scholarly journals Use of meteorological data in biosecurity

2020 ◽  
Vol 4 (5) ◽  
pp. 497-511 ◽  
Author(s):  
Deborah Hemming ◽  
Katrina Macneill
Keyword(s):  
Meteorological Data ◽  
Social Systems ◽  
Early Warning Systems ◽  
Data Types ◽  
Wide Audience ◽  
Outbreak Management ◽  
Wide Range ◽  
Bioclimatic Modelling ◽  
Future Potential ◽  
Living Organisms

Pests, pathogens and diseases cause some of the most widespread and damaging impacts worldwide — threatening lives and leading to severe disruption to economic, environmental and social systems. The overarching goal of biosecurity is to protect the health and security of plants and animals (including humans) and the wider environment from these threats. As nearly all living organisms and biological systems are sensitive to weather and climate, meteorological, ‘met’, data are used extensively in biosecurity. Typical applications include, (i) bioclimatic modelling to understand and predict organism distributions and responses, (ii) risk assessment to estimate the probability of events and horizon scan for future potential risks, and (iii) early warning systems to support outbreak management. Given the vast array of available met data types and sources, selecting which data is most effective for each of these applications can be challenging. Here we provide an overview of the different types of met data available and highlight their use in a wide range of biosecurity studies and applications. We argue that there are many synergies between meteorology and biosecurity, and these provide opportunities for more widespread integration and collaboration across the disciplines. To help communicate typical uses of meteorological data in biosecurity to a wide audience we have designed the ‘Meteorology for biosecurity’ infographic.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals A data-driven method for reconstructing and modelling social interactions in moving animal groups

2020 ◽  
Vol 375 (1807) ◽  
pp. 20190380 ◽  
Author(s):  
R. Escobedo ◽  
V. Lecheval ◽  
V. Papaspyros ◽  
F. Bonnet ◽  
F. Mondada ◽  
...  
Keyword(s):  
Social Interactions ◽  
Social Systems ◽  
Quantitative Description ◽  
Group Living ◽  
Quantitative Model ◽  
Individual Level ◽  
Wide Range ◽  
Living Organisms ◽  
Individual Scale ◽  
The Individual

Group-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish, and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and can be described as a combination of interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate datasets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of studies, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummy-nose tetra ( Hemigrammus rhodostomus ) and the zebrafish ( Danio rerio ), which both present a burst-and-coast motion. From the detailed quantitative description of individual-level interactions, it is thus possible to develop a quantitative model of the emergent dynamics observed at the group level, whose predictions can be checked against experimental results. This method can be applied to a wide range of biological and social systems. This article is part of the theme issue ‘Multi-scale analysis and modelling of collective migration in biological systems’.

scholarly journals A data-driven method for reconstructing and modelling social interactions in moving animal groups

2019 ◽  
Author(s):  
R. Escobedo ◽  
V. Lecheval ◽  
V. Papaspyros ◽  
F. Bonnet ◽  
F. Mondada ◽  
...  
Keyword(s):  
Social Interactions ◽  
Social Systems ◽  
Quantitative Description ◽  
Group Living ◽  
Data Sets ◽  
Individual Level ◽  
Wide Range ◽  
Living Organisms ◽  
Individual Scale ◽  
The Individual

AbstractGroup-living organisms that collectively migrate range from cells and bacteria to human crowds, and include swarms of insects, schools of fish and flocks of birds or ungulates. Unveiling the behavioural and cognitive mechanisms by which these groups coordinate their movements is a challenging task. These mechanisms take place at the individual scale and they can be described as a combination of pairwise interactions between individuals and interactions between these individuals and the physical obstacles in the environment. Thanks to the development of novel tracking techniques that provide large and accurate data sets, the main characteristics of individual and collective behavioural patterns can be quantified with an unprecedented level of precision. However, in a large number of works, social interactions are usually described by force map methods that only have a limited capacity of explanation and prediction, being rarely suitable for a direct implementation in a concise and explicit mathematical model. Here, we present a general method to extract the interactions between individuals that are involved in the coordination of collective movements in groups of organisms. We then apply this method to characterize social interactions in two species of shoaling fish, the rummynose tetra (Hemigrammus rhodostomus) and the zebrafish (Danio rerio), which both present a burst-and-coast motion. The detailed quantitative description of microscopic individual-level interactions thus provides predictive models of the emergent dynamics observed at the macroscopic group-level. This method can be applied to a wide range of biological and social systems.

