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Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.

Deficiencies and inconsistencies introduced during the modeling of software systems may result in high costs and negatively impact the quality of all developments performed using these models. Therefore, developing more accurate models will aid software architects in developing software systems that match and exceed expectations. This paper proposes a graph neural network (GNN) method for predicting missing connections, or links, in graphical models, which are widely employed in modeling software systems. The proposed method utilizes graphs as allegedly incomplete, primitive graphical models of the system under consideration (SUC) as input and proposes links between its elements through the following steps: (i) transform the models into graph-structured data and extract features from the nodes, (ii) train the GNN model, and (iii) evaluate the performance of the trained model. Two GNN models based on SEAL and DeepLinker are evaluated using three performance metrics, namely cross-entropy loss, area under curve, and accuracy. Event sequence graphs (ESGs) are used as an example of applying the approach to an event-based behavioral modeling technique. Examining the results of experiments conducted on various datasets and variations of GNN reveals that missing connections between events in an ESG can be predicted even with relatively small datasets generated from ESG models.

Geospatial research has long centered around objects. While attention to events is growing rapidly, events remain objectified in spatial databases. This paper aims to highlight the importance of events in scientific inquiries and overview general event-based approaches to data modeling and computing. As machine learning algorithms and big data become popular in geospatial research, many studies appear to be the products of convenience with readily adaptable data and codes rather than curiosity. By asking why events are important and how to compute events in geospatial research, the author intends to provoke thinking into the rationale and conceptual basis of event-based modeling and to emphasize the epistemological role of events in geospatial information science. Events are essential to understanding the world and communicating the understanding, events provide points of entry for knowledge inquiries and the inquiry processes, and events mediate objects and scaffold causality. We compute events to improve understanding, but event computing and computability depend on event representation. The paper briefly reviews event-based data models in spatial databases and methods to compute events for site understanding and prediction, for spatial impact assessment, and for discovering events' dynamic structures. Concluding remarks summarize key arguments and comment on opportunities to extend event computability.

Hydrologic models are important for effective water resources management at a basin level. This paper describes an application of the HEC-HMS hydrologic model for simulations of flood hydrographs in the Lukovska River basin. Five flood events observed at the Merćez stream gauge were available for modelling purposes. These events are from two distinct periods and two seasons with different prevailing runoff generation mechanisms. Hence the events are assigned to either “present” or “past”, and “spring” or “summer” group. The optimal parameter sets of each group are obtained by averaging the optimal parameters for individual events within the group. To assess model transferability, its applicability for simulation of flood events which are not considered in the model calibration, a cross-validation is performed. The results indicate that model parameters vary across the events, and that parameter transfer generally leads to considerable errors in hydrograph peaks and volumes, with the exception of simulation of summer events with “spring” parameters. Based on these results, recommendations for event-based modeling are given.

Geospatial research has long centered around objects. While attention to events is growing rapidly, events remain objectified in spatial databases. This paper aims to highlight the importance of events in scientific inquiries and overview general event-based approaches to data modeling and computing. As machine learning algorithms and big data become popular in geospatial research, many studies appear to be the products of convenience with readily adaptable data and codes rather than curiosity. By asking why events are important and how to compute events in geospatial research, the author intends to provoke thinking into the rationale and conceptual basis of event-based modeling and to emphasize the epistemological role of events in geospatial information science. Events are essential to understanding the world and communicating the understanding, events provide points of entry for knowledge inquiries and the inquiry processes, and events mediate objects and scaffold causality. We compute events to improve understanding, but event computing and computability depend on event representation. The paper briefly reviews event-based data models in spatial databases and methods to compute events for site understanding and prediction, for spatial impact assessment, and for discovering events' dynamic structures. Concluding remarks summarize key arguments and comment on opportunities to extend event computability. [ABSTRACT FROM AUTHOR]

