195 research outputs found

    Ekologické a evoluční procesy určující strukturu sítí rostlin a opylovačů

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    Abtrakt Rozmnožování většiny druhů rostlin a potrava značné části diverzity živočichů na této planetě přímo závisí na vztazích mezi květy a opylovači. Donedávna se však převážná většina výzkumu opylování zaměřovala pouze na studium opylování konkrétních rostlin a jen málo pozornosti bylo věnováno celým společenstvům rostlin i opylovačů. V posledních desetiletích se však zaměření ekologie opylování posunulo díky zavedení konceptu opylovacích sítí. Tento koncept umožnil zabývat se opylováním v kontextu celého společenstva, poukázal na rozmanitost i komplexitu vztahů mezi rostlinami a jejich opylovači a otevřel řadu nových možností výzkumu těchto vztahů z pohledu jeho významu pro živočichy nebo z pohledu časové a prostorové dynamiky opylovacích interakcí. Přesto však dosud máme jen matné představy o tom, jaké procesy jsou zodpovědné za strukturu a dynamiku těchto sítí. Podoba opylovací sítě je formována jak ekologickými, tak evolučními procesy. Z ekologického pohledu hraje roli například to, jak se druhy v čase a prostoru potkávají nebo jak si jednotlivé taxony opylovačů vybírají mezi rostlinami v závislosti na kontextu prostředí, aktuálních potravních potřebách či nabídce květních zdrojů. Z evolučního pohledu je pak podoba sítě vztahů mezi rostlinami a opylovači určena tím, jak se druhy na sebe vzájemně...Associations between flowers and pollinators are responsible for reproduction of majority of plant species as well as food supply for substantial part of animal diversity on the Earth. Until recently, the studies on plant-pollinator relationship were focused predominantly on pollination of particular plant species, with only little or no accent on community perspective. In recent decades, however, pollination ecology shifted its focus rather to community context by introducing so called pollination networks. This approach allows us to view the ubiquity and complexity of the interactions between plants and their pollinators and it opened up many new opportunities to study the pollination from animal perspective or to access spatio-temporal variability in the interactions. However, we still have only limited insight into the processes driving the structure and dynamics of such networks. The assembly of plants, pollinators and their interactions are driven by various ecological as well as evolutionary processes. From the ecological point of view, species co-occurrence in time and space may affect the interactions, or species flexibility for various community contexts providing different food sources may play role. In the evolutionary perspective, species may have various co-adaptations due to their...Katedra zoologieDepartment of ZoologyPřírodovědecká fakultaFaculty of Scienc

    Advances in Modelling of Rainfall Fields

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    Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

    Strong influence of trees outside forest in regulating microclimate of intensively modified Afromontane landscapes

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    Climate change is expected to have detrimental consequences on fragile ecosystems, threatening biodiversity, as well as food security of millions of people. Trees are likely to play a central role in mitigating these impacts. The microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate as vegetation can buffer temperature changes and variability. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. The aim of this study was to examine the effect of canopy cover on microclimate in an intensively modified Afromontane landscape in Taita Taveta, Kenya. We studied temperatures recorded by 19 microclimate sensors under different canopy covers, as well as land surface temperature (LST) estimated by Landsat 8 thermal infrared sensor. We combined the temperature records with high-resolution airborne laser scanning data to untangle the combined effects of topography and canopy cover on microclimate. We developed four multivariate regression models to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between canopy cover percentage and daytime mean (R-2 = 0.65) and maximum (R-2 = 0.75) temperatures. Any increase in canopy cover contributed to reducing temperatures. The average difference between 0 % and 100 % canopy cover sites was 5.2 degrees C in mean temperatures and 10.2 degrees C in maximum temperatures. Canopy cover (CC) reduced LST on average by 0.05 degrees C per percent CC. The influence of canopy cover on microclimate was shown to vary strongly with elevation and ambient temperatures. These results demonstrate that trees have a substantial effect on microclimate, but the effect is dependent on macroclimate, highlighting the importance of maintaining tree cover particularly in warmer conditions. Hence, we demonstrate that trees outside forests can increase climate change resilience in fragmented landscapes, having strong potential for regulating regional and local temperatures

    Mapping poverty at multiple geographical scales

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    Poverty mapping is a powerful tool to study the geography of poverty. The choice of the spatial resolution is central as poverty measures defined at a coarser level may mask their heterogeneity at finer levels. We introduce a small area multi-scale approach integrating survey and remote sensing data that leverages information at different spatial resolutions and accounts for hierarchical dependencies, preserving estimates coherence. We map poverty rates by proposing a Bayesian Beta-based model equipped with a new benchmarking algorithm that accounts for the double-bounded support. A simulation study shows the effectiveness of our proposal and an application on Bangladesh is discussed.Comment: 22 pages, 7 figure

    Studies of the Ionosphere-Thermosphere Responses to Multi-Scale Energy Deposition Processes