Emergence of Cooperation: State of the Art

2005 ◽  
Vol 11 (3) ◽  
pp. 367-396 ◽  
Author(s):  
Geoff Nitschke
Keyword(s):  
Cooperative Behavior ◽  
Social Systems ◽  
Limiting Factors ◽  
Biologically Inspired ◽  
Future Directions ◽  
Pursuit Evasion ◽  
Wide Range ◽  
Emergence Of Cooperation ◽  
Future Potential ◽  
Biologically Inspired Design

This review presents a review of prevalent results within research pertaining to emergent cooperation in biologically inspired artificial social systems. Results reviewed maintain particular reference to biologically inspired design principles, given that current mathematical and empirical tools have provided only a partial insight into elucidating mechanisms responsible for emergent cooperation, and then only in systems of an abstract nature. This review aims to provide an overview of important and disparate research contributions that investigate utilization of biologically inspired concepts such as emergence, evolution, and self-organization as a means of attaining cooperation in artificial social systems. An introduction and overview of emergent cooperation in artificial life is presented, followed by a survey of emergent cooperation in swarm-based systems, the pursuit-evasion domain, and RoboCup soccer. The final section draws conclusions regarding future directions of emergent cooperation as a problem-solving methodology that is potentially applicable in a wide range of problem domains. Within each of these sections and their respective themes of research, the mechanisms deemed to be responsible for emergent cooperation are elucidated and their key limitations highlighted. The review concludes that current studies in emergent cooperative behavior are limited by a lack of situated and embodied approaches, and by the research infancy of current biologically inspired design approaches. Despite these limiting factors, emergent cooperation maintains considerable future potential in a wide variety of application domains where systems composed of many interacting components must cooperatively perform unanticipated global tasks.

scholarly journals A multi-site, year-round turbulence microstructure atlas for the deep perialpine Lake Garda

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sebastiano Piccolroaz ◽  
Bieito Fernández-Castro ◽  
Marco Toffolon ◽  
Henk A. Dijkstra
Keyword(s):  
Turbulent Mixing ◽  
Meteorological Data ◽  
Time Of Day ◽  
Monthly Basis ◽  
Lake Garda ◽  
Standardized Protocol ◽  
Wide Range ◽  
Buoyancy Driven Convection ◽  
Monitoring Activity ◽  
Additional Monitoring

AbstractA multi-site, year-round dataset comprising a total of 606 high-resolution turbulence microstructure profiles of shear and temperature gradient in the upper 100 m depth is made available for Lake Garda (Italy). Concurrent meteorological data were measured from the fieldwork boat at the location of the turbulence measurements. During the fieldwork campaign (March 2017-June 2018), four different sites were sampled on a monthly basis, following a standardized protocol in terms of time-of-day and locations of the measurements. Additional monitoring activity included a 24-h campaign and sampling at other sites. Turbulence quantities were estimated, quality-checked, and merged with water quality and meteorological data to produce a unique turbulence atlas for a lake. The dataset is open to a wide range of possible applications, including research on the variability of turbulent mixing across seasons and sites (demersal vs pelagic zones) and driven by different factors (lake-valley breezes vs buoyancy-driven convection), validation of hydrodynamic lake models, as well as technical studies on the use of shear and temperature microstructure sensors.

scholarly journals A heat-health watch and warning system with extended season and evolving thresholds

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Mahamat Abdelkerim Issa ◽  
Fateh Chebana ◽  
Pierre Masselot ◽  
Céline Campagna ◽  
Éric Lavigne ◽  
...  
Keyword(s):  
Excess Mortality ◽  
Heat Waves ◽  
Meteorological Data ◽  
Warm Season ◽  
Warning System ◽  
Early Warning Systems ◽  
Climate Projections ◽  
Warning Systems ◽  
Extreme Heat Events ◽  
Watch And Warning System

Abstract Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

scholarly journals Graphene Oxide-Based Nanofiltration for Hg Removal from Wastewater: A Mini Review

Membranes ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 269
Author(s):  
Megawati Zunita
Keyword(s):  
Graphene Oxide ◽  
Negative Impact ◽  
Treatment Area ◽  
Functionalized Graphene Oxide ◽  
Flexible Membrane ◽  
High Mechanical Strength ◽  
Wide Range ◽  
Living Organisms ◽  
Filtration Properties ◽  
High Chemical Stability

Mercury (Hg) is one of heavy metals with the highest toxicity and negative impact on the biological functions of living organisms. Therefore, many studies are devoted to solving the problem of Hg separation from wastewater. Membrane-based separation techniques have become more preferable in wastewater treatment area due to their ease of operation, mild conditions and also more resistant to toxic pollutants. This technique is also flexible and has a wide range of possibilities to be integrated with other techniques. Graphene oxide (GO) and derivatives are materials which have a nanostructure can be used as a thin and flexible membrane sheet with high chemical stability and high mechanical strength. In addition, GO-based membrane was used as a barrier for Hg vapor due to its nano-channels and nanopores. The nano-channels of GO membranes were also used to provide ion mobility and molecule filtration properties. Nowadays, this technology especially nanofiltration for Hg removal is massively explored. The aim of the review paper is to investigate Hg removal using functionalized graphene oxide nanofiltration. The main focus is the effectiveness of the Hg separation process.

