FrescoDiffusion: 4K Image-to-Video with Prior-Regularized Tiled Diffusion

The relative importance of spatial versus temporal structure in the perception of biological motion: An event-related potential study

The goal of this study is to calculate regionalized fate and characterization factors for zinc (Zn) emitted to soil considering the Zn transfer through groundwater in USETox and Zn speciation in soil, groundwater, and surface water using regionalized soil, subsoil, and freshwater parameters. Partition coefficients for Zn in soil and water and effect factors (EF) are calculated using the WHAM7 software. Soil and watershed maps are intersected with a geographic information system software to obtain native geographic resolution. USETox is modified by linking (a) the soil with subsoil and groundwater compartment and (b) subsoil and groundwater compartment to the freshwater compartment. Soil to water fate factors (FFsw) for each native resolution cell are calculated using these soil, subsoil, and watershed partition coefficients with the modified version of USETox. These specific FFsw’s are multiplied with bioavailability factors (BF) and effect factors (EF) to generate characterization factors (CFsw) for all the native resolution cells. The results obtained at the native resolution scale are aggregated at different more operational regionalization scales: country, continent, and global level, with the corresponding spatial variability determination. The newly obtained results are compared with the default values of USETox. Regionalized freshwater ecotoxicity characterization factors for Zn emitted to soil have a global spatial variability of 3 orders of magnitude. The aggregated global value is in the same order of magnitude with the default USETox value. The spatial variability of soil to water fate (FFsw) and the characterization factors (CFsw) for Zn within each watershed are quantified. The results are illustrated on a world map for all the native resolution cells. With the exception of Europe, all the regional and continental FFsw and CFsw varied over 2 orders of magnitude. The inclusion of the transfer through groundwater for Zn soil emissions in fate and in characterization calculation along with the Zn speciation allow better prediction of the potential impacts in freshwater systems. The spatial variability of Zn CFsw at continental scale is close to the uncertainty of USEtox’s CFsw (2 orders of magnitude), meaning that using a continental level CFsw seems a reasonable compromise between a too intensive data collection and imprecise impact assessment.

Low-rank representation with local constraint for graph construction

<p>Extreme precipitation events are expected to occur more frequently in a warming climate. Understanding their structure and predicting the exact time and location of precipitation events still remains a challenge because of the high temporal and spatial variability of rainfall. Nationwide weather radar networks are a common tool for investigating precipitation events and their spatial and temporal structure. The German Weather Service (DWD) provides a nationwide climatological radar data set from 2001 to 2020. A reprocessing procedure has been applied to reflectivity measurements in order to obtain precipitation estimates as homogeneous as possible. With an object-oriented analysis, all precipitation events for 11 different durations from 1 to 72 hours exceeding DWD’s official warning level for heavy precipitation have been detected and statistically analysed.</p><p>We will present a comprehensive analysis of all heavy precipitation events that occurred in Germany between 2001 and 2020. We examined their size, duration, location, spatial structure and distribution as well as regional and climatological differences and demonstrate how this information is collected in an online tool for easy access. An assessment of how well these heavy precipitation events were captured by DWD’s network of precipitation stations will be given. Finally, we will present the possibility to use the event detection procedure as an operational tool for assessing and classifying heavy precipitation events and their potential impact in near real-time.</p>

This paper presents an in-depth study and analysis of the restoration of distorted electronic technology archives using Markov models and proposes a corresponding fusion algorithm. Using the image gradient parametrization as a regular term, the filtering restoration process is constrained and the fuzzy kernel is estimated to solve the degradation problem existing in the Tibetan antiquarian literature. In the algorithmic framework of nonlocal mean filtering, the calculation of the weight function is improved to reduce the computational effort. In the simulation results, it is shown that the improved nonlocal mean filtering restoration algorithm in this paper has good overall quality evaluation performance in the restoration of text-based images. The structural similarity between the generated image and the real image is guaranteed, and the internal mixed-mode learning of a single SAR image is performed by combining the pyramidal hierarchical network structure to improve the effectiveness of the generated image in general. The method further improves the similarity between the generated and real images, while improving the accuracy of the classification based on the data expanded by the generation method. The feasibility and accuracy of the online algorithm for the parameter estimation problem of the model are illustrated through numerical experiments with two specific examples of hidden Markov models, namely, a double Gaussian mixture model and a finite-state Markov chain model with Gaussian noise. At the same time, the advantages of the algorithm proposed in this paper are demonstrated by comparing the experimental results of the online EM algorithm with those of the offline EM algorithm on a unified model. Finally, the empirical analysis is used to illustrate the application of the algorithm in practical scenarios.

