Level Of Detail Research Articles

Structuring of 2‐oleodipalmitin at the air interface from all‐atom, united‐atom, and coarse‐grained molecular dynamics simulations

AbstractThis study employs classical molecular dynamics (MD) simulations to investigate the behavior of 1,3‐dipalmitoyl‐2‐oleoyl glycerol (POP), a typical triglyceride found in food at the air interface. The investigation utilizes three force fields (FF) with varying levels of detail, including all‐atom, united‐atom, and coarse‐grained simulations, to elucidate the structural evolution of POP at the air interface. The results confirm that the structuring processes observed in nonfood triglyceride systems also occur in POP. Aliphatic chains orient toward the air phase, while glycerol backbones face the inner triglyceride phase. Additionally, the study observes the formation of clusters of glycerol head groups at the interface, and the choice of the FF significantly affects the simulated morphological structure. The Berger FF exhibits the most substantial structuring effects after 200 ns of simulation, followed by the General AMBER FF, while the Martini FF shows the weakest effects. Number density plots of aliphatic chains and glycerol backbones at different temperatures monitor the evolution of structuring effects over time and determine the approach toward equilibrium conditions. The temperatures investigated include the solid state of POP at ambient temperature (293 K), the liquid state at human body temperature (310 K), and a typical processing temperature of the chocolate conching process (333 K).Practical Applications: Tailored molecular design of interfaces according to their purpose in food.

Research on the scene design of virtual reality technology in film and television animation

Abstract In this paper, firstly, we study the modeling of film and television animation based on virtual reality technology and use quadratic polynomial inverse to control the intensity of light source to establish the lighting model. The image alignment is designed for the scene based on multi-resolution decomposition, and the filtered image layer is obtained by calculating the difference between two layers of Gaussian filtered images, and the Laplace pyramid is established to match the image resolution consistently. Then the scene is chunked and locally rendered, and a smooth transition between geometry and texture is achieved by morphing the geometry next to the texture. Finally, the scene effect, LOD detail level and interactive experience effect before and after simplification are compared and analyzed. The scene design of virtual reality technology basically scores 5-6 on each index, while the scene design of common technology only scores 3-4, indicating that the scene design of virtual reality technology works well.

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/.

Factors affecting judgment accuracy when scoring children's responses to non-word repetition stimuli in real time.

Non-word repetition (NWR) tests are an important way speech and language therapists (SaLTs) assess language development. NWR tests are often scored whilst participants make their responses (i.e., in real time) in clinical and research reports (documented here via a secondary analysis of a published systematic review). The main aim was to determine the extent to which real-time coding of NWR stimuli at the whole-item level (as correct/incorrect) was predicted by models that had varying levels of detail provided from phonemic transcriptions using several linear mixed method (LMM) models. Live scores and recordings of responses on the universal non-word repetition (UNWR) test were available for 146 children aged between 3 and 6 years where the sample included all children starting in five UK schools in one year or two consecutive years. Transcriptions were made of responses to two-syllable NWR stimuli for all children and these were checked for reliability within and between transcribers. Signal detection analysis showed that consonants were missed when judgments were made live. Statistical comparisons of the discrepancies between target stimuli and transcriptions of children's responses were then made and these were regressed against live score accuracy. Six LMM models (three normalized: 1a, 2a, 3a; and three non-normalized: 1b, 2b, 3b) were examined to identify which model(s) best captured the data variance. Errors on consonants for live scores were determined by comparison with the transcriptions in the following ways (the dependent variables for each pair of models): (1) consonants alone; (2) substitutions, deletions and insertions of consonants identified after automatic alignment of live and transcribed materials; and (3) as with (2) but where substitutions were coded further as place, manner and voicing errors. The normalized model that coded consonants in non-words as 'incorrect' at the level of substitutions, deletions and insertions (2b) provided the best fit to the real-time coding responses in terms of marginal R2, Akaike's information criterion (AIC) and Bayesian information criterion (BIC) statistics. Errors that occur on consonants when non-word stimuli are scored in real time are characterized solely by the substitution, deletion and insertion measure. It is important to know that such errors arise when real-time judgments are made because NWR tasks are used to assess and diagnose several cognitive-linguistic impairments. One broader implication of the results is that future work could automate the analysis procedures to provide the required information objectively and quickly without having to transcribe data. What is already known on this subject Children and patients with a wide range of cognitive and language difficulties are less accurate relative to controls when they attempt to repeat non-words. Responses to non-words are often scored as correct or incorrect at the time the test is conducted. Limited assessments of this scoring procedure have been conducted to date. What this study adds to the existing knowledge Live NWR scores made by 146 children were available and the accuracy of these judgements was assessed here against ones based on phonemic transcriptions. Signal detection analyses showed that live scoring missed consonant errors in children's responses. Further analyses, using linear mixed effect models, showed that live judgments led to consonant substitution, deletion and insertion errors. What are the practical and clinical implications of this work? Improved and practicable NWR scoring procedures are required to provide SaLTs with better indications about children's language development (typical and atypical) and for clinical assessments of older people. The procedures currently used miss substitutions, deletions and insertions. Hence, procedures are required that provide the information currently only available when materials are transcribed manually. The possibility of training automatic speech recognizers to provide this level of detail is raised.

