Planning to Practice: Impacts of Large-Scale and Rapid Urban Afforestation on Greenspace Patterns in the Beijing Plain Area

(1) Research Highlights: Afforestation is one of the most effective urban greening practices for mitigating a variety of environmental issues. Globally, municipal governments have launched large-scale afforestation programs in metropolitan areas during the last decades. However, the spatiotemporal dynamics of urban greenspace patterns are seldom studied during such afforestation programs. (2) Background and Objectives: In this study, the Beijing Plain Afforestation Project (BPAP), which planted 70,711 ha of trees in only four years, was examined by integrating spatial and landscape analysis. To evaluate the real-world outcomes of this massive program, we investigated the spatial-temporal dynamics of landscape patterns during the implementation process to identify potential impacts and challenges for future management of new afforestation. (3) Materials and Methods: We analyzed the transition of various patch types and sizes, applied landscape indicators to measure the temporal changes in urban greenspace patterns, and used the landscape expansion index to quantify the rate and extent of greenspace spatial expansion. (4) Results: Our results illustrated that the implementation of afforestation in the Beijing plain area had generally achieved its initial goal of increasing the proportion of land devoted to forest (increased 8.43%) and parks (increased 0.23%). Afforestation also accelerated the conversion of small-size greenspaces to large-size patches. However, the significant discrepancies found between planned and actual afforestation sites, as well as the large conversion of cropland to forest, may present major challenges for project optimization and future management. (5) Conclusions: This study demonstrated that spatial analysis is a useful and potentially replicable method that can rapidly provide new data to support further afforestation ecosystem assessments and provide spatial insights into the optimization of large inner-city afforestation projects.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Multi-Scale Ionospheric Anomalies Monitoring and Spatio-Temporal Analysis during Intense Storm

Atmosphere ◽

10.3390/atmos12020215 ◽

2021 ◽

Vol 12 (2) ◽

pp. 215

Author(s):

Na Cheng ◽

Shuli Song ◽

Wei Li

Keyword(s):

Magnetic Storm ◽

Large Scale ◽

Temporal Dynamics ◽

Ionospheric Disturbance ◽

Ionospheric Scintillation ◽

Small Scale ◽

Electron Content ◽

Total Electron ◽

Multi Scale ◽

Intense Storm

The ionosphere is a significant component of the geospace environment. Storm-induced ionospheric anomalies severely affect the performance of Global Navigation Satellite System (GNSS) Positioning, Navigation, and Timing (PNT) and human space activities, e.g., the Earth observation, deep space exploration, and space weather monitoring and prediction. In this study, we present and discuss the multi-scale ionospheric anomalies monitoring over China using the GNSS observations from the Crustal Movement Observation Network of China (CMONOC) during the 2015 St. Patrick’s Day storm. Total Electron Content (TEC), Ionospheric Electron Density (IED), and the ionospheric disturbance index are used to monitor the storm-induced ionospheric anomalies. This study finally reveals the occurrence of the large-scale ionospheric storms and small-scale ionospheric scintillation during the storm. The results show that this magnetic storm was accompanied by a positive phase and a negative phase ionospheric storm. At the beginning of the main phase of the magnetic storm, both TEC and IED were significantly enhanced. There was long-duration depletion in the topside ionospheric TEC during the recovery phase of the storm. This study also reveals the response and variations in regional ionosphere scintillation. The Rate of the TEC Index (ROTI) was exploited to investigate the ionospheric scintillation and compared with the temporal dynamics of vertical TEC. The analysis of the ROTI proved these storm-induced TEC depletions, which suppressed the occurrence of the ionospheric scintillation. To improve the spatial resolution for ionospheric anomalies monitoring, the regional Three-Dimensional (3D) ionospheric model is reconstructed by the Computerized Ionospheric Tomography (CIT) technique. The spatial-temporal dynamics of ionospheric anomalies during the severe geomagnetic storm was reflected in detail. The IED varied with latitude and altitude dramatically; the maximum IED decreased, and the area where IEDs were maximum moved southward.