The article presents the results of scientific developments concerning the structural organization of sacred landscapes. The methodological basis of the study is the concept of constructive-geographic analysis, which is based on the approaches of the natural and the humanitarian sciences. The system approach to the study of sacred landscapes as a holistic organized territorial structure and a set of methods is used in this work, in particular: structural and logical generalization and system analysis, comparative and geographical, historical and geographical. The author considers the significance of the notion of sacral landscape as being broader than religion per se, and considers it a natural, natural-anthropogenic and anthropogenic system associated with certain symbols of life, myths, significant events, and , indeed ,religious feelings that are of great importance to a person or group of people and requires special respect and protection. The structural organization of all sacred landscapes is characterized by their properties and spatial structure and is closely connected with their social and functional purpose. As a result, such territorial systems can be divided into: confessional, taphal, active, abnormal. The sacred landscape is characterized by polystructurality, that is, the presence of spatial, temporal and morphological structure. In the spatial structure of the sacral landscape, the following components can be distinguished: the sacred object, anthropogenic and technogenic component, the landscape structure and a person with his/ her spiritual experience. In addition, such a structure has a hierarchical construction, where individual, local, regional, national and global levels can be distinguished. This article presents the peculiarities of the temporal structure of sacral landscapes and outlines the external, internal, and the functioning time. Particular attention is paid to the characteristic of internal time, where one can distinguish the following phases of development: the formation of a natural, natural-anthropogenic or anthropogenic landscape; the creation of a spiritual component; loss of sacred human perception of a natural, natural-anthropogenic or anthropogenic landscape; the disappearance of the natural or natural- anthropogenic landscape. Taking into account the morphological structure of the sacred landscape, it is substantiated that religious objects serving as markers of sacred landscapes cannot correspond to one or another morphological unit of the landscape, that is, completely repeat its outlines and boundaries. However, there is a correlation between the type of landscape and the features of the sacred objects that were formed there.

The widespread of rumors on social media, carrying unreal or even malicious information, brings negative effects on society and individuals, which makes the automatic detection of rumors become particularly important. Most of the previous studies focused on text mining using supervised models based on feature engineering or deep learning models. In recent years, another parallel line of works, which focuses on the spatial structure of message propagation, provides an alternative and promising solution. However, these existing methods in this parallel line largely overlooked the temporal structure information associated with the spatial structure in message propagation. Actually the addition of temporal structure information can make the message propagations be classified from the perspective of spatial–temporal structure, a more fine-grained perspective. Under these observations, this paper proposes a spatial–temporal structure neural network for rumor detection, termed as STS-NN, which treats the spatial structure and the temporal structure as a whole to model the message propagation. All the STS-NN units are parameter shared and consist of three components, including spatial capturer, temporal capturer and integrator, to capture the spatial–temporal information for the message propagation. The results show that our approach obtains better performance than baselines in both rumor classification and early detection.

Characterising spatial microbial community structure is important to understand and explain the consequences of continuous plantation of one crop species on the performance of subsequent crops, especially where this leads to reduced growth vigour and crop yield. We investigated the spatial structure, specifically distance-decay of similarity, of soil bacterial and fungal communities in two long-established orchards with contrasting agronomic characteristics. A spatially explicit sampling strategy was used to collect soil from under recently grubbed rows of apple trees and under the grassed aisles. Amplicon-based metabarcoding technology was used to characterise the soil microbial communities. The results suggested that (1) most of the differences in soil microbial community structure were due to large-scale differences (i.e. between orchards), (2) within-orchard, small-scale (1–5 m) spatial variability was also present, but spatial relationships in microbial community structure differed between orchards and were not predictable, and (3) vegetation type (i.e. trees or grass and their associated management) can significantly alter the structure of soil microbial communities, affecting a large proportion of microbial groups. The discontinuous nature of soil microbial community structure in the tree stations and neighbouring grass aisles within an orchard illustrate the importance of vegetation type and allied weed and nutrient management on soil microbial community structure.

