Interaction Between Electromagnetic Waves and Transport in Saturated Media

Air sparging is one of the most popular remediation technologies. However, it is limited to a small radius of influence (ROI) surrounding the air injection well. Hence, there have been several efforts to improve its effectiveness. To study the possibility of improving the effectivity of air sparging electromagnetic (EM) waves, an easily visible analogous problem (dye transport in water) is studied in this paper. In order to quantify the effects of EM stimulation on flow of an inert, nonreactive dye in water, EM-stimulated and unstipulated dye transport experiments tests were performed and compared. To quantify this interaction, both dye transport and EM wave propoagation (only the electric field component Z) are quantified experimentally in lab-scale. In addition to the experimental mapping of the electric field at limited location on depth (i.e., vertical) slices, the electric field is simulated in COMSOL Multiphysics 4.1 in three dimensions (3D) for accurate field analysis. Transport analysis of the dye was performed using digital imaging to determine temporal and spatial concentration variations. The results show a visible effect on the dye transport mechanisms (i.e., fingering and diffusion). However, further study is needed to validate the proposed correlation between the electric field and the transport mechanisms

Electromagnetically Induced Transport in Water for Geoenvironmental Applications

Air sparging is a popular soil remediation technique that enables the removal of contaminants through diffusing air into soil. The removal process is, however, slow. The goal of this work is to study the effect of electromagnetic (EM) waves —with minimal heat generation— on transport mechanisms such as diffusion, in order to improve airflow or contaminant transport in order to expedite the cleanup process using air sparging or similar technologies. This effect is studied through an experimental setup that examines the diffusion of a nonreactive dye in water under EM waves at a range of frequencies (50-200 MHz). The electric field was simulated using COMSOL Multiphysics for better three-dimensional (3D) visualization and analysis and then validated using the experimental measurements. A dielectrophoretic study was then performed using the simulated electric field. Various dye flows under EM stimulation at different frequencies were compared. At 65 MHz and 76 MHz, the dye flow was in the direction of the dielectrophoretic forces, which are believed to be the governing mechanism for the EM-stimulated dye transport

Analysis of Electromagnetic Stimulation of Transport in Water for Geoenvironmental Applications

Air sparging is a popular soil and groundwater remediation technique, which enables the removal of volatile organic compounds (VOCs) through diffusing contaminant-free air into saturated zones of soil. However, the VOC removal process is slow due to the soils\u27 low permeability, and might take months to years depending on the type of the soil and contaminant. The goal of this thesis is to study the effect of electromagnetic (EM) waves —with minimal heat generation— on transport mechanisms such as diffusion, in order to improve airflow and expedite the cleanup process using air sparging or similar technologies. Because water dipole molecules oscillate under alternating electric fields, EM waves can enhance transport mechanisms such as diffusion in saturated media. This effect is studied through an experimental setup that examines the diffusion of a nonreactive dye into water. Prior experimental work is analyzed and simulated by the author to find the potential correlation between the electric field magnitude/power and the flow characteristics of the dye. The experiment is simulated using the finite element software, COMSOL Multiphysics, to obtain a full vector representation of the EM field. In addition, the results from the digital analysis of the prior work are manipulated to study the concentration of the dye as well as the flow rate at different locations and times for all tests. The study, however, proved the necessity of a modified experimental setup for finding the correlation between the electric field pattern and the flow of the dye. For this reason, a modified experimental setup was developed. The new setup was tested at a range of frequencies 50-200 MHz. Measurement of the electric field component of EM waves is taken to map the electric field. In addition, the electric fields are simulated in COMSOL Multiphysics for better 3D visualization and analysis. A dielectrophoretic study is performed on the simulation data. The result of this study is in agreement with the experimental result of the dye flow. Recording the temperature change of the medium for different frequencies shows the same trend of the temperature change (less than 1°C) for all tests. However, only at the specific frequency of 65 MHz did the dye flow occur. Therefore, this observation suggest that thermal effects are not controlling the movement of the dye in the water. Since the flow of the dye is in the direction of the dielectrophoretic forces, it is believed that the governing mechanism for the dye transport is mainly dielectrophoresis

Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review

Planning an autonomous vehicle's (AV) path in a space shared with pedestrians requires reasoning about pedestrians' future trajectories. A practical pedestrian trajectory prediction algorithm for the use of AVs needs to consider the effect of the vehicle's interactions with the pedestrians on pedestrians' future motion behaviours. In this regard, this paper systematically reviews different methods proposed in the literature for modelling pedestrian trajectory prediction in presence of vehicles that can be applied for unstructured environments. This paper also investigates specific considerations for pedestrian-vehicle interaction (compared with pedestrian-pedestrian interaction) and reviews how different variables such as prediction uncertainties and behavioural differences are accounted for in the previously proposed prediction models. PRISMA guidelines were followed. Articles that did not consider vehicle and pedestrian interactions or actual trajectories, and articles that only focused on road crossing were excluded. A total of 1260 unique peer-reviewed articles from ACM Digital Library, IEEE Xplore, and Scopus databases were identified in the search. 64 articles were included in the final review as they met the inclusion and exclusion criteria. An overview of datasets containing trajectory data of both pedestrians and vehicles used by the reviewed papers has been provided. Research gaps and directions for future work, such as having more effective definition of interacting agents in deep learning methods and the need for gathering more datasets of mixed traffic in unstructured environments are discussed.Comment: Published in IEEE Transactions on Intelligent Transportation System

Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks

Predicting pedestrians' trajectories is a crucial capability for autonomous vehicles' safe navigation, especially in spaces shared with pedestrians. Pedestrian motion in shared spaces is influenced by both the presence of vehicles and other pedestrians. Therefore, effectively modelling both pedestrian-pedestrian and pedestrian-vehicle interactions can increase the accuracy of the pedestrian trajectory prediction models. Despite the huge literature on ways to encode the effect of interacting agents on a pedestrian's predicted trajectory using deep-learning models, limited effort has been put into the effective selection of interacting agents. In the majority of cases, the interaction features used are mainly based on relative distances while paying less attention to the effect of the velocity and approaching direction in the interaction formulation. In this paper, we propose a heuristic-based process of selecting the interacting agents based on collision risk calculation. Focusing on interactions of potentially colliding agents with a target pedestrian, we propose the use of time-to-collision and the approach direction angle of two agents for encoding the interaction effect. This is done by introducing a novel polar collision grid map. Our results have shown predicted trajectories closer to the ground truth compared to existing methods (used as a baseline) on the HBS dataset.Comment: Accepted for publication as a conference paper in IEEE Intelligent Transportation Systems Conference (ITSC), 202

Uncertainty-Aware DRL for Autonomous Vehicle Crowd Navigation in Shared Space

Safe, socially compliant, and efficient navigation of low-speed autonomous vehicles (AVs) in pedestrian-rich environments necessitates considering pedestrians' future positions and interactions with the vehicle and others. Despite the inevitable uncertainties associated with pedestrians' predicted trajectories due to their unobserved states (e.g., intent), existing deep reinforcement learning (DRL) algorithms for crowd navigation often neglect these uncertainties when using predicted trajectories to guide policy learning. This omission limits the usability of predictions when diverging from ground truth. This work introduces an integrated prediction and planning approach that incorporates the uncertainties of predicted pedestrian states in the training of a model-free DRL algorithm. A novel reward function encourages the AV to respect pedestrians' personal space, decrease speed during close approaches, and minimize the collision probability with their predicted paths. Unlike previous DRL methods, our model, designed for AV operation in crowded spaces, is trained in a novel simulation environment that reflects realistic pedestrian behaviour in a shared space with vehicles. Results show a 40% decrease in collision rate and a 15% increase in minimum distance to pedestrians compared to the state of the art model that does not account for prediction uncertainty. Additionally, the approach outperforms model predictive control methods that incorporate the same prediction uncertainties in terms of both performance and computational time, while producing trajectories closer to human drivers in similar scenarios.Comment: Accepted for publication in IEEE Transactions on Intelligent Vehicle

Aberrant DNA Methylation Status and mRNA Expression Level of SMG1 Gene in Chronic Myeloid Leukemia: A Case-Control Study

Objective Chronic myeloid leukemia (CML) is a myeloproliferative malignancy with different stages. Aberrant epigeneticmodifications, such as DNA methylation, have been introduced as a signature for diverse cancers which also plays acrucial role in CML pathogenesis and development. Suppressor with morphogenetic effect on genitalia (SMG1) generecently has been brought to the spotlight as a potent tumor suppressor gene that can be suppressed by tumors forfurther progress. The present study aims to investigate SMG1 status in CML patients. Materials and Methods In this case-control study, peripheral blood from 30 patients with different phases of CML [newcase (N)=10, complete molecular remission (CMR)=10, blastic phase (BP)=10] and 10 healthy subjects were collected.Methylation status and expression level of SMG1 gene promoter was assessed by methylation-specific polymerasechain reaction (MSP) and quantitative reverse-transcription PCR, respectively. Results MSP results of SMG1 gene promotor in the new case group were methylated (60% methylated, 30%hemimethylated and 10% unmethylated). All CMR and control group patients were unmethylated in the SMG1 genepromoter. In the BP group, methylated SMG1 promoter was seen (50% of patients had a methylated status and 50%had hemimethylated status). In comparison with the healthy subjects, expression level of SMG1 in the new case groupwas decreased (P<0.01); in the CMR group and BP-CML groups, it was increased (P<0.05). No significant correlationbetween patients’ hematological features and SMG1 methylation was seen. Conclusion Our results demonstrated that aberrant methylation of SMG1 occurred in CML patients and it had asignificant association with SMG1 expression. SMG1 gene promoter showed diverse methylated status and subsequentexpression levels in different phases of CML. These findings suggested possible participation of SMG1 suppression inthe CML pathogenesis

Global systematic review of primary immunodeficiency registries

Introduction During the last 4 decades, registration of patients with primary immunodeficiencies (PID) has played an essential role in different aspects of these diseases worldwide including epidemiological indexes, policymaking, quality controls of care/life, facilitation of genetic studies and clinical trials as well as improving our understanding about the natural history of the disease and the immune system function. However, due to the limitation of sustainable resources supporting these registries, inconsistency in diagnostic criteria and lack of molecular diagnosis as well as difficulties in the documentation and designing any universal platform, the global perspective of these diseases remains unclear. Areas covered Published and unpublished studies from January 1981 to June 2020 were systematically reviewed on PubMed, Web of Science and Scopus. Additionally, the reference list of all studies was hand-searched for additional studies. This effort identified a total of 104614 registered patients and suggests identification of at least 10590 additional PID patients, mainly from countries located in Asia and Africa. Molecular defects in genes known to cause PID were identified and reported in 13852 (13.2% of all registered) patients. Expert opinion Although these data suggest some progress in the identification and documentation of PID patients worldwide, achieving the basic requirement for the global PID burden estimation and registration of undiagnosed patients will require more reinforcement of the progress, involving both improved diagnostic facilities and neonatal screening.Peer reviewe

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation