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1. In harm’s way: Non-migration decisions of people at risk of slow-onset coastal hazards in Bangladesh

   https://doi.org/10.1007/s13280-021-01552-8  

   Mallick, Bishawjit; Rogers, Kimberly G.; Sultana, Zakia (January 2021, Ambio)

   null (Ed.)

   Abstract Non-migration is an adaptive strategy that has received little attention in environmental migration studies. We explore the leveraging factors of non-migration decisions of communities at risk in coastal Bangladesh, where exposure to both rapid- and slow-onset natural disasters is high. We apply the Protection Motivation Theory (PMT) to empirical data and assess how threat perception and coping appraisal influences migration decisions in farming communities suffering from salinization of cropland. This study consists of data collected through quantitative household surveys ( n  = 200) and semi-structured interviews from four villages in southwest coastal Bangladesh. Results indicate that most respondents are unwilling to migrate, despite better economic conditions and reduced environmental risk in other locations. Land ownership, social connectedness, and household economic strength are the strongest predictors of non-migration decisions. This study is the first to use the PMT to understand migration-related behaviour and the findings are relevant for policy planning in vulnerable regions where exposure to climate-related risks is high but populations are choosing to remain in place. 

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2. Bangladesh Environment and Migration Survey (BEMS), 2019 :Version 1

   https://doi.org/10.3886/ICPSR38846.v1  

   Carrico, Amanda; Donato, Katharine M. (January 2023, ICPSR - Interuniversity Consortium for Political and Social Research)

   The Bangladesh Environment and Migration Survey (BEMS) collects detailed retrospective information about migration trips in southwest Bangladesh, including the first, last, and second-to-last to internal destinations, India, and other international destinations. BEMS collects information about the year, origin, destination, and duration of all trips. Furthermore, BEMS includes information on migration and livelihood histories, socioeconomic conditions, agricultural resources and practices, disasters and perceptions about environment, and self-reported health.</p> Dataset 1 is a household-level file with information about household composition, economic and migratory activity of household members, land ownership/usage, business ownership, household environmental perceptions, environmental conditions, agricultural activities, and physical and psychological health/well-being of household members. Dataset 2 is an individual-level file containing details of internal and international migration trips, as well as measures of economic and social activity during those trips. It also contains information provided by household heads, spouses, and other migrants in the household. Dataset 3 is an individual-level data file that provides general demographic information and brief migration history for each member of a surveyed household. It also includes health information for the head of household and spouse.</p> The purpose of the Bangladesh Environment and Migration Survey (BEMS) is to understand patterns and processes of contemporary internal and international migration in Bangladesh. The project derives from a multi-disciplinary research effort that will generate data on the characteristics and behavior of Bangladeshi migrants and non-migrants and the communities in which they live, and examine whether and how environmental stressors (e.g., salinity, riverbank erosion) affect patterns of migration in this region. The household ethnosurvey is administered to self-identified household heads and spouses in randomly selected households. After gathering social, demographic, and economic information on households and their members, interviewers will collect basic information on each person's first, 2nd to last, and last (or most recent) internal and international migration trips. From household heads and spouses, they will compile migration histories and administer a detailed series of questions about a selection of these trips, focusing on economic livelihoods, methods of moving, connections to other migrants, and use of health and school services. In addition to detailed migration histories, the BEMS will collect information about household wealth, physical conditions of households and communities, and perceptions of environmental conditions. It will also gather some self-reported health information about household members, such as recent illnesses, use of health services, height and weight, and diet. The BEMS is closely modeled on the sampling design and ethnosurvey used in the Mexican Migration Project. The BEMS data were collected in 20 research sites from a random sample of 200 households in each site in 2019. BEMS data include a total of 4,000 households in communities broadly covering the southwest region of Bangladesh. Households in southwest Bangladesh. Smallest Geographic Unit: Administrative region For more information about this study, please visit the ISEE Bangladesh project website.</p> 

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3. RFtest: A Robust and Flexible Community-Level Test for Microbiome Data Powerfully Detects Phylogenetically Clustered Signals

   https://doi.org/10.3389/fgene.2021.749573  

   Zhang, Lujun; Wang, Yanshan; Chen, Jingwen; Chen, Jun (January 2022, Frontiers in Genetics)

   Random forest is considered as one of the most successful machine learning algorithms, which has been widely used to construct microbiome-based predictive models. However, its use as a statistical testing method has not been explored. In this study, we propose “Random Forest Test” (RFtest), a global (community-level) test based on random forest for high-dimensional and phylogenetically structured microbiome data. RFtest is a permutation test using the generalization error of random forest as the test statistic. Our simulations demonstrate that RFtest has controlled type I error rates, that its power is superior to competing methods for phylogenetically clustered signals, and that it is robust to outliers and adaptive to interaction effects and non-linear associations. Finally, we apply RFtest to two real microbiome datasets to ascertain whether microbial communities are associated or not with the outcome variables. 