Quantitative risk assessment of landslides induced by slope failure is an important precondition for formulating effective disaster prevention, mitigation measures, and establishing a landslide risk warning system. In general, the location of the critical slip surface and the failure mode is unlikely to be predicted due to the spatial variability in soil. It remains a challenging task to effectively identify the critical slip surface and conduct the efficient risk assessment based on a three‐dimensional (3‐D) slope with spatial variability. Based on Monte Carlo simulation and the random field method, a quantitative risk evaluation method for slope failure considering the spatial variability of soil parameters is proposed in the study. Compared with a uniform soil slope, the landslide volume, the critical slip surface, and the factor of safety considering the spatial variability of soil are all uncertain; thus, the soil spatial variability has a significant effect on the failure mode and stability of the slope. By using the random finite element method, the critical slip surface of the slope is accurately identified, the corresponding landslide volume and slide distance are calculated, and the modified risk index for a landslide is further enriched, which can provide the reference basis for predicting the landslide deformation, quantitatively evaluating the landslide risk, and mitigating the landslide disaster.

Single image super-resolution using regularization of non-local steering kernel regression

Phragmites australis is a perennial grass that has invaded wetlands of the northeastern United States over the past century. The Hudson River Estuary and surrounding watersheds are no exception in that populations of P. australis have spread dramatically along its shores and tributaries in the past 40 years. Recent studies have shown that genetically variable populations of P. australis can spread by seed dispersal in addition to clonal mechanisms. It is important to characterize the genetic variation of Hudson River populations as part of a management strategy for this species to determine the mechanisms by which its spreads and colonizes new habitats, particularly those with frequent anthropogenic disturbances. The goals of this study were to quantify levels of genetic variation and structuring in Hudson River populations of P. australis using microsatellite DNA analysis. A total of 354 culms of P. australis were collected from nine locations ranging from Albany, New York to Staten Island, New York in the summers of 2004 (N = 174) and 2011 (N = 180). Microsatellite data from eight loci indicated that the Hudson River Estuary has some of the highest levels of genetic variation of all U. S. Atlantic Coast regions containing P. australis. Gene diversity (Hs) across all loci in the 2004 collection was 0.45 (±0.02) and that of the 2011 collection was 0.47 (±0.07). Patches within sample sites were rarely monoclonal and had multiple genetic phenotypes. Moran’s Identity tests indicated that individuals within a patch were closely related, whereas little genetic relatedness was evident among individuals from sample sites >1 km apart. Spatial structuring was also not evident in autospatial correlation and principle coordinate analyses. These findings suggest that genetic diversity is maintained within stands by sexual reproduction and that seeds are important in dispersal of P. australis across the Hudson River Estuary. Ample habitats are available for establishment of new Phragmites stands due to high levels of anthropogenic disturbance from populations living along the Estuary. Wildlife managers should focus on monitoring habitats that provide seedbed for Phragmites and promote land use practices that prevent soil disturbance and establishment of new stands.

Counts in epidemiology often deviate from equidispersion and exhibit spatial, temporal, and nonlinear structure that the Poisson model cannot accommodate. We introduce a gamma-count structured additive regression model that strategically integrates penalized complexity priors in two critical aspects: (i) a principled penalized complexity prior on the dispersion parameter of the gamma-count distribution, which naturally shrinks toward the base Poisson model when the data support equidispersion, and (ii) scale-dependent penalized complexity hyperpriors on the smoothing variances for nonlinear, spatial, and temporal effects. By formulating the model within a latent Gaussian framework, we enable efficient approximate Bayesian inference through integrated nested Laplace approximations. Simulation studies across under-, equi-, and over-dispersed regimes show that the penalized complexity prior for dispersion parameter combined with scale-dependent hyperpriors yields accurate estimation of dispersion and smooth effects, favorable predictive scores, and robust inference. In empirical applications to larynx cancer mortality in Germany, COVID-19 incidence in Georgia (USA), and lung and bronchus cancer in Iowa (USA), the gamma-count structured additive regression model exhibits competitive or enhanced fit relative to Poisson and negative binomial counterparts, while elucidating interpretable nonlinear and spatial structures. This framework delivers robust, spatially resolved estimates of disease burden in the presence of non-equidispersion, thereby facilitating evidence-based resource allocation, epidemiological surveillance, and monitoring of health disparities, contributing to Sustainable Development Goals (SDGs) 3 (Good Health and Well-Being) and 10 (Reduced Inequalities). For geographically targeted analyses, it further supports informed decision-making in urban and community planning, aligning with SDG 11 (Sustainable Cities and Communities).

