◽  
River Runoff ◽  
Climate Change Impact ◽  
Large Scale ◽  
River Basins ◽  
Surface Model ◽  
Second Phase ◽  
Rcp Scenarios ◽  
Data Set ◽  
Change Impact

Abstract. Climate change impact on river runoff was investigated within the framework of the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP2) using a physically-based land surface model Soil Water – Atmosphere – Plants (SWAP) (developed in the Institute of Water Problems of the Russian Academy of Sciences) and meteorological projections (for 2006–2099) simulated by five General Circulation Models (GCMs) (including GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) for each of four Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Eleven large-scale river basins were used in this study. First of all, SWAP was calibrated and validated against monthly values of measured river runoff with making use of forcing data from the WATCH data set and all GCMs' projections were bias-corrected to the WATCH. Then, for each basin, 20 projections of possible changes in river runoff during the 21st century were simulated by SWAP. Analysis of the obtained hydrological projections allowed us to estimate their uncertainties resulted from application of different GCMs and RCP scenarios. On the average, the contribution of different GCMs to the uncertainty of the projected river runoff is nearly twice larger than the contribution of RCP scenarios. At the same time the contribution of GCMs slightly decreases with time.

scholarly journals Evaluative application of UKCP09‐based downscaled future weather years to simulate overheating risk in typical English homes

2013 ◽  
Vol 31 (4) ◽  
pp. 231-252 ◽  
Author(s):  
Rajat Gupta ◽  
Matthew Gregg ◽  
Hu Du ◽  
Katie Williams
Keyword(s):  
Climate Change ◽  
Simulation Modelling ◽  
Simulation Software ◽  
Building Simulation ◽  
Future Research ◽  
Climate Projections ◽  
Data Sets ◽  
Data Set ◽  
Content Type ◽  
The Difference

PurposeTo critically compare three future weather year (FWY) downscaling approaches, based on the 2009 UK Climate Projections, used for climate change impact and adaptation analysis in building simulation software.Design/methodology/approachThe validity of these FWYs is assessed through dynamic building simulation modelling to project future overheating risk in typical English homes in 2050s and 2080s.FindingsThe modelling results show that the variation in overheating projections is far too significant to consider the tested FWY data sets equally suitable for the task.Research and practical implicationsIt is recommended that future research should consider harmonisation of the downscaling approaches so as to generate a unified data set of FWYs to be used for a given location and climate projection. If FWY are to be used in practice, live projects will need viable and reliable FWY on which to base their adaptation decisions. The difference between the data sets tested could potentially lead to different adaptation priorities specifically with regard to time series and adaptation phasing through the life of a building.Originality/valueThe paper investigates the different results derived from FWY application to building simulation. The outcome and implications are important considerations for research and practice involved in FWY data use in building simulation intended for climate change adaptation modelling.

Electroconvulsive therapy versus transcranial magnetic stimulation for major depression: a review with recommendations for future research

2008 ◽  
Vol 20 (6) ◽  
pp. 291-294 ◽  
Author(s):  
Keith G. Rasmussen
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Major Depression ◽  
Electroconvulsive Therapy ◽  
Magnetic Stimulation ◽  
Large Scale ◽  
The Elderly ◽  
Future Research ◽  
Sample Sizes ◽  
Data Set ◽  
Differential Outcomes

Objective:To review the literature comparing electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) for major depression.Methods:Data from the six randomised, prospective studies were agglutinated into one data set. Special attention was given to the methods of both TMS and ECT as well as data pertaining to differential outcomes in subgroups such as psychotic depressives and the elderly.Results:There is a highly significant advantage for ECT in the prospective, randomised trials. The two non-randomised, retrospective comparative trials found the treatments to be equal in one study and superior for ECT in another. However, sample sizes are small in these studies, and both TMS and ECT may have been used suboptimally. Furthermore, the possibilities of differential efficacy of ECT or TMS for psychotic depressives or as a function of age have yet to be fully explored.Conclusions:The data to date do not support the contention that TMS is equivalent in efficacy to ECT. It is recommended that a large-scale trial be undertaken using aggressive forms of both TMS and ECT with sample sizes sufficiently large to detect effects of moderating variables such as age and psychosis status.

scholarly journals Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

2021 ◽  
Vol 9 ◽  
pp. 1061-1080
Author(s):  
Prakhar Ganesh ◽  
Yao Chen ◽  
Xin Lou ◽  
Mohammad Ali Khan ◽  
Yin Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Research Attention ◽  
Model Compression ◽  
Future Research Directions ◽  
Or Applications

