Sensor Fusion and Deep Learning for Indoor Agent Localization

Autonomous, self-navigating agents have been rising in popularity due to a push for a more technologically aided future. From cars to vacuum cleaners, the applications of self-navigating agents are vast and span many different fields and aspects of life. As the demand for these autonomous robotic agents has been increasing, so has the demand for innovative features, robust behavior, and lower cost hardware. One particular area with a constant demand for improvement is localization, or an agent\u27s ability to determine where it is located within its environment. Whether the agent\u27s environment is primarily indoor or outdoor, dense or sparse, static or dynamic, an agent must be able to have knowledge of its location. Many different techniques exist today for localization, each having its strengths and weaknesses. Despite the abundance of different techniques, there is still room for improvement. This research presents a novel indoor localization algorithm that fuses data from multiple sensors for a relatively low cost. Inspired by recent innovations in deep learning and particle filters, a fast, robust, and accurate autonomous localization system has been created. Results demonstrate that the proposed system is both real-time and robust against changing conditions within the environment

Modelling of Usual Nutrient Intakes: Potential Impact of the Choices Programme on Nutrient Intakes in Young Dutch Adults

Introduction The Choices Programme is an internationally applicable nutrient profiling system with nutrition criteria for trans fatty acids (TFA), saturated fatty acids, sodium, added sugar and for some product groups energy and fibre. These criteria determine whether foods are eligible to carry a “healthier option” stamp. In this paper a nutrient intake modelling method is described to evaluate these nutritional criteria by investigating the potential effect on nutrient intakes. Methods Data were combined from the 2003 Dutch food consumption survey in young adults (aged 19–30) and the Dutch food composition table into the Monte Carlo Risk Assessment model. Three scenarios were calculated: the “actual intakes” (scenario 1) were compared to scenario 2, where all foods that did not comply were replaced by similar foods that did comply with the Choices criteria. Scenario 3 was the same as scenario 2 adjusted for the difference in energy density between the original and replacement food. Additional scenarios were calculated where snacks were not or partially replaced and stratified analyses for gender, age, Body Mass Index (BMI) and education. Results Calculated intake distributions showed that median energy intake was reduced by 16% by replacing normally consumed foods with Choices compliant foods. Intakes of nutrients with a maximal intake limit were also reduced (ranging from -23% for sodium and -62% for TFA). Effects on intakes of beneficial nutrients varied from an unintentional reduction in fat soluble vitamin intakes (-15 to -28%) to an increase of 28% for fibre and 17% calcium. Stratified analyses in this homogeneous study population showed only small differences across gender, age, BMI and education. Conclusions This intake modelling method showed that with consumption of Choices compliant foods, nutrient intakes shift towards population intake goals for the nutrients for which nutrition criteria were defined, while effects on beneficial nutrients were diverse

The LifeCycle Project-EU Child Cohort Network : a federated analysis infrastructure and harmonized data of more than 250,000 children and parents

Early life is an important window of opportunity to improve health across the full lifecycle. An accumulating body of evidence suggests that exposure to adverse stressors during early life leads to developmental adaptations, which subsequently affect disease risk in later life. Also, geographical, socio-economic, and ethnic differences are related to health inequalities from early life onwards. To address these important public health challenges, many European pregnancy and childhood cohorts have been established over the last 30 years. The enormous wealth of data of these cohorts has led to important new biological insights and important impact for health from early life onwards. The impact of these cohorts and their data could be further increased by combining data from different cohorts. Combining data will lead to the possibility of identifying smaller effect estimates, and the opportunity to better identify risk groups and risk factors leading to disease across the lifecycle across countries. Also, it enables research on better causal understanding and modelling of life course health trajectories. The EU Child Cohort Network, established by the Horizon2020-funded LifeCycle Project, brings together nineteen pregnancy and childhood cohorts, together including more than 250,000 children and their parents. A large set of variables has been harmonised and standardized across these cohorts. The harmonized data are kept within each institution and can be accessed by external researchers through a shared federated data analysis platform using the R-based platform DataSHIELD, which takes relevant national and international data regulations into account. The EU Child Cohort Network has an open character. All protocols for data harmonization and setting up the data analysis platform are available online. The EU Child Cohort Network creates great opportunities for researchers to use data from different cohorts, during and beyond the LifeCycle Project duration. It also provides a novel model for collaborative research in large research infrastructures with individual-level data. The LifeCycle Project will translate results from research using the EU Child Cohort Network into recommendations for targeted prevention strategies to improve health trajectories for current and future generations by optimizing their earliest phases of life.Peer reviewe

Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy

The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk

Enhancing vector refractoriness to trypanosome infection : achievements, challenges and perspectives

With the absence of effective prophylactic vaccines and drugs against African trypanosomosis, control of this group of zoonotic neglected tropical diseases depends the control of the tsetse fly vector. When applied in an area-wide insect pest management approach, the sterile insect technique (SIT) is effective in eliminating single tsetse species from isolated populations. The need to enhance the effectiveness of SIT led to the concept of investigating tsetse-trypanosome interactions by a consortium of researchers in a five-year (2013-2018) Coordinated Research Project (CRP) organized by the Joint Division of FAO/IAEA. The goal of this CRP was to elucidate tsetse-symbiome-pathogen molecular interactions to improve SIT and SIT-compatible interventions for trypanosomoses control by enhancing vector refractoriness. This would allow extension of SIT into areas with potential disease transmission. This paper highlights the CRP's major achievements and discusses the science-based perspectives for successful mitigation or eradication of African trypanosomosis.</p

Coronal Heating as Determined by the Solar Flare Frequency Distribution Obtained by Aggregating Case Studies

Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counter-intuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfv\'en waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, $\alpha=2$ as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed $>$600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: pre-flare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that $\alpha = 1.63 \pm 0.03$. This is below the critical threshold, suggesting that Alfv\'en waves are an important driver of coronal heating.Comment: 1,002 authors, 14 pages, 4 figures, 3 tables, published by The Astrophysical Journal on 2023-05-09, volume 948, page 7