Soil and land quality indicators play an important role in the assessment and evaluation of soil and land quality. In contrast with the general definitions of soil and land quality, working with indicators demands a better awareness of at which scale level measurements were made, at which scale calculations and models were developed and validated, and at which scale answers are needed. We propose that soil and land quality indicators may be classified by three characteristics: 1) scale level, 2) complexity, and 3) transferability. Each characteristic is represented by an axis in the Soil and Land Quality Indicator Diagram. Indicators with a high complexity can not be measured directly, but need to be calculated with one or more models, eg. pedotransfer functions and hydrological simulation models. For the application of the indicator it is then important to know how the indicator value was obtained, i.e. which models were used. A specific sequence of models used for obtaining an indicator value is called a ‘research chain’ and is indicated in the Scale Hierarchy and Knowledge Type Diagram. The use of research chains allows the user to consider and evaluate alternative options for the assessment of a specific indicator. In this study values for three soil quality indicators were obtained through two alternative research chains. The research chains differed by the choice of used pedotransfer functions and soil hydrological models. The two research chains yielded for each of the three indicators two sets of thirty year averages for 166 locations in the study area. Per location the obtained indicator values were compared with a t-test. The research chains were found to yield significantly different values for all three indicators. The spatial and temporal variability of the data was analyzed for each step, i.e. per model, along both research chains. Alternative models yielded different spatial and temporal variability structures. Therefore, the choice of research chain not only affects the mean value of an indicator, but also the associated spatial and temporal variability structure. Knowledge of the spatial and temporal variability is important for upscaling purposes. Based on these results we conclude that the successful application of soil and land quality indicators depends on: 1) the definition of suitable indicators based on scale level, complexity, and transferability; 2) the careful selection and definition of research chains; and 3) the combined presentation of indicator values and used research chains.

We analyze the relative contribution of environmental and spatial variables to the alpha and beta components of taxonomic (TD), phylogenetic (PD), and functional (FD) diversity in ant communities found along different climate and anthropogenic disturbance gradients across western and central Europe, in order to assess the mechanisms structuring ant biodiversity. To this aim we calculated alpha and beta TD, PD, and FD for 349 ant communities, which included a total of 155 ant species; we examined 10 functional traits and phylogenetic relatedness. Variation partitioning was used to examine how much variation in ant diversity was explained by environmental and spatial variables. Autocorrelation in diversity measures and each trait’s phylogenetic signal were also analyzed. We found strong autocorrelation in diversity measures. Both environmental and spatial variables significantly contributed to variation in TD, PD, and FD at both alpha and beta scales; spatial structure had the larger influence. The different facets of diversity showed similar patterns along environmental gradients. Environment explained a much larger percentage of variation in FD than in TD or PD. All traits demonstrated strong phylogenetic signals. Our results indicate that environmental filtering and dispersal limitations structure all types of diversity in ant communities. Strong dispersal limitations appear to have led to clustering of TD, PD, and FD in western and central Europe, probably because different historical and evolutionary processes generated different pools of species. Remarkably, these three facets of diversity showed parallel patterns along environmental gradients. Trait-mediated species sorting and niche conservatism appear to structure ant diversity, as evidenced by the fact that more variation was explained for FD and that all traits had strong phylogenetic signals. Since environmental variables explained much more variation in FD than in PD, functional diversity should be a better indicator of community assembly processes than phylogenetic diversity.

Currently, little is known about the spatial and temporal variability of rice (Oryza sativa L.) yield patterns. This information is needed before implementing any site-specific management strategy. The objective of this research was to characterize the spatial and temporal yield variability of rice grown in commercial fields in California. Rice cultivars M-202 and Koshihikari were grown and managed by a cooperating farmer, who collected yield monitor data over a 4-yr period. Alternative methods of data quality analysis were applied to the data. To evaluate temporal variability, yields from different years must be placed on a common grid. The appropriate size for these grids was tested. Large-scale spatial structure was determined using median polish while small-scale spatial structure was evaluated by computing variograms of the yield residuals after subtracting the trends. Temporal variability was determined using two approaches: (i) computation of the variance among years of the original, trend, and residual yield values at fixed points in the field and (ii) cluster analysis of the standardized trend yield values. Results from the study showed that the grid density necessary to capture the spatial variability depended on site and year. Trend surface spatial behaviors depended on year, indicating a lack of temporal stability. Variograms showed strong spatial structure of yield residuals. Cluster analysis reduced the considerable complexity in a sequence of yield maps of these fields to a few general patterns of variations among years.