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4. Evaluating the Performance of Topic Modeling Techniques with Human Validation to Support Qualitative Analysis

   https://doi.org/10.3390/bdcc8100132  

   Romero, Julian D; Feijoo-Garcia, Miguel A; Nanda, Gaurav; Newell, Brittany; Magana, Alejandra J (October 2024, Big Data and Cognitive Computing)

   Leung, Carson (Ed.)

   Examining the effectiveness of machine learning techniques in analyzing engineering students’ decision-making processes through topic modeling during simulation-based design tasks is crucial for advancing educational methods and tools. Thus, this study presents a comparative analysis of different supervised and unsupervised machine learning techniques for topic modeling, along with human validation. Hence, this manuscript contributes by evaluating the effectiveness of these techniques in identifying nuanced topics within the argumentation framework and improving computational methods for assessing students’ abilities and performance levels based on their informed decisions. This study examined the decision-making processes of engineering students as they participated in a simulation-based design challenge. During this task, students were prompted to use an argumentation framework to articulate their claims, evidence, and reasoning, by recording their informed design decisions in a design journal. This study combined qualitative and computational methods to analyze the students’ design journals and ensured the accuracy of the findings through the researchers’ review and interpretations of the results. Different machine learning models, including random forest, SVM, and K-nearest neighbors (KNNs), were tested for multilabel regression, using preprocessing techniques such as TF-IDF, GloVe, and BERT embeddings. Additionally, hyperparameter optimization and model interpretability were explored, along with models like RNNs with LSTM, XGBoost, and LightGBM. The results demonstrate that both supervised and unsupervised machine learning models effectively identified nuanced topics within the argumentation framework used during the design challenge of designing a zero-energy home for a Midwestern city using a CAD/CAE simulation platform. Notably, XGBoost exhibited superior predictive accuracy in estimating topic proportions, highlighting its potential for broader application in engineering education. 

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5. High-Resolution Mapping of Soil Organic Carbon Under Different Land-Use Types Using NEON Remote Sensing Data.

   Sihi, D; Gelsleichter, YA; Konduri, VS; Wang, Z; Kumar, J; Weintraub-Leff, SR (November 2024, ASA, CSSA, SSSA International Annual Meeting, San Antonio, TX. https://scisoc.confex.com/scisoc/2024am/meetingapp.cgi/Paper/160215)

   Soil organic carbon (SOC) represents the largest terrestrial carbon pool. Effectively monitoring SOC at high spatial resolution is crucial for estimating carbon budgets at the ecosystem scale and informing climate change mitigation efforts at the regional scale. Traditional soil sampling methods, however, are laborious and expensive. Remote sensing platforms can be used to survey large landscapes to meet the need for rapid and cost-effective approaches for quantifying SOC at landscape to regional scales, if relationships between remotely sensed variables and SOC can be established. We developed a workflow to analyze and predict SOC content based on National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) remote sensing data. First, we benchmarked related tools and developed reproducible workflows using NEON remote sensing datasets. Hyperspectral data were extracted from the locations where NEON soil data exist. Additional variables from the LiDAR data and key metadata (climate and land cover) were extracted for those locations. Random Forest and Partial Least Squares Regression techniques were then used to create models for fine-scale SOC prediction. Cross-validation was embedded in the model creation step. The most important covariates were selected through recursive feature elimination, stepwise selection, and expert judgment. Preliminary results indicate that machine learning models can re-produce SOC measurements in testing datasets. Key predictors include topographic variables, vegetation indices, and specific wavelength bands in hyperspectral images. We are further validating our algorithms using SOC data from ISCN (International Soil Carbon Network) and SoDaH (SOils DAta Harmonization database) that are co-located with NEON sites. We are creating high-resolution SOC maps for 0-30 cm depth at NEON sites and testing our algorithms for different land use types. Our work paves the way for a broader assessment of SOC stocks using remote sensing observations, and our high-resolution SOC maps will potentially help quantify carbon budgets across heterogeneous landscapes. 

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