The typical Mediterranean climate is marked at certain times of the year by sudden torrential rains causing high water flows, which leads to heavy flooding and hydroclimatic fluctuations due to a semi-arid climate. This explains the need for hydrological modeling for water resource management in these contexts. This work concerns the hydrological modeling of the Azzaba catchment area in Haut-Sebou "Morocco". In the first part of this work, a bibliographic synthesis was carried out to characterize certain factors (physical, geological and climatic), and a hydrological study was carried out by processing rainfall and hydrometric data from the considered time periods. Ultimately, the use of the "ATHYS" platform is beginning to reproduce the flows at the Azzaba outlet. This model is really applicable in the semi-arid context based on several studies carried out on these contexts, since it has to consider the chronological sequence of phenomena on one hand and the influence of the climatic and physicalhydrogeological parameters of the basin (humidity and soil exchange) on the other. Several criteria were used in this study to estimate the model performance; the most common is Nash-Sutcliffe. After observation and analysis of the overall results, it can be concluded that the model reproduces flows in the Azzaba River watershed well, especially in event mode (mean Nash-Sutcliffe value of 0.71). The use of a historical meteorological time series to simulate flow using a daily time step gives average results with a Nash of 0.50, which strengthens the reliability of the ATHYS platform in the Mediterranean climate area. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Faculty of Forestry / Glasnik Šumarskog Fakulteta is the property of University of Belgrade, Faculty of Forestry and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The scope of this study is an evaluation of potential soil water erosion using the physically-based erosion model, EROSION-3D. This model is event-based and calculates soil water erosion during an actual measured rainfall event. The calculations of the erosion model take advantage of a digital elevation model, precipitation and soil parameters, which are established in a specific parameter catalogue. The soil water erosion was modeled in two small catchments in the Myjava Hill Land (Slovakia), using 9 soil moisture scenarios and two different crops on arable areas. When considering the last 35 years of rainfall records at the Myjava meteorological station, three storm rainfall events were applied in the modeling. The results were statistically analyzed to figure out the differences between the model's functional possibilities; the modeling under the various scenarios proved a strong interaction between the values of the input factors and the results of the soil erosion. [ABSTRACT FROM AUTHOR]

Mathematical models are often required in process modeling, control, and evaluation. To meet the increasing demand of production system real-time performance evaluation and improvement, in this paper: 1) an innovative event-based, data-driven mathematical model is established for network structured manufacturing systems; 2) important properties of network structured manufacturing systems are obtained through the concept development of a virtual ideal clean system; and 3) a system performance diagnostic method is developed based on the mathematical model and system properties, as well as available sensor data. The mathematical model and system performance identification methodology are studied analytically and validated by simulation studies. This data-driven mathematical framework and system real-time performance diagnostic methodology are invaluable for real-time production control to improve system responsiveness and efficiency. Note to Practitioners—For production systems with complex networked structure layouts, the dependence relationship between machines and buffers is more complicated compared with serial production lines. Therefore, it is more difficult to analyze and evaluate their real-time production performance such as permanent production loss. Such information is critical for real-time production management and control to achieve higher system responsiveness and efficiency. This paper establishes an event-based data-driven mathematical model to describe the real-time dynamic behavior of manufacturing systems with complex networked structures. Furthermore, an analysis method for networked manufacturing system properties and a data-driven system performance diagnostic method are proposed. The methods provide a quantitative solution to evaluate production capacity and production constraints of complex networked manufacturing systems, and to evaluate the system real-time performance such as permanent production loss and its attribution to each disruption event and machine. [ABSTRACT FROM AUTHOR]

This paper aims at providing a flexible and compact volumetric object model capable of representing many sedimentary structures at different scales. Geobodies are defined by a boundary representation; each bounding surface is constructed as a parametric deformable surface. A three-dimensional sedimentary object with a compact parametrization which allows for representing various geometries and provides a curvilinear framework for modeling internal heterogeneities is proposed. This representation is based on non-uniform rational basis splineswhich smoothly interpolate between a set of points. The three-dimensional models of geobodies are generated using a small number of parameters, and hence can be easily modified. This can be done by a point and click user interaction for manual editing or by a Monte-Carlo sampling for stochastic simulation. Each elementary shape is controlled by deformation rules and has connection constraints with associated objects to maintain geometric consistency through editing. The boundary representations of the different sedimentary structures are used to construct hexahedral conformal grids to perform petrophysical property simulations following the particular three-dimensional parametric space of each object. Finally these properties can be upscaled, according to erosion rules, to a global grid that represents the global depositional environment. [ABSTRACT FROM AUTHOR]

