scholarly journals Spatial, but not temporal, aspects of orientation are controlled by the fine scale distribution of chemical cues in turbulent odor plumes

2021 ◽  
pp. jeb.240457
Author(s):  
Paul A. Moore ◽  
David Edwards ◽  
Ana Jurcak-Detter ◽  
Sara Lahman
Keyword(s):  
Chemical Cues ◽  
Electrochemical Sensors ◽  
Spatial Scales ◽  
Orientation Behavior ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Odor Cues ◽  
Temporal Aspects ◽  
Odor Plumes ◽  
Vast Range

Orientation within turbulent odor plumes occurs across a vast range of spatial and temporal scales. From salmon homing across featureless oceans to microbes forming reproductive spores, the extraction of spatial and temporal information from chemical cues is a common sensory phenomenon. Yet, given the difficulty of quantifying chemical cues at the spatial and temporal scales used by organisms, discovering what aspects of chemical cues controls orientation behavior has remain elusive. In this study, we place electrochemical sensors on the carapace of orienting crayfish and measure, with fast temporal rates and small spatial scales, the concentration fluctuations arriving at the olfactory appendages during orientation. The results of these studies show that the spatial aspects of orientation (turning and heading angles) are controlled by the temporal aspects of odor cues.

scholarly journals Adaptive Multiscale Symbolic-Dynamics Entropy for Condition Monitoring of Rotating Machinery

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1138
Author(s):  
Chunhong Dou ◽  
Jinshan Lin
Keyword(s):  
Symbolic Dynamics ◽  
Spatial Scales ◽  
Approximate Entropy ◽  
Rotating Machinery ◽  
Temporal Scale ◽  
Permutation Entropy ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Vibration Data ◽  
Temporal Statistics

Vibration data from rotating machinery working in different conditions display different properties in spatial and temporal scales. As a result, insights into spatial- and temporal-scale structures of vibration data of rotating machinery are fundamental for describing running conditions of rotating machinery. However, common temporal statistics and typical nonlinear measures have difficulties in describing spatial and temporal scales of data. Recently, statistical linguistic analysis (SLA) has been pioneered in analyzing complex vibration data from rotating machinery. Nonetheless, SLA can examine data in spatial scales but not in temporal scales. To improve SLA, this paper develops symbolic-dynamics entropy for quantifying word-frequency series obtained by SLA. By introducing multiscale analysis to SLA, this paper proposes adaptive multiscale symbolic-dynamics entropy (AMSDE). By AMSDE, spatial and temporal properties of data can be characterized by a set of symbolic-dynamics entropy, each of which corresponds to a specific temporal scale. Afterward, AMSDE is employed to deal with vibration data from defective gears and rolling bearings. Moreover, the performance of AMSDE is benchmarked against five common temporal statistics (mean, standard deviation, root mean square, skewness and kurtosis) and three typical nonlinear measures (approximate entropy, sample entropy and permutation entropy). The results suggest that AMSDE performs better than these benchmark methods in characterizing running conditions of rotating machinery.

scholarly journals Variable Urbanization Warming Effects across Metropolitans of China and Relevant Driving Factors

2020 ◽  
Vol 12 (9) ◽  
pp. 1500 ◽  
Author(s):  
Qiang Zhang ◽  
Zixuan Wu ◽  
Huiqian Yu ◽  
Xiudi Zhu ◽  
Zexi Shen
Keyword(s):  
Remote Sensing ◽  
Spatial Scales ◽  
River Delta ◽  
Driving Factors ◽  
Landsat 8 ◽  
Temporal Scales ◽  
Linear Relations ◽  
Urban Agglomerations ◽  
Spatial And Temporal Scales ◽  
Urbanized Region