scholarly journals Product formalisms for measures on spaces with binary tree structures: representation, visualization, and multiscale noise

2020 ◽  
Vol 8 ◽  
Author(s):  
Devasis Bassu ◽  
Peter W. Jones ◽  
Linda Ness ◽  
David Shallcross
Keyword(s):  
Data Science ◽  
Theoretical Foundation ◽  
Continuous Functions ◽  
Noise Model ◽  
Data Types ◽  
Data Set ◽  
Tree Structures ◽  
Simple Concept ◽  
Wide Range ◽  
Computer Scientists

Abstract In this paper, we present a theoretical foundation for a representation of a data set as a measure in a very large hierarchically parametrized family of positive measures, whose parameters can be computed explicitly (rather than estimated by optimization), and illustrate its applicability to a wide range of data types. The preprocessing step then consists of representing data sets as simple measures. The theoretical foundation consists of a dyadic product formula representation lemma, and a visualization theorem. We also define an additive multiscale noise model that can be used to sample from dyadic measures and a more general multiplicative multiscale noise model that can be used to perturb continuous functions, Borel measures, and dyadic measures. The first two results are based on theorems in [15, 3, 1]. The representation uses the very simple concept of a dyadic tree and hence is widely applicable, easily understood, and easily computed. Since the data sample is represented as a measure, subsequent analysis can exploit statistical and measure theoretic concepts and theories. Because the representation uses the very simple concept of a dyadic tree defined on the universe of a data set, and the parameters are simply and explicitly computable and easily interpretable and visualizable, we hope that this approach will be broadly useful to mathematicians, statisticians, and computer scientists who are intrigued by or involved in data science, including its mathematical foundations.

scholarly journals A Mobile Sensing System for UrbanPM2.5Monitoring with Adaptive Resolution

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Hongjie Guo ◽  
Guojun Dai ◽  
Jin Fan ◽  
Yifan Wu ◽  
Fangyao Shen ◽  
...  
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Computational Cost ◽  
Estimation Method ◽  
Mobile Sensing ◽  
Prototype System ◽  
Adaptive Grids ◽  
Sensing System ◽  
Adaptive Resolution ◽  
Wide Range

This paper develops a mobile sensing system, the first system used in adaptive resolution urban air quality monitoring. In this system, we employ several taxis as sensor carries to collect originalPM2.5data and collect a variety of datasets, including meteorological data, traffic status data, and geographical data in the city. This paper also presents a novel method AG-PCEM (Adaptive Grid-Probabilistic Concentration Estimation Method) to infer thePM2.5concentration for undetected grids using dynamic adaptive grids. We gradually collect the measurements throughout a year using a prototype system in Xiasha District of Hangzhou City, China. Experimental data has verified that the proposed system can achieve good performance in terms of computational cost and accuracy. The computational cost of AG-PCEM is reduced by about 40.2% compared with a static grid method PCEM under the condition of reaching the close accuracy, and the accuracy of AG-PCEM is far superior as widely used artificial neural network (ANN) and Gaussian process (GP), enhanced by 38.8% and 14.6%, respectively. The system can be expanded to wide-range air quality monitor by adjusting the initial grid resolution, and our findings can tell citizens actual air quality and help official management find pollution sources.

scholarly journals Nanotechnology Innovations in Construction Industry and Environmental Sustainability

2019 ◽  
Vol 8 (11S) ◽  
pp. 286-291
Keyword(s):  
Construction Industry ◽  
Environmental Sustainability ◽  
Research Review ◽  
Environmental Damage ◽  
The Sustainable Development ◽  
Future Challenges ◽  
Future Potential ◽  
Living Organisms ◽  
The Impact ◽  
Near Future

Nanotechnology is a speedily increasing and innovating range of research, where advanced characteristics of resources manufactures on the nanoscale can be exploited as advantages for people at large through various methods or mechanisms of construction. Being a varied technical and scientific arena that brings and covers numerous application kinds, the contribution of nanotechnological innovations is immense for various sectors of construction industries. It also possesses a large future potential for ecological efficiency, which is dire need of the hour. In construction industry there are many factors involved to achieve the major goal of sustainability like smart design, planning through which energy can be saved, resource usage can be reduced and environmental damage can be avoided. No doubt, the application of nanotechnology materials and its various causes on the atmosphere and living organisms are not clearly defined yet which can become a problem. For instance, leakage of materials into the water resources, and seas, as well, discharge of nanoparticles into the air generating dust and exposure to harmful materials during various construction, maintenance and use etc. Considering tremendous revolution in Nanotechnological field there is an important aspect in relation to the impact of nanoparticles and nanomaterials on human health and environment which should be discussed at length. This paper intends to give a research review of current and near future, safety and eco-efficiency applications of nanotechnology to not only improve and maintain but also deal with certain future challenges and directions related to the sustainable development.

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