The Feasibility of Using Data-Driven Algorithmic Recommendations for Refugee Placement in Norway

A growing body of research suggests that refugees’ initial settlement area can have a long-run impact on subsequent integration outcomes. As a result, matching refugees and asylum seekers to initial locations where they are likely to succeed holds the potential to improve their labor market integration. In this report we focus on the GeoMatch algorithm, which is a recommendation tool that provides settlement officers with data-driven location recommendations for incoming refugees and asylum seekers. Leveraging machine learning on historical data, the tool predicts labor market outcomes for individuals across possible settlement areas. A flexible allocation algorithm then provides location recommendations for each family unit while taking capacity constraints into account. Drawing on administrative data from Statistics Norway and incorporating a set of realistic constraints, we find that using GeoMatch recommendations could improve refugees’ monthly earnings by up to 55% over baseline. The report ends with a discussion of how the tool can be implemented in the Norwegian context.The Feasibility of Using Data-Driven Algorithmic Recommendations for Refugee Placement in NorwaypublishedVersio

Sustainable product development of biomass briquette from Samanea saman leaf waste with rejected papaya as the binding agent in Indonesia

The depletion of solid fuels in the world triggers the requirement for the existence of an alternative fuel product as a substitute. In several studies, briquettes have become one of the alternatives used to deal with this problem. However, the selection of briquette material that considers its impact on economic, social, and environmental aspects has not been widely carried out. To solve this problem, the selection of raw materials for briquettes must be determined using the concept of sustainable product development (SPD). SPD is a concept that can be used to determine a new product by considering various aspects, including economic, environmental, and social aspects. Briquette fuel that is derived from leaf waste and rejected fruit as a binding agent can be an alternative to renewable solid fuels since the raw materials come from wastes that have no selling value. The methodology used in this research is by conducting a survey at five markets in Surabaya and proceeding with interviews with the local government official. Pugh Matrix Concept Selection (PMCS) method is used to determine the suitable raw materials and to analyze the sustainability of the product development. Through PMCS, the results show that the best material for briquette production based on economic, social, and environmental aspects is a mixture of Samanea saman leaf waste with a 12.83 rating value and rejected papaya as the binding agent with a 10.44 rating value. Briquette with a mixture of 95% Samanea saman leaf waste and 5% rejected papaya is produced with a mesh size of 60 or 250 μm and a compression pressure of 2 MPa and is identified to have a heating value of 4025.87 Kcal/Kg

Mosquito transcriptome changes and filarial worm resistance in Armigeres subalbatus

<p>Abstract</p> <p>Background</p> <p><it>Armigeres subalbatus </it>is a natural vector of the filarial worm <it>Brugia pahangi</it>, but it rapidly and proficiently kills <it>Brugia malayi </it>microfilariae by melanotic encapsulation. Because <it>B. malayi </it>and <it>B. pahangi </it>are morphologically and biologically similar, the <it>Armigeres-Brugia </it>system serves as a valuable model for studying the resistance mechanisms in mosquito vectors. We have initiated transcriptome profiling studies in <it>Ar. subalbatus </it>to identify molecular components involved in <it>B. malayi </it>refractoriness.</p> <p>Results</p> <p>These initial studies assessed the transcriptional response of <it>Ar. subalbatus </it>to <it>B. malayi </it>at 1, 3, 6, 12, 24, 48, and 72 hrs after an infective blood feed. In this investigation, we initiated the first holistic study conducted on the anti-filarial worm immune response in order to effectively explore the functional roles of immune-response genes following a natural exposure to the parasite. Studies assessing the transcriptional response revealed the involvement of unknown and conserved unknowns, cytoskeletal and structural components, and stress and immune responsive factors. The data show that the anti-filarial worm immune response by <it>Ar. subalbatus </it>to be a highly complex, tissue-specific process involving varied effector responses working in concert with blood cell-mediated melanization.</p> <p>Conclusion</p> <p>This initial study provides a foundation and direction for future studies, which will more fully dissect the nature of the anti-filarial worm immune response in this mosquito-parasite system. The study also argues for continued studies with RNA generated from both hemocytes and whole bodies to fully expound the nature of the anti-filarial worm immune response.</p

Persistence of Brugia malayi DNA in vector and non-vector mosquitoes: Implications for xenomonitoring and transmission monitoring of lymphatic filariasis

Abstract. Xenomonitoring (detection of filarial larvae or their DNA in mosquitoes) is a sensitive marker for as-sessing the endemicity of filariasis and a useful tool for evaluating elimination programs. To examine the fate of microfilariae (mf) and filarial DNA in vector competent and non-competent mosquito strains, we compared the detec-tion of Brugia malayi parasites by dissection and by quantitative real-time polymerase chain reaction (PCR) in three different mosquito strains. We conclude that PCR is much more sensitive than dissection for detecting filarial larvae, especially their remnants in mosquitoes. However, parasite DNA can be detected in both vector and non-vector mosquitoes for two weeks or longer after they ingest mf-positive blood. Thus, although xenomonitoring with vector and non-vector mosquito species may be a sensitive method for indirectly detecting filarial parasites in human populations, positive test results for parasite DNA in mosquitoes do not necessarily prove that transmission is ongoing in the study area