Urbanization is mainly characterized by the expansion of impervious surface (IS) and hence modifies hydrothermal properties of the urbanized areas. This process results in rising land surface temperature (LST) of the urbanized regions, i.e., urban heat island (UHI). Previous studies mainly focused on relations between LST and IS over individual city. However, because of the spatial heterogeneity of UHI from individual cities to urban agglomerations and the influence of relevant differences in climate background across urban agglomerations, the spatial-temporal scale independence of the IS-LST relationship still needs further investigation. In this case, based on Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS) remote sensing image and multi-source remote sensing data, we extracted IS using VrNIR-BI (Visible red and NIR-based built-up Index) and calculated IS density across three major urban agglomerations across eastern China, i.e., the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) to investigate the IS-LST relations on different spatial and temporal scales and clarify the driving factors of LST. We find varying warming effects of IS on LST in diurnal and seasonal sense at different time scales. Specifically, the IS has stronger impacts on increase of LST during daytime than during nighttime and stronger impacts on increase of LST during summer than during winter. On different spatial scales, more significant enhancing effects of IS on LST can be observed across individual city than urban agglomerations. The Pearson correlation coefficient (r) between IS and LST at the individual urbanized region can be as high as 0.94, indicating that IS can well reflect LST changes within individual urbanized region. However, relationships between IS and LST indicate nonlinear effects of IS on LST. Because of differences in spatial scales, latitudes, and local climates, we depicted piecewise linear relations between IS and LST across BTH when the IS density was above 10% to 17%. Meanwhile, linear relations still stand between IS density and LST across YRD and PRD. Besides, the differences in the IS-LST relations across urban agglomeration indicate more significant enhancing effects of IS on LST across PRD than YRD and BTH. These findings help to enhance human understanding of the warming effects of urbanization or UHI at different spatial and temporal scales and is of scientific and practical merits for scientific urban planning.

Measuring temporal and spatial scales of compound events in the United Kingdom

2020 ◽  
Author(s):  
Aloïs Tilloy ◽  
Bruce Malamud ◽  
Hugo Winter ◽  
Amelie Joly-Laugel
Keyword(s):  
Natural Hazards ◽  
Clustering Algorithm ◽  
Spatial Scales ◽  
Wind Gust ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Extreme Wind ◽  
Compound Events ◽  
Temporal And Spatial ◽  
The Impact

<p>Multi-hazard events have the potential to cause damages to infrastructures and people that may differ greatly from the associated risks posed by singular hazards. Interrelations between natural hazards also operate on different spatial and temporal scales than single natural hazards. Therefore, the measure of spatial and temporal scales of natural hazard interrelations still remain challenging. The objective of this study is to refine and measure temporal and spatial scales of natural hazards and their interrelations by using a spatiotemporal clustering technique. To do so, spatiotemporal information about natural hazards are extracted from the ERA5 climate reanalysis. We focus here on the interrelation between two natural hazards (extreme precipitation and extreme wind gust) during the period 1969-2019 within a region including Great Britain and North-West France. The characteristics of our input data (i.e. important size, high noise level) and the absence of assumption about the shape of our hazard clusters guided the choice of a clustering algorithm toward the DBSCAN clustering algorithm. To create hazard clusters, we retain only extreme values (above the 99% quantile) of precipitation and wind gust. We analyse the characteristics (eg., size, duration, season, intensity) of single and compound events of rain and wind impacting our study area. We then measure the impact of the spatial and temporal scales defined in this study on the nature of the interrelation between extreme rainfall and extreme wind in the UK. We therefore demonstrate how this methodology can be applied to a different set of natural hazards.</p>

scholarly journals Using iNaturalist observations to detect disease in Red Mangroves (Rhizophora mangle)

2017 ◽  
Author(s):  
Ryann E Rossi
Keyword(s):  
Citizen Science ◽  
Passive Sampling ◽  
Spatial Scales ◽  
Disease Detection ◽  
Temporal Scales ◽  
Disease Symptoms ◽  
Foliar Disease ◽  
Spatial And Temporal Scales ◽  
Red Mangrove ◽  
Disease Surveys

Detection of disease over broad spatial scales is important to managing the spread of many diseases. One way to do this is to work with citizen scientists to collect data over broad spatial and temporal scales. Citizen science observations are becoming more widely available through web and app interfaces such as iNaturalist.org. iNaturalist.org provides passive sampling of organisms through photographs with a geolocation. These observations are often used to examine biodiversity and species monitoring, but, disease detection is also possible. Here, I demonstrate the utility of using iNaturlist.org observations of red mangrove to detect foliar disease symptoms such as lesions. I downloaded observations of red mangrove from iNaturalist.org, filtered them and examined images for foliar disease symptoms. Out of 153 filtered images, I found that 42% showed no signs of foliar disease while 58% did show foliar disease symptoms. I also found that observations of red mangrove were recorded from 15 countries in total, with 11 countries having at least one observation with foliar disease symptoms present. While small, this study demonstrates the utility of using resources such as iNaturalist.org to obtain preliminary disease observations which can be used to further focus in person disease surveys and sampling.