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    The Ionosphere-Thermosphere (I-T) system is greatly affected by the magnetospheric energy deposition from above and subjected to forcing from the lower atmosphere simultaneously. A central problem in studying the coupled I-T system is to analyze the multi-scale processes naturally embedded in both ways. Magnetospheric energy input such as auroral precipitation and electric field demonstrates multi-scale structures during magnetic storms, resulting in multi-scale I-T responses when deposited into the I-T system. To better quantify the multi-scale aurora and electric field, we developed a new data assimilation model based on a multi-resolution Gaussian process model to synthesize empirical models and observational data from various sources and provide estimates in regions without observations. The new method mitigates the discrepancy between empirical models and observations by successfully capturing the dynamic evolutions of large-scale and mesoscale auroral and electric field structures. By further incorporating the assimilated aurora and electric fields into Thermosphere Ionosphere Electrodynamics General Circulation Model (TIEGCM) during the 2015 St. Patrick\u27s Day storm, we significantly elevate Joule heating and largely reproduce the global and local I-T responses as observed, including the enhanced electron density and vertical wind. Data assimilation also helps introduce more spatial and temporal variabilities in TIEGCM, which propagate to low-latitude regions through Traveling Atmospheric Disturbance (TAD). In the other direction, to study the atmospheric wave forcing from below and how it impacts the I-T system, we develop a nested-grid extension to TIEGCM to study the Gravity Wave (GW) propagation process and its ionospheric effect during the 2022 Tonga volcano eruption. Such a hybrid-grid design helps to better simulate the variations of a smaller scale than the standard model resolution while reducing computation costs at the same time. With proper seeding at the lower boundary, GW propagation in the thermosphere is successfully reproduced. The resulting Traveling Ionospheric Disturbance (TID) in the ionosphere has a similar speed to observations. The wave spectrum at different altitudes also indicates that the dominant GW has a shorter period and horizontal wavelength at higher altitudes. This dissertation discusses the detailed tool development, including data assimilation and TIEGCM-NG, which enables a better understanding of the influences of multi-scale magnetospheric forcing and lower-atmosphere wave forcing on the I-T system. This work provides a powerful set of tools for a better simulation of space weather

    Metallurgical Process Simulation and Optimization

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    Metallurgy involves the art and science of extracting metals from their ores and modifying the metals for use. With thousands of years of development, many interdisciplinary technologies have been introduced into this traditional and large-scale industry. In modern metallurgical practices, modelling and simulation are widely used to provide solutions in the areas of design, control, optimization, and visualization, and are becoming increasingly significant in the progress of digital transformation and intelligent metallurgy. This Special Issue (SI), entitled “Metallurgical Process Simulation and Optimization”, has been organized as a platform to present the recent advances in the field of modelling and optimization of metallurgical processes, which covers the processes of electric/oxygen steel-making, secondary metallurgy, (continuous) casting, and processing. Eighteen articles have been included that concern various aspects of the topic

    Nonlocal Graph-PDEs and Riemannian Gradient Flows for Image Labeling

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    In this thesis, we focus on the image labeling problem which is the task of performing unique pixel-wise label decisions to simplify the image while reducing its redundant information. We build upon a recently introduced geometric approach for data labeling by assignment flows [ APSS17 ] that comprises a smooth dynamical system for data processing on weighted graphs. Hereby we pursue two lines of research that give new application and theoretically-oriented insights on the underlying segmentation task. We demonstrate using the example of Optical Coherence Tomography (OCT), which is the mostly used non-invasive acquisition method of large volumetric scans of human retinal tis- sues, how incorporation of constraints on the geometry of statistical manifold results in a novel purely data driven geometric approach for order-constrained segmentation of volumetric data in any metric space. In particular, making diagnostic analysis for human eye diseases requires decisive information in form of exact measurement of retinal layer thicknesses that has be done for each patient separately resulting in an demanding and time consuming task. To ease the clinical diagnosis we will introduce a fully automated segmentation algorithm that comes up with a high segmentation accuracy and a high level of built-in-parallelism. As opposed to many established retinal layer segmentation methods, we use only local information as input without incorporation of additional global shape priors. Instead, we achieve physiological order of reti- nal cell layers and membranes including a new formulation of ordered pair of distributions in an smoothed energy term. This systematically avoids bias pertaining to global shape and is hence suited for the detection of anatomical changes of retinal tissue structure. To access the perfor- mance of our approach we compare two different choices of features on a data set of manually annotated 3 D OCT volumes of healthy human retina and evaluate our method against state of the art in automatic retinal layer segmentation as well as to manually annotated ground truth data using different metrics. We generalize the recent work [ SS21 ] on a variational perspective on assignment flows and introduce a novel nonlocal partial difference equation (G-PDE) for labeling metric data on graphs. The G-PDE is derived as nonlocal reparametrization of the assignment flow approach that was introduced in J. Math. Imaging & Vision 58(2), 2017. Due to this parameterization, solving the G-PDE numerically is shown to be equivalent to computing the Riemannian gradient flow with re- spect to a nonconvex potential. We devise an entropy-regularized difference-of-convex-functions (DC) decomposition of this potential and show that the basic geometric Euler scheme for inte- grating the assignment flow is equivalent to solving the G-PDE by an established DC program- ming scheme. Moreover, the viewpoint of geometric integration reveals a basic way to exploit higher-order information of the vector field that drives the assignment flow, in order to devise a novel accelerated DC programming scheme. A detailed convergence analysis of both numerical schemes is provided and illustrated by numerical experiments