How Protected Areas Are Transforming Within Megapolis: An Advanced Spatiotemporal Legislative Model

Compared to pristine ecosystems, urban protected areas (PAs) are exposed to intensified pressure and deterioration due to rapid population growth and entangled stakeholders’ interests. At the same time, these valuable ecosystems provide cities with ecosystem services, including cultural ones, and enhance the quality of life. Spatial analysis of PAs’ transformations in the context of the multidisciplinary approach contributes to the detection and safeguarding of vulnerable ecosystems. The study object is the protected areas of Moscow megapolis (within boundaries until 2012), whereas the study subject is the spatial and temporal PA’s transformations established by legislative acts. The research question is to devise a model of transformations designated by law within urban PAs and affecting their borders, land use, and rate of ecosystem deterioration. To achieve the research question, three goals were set: to gather spatial data on PAs’ transformations within Moscow designated by legislative acts; to design a comprehensive and exhaustive classification of PAs’ transformations established by legislative acts; to model spatial and temporal trends in transformations of Moscow PAs (1985-2022), according to the classification devised. The 3-compound framework for the analysis of legislative transformations (downgrading, downsizing, degazettment of protected areas) was coupled by content analysis of transformation events, GIS mapping, and spatial analysis of urban vegetation through NDVI (normalized difference vegetation index) estimations and raster computations in QGIS and GDAL software. The originality of our study derives from: the analysis of the 4th transformations’ compound (design failures of new PAs); spatial comparison with positive transformations, strengthening nature conservation; uncovering detailed subtypes and levels of transformations; applying this approach to the local scale of megapolis. Our study is based on: 1985-2022 legislative acts with text and map representations of PAs’ borders, zones and land-use designated by regional government and national ministries; national and Moscow open-access spatial data hubs; Moscow online news; 2001-2021 Landsat imageries and Global Forest Change data on Moscow region. Adverse transformations affected a larger area than positive ones (53.8% of a total PA area compared to 22.6%). Positive transformations contributed by PAs’ design (49.5%) mostly, while adverse ones – by easing of restrictions on land use (60.3%) and failures in the design of new PAs (22.8%). Adverse transformations are mainly reflected in the downsizing of zones with the strictest prohibitions on land use (-68% on average) and a low share of designed PAs (54%) through the period 1985-2022. Woodland plantations dramatically expanded (+86.5%), replacing seminatural urban forests (2005-2021). Hence, PA’s ability to supply ecosystem services has been considerably diminished. In regard to Moscow, considerable adverse trends in nature protection were revealed, generally hidden from the public. The analyzed typology of Moscow PAs’ transformations is quite conventional and may be improved through comparisons with other megapolises abundant in natural heritage to advance the model devised and elicit threats to nature conservation.

Site-specific alloying through binder jet 3D printing

Controlling the properties of materials site-specifically enables the design and production of multifunctional parts that satisfy multiple constraints at once. In this work, we demonstrate the capability of tuning the mechanical properties of steel site-specifically by varying the local alloy composition through binder jet 3D printing. We deposit a modified binder containing carbon nanoparticles to produce samples with carbon-rich regions that display > 90 % higher hardness compared to the rest of the part. We discuss this feature in terms of carbon diffusion—the limiting factor in site-specific alloying in our case-study material—which we mitigate by employing a 2-stage sintering strategy of our design. Our results showcase new opportunities to design and produce multi-functional metal alloys by controlling the distribution of their alloying elements with an unprecedented level of detail.

Design and implementation of an efficient container tag dynamic taint analysis

Dynamic taint analysis is a commonly used technique in software security. By tracking the processing of tainted data in the program, dynamic taint analysis can provide users with information on how the variables they are interested in are affected by the program input. The information is stored in tags corresponding to the variables, and the type of the tag determines the level of detail it can store. While integer tags can only indicate whether the taint exists, container tags provide knowledge of which part of the input the taint originated from. This knowledge is crucial for fields such as protocol reverse engineering and fuzzing. Despite their advantages, container tags suffer from low execution efficiency. In some applications, the execution time can increase by thousands of times as compared to the use of integer tags. In this paper, we propose an efficient container tag scheme based on the Reduced Ordered Binary Decision Diagram. The test results indicate that our container tag scheme achieves average speedups of 7.53x and 100.96x compared to the two container tag schemes utilized in libdft64.