scholarly journals Ecosystem-based adaptation in Lake Victoria Basin; synergies and trade-offs

2021 ◽  
Vol 8 (6) ◽  
pp. 201847
Author(s):  
Dorice Agol ◽  
Hannah Reid ◽  
Florence Crick ◽  
Hausner Wendo
Keyword(s):  
Climate Change ◽  
Lake Victoria ◽  
Spatial Scales ◽  
Analytical Framework ◽  
Lake Victoria Basin ◽  
Adaptation To Climate Change ◽  
Group Discussions ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Trade Offs

Healthy ecosystems such as forests and wetlands have a great potential to support adaptation to climate change and are the foundation of sustainable livelihoods. Ecosystem-based adaptation (EbA) can help to protect and maintain healthy ecosystems providing resilience against the impacts of climate change. This paper explores the role of EbA in reconciling socio-economic development with the conservation and restoration of nature in Lake Victoria Basin, Kenya, East Africa. Using selected ecosystems in the Lake region, the paper identifies key EbA approaches and explores trade-offs and synergies at spatial and temporal scales and between different stakeholders. The research methods used for this study include site visits, key informant interviews, focus group discussions, participatory workshops and literature reviews. An analytical framework is applied to advance the understanding of EbA approaches and how they lead to synergies and trade-offs between ecosystem services provision at spatial and temporal scales and multiple stakeholders. Our results show that EbA approaches such as ecosystem restoration have the potential to generate multiple adaptation benefits as well as synergies and trade-offs occurring at different temporal and spatial scales and affecting various stakeholder groups. Our paper underscores the need to identify EbA trade-offs and synergies and to explore the ways in which they are distributed in space and time and between different stakeholders to design better environmental and development programmes.

scholarly journals Extracellular matrix, mechanotransduction and structural hierarchies in heart tissue engineering

2007 ◽  
Vol 362 (1484) ◽  
pp. 1267-1279 ◽  
Author(s):  
Kevin K Parker ◽  
Donald E Ingber
Keyword(s):  
Extracellular Matrix ◽  
Conformational Changes ◽  
Structural Control ◽  
Artificial Heart ◽  
Spatial Scales ◽  
Heart Tissue ◽  
Control Mechanisms ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
And Function

The spatial and temporal scales of cardiac organogenesis and pathogenesis make engineering of artificial heart tissue a daunting challenge. The temporal scales range from nanosecond conformational changes responsible for ion channel opening to fibrillation which occurs over seconds and can lead to death. Spatial scales range from nanometre pore sizes in membrane channels and gap junctions to the metre length scale of the whole cardiovascular system in a living patient. Synchrony over these scales requires a hierarchy of control mechanisms that are governed by a single common principle: integration of structure and function. To ensure that the function of ion channels and contraction of muscle cells lead to changes in heart chamber volume, an elegant choreography of metabolic, electrical and mechanical events are executed by protein networks composed of extracellular matrix, transmembrane integrin receptors and cytoskeleton which are functionally connected across all size scales. These structural control networks are mechanoresponsive, and they process mechanical and chemical signals in a massively parallel fashion, while also serving as a bidirectional circuit for information flow. This review explores how these hierarchical structural networks regulate the form and function of living cells and tissues, as well as how microfabrication techniques can be used to probe this structural control mechanism that maintains metabolic supply, electrical activation and mechanical pumping of heart muscle. Through this process, we delineate various design principles that may be useful for engineering artificial heart tissue in the future.