    Machine Learning Techniques for Improved Functional Brain Parcellation

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    Brain parcellation studies are fundamental for neuroscience as they serve as a bridge between anatomy and function, helping researchers interpret how functions are distributed across different brain regions. However, two substantial challenges exist in current imaging-based brain parcellation studies: large variations in the functional organization across individuals and the intrinsic spatial dependence which causes nearby brain locations to have a similar function. This thesis presents a series of projects aimed to tackle these challenges from different perspectives by using advanced machine learning techniques. To handle the challenge of individual variability in building precise individual parcellations, Chapter 3 introduces a novel hierarchical Bayesian brain parcellation framework. This framework learns a brain probabilistic parcellation by integrating across diverse datasets. For single individuals, the framework optimally combines the limited individual data with the group probability map, resulting in improved individual maps. We found that the resultant individual parcellation based on only 10 minutes of imaging scans can achieve an equivalent performance to the ones using 100 minutes of data alone. These improved individual parcellations are essential to accurately capture functional variations across studied populations. The intrinsic spatial dependence between brain locations poses a significant challenge in both evaluating and generating brain parcellations. To address this, Chapter 2 presents a bias-free method for evaluating different brain parcellations, the distance-controlled boundary coefficient (DCBC). Compared to existing evaluation metrics that bias toward finer and spatial contiguous parcellations due to spatial smoothness, DCBC provides a fair evaluation by controlling the distance of brain location pairs, ensuring a direct comparison of parcellations in different resolutions. To address the intrinsic spatial dependence when learning parcellations, I propose a new model in Chapter 4, the multinomial restricted Boltzmann machine (m-RBM), that can be incorporated into the learning framework in Chapter 3. This model captures spatial structure between brain locations in its architecture. While simulations showed the utility of this type of model in estimating individual parcellations, we could not demonstrate better performance using real imaging data. Together, this thesis significantly advances the technical toolkit for deriving brain parcellations from functional imaging data. The developments open up new avenues for future research into human brain organization

    Ekologické a evoluční procesy určující strukturu sítí rostlin a opylovačů

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    Associations between flowers and pollinators are responsible for reproduction of majority of plant species as well as food supply for substantial part of animal diversity on the Earth. Until recently, the studies on plant-pollinator relationship were focused predominantly on pollination of particular plant species, with only little or no accent on community perspective. In recent decades, however, pollination ecology shifted its focus rather to community context by introducing so called pollination networks. This approach allows us to view the ubiquity and complexity of the interactions between plants and their pollinators and it opened up many new opportunities to study the pollination from animal perspective or to access spatio-temporal variability in the interactions. However, we still have only limited insight into the processes driving the structure and dynamics of such networks. The assembly of plants, pollinators and their interactions are driven by various ecological as well as evolutionary processes. From the ecological point of view, species co-occurrence in time and space may affect the interactions, or species flexibility for various community contexts providing different food sources may play role. In the evolutionary perspective, species may have various co-adaptations due to their...Abtrakt Rozmnožování většiny druhů rostlin a potrava značné části diverzity živočichů na této planetě přímo závisí na vztazích mezi květy a opylovači. Donedávna se však převážná většina výzkumu opylování zaměřovala pouze na studium opylování konkrétních rostlin a jen málo pozornosti bylo věnováno celým společenstvům rostlin i opylovačů. V posledních desetiletích se však zaměření ekologie opylování posunulo díky zavedení konceptu opylovacích sítí. Tento koncept umožnil zabývat se opylováním v kontextu celého společenstva, poukázal na rozmanitost i komplexitu vztahů mezi rostlinami a jejich opylovači a otevřel řadu nových možností výzkumu těchto vztahů z pohledu jeho významu pro živočichy nebo z pohledu časové a prostorové dynamiky opylovacích interakcí. Přesto však dosud máme jen matné představy o tom, jaké procesy jsou zodpovědné za strukturu a dynamiku těchto sítí. Podoba opylovací sítě je formována jak ekologickými, tak evolučními procesy. Z ekologického pohledu hraje roli například to, jak se druhy v čase a prostoru potkávají nebo jak si jednotlivé taxony opylovačů vybírají mezi rostlinami v závislosti na kontextu prostředí, aktuálních potravních potřebách či nabídce květních zdrojů. Z evolučního pohledu je pak podoba sítě vztahů mezi rostlinami a opylovači určena tím, jak se druhy na sebe vzájemně...Katedra zoologieDepartment of ZoologyFaculty of SciencePřírodovědecká fakult
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