Settlement and Community Patterns at Sayil, Yucatan, Mexico: The 1984 Season

The lowlands of the vast Yucatan Peninsula, where ancient Maya civilizations flourished for 2000 years until the Spanish Conquest in the 16th century, are conventionally divided into the wet tropical Southern Lowlands of Guatemala and Belize and the dry tropical Northern Lowlands in Mexico. The apparent peak of Maya civilization, or at least of archaeologists\u27 attention to it, is in the Southern Lowlands in the centuries before its collapse around A.D. 900 (the Classic Period), with thereafter the finest remains occurring in the Northern Lowlands, especially in the Puuc (i.e. hilly) region in the northwest corner of the peninsula. Our attention has been brought to this Puuc region in part because its florescence spans the critical A.D. 800-1000 period during which great, if not cataclysmic, changes occurred in the course and locales of Maya civilization and its people. This region should, therefore, offer a most constructive contrast to the chaos of other contemporaneous and, so far, better studied regions. Furthermore, study of the dwelling places of the people should provide evidence not only for just how many they were and how they lived through those tumultuous times, but also for their trade with and possible origins from other regions, and even, perhaps, evidence that they had a significant role in the changes occurring there

A short leucocyte telomere length is associated with development of insulin resistance

International audienceAIMS/HYPOTHESIS:A number of studies have shown that leucocyte telomere length (LTL) is inversely associated with insulin resistance and type 2 diabetes mellitus. The aim of the present longitudinal cohort study, utilising a twin design, was to assess whether shorter LTL predicts insulin resistance or is a consequence thereof.METHODS:Participants were recruited between 1997 and 2000 through the population-based national Danish Twin Registry to participate in the GEMINAKAR study, a longitudinal evaluation of metabolic disorders and cardiovascular risk factors. Baseline and follow-up measurements of LTL and insulin resistance over an average of 12 years were performed in a subset of the Registry consisting of 338 (184 monozygotic and 154 dizygotic) same-sex twin pairs.RESULTS:Age at baseline examination was 37.4 ± 9.6 (mean ± SD) years. Baseline insulin resistance was not associated with age-dependent changes in LTL (attrition) over the follow-up period, whereas baseline LTL was associated with changes in insulin resistance during this period. The shorter the LTL at baseline, the more pronounced was the increase in insulin resistance over the follow-up period (p < 0.001); this effect was additive to that of BMI. The co-twin with the shorter baseline LTL displayed higher insulin resistance at follow-up than the co-twin with the longer LTL.CONCLUSIONS/INTERPRETATION:These findings suggest that individuals with short LTL are more likely to develop insulin resistance later in life. By contrast, presence of insulin resistance does not accelerate LTL attrition

Non-inferiority of Deep Learning Model to Segment Acute Stroke on Non-contrast CT Compared to Neuroradiologists

Purpose: To develop a deep learning model to segment the acute ischemic infarct on non-contrast Computed Tomography (NCCT). Materials and Methods In this retrospective study, 227 Head NCCT examinations from 200 patients enrolled in the multicenter DEFUSE 3 trial were included. Three experienced neuroradiologists (experts A, B and C) independently segmented the acute infarct on each study. The dataset was randomly split into 5 folds with training and validation cases. A 3D deep Convolutional Neural Network (CNN) architecture was optimized for the data set properties and task needs. The input to the model was the NCCT and the output was a segmentation mask. The model was trained and optimized on expert A. The outcome was assessed by a set of volume, overlap and distance metrics. The predicted segmentations of the best model and expert A were compared to experts B and C. Then we used a paired Wilcoxon signed-rank test in a one-sided test procedure for all metrics to test for non-inferiority in terms of bias and precision. Results: The best performing model reached a Surface Dice at Tolerance (SDT)5mm of 0.68 \pm 0.04. The predictions were non-inferior when compared to independent experts in terms of bias and precision (paired one-sided test procedure for differences in medians and bootstrapped standard deviations with non-inferior boundaries of -0.05, 2ml, and 2mm, p < 0.05, n=200). Conclusion: For the segmentation of acute ischemic stroke on NCCT, our 3D CNN trained with the annotations of one neuroradiologist is non-inferior when compared to two independent neuroradiologists

Integrating Climate Change Adaptation into Public Health Practice: Using Adaptive Management to Increase Adaptive Capacity and Build Resilience

Background: Climate change is expected to have a range of health impacts, some of which are already apparent. Public health adaptation is imperative, but there has been little discussion of how to increase adaptive capacity and resilience in public health systems