Airborne Hyperspectral Trace Gas Sensors as Testbeds for Geostationary Air Quality Mission Validation

2020 ◽  
Author(s):  
Scott Janz ◽  
Matthew Kowalewski ◽  
Lok Lamsal ◽  
Laura Judd ◽  
Caroline Nowlan ◽  
...  
Keyword(s):  
Air Quality ◽  
Spatial Scales ◽  
Lower Troposphere ◽  
Weather Conditions ◽  
Trace Gas ◽  
Radiometric Calibration ◽  
Temporal Scales ◽  
Vertical Column ◽  
Spatial And Temporal Scales ◽  
Forecast Models

<p>Next generation air quality sensors are currently planned to launch within the next couple of years. The Tropospheric Emissions: Monitory of Pollution (TEMPO-United States) and Geostationary Environment Monitoring Sensor (GEMS-South Korea) are two such missions that will probe the boundary layer/lower troposphere at unprecedented spatial and temporal scales. These missions are designed to provide constraints on chemical forecast models and specifically to answer the question: "What are the temporal and spatial variations of emissions of gases and aerosols important for air quality and climate?" In preparation for these missions a number of airborne air quality field missions have been performed to collect data at similar spatial and temporal scales, and during relevant seasonal air quality episodes including fires. This data is being used to improve the trace gas retrieval algorithms and explore the unique spatial scales and diurnal patterns that will be encountered when the geostationary experiments are operational. This overview will present details of two of the instruments used during these campaigns, the GeoCAPE Airborne Simulator (GCAS) and the Geostationary Trace Gas and Aerosol Sensor Optimization (GeoTASO) instruments. Maintained at the Goddard Space Flight Center's Radiometric Calibration and Development Facility (RCDF), these instruments are similar in design and sensitivty to what will be measured on-orbit by the TEMPO and GEMS sensors. Results of the retrieval of high spatial resolution nitrogen dioxide and formaldehyde will presented. Examples of vertical column retrievals will be presented under various source/weather conditions as well as the uncertainties that result from both instrument and radiative transfer assumptions.</p>

5. Eroded by ice

2018 ◽  
pp. 97-109
Author(s):  
David J. A. Evans
Keyword(s):  
Spatial Scales ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Wide Range

There is a very wide range of spatial and temporal scales reflected in the types of glacial erosional landforms, from individual millimetre-wide striae that can form over a few days to fjords tens of kilometres long that require hundreds of thousands of years to develop. Erosional landforms can be discussed in categories defined by three spatial scales: microscale, macroscale, and megascale, the latter also including whole landscapes that have unmistakable glacial erosional origins. ‘Eroded by ice’ describes these different erosional forms and explains that they are rarely viewed in isolation because microscale erosional marks are superimposed on macroscale forms, which in turn are superimposed on megascale surfaces to constitute erosional landscapes.

Multimodal integration across spatiotemporal scales to guide invertebrate locomotion

2021 ◽  
Author(s):  
Jean-Michel Mongeau ◽  
Lorian E Schweikert ◽  
Alexander L Davis ◽  
Michael S Reichert ◽  
Jessleen K Kanwal
Keyword(s):  
Genetic Model ◽  
Spatial Scales ◽  
Fruit Fly ◽  
Model Systems ◽  
Ecological Niches ◽  
Multimodal Integration ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Challenges And Opportunities ◽  
Spatiotemporal Scales

SYNOPSIS Locomotion is a hallmark of organisms that has enabled adaptive radiation to an extraordinarily diverse class of ecological niches, and allows animals to move across vast distances. Sampling from multiple sensory modalities enables animals to acquire rich information to guide locomotion. Locomotion without sensory feedback is haphazard, therefore sensory and motor systems have evolved complex interactions to generate adaptive behavior. Notably, sensory-guided locomotion acts over broad spatial and temporal scales to permit goal-seeking behavior, whether to localize food by tracking an attractive odor plume or to search for a potential mate. How does the brain integrate multimodal stimuli over different temporal and spatial scales to effectively control behavior? In this review, we classify locomotion into three ordinally ranked hierarchical layers that act over distinct spatiotemporal scales: stabilization, motor primitives, and higher-order tasks, respectively. We discuss how these layers present unique challenges and opportunities for sensorimotor integration. We focus on recent advances in invertebrate locomotion due to their accessible neural and mechanical signals from the whole brain, limbs and sensors. Throughout, we emphasize neural-level description of computations for multimodal integration in genetic model systems, including the fruit fly, Drosophila melanogaster, and the yellow fever mosquito, Aedes aegypti. We identify that summation (e.g. gating) and weighting—which are inherent computations of spiking neurons—underlie multimodal integration across spatial and temporal scales, therefore suggesting collective strategies to guide locomotion.

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