Abstract Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

scholarly journals Latent Dirichlet Allocation in predicting clinical trial terminations

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Simon Geletta ◽  
Lendie Follett ◽  
Marcia Laugerman
Keyword(s):  
Clinical Trial ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Latent Dirichlet Allocation ◽  
Structured Data ◽  
Unstructured Data ◽  
Future Research ◽  
Funding Agencies ◽  
Dirichlet Allocation

Abstract Background This study used natural language processing (NLP) and machine learning (ML) techniques to identify reliable patterns from within research narrative documents to distinguish studies that complete successfully, from the ones that terminate. Recent research findings have reported that at least 10 % of all studies that are funded by major research funding agencies terminate without yielding useful results. Since it is well-known that scientific studies that receive funding from major funding agencies are carefully planned, and rigorously vetted through the peer-review process, it was somewhat daunting to us that study-terminations are this prevalent. Moreover, our review of the literature about study terminations suggested that the reasons for study terminations are not well understood. We therefore aimed to address that knowledge gap, by seeking to identify the factors that contribute to study failures. Method We used data from the clinicialTrials.gov repository, from which we extracted both structured data (study characteristics), and unstructured data (the narrative description of the studies). We applied natural language processing techniques to the unstructured data to quantify the risk of termination by identifying distinctive topics that are more frequently associated with trials that are terminated and trials that are completed. We used the Latent Dirichlet Allocation (LDA) technique to derive 25 “topics” with corresponding sets of probabilities, which we then used to predict study-termination by utilizing random forest modeling. We fit two distinct models – one using only structured data as predictors and another model with both structured data and the 25 text topics derived from the unstructured data. Results In this paper, we demonstrate the interpretive and predictive value of LDA as it relates to predicting clinical trial failure. The results also demonstrate that the combined modeling approach yields robust predictive probabilities in terms of both sensitivity and specificity, relative to a model that utilizes the structured data alone. Conclusions Our study demonstrated that the use of topic modeling using LDA significantly raises the utility of unstructured data in better predicating the completion vs. termination of studies. This study sets the direction for future research to evaluate the viability of the designs of health studies.

Mother nature’s son?

2016 ◽  
Vol 8 (2) ◽  
pp. 137-172 ◽  
Author(s):  
Diana M. Hechavarría
Keyword(s):  
Cultural Values ◽  
Large Scale ◽  
Covariance Structure ◽  
Gender Socialization ◽  
Entrepreneurial Activity ◽  
Future Research ◽  
Female Entrepreneurs ◽  
Data Set ◽  
Content Type ◽  
Country Level

Purpose Drawing on the multiplicity of context approach, this study investigates whether female entrepreneurs are more likely than male entrepreneurs to create environmentally oriented organizations. This study aims to examine how context, measured by gender socialization stereotypes and post-materialism, differentially affects the kinds of organizations entrepreneurs choose to create. Design/methodology/approach To test the hypotheses, this study utilizes Global Entrepreneurship Monitor data from 2009 (n = 17,364) for nascent entrepreneurs, baby businesses owners and established business owners in 47 counties. This study also utilizes the World Values Surveys to measure gender ideologies and post-materialist cultural values at the country level. To test the hypotheses, a logistic multi-level model is estimated to identify the drivers of environmental venturing. Data are nested by countries, and this allows random intercepts by countries with a variance components covariance structure. Findings Findings indicate that female entrepreneurs are more likely to engage in ecological venturing. Societies with high levels of post-materialist national values are significantly more likely to affect female entrepreneurs to engage in environmental ventures when compared to male entrepreneurs. Moreover, traditional gender socialization stereotypes decrease the probability of engaging in environmental entrepreneurship. Likewise, female entrepreneurs in societies with strong stereotypes regarding gender socialization will more likely engage in environmental entrepreneurship than male entrepreneurs. Research limitations/implications The present study uses a gender analysis approach to investigate empirical differences in environmental entrepreneurial activity based on biological sex. However, this research assumes that gender is the driver behind variations in ecopreneurship emphasis between the engagement of males and females in venturing activity. The findings suggest that female entrepreneurs pursuing ecological ventures are more strongly influenced by contextual factors, when compared to male entrepreneurs. Future research can build upon these findings by applying a more nuanced view of gender via constructivist approaches. Originality/value This study is one of the few to investigate ecologically oriented ventures with large-scale empirical data by utilizing a 47-country data set. As a result, it begins to open the black box of environmental entrepreneurship by investigating the role of gender, seeking to understand if men and women entrepreneurs equally engage in environmental venturing. And it responds to calls that request more research at the intersection of gender and context in terms of environmental entrepreneurship.

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