scholarly journals Global and country-level estimates of human population at high altitude

2021 ◽  
Vol 118 (18) ◽  
pp. e2102463118
Author(s):  
Joshua C. Tremblay ◽  
Philip N. Ainslie
Keyword(s):  
High Altitude ◽  
Human Population ◽  
Population Distribution ◽  
Disease Prevalence ◽  
Country Level ◽  
Elevation Data ◽  
Terrain Elevation ◽  
Global Population ◽  
Absolute Population ◽  
Epidemiology And Public Health

Estimates of the global population of humans living at high altitude vary widely, and such data at the country level are unavailable. Herein, we use a geographic information system (GIS)-based approach to quantify human population at 500-m elevation intervals for each country. Based on georeferenced data for population (LandScan Global 2019) and elevation (Global Multiresolution Terrain Elevation Data), 500.3 million humans live at ≥1,500 m, 81.6 million at ≥2,500 m, and 14.4 million at ≥3,500 m. Ethiopia has the largest absolute population at ≥1,500 m and ≥2,500 m, while China has the greatest at ≥3,500 m. Lesotho has the greatest percentage of its population above 1,500 m, while Bolivia has the greatest at ≥2,500 m and ≥3,500 m. High altitude presents a myriad of environmental stresses that provoke physiological responses and adaptation, and consequently impact disease prevalence and severity. While the majority of high-altitude physiology research is based upon lowlanders from western, educated, industrialized, rich, and democratic countries ascending to high altitude, the global population distribution of high-altitude residents encourages an increased emphasis on understanding high-altitude physiology, adaptation, epidemiology, and public health in the ∼500 million permanent high-altitude residents.

scholarly journals A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015

2020 ◽  
Vol 42 (2) ◽  
pp. 255-277
Author(s):  
Maria Francisca Archila Bustos ◽  
Ola Hall ◽  
Thomas Niedomysl ◽  
Ulf Ernstson
Keyword(s):  
High Resolution ◽  
Human Activity ◽  
Human Population ◽  
Population Distribution ◽  
Swedish Population ◽  
Policy Makers ◽  
Modeling Techniques ◽  
Global Population ◽  
Over Time

Abstract Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is fundamental and use of freely available, high-resolution, gridded datasets on global population as a source of such information is increasing. However, there is little research to guide users in dataset choice. This study evaluates five of the most commonly used global gridded population datasets against a high-resolution Swedish population dataset on a pixel level. We show that datasets which employ more complex modeling techniques exhibit lower errors overall but no one dataset performs best under all situations. Furthermore, differences exist in how unpopulated areas are identified and changes in algorithms over time affect accuracy. Our results provide guidance in navigating the differences between the most commonly used gridded population datasets and will help researchers and policy makers identify the most suitable datasets under varying conditions.

scholarly journals Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Brandon Malone ◽  
Boris Simovski ◽  
Clément Moliné ◽  
Jun Cheng ◽  
Marius Gheorghe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Monte Carlo ◽  
Cell Surface ◽  
Antigen Presentation ◽  
Human Population ◽  
Host Immune System ◽  
T Cell Epitopes ◽  
Digital Twin ◽  
Novel Coronavirus ◽  
Global Population

AbstractThe global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

scholarly journals A Novel LOS Decision Technique Reflecting 3D DTED for Modeling an Electronic Warfare Environment

2021 ◽  
Vol 11 (1) ◽  
pp. 409
Author(s):  
Jaejoong Lee ◽  
Chiho Lee ◽  
Hyeon Hwi Lee ◽  
Kyung Tae Park ◽  
Hyun-Kyo Jung ◽  
...  
Keyword(s):  
Interpolation Method ◽  
Three Dimensional ◽  
Superior Performance ◽  
Line Of Sight ◽  
Electronic Warfare ◽  
Decision Algorithm ◽  
Elevation Data ◽  
The Republic ◽  
Point Distance ◽  
Terrain Elevation

A new line-of-sight (LOS) decision algorithm applicable to simulation of electronic warfare (EW) is developed. For accurate simulation, the digital terrain elevation data (DTED) of the region to be analyzed must be reflected in the simulation, and millions of datasets are necessary in the EW environment. In order to obtain real-time results in such an environment, a technology that determines line-of-sight (LOS) quickly and accurately is very important. In this paper, a novel algorithm is introduced for determining LOS that can be applied in an EW environment with three-dimensional (3D) DTED. The proposed method shows superior performance as compared with the simplest point-to-point distance calculation method and it is also 50% faster than the conventional interpolation method. The DTED used in this paper is the data applied as level 0 for the Republic of Korea, and the decision of the LOS at approximately 1.8 million locations viewed by a reconnaissance plane flying 10 km above the ground is determined within 0.026 s.

