Refugee Integration: Issues and Challenges

Majka, Theo J.; Majka, Linda C.

doi:10.1007/978-94-007-6179-7_106-1

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…For each of the non-linear CTGs variables with p < 0.2, univariate BLR models and Naive-Bayes models were developed. Asphyxia prediction models were implemented in R ( 48 ) using the Generalized Linear Models function to binomial logistic and the naivebayes package ( 49 ), respectively.…”

Section: Methodsmentioning

confidence: 99%

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Ribeiro

Nunes

Castro

et al. 2023

Front. Public Health

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IntroductionPerinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model.ObjectivesThis exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.MethodsSingle gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models.ResultsThe data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%].ConclusionBoth BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

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Section: Methodsmentioning

confidence: 99%

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Ribeiro

Nunes

Castro

et al. 2023

Front. Public Health

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“…Majka and Majka write that in the 1940s 'all of the white grower organizations supported [Japanese] evacuation, and the Western Growers Protective Association, the Associated Farmers, and California Farm Bureau'. 25 Even so, in the midst of war, the uS state concerned itself with agricultural production and stayed keenly aware that removal of Japanese farmers from California agriculture would adversely affect production levels. Further, the state envisioned a plan that was mapped out in racial terms.…”

Section: The State Sees An 'Enemy Race' In the Fieldsmentioning

confidence: 99%

“…33 Subsequently, in 1948, the state relinquished control of the Program to the Bureau of Employment Security that would, in turn, give the reins over to the California Placement Bureau, which had historically worked closely with growers and neglected farm labourers. 34 Still, the state's role as a labour contractor institutionalised a mode of racialising Mexican workers so that their role as farm workers was mutually constitutive with that of being foreign labour. And the deportability of Braceros was, as Don Mitchell astutely showed, a key characteristic of the Program.…”

Section: Deportable Mexican Labourmentioning

confidence: 99%

Labour militancy deferred: racial state interventions and the California farm worker struggle

Cruz¹

2014

Race & Class

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This article explains how the uS's employment of racial state projects directly affected efforts to unionise farm workers in California -the 1942 Bracero Program which imported Mexican agricultural workers into the uS Southwest, and Japanese internment, which forcibly moved Japanese people from their homes in uS Pacific coast states to internment camps situated throughout the country. Both projects decimated the momentum and militancy of the farm worker movement. Thus, the projects created a period of suspended social movement activity in which farm labour organisers directed their energies away from confrontation with growers and towards confrontations with the state. The state's emergence as a central actor in the farm labour struggle managed to exploit the differences in terms of land-ownership/class and citizenship rights between groups of workers: 'white' Americans, Mexican Americans, Japanese, Filipino and Braceros -non-citizen Mexican workers brought in on a temporary basis. All of which weakened the capacity for joint action -until the mid-1960s.

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“…The hyperparameter tuning procedure used a 10-fold cross-validation framework, while selecting predictors was conducted through a variable importance assessment. Both of these steps were executed using the 'h2o.grid' and 'h2o.varimp' functions within the 'h2o' package 'h2o' [54], 'caret' [55], 'naivebayes' [56], and 'foreach' [57]. Optimal hyperparameters were derived from the best model reporting the highest 'logloss' values (from 0% to 100%; the binary-class classification logloss equation was 1) and the 'h2o.getGrid' function.…”

mentioning

confidence: 99%

Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

Alvites,

Maesano,

Molina-Valero

et al. 2023

Remote Sensing

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Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level.

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Refugee Integration: Issues and Challenges

Cited by 3 publications

References 13 publications

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Labour militancy deferred: racial state interventions and the California farm worker struggle

Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

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