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Leadership assumptions on implementation of patient involvement methods

BMC Health Services Research ◽

10.1186/s12913-021-06497-y ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kathrine Håland Jeppesen ◽

Kirsten Frederiksen ◽

Marianne Johansson Joergensen ◽

Kirsten Beedholm

Keyword(s):

Health Care ◽

Organizational Learning ◽

Health Care Professionals ◽

Large Scale ◽

Patient Involvement ◽

Hospital Setting ◽

Implementation Process ◽

University Hospital ◽

Structured Interviews ◽

Implementation Processes

Abstract Background From 2014 to 17, a large-scale project, ‘The User-involving Hospital’, was implemented at a Danish university hospital. Research highlights leadership as crucial for the outcome of change processes in general and for implementation processes in particular. According to the theory on organizational learning by Agyris and Schön, successful change requires organizational learning. Argyris and Schön consider that the assumptions of involved participants play an important role in organizational learning and processes. The purpose was to explore leaders’ assumptions concerning implementation of patient involvement methods in a hospital setting. Methods Qualitative explorative interview study with the six top leaders in the implementation project. The semi-structured interviews were conducted and analyzed in accordance with Kvale and Brinkmanns’ seven stages of interview research. Result The main leadership assumptions on what is needed in the implementation process are in line with the perceived elements in organizational learning according to the theory of Argyris and Schön. Hence, they argued that implementation of patient involvement requires a culture change among health care professionals. Two aspects on how to obtain success in the implementation process were identified based on leadership assumptions: “The health care professionals’ roles in the implementation process” and “The leaders’ own roles in the implementation process”. Conclusion The top leaders considered implementation of patient involvement a change process that necessitates a change in culture with health care professionals as crucial actors. Furthermore, the top leaders considered themselves important facilitators of this implementation process.

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Modelling Tree Growth in Monospecific Forests from Forest Inventory Data

Forests ◽

10.3390/f12060753 ◽

2021 ◽

Vol 12 (6) ◽

pp. 753

Author(s):

Guadalupe Sáez-Cano ◽

Marcos Marvá ◽

Paloma Ruiz-Benito ◽

Miguel A. Zavala

Keyword(s):

Tree Growth ◽

Large Scale ◽

Temporal Dynamics ◽

Carbon Sink ◽

Size Variation ◽

Biotic Interactions ◽

Richards Equation ◽

Tree Level ◽

Forest Inventories ◽

Large Scale Data

The prediction of tree growth is key to further understand the carbon sink role of forests and the short-term forest capacity on climate change mitigation. In this work, we used large-scale data available from three consecutive forest inventories in a Euro-Mediterranean region and the Bertalanffy–Chapman–Richards equation to model up to a decade’s tree size variation in monospecific forests in the growing stages. We showed that a tree-level fitting with ordinary differential equations can be used to forecast tree diameter growth across time and space as function of environmental characteristics and initial size. This modelling approximation was applied at different aggregation levels to monospecific regions with forest inventories to predict trends in aboveground tree biomass stocks. Furthermore, we showed that this model accurately forecasts tree growth temporal dynamics as a function of size and environmental conditions. Further research to provide longer term prediction forest stock dynamics in a wide variety of forests should model regeneration and mortality processes and biotic interactions.

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Interactions of sapsuckers and Cytospora canker can facilitate decline of riparian willows

Botany ◽

10.1139/cjb-2014-0019 ◽

2014 ◽

Vol 92 (7) ◽

pp. 485-493 ◽

Cited By ~ 3

Author(s):

Kristen M. Kaczynski ◽

David J. Cooper ◽

William R. Jacobi

Keyword(s):