Surface modelling of human population distribution in China

2005 ◽  
Vol 181 (4) ◽  
pp. 461-478 ◽  
Author(s):  
Tian Xiang Yue ◽  
Ying An Wang ◽  
Ji Yuan Liu ◽  
Shu Peng Chen ◽  
Dong Sheng Qiu ◽  
...  
Keyword(s):  
Human Population ◽  
Population Distribution ◽  
Surface Modelling

scholarly journals Population Backfire: Hindsight to Malthusian Catastrophe

2020 ◽  
Vol 8 (1) ◽  
pp. 121-126
Author(s):  
R Ramya ◽  
C C Babu ◽  
P Akshay
Keyword(s):  
Food Supply ◽  
Human Population ◽  
Future Generation ◽  
Population Pressure ◽  
Basic Tenet ◽  
Sufficient Food Supply ◽  
Per Capita ◽  
Sufficient Food ◽  
Global Population ◽  
Malthusian Trap

The basic tenet of Economics lies in the scarcity principle and unlimited nature of human wants, but allocating a definite amount of resources to satisfy the growing per capita needs in an economy is a difficult task. Things become more complicated when the population pressure generates backfire. The global population has increased to 7.8 billion, and it is essential to ensure sufficient food supply for the growing human population as well as for other species without destroying ecological balance is a serious matter to consider. An evaluation of Malthusian population theories has great importance in this context. This paper intends to analyze the Malthusian theory of population and what happens if population backfire happens and also looks into the intensity of positive checks on population along with the Malthusian trap and its effect on the present as well as the future generation.

scholarly journals Spatially-explicit estimates of global wastewater production, collection, treatment and re-use

2020 ◽  
Author(s):  
Edward R. Jones ◽  
Michelle T. H. van Vliet ◽  
Manzoor Qadir ◽  
Marc F. P. Bierkens
Keyword(s):  
Large Scale ◽  
Western Europe ◽  
Treated Wastewater ◽  
Spatially Explicit ◽  
Wastewater Management ◽  
Online Data ◽  
Untreated Wastewater ◽  
Country Level ◽  
Data Driven Approach ◽  
Global Population

Abstract. Continually improving and affordable wastewater management provides opportunities for both pollution reduction and clean water supply augmentation, whilst simultaneously promoting sustainable development and supporting the transition to a circular economy. This study aims to provide the first comprehensive and consistent global outlook on the state of domestic and industrial wastewater production, collection, treatment and re-use. We use a data-driven approach, collating, cross-examining and standardising country-level wastewater data from online data resources. Where unavailable, data is estimated using multiple linear regression. Country-level wastewater data are subsequently downscaled and validated at 5 arc-minute (~ 10 km) resolution. This study estimates global wastewater production at 359.5 billion m3 yr−1, of which 63 % (225.6 billion m3 yr−1) is collected and 52 % (188.1 billion m3 yr−1) is treated. By extension, we estimate that 48 % of global wastewater production is released to the environment untreated, which is significantly lower than previous estimates of ~ 80 %. An estimated 40.7 billion m3 yr−1 of treated wastewater is intentionally re-used. Substantial differences in per capita wastewater production, collection and treatment are observed across different geographic regions and by level of economic development. For example, just over 16 % of the global population in high income countries produce 41 % of global wastewater. Treated wastewater re-use is particularly significant in the Middle East and North Africa (15 %) and Western Europe (16 %), while containing just 5.8 % and 5.7 % of the global population, respectively. Our database serves as a reference for understanding the global wastewater status and for identifying hotspots where untreated wastewater is released to the environment, which are found particularly in South and Southeast Asia. Importantly, our results also serve as a baseline for evaluating progress towards many policy goals that are both directly and indirectly connected to wastewater management (e.g. SDGs). Our spatially-explicit results available at 5 arc-minute resolution are well suited for supporting more detailed hydrological analyses such as water quality modelling and large-scale water resource assessments, and can be accessed at: https://doi.pangaea.de/10.1594/PANGAEA.918731 (Jones et al., 2020). A temporary link to this dataset for the review process can be accessed at: https://www.pangaea.de/tok/6631ef8746b59999071fa2e692fbc492c97352aa.

scholarly journals Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 509
Author(s):  
Dipayan Mitra ◽  
Aranee Balachandran ◽  
Ratnasingham Tharmarasa
Keyword(s):  
Lower Bound ◽  
Estimation Error ◽  
Common Source ◽  
Bias Estimation ◽  
Tracking Accuracy ◽  
Digital Terrain ◽  
Single Target ◽  
Elevation Data ◽  
Measurement Biases ◽  
Terrain Elevation

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

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