Thermal Mapping from Point Clouds to 3D Building Model Facades

Thermal inspection of buildings regarding efficient energy use is an increasing need in today’s energy-demanding world. This paper proposes a framework for mapping temperature attributes from thermal point clouds onto building facades. The goal is to generate thermal textures for three-dimensional (3D) analysis. Classical texture generation methods project facade images directly onto a 3D building model. Due to the limited level of detail of these models, projection errors occur. Therefore, we use point clouds from mobile laser scanning extended by intensities extracted from thermal infrared (TIR) image sequences. We are not using 3D reconstructed point clouds because of the limited geometric density and accuracy of TIR images, which can lead to poor 3D reconstruction. We project these thermal point clouds onto facades using a mapping algorithm. The algorithm uses a nearest neighbor search to find an optimal nearest point with three different approaches: “Minimize angle to normal”, “Minimize perpendicular distance to normal”, and “Minimize only distance”. Instead of interpolation, nearest neighbor is used because it retains the original temperature values. The thermal intensities of the optimal nearest points are weighted by resolution layers and mapped to the facade. The approach “Minimize perpendicular distance to normal” yields the finest texture resolution at a reasonable processing time. The accuracy of the generated texture is evaluated based on estimating the shift of the window corner points from a ground truth texture. A performance metric root-mean-square deviation (RMSD) value that measures this shift is calculated. In terms of accuracy, the nearest neighbor method outperformed bilinear interpolation and an existing TIR image-based texturing method.

A method of reconstructing compressive spectral imaging with a complementary prior constraint

Compressed spectral imaging (CSI) is a technique for acquiring a cube of spectral image data in a single snapshot. In this paper, we propose a reconstruction method for the CSI system that integrates complementary prior constraints on spectral data to significantly enhance the accuracy of reconstructed data. The fundamental concepts are as follows: (1) Firstly, by conducting a thorough analysis of the corresponding feature maps of spectral data on the convolution dictionaries, we confirm the feasibility of utilizing the stationary response characteristics of spectral data on the convolution dictionaries as a reconstruction constraint to enhance spectral accuracy. (2) To compensate for the limitations of Convolutional Sparse Coding in reconstructing low-frequency signals, this paper proposes utilizing iterative Total Variation operators to estimate the low-frequency portion of spectral data. This approach results in improved performance in reconstructing low-frequency data and noise suppression. (3) Convolutional Sparse Coding and Total Variation are utilized as constraints for high and low-frequency complementary reconstruction, resulting in a multi-constraint solution problem within the Plug-and-Play framework that simplifies the overall reconstruction process. The proposed method surpasses the state-of-the-art reconstruction methods in both spatial reconstruction quality and spectral accuracy, while also significantly enhancing the level of detail in reconstructed spectral images.

Standardized local assortativity in networks and systemic risk in financial markets.

The study of assortativity allows us to understand the heterogeneity of networks and the implication of network resilience. While a global measure has been predominantly used to characterize this network feature, there has been little research to suggest a local coefficient to account for the presence of local (dis)assortative patterns in diversely mixed networks. We build on existing literature and extend the concept of assortativity with the proposal of a standardized scale-independent local coefficient to observe the assortative characteristics of each entity in networks that would otherwise be smoothed out with a global measure. This coefficient provides a lens through which the granular level of details can be observed, as well as capturing possible pattern (dis)formation in dynamic networks. We demonstrate how the standardized local assortative coefficient discovers the presence of (dis)assortative hubs in static networks on a granular level, and how it tracks systemic risk in dynamic financial networks.

Predicting enzymatic function of protein sequences with attention.

There is a growing number of available protein sequences, but only a limited amount has been manually annotated. For example, only 0.25% of all entries of UniProtKB are reviewed by human annotators. Further developing automatic tools to infer protein function from sequence alone can alleviate part of this gap. In this article, we investigate the potential of Transformer deep neural networks on a specific case of functional sequence annotation: the prediction of enzymatic classes. We show that our EnzBert transformer models, trained to predict Enzyme Commission (EC) numbers by specialization of a protein language model, outperforms state-of-the-art tools for monofunctional enzyme class prediction based on sequences only. Accuracy is improved from 84% to 95% on the prediction of EC numbers at level two on the EC40 benchmark. To evaluate the prediction quality at level four, the most detailed level of EC numbers, we built two new time-based benchmarks for comparison with state-of-the-art methods ECPred and DeepEC: the macro-F1 score is respectively improved from 41% to 54% and from 20% to 26%. Finally, we also show that using a simple combination of attention maps is on par with, or better than, other classical interpretability methods on the EC prediction task. More specifically, important residues identified by attention maps tend to correspond to known catalytic sites. Quantitatively, we report a max F-Gain score of 96.05%, while classical interpretability methods reach 91.44% at best. Source code and datasets are respectively available at https://gitlab.inria.fr/nbuton/tfpc and https://doi.org/10.5281/zenodo.7253910.

Microwave Breast Sensing via Deep Learning for Tumor Spatial Localization by Probability Maps.

microwave imaging (MWI) has emerged as a promising modality for breast cancer screening, offering cost-effective, rapid, safe and comfortable exams. However, the practical application of MWI for tumor detection and localization is hampered by its inherent