Large Scale ◽

Temporal Dynamics ◽

Rocky Mountain ◽

Aerial Photo ◽

Stream Flows ◽

The Past ◽

Cytospora Chrysosperma ◽

Willow Bark ◽

Moose Population ◽

Cytospora Canker

Drought has caused large-scale plant mortality in ecosystems around the globe. Most diebacks have affected upland forest species. In the past two decades, a large-scale decline of riparian willows (Salix L.) has occurred in Rocky Mountain National Park, Colorado. We examined whether climatic or biotic factors drive and maintain the willow community decline. We compared annual growth and dieback of willows inside and outside of 14-year-old ungulate exclosures and measured groundwater depth and predawn xylem pressures of stems as indicators of drought stress. We also performed an aerial photo analysis to determine the temporal dynamics of the decline. Aerial photo analysis indicated willow decline occurred between 2001 and 2005 and was best explained by an increase in moose population and a decrease in peak stream flows. A new mechanism for willow stem dieback was identified, initiated by red-naped sapsucker wounding willow bark. Wounds became infected with fungus that girdled the stem. DNA analyses confirmed Valsa sordida (Cytospora chrysosperma) as the lethal fungus. Captured sapsuckers had V. sordida spores on feet and beaks identifying them as one possible vector of spread. Predawn xylem pressure potentials remained high through the growing season on all study willows regardless of depth to ground water. Our results indicate that additional mechanisms may be involved in tall willow decline.

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Uncovering the structure and temporal dynamics of information propagation

Network Science ◽

10.1017/nws.2014.3 ◽

2014 ◽

Vol 2 (1) ◽

pp. 26-65 ◽

Cited By ~ 62

Author(s):

MANUEL GOMEZ RODRIGUEZ ◽

JURE LESKOVEC ◽

DAVID BALDUZZI ◽

BERNHARD SCHÖLKOPF

Keyword(s):

News Media ◽

Information Diffusion ◽

Large Scale ◽

Temporal Dynamics ◽

Inference Algorithm ◽

Information Propagation ◽

Transmission Rates ◽

Diffusion Dynamics ◽

Underlying Network ◽

News Events

AbstractTime plays an essential role in the diffusion of information, influence, and disease over networks. In many cases we can only observe when a node is activated by a contagion—when a node learns about a piece of information, makes a decision, adopts a new behavior, or becomes infected with a disease. However, the underlying network connectivity and transmission rates between nodes are unknown. Inferring the underlying diffusion dynamics is important because it leads to new insights and enables forecasting, as well as influencing or containing information propagation. In this paper we model diffusion as a continuous temporal process occurring at different rates over a latent, unobserved network that may change over time. Given information diffusion data, we infer the edges and dynamics of the underlying network. Our model naturally imposes sparse solutions and requires no parameter tuning. We develop an efficient inference algorithm that uses stochastic convex optimization to compute online estimates of the edges and transmission rates. We evaluate our method by tracking information diffusion among 3.3 million mainstream media sites and blogs, and experiment with more than 179 million different instances of information spreading over the network in a one-year period. We apply our network inference algorithm to the top 5,000 media sites and blogs and report several interesting observations. First, information pathways for general recurrent topics are more stable across time than for on-going news events. Second, clusters of news media sites and blogs often emerge and vanish in a matter of days for on-going news events. Finally, major events, for example, large scale civil unrest as in the Libyan civil war or Syrian uprising, increase the number of information pathways among blogs, and also increase the network centrality of blogs and social media sites.

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Spatio–Temporal Dynamics of Wetland Landscape Patterns Based on Remote Sensing in Yellow River Delta, China

Wetlands ◽

10.1007/s13157-014-0542-1 ◽

2014 ◽

Vol 34 (4) ◽

pp. 787-801 ◽

Cited By ~ 43

Author(s):

Guilin Liu ◽

Luocheng Zhang ◽

Qian Zhang ◽

Zipporah Musyimi ◽

Qinghu Jiang

Keyword(s):

Remote Sensing ◽

Temporal Dynamics ◽

Yellow River ◽

Yellow River Delta ◽

River Delta ◽

Landscape Patterns ◽

Wetland Landscape ◽

Spatio Temporal

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Implementation aspects of agile methods in large organizations

e-mentor ◽

10.15219/em90.1523 ◽

2021 ◽

Vol 90 (3) ◽

pp. 64-72

Author(s):

Robert Pawlak ◽

Keyword(s):

Best Practices ◽

Large Scale ◽

Success Factors ◽

Implementation Process ◽

Business Practice ◽

Organization Culture ◽

Agile Methods ◽

Implementation Challenges ◽

Depth Analysis ◽

Implementation Aspects

The aim of this article is to analyze the challenges and success factors on organizations’ path to agile transformation, as frequently discussed in the literature and encountered in business practice. The research conducted proved that large-scale agile transformations require a dedicated approach with set of tools and best practices in place. The implementation challenges and barriers have been categorized into method-, organization-, culture- and technology-oriented groups. As a result of an in-depth analysis carried on for the purpose of this paper, a dedicated methodology of agile transformation has been proposed to ease the implementation process.

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Impact of the first COVID-19 shelter-in-place order in the United States on emergency department utilization, Marin County, California

10.1101/2020.07.01.20144691 ◽

2020 ◽

Author(s):

Brett R. Bayles ◽

Michaela F George ◽

Haylea Hannah ◽

Patti Culross ◽

Rochelle R. Ereman ◽

...

Keyword(s):

United States ◽

San Francisco ◽

Large Scale ◽

Temporal Dynamics ◽

The United States ◽

Emergency Department Utilization ◽

Marin County ◽

Before And After ◽

Ed Visits ◽

The Impact

Background: The first shelter-in-place (SIP) order in the United States was issued across six counties in the San Francisco Bay Area to reduce the impact of COVID-19 on critical care resources. We sought to assess the impact of this large-scale intervention on emergency departments (ED) in Marin County, California. Methods: We conducted a retrospective descriptive and trend analysis of all ED visits in Marin County, California from January 1, 2018 to May 4, 2020 to quantify the temporal dynamics of ED utilization before and after the March 17, 2020 SIP order. Results: The average number of ED visits per day decreased by 52.3% following the SIP order compared to corresponding time periods in 2018 and 2019. Both respiratory and non-respiratory visits declined, but this negative trend was most pronounced for non-respiratory admissions. Conclusions: The first SIP order to be issued in the United States in response to COVID-19 was associated with a significant reduction in ED utilization in Marin County.

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Nonlinear visuoauditory integration in the mouse superior colliculus

10.1101/2021.01.26.428325 ◽

2021 ◽

Author(s):

Shinya Ito ◽

Yufei Si ◽

Alan M. Litke ◽

David A. Feldheim

Keyword(s):

Superior Colliculus ◽

Sensory Integration ◽

Large Scale ◽

Temporal Dynamics ◽

Critical Role ◽

Sensory Information ◽

Population Level ◽

Object Identification ◽

Response Properties ◽

The Individual

AbstractSensory information from different modalities is processed in parallel, and then integrated in associative brain areas to improve object identification and the interpretation of sensory experiences. The Superior Colliculus (SC) is a midbrain structure that plays a critical role in integrating visual, auditory, and somatosensory input to assess saliency and promote action. Although the response properties of the individual SC neurons to visuoauditory stimuli have been characterized, little is known about the spatial and temporal dynamics of the integration at the population level. Here we recorded the response properties of SC neurons to spatially restricted visual and auditory stimuli using large-scale electrophysiology. We then created a general, population-level model that explains the spatial, temporal, and intensity requirements of stimuli needed for sensory integration. We found that the mouse SC contains topographically organized visual and auditory neurons that exhibit nonlinear multisensory integration. We show that nonlinear integration depends on properties of auditory but not visual stimuli. We also find that a heuristically derived nonlinear modulation function reveals conditions required for sensory integration that are consistent with previously proposed models of sensory integration such as spatial matching and the principle of inverse effectiveness.

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