Objective: Prediction of disease progression is challenging in multiple sclerosis (MS) as the sequence of lesion development and retention of inflammation within a subset of chronic lesions is heterogeneous among patients. We investigated the sequence of lesion-related regional structural disconnectivity across the spectrum of disability and cognitive impairment in MS., Methods: In a full cohort of 482 patients, the Expanded Disability Status Scale was used to classify patients into (i) no or mild vs (ii) moderate or severe disability groups. In 363 out of 482 patients, Quantitative Susceptibility Mapping was used to identify paramagnetic rim lesions (PRL), which are maintained by a rim of iron-laden innate immune cells. In 171 out of 482 patients, Brief International Cognitive Assessment was used to identify subjects with cognitive impairment. Network Modification Tool was used to estimate the regional structural disconnectivity due to MS lesions. Discriminative event-based modeling was applied to investigate the sequence of regional structural disconnectivity due to all representative lesions across the spectrum of disability and cognitive impairment., Results: Structural disconnection in the ventral attention and subcortical networks was an early biomarker of moderate or severe disability. The earliest biomarkers of disability progression were structural disconnections due to PRL in the motor-related regions. Subcortical structural disconnection was an early biomarker of cognitive impairment., Interpretation: MS lesion-related structural disconnections in the subcortex is an early biomarker for both disability and cognitive impairment in MS. PRL-related structural disconnection in the motor cortex may identify the patients at risk for moderate or severe disability in MS., Competing Interests: Disclosure of competing interests The authors declare that they have no competing interest.

Nitrate and phosphate export coefficient models were developed for coastal watersheds along the Santa Barbara Channel in central California. One approach was based on measurements of nutrient fluxes in streams from specific land use classes and included a watershed response function that scaled export up or down depending on antecedent moisture conditions. The second approach for nutrient export coefficient modeling used anthropogenic nutrient loading for land use classes and atmospheric nutrient deposition to model export. In an application of the first approach to one watershed, the nitrate and phosphate models were within 20% of measured values for most storms. When applied to another year, both nitrate and phosphate models generally performed adequately with annual, storm-flow, and base-flow values within 20% of measured nutrient loadings. Less satisfactory results were found when applied to neighboring watersheds with difference percentages of land use and hydrologic conditions. Application of the second approach was less successful than the first approach. [ABSTRACT FROM AUTHOR]

Facies bodies in geostatistical models of deep-water depositional environments generally represent channel-levee-overbank-lobe morphologies. Such models adequately capture one set of the erosional and depositional processes resulting from turbidity currents traveling downslope to the ocean basin floor. However, depositional morphologies diverge from the straight forward channel-levee-overbank-lobe paradigm when the topography of the slope or the shape of the basin impacts the timing and magnitude of turbidity current deposition. Subaqueous mass-transport-deposits (MTDs) present the need for an exception to the channel-levee-overbank-lobe archetype. Irregular surface topography of subaqueous MTDs can play a primary role in controlling sand deposition from turbidity currents. MTD topography creates mini-basins in which sand accumulates in irregularly-shaped deposits. These accumulations are difficult to laterally correlate using well-log data due to their variable and unpredictable shape and size. Prediction is further complicated because sandstone bodies typical of this setting are difficult to resolve in seismic-reflection data. An event-based model is presented, called DFTopoSim, which simulates debris flows and turbidity currents. The accommodation space on top of and between debris flow lobes is filled in by sand from turbidity currents. When applied to a subsurface case in the Molasse Basin of Upper Austria, DFTopoSim predicts sand packages consistent with observations from core, well, and seismic data and the interpretation of the sedimentologic processes. DFTopoSim expands the set of available geostatistical deep-water depositional models beyond the standard channel-levee-overbank-lobe model. [ABSTRACT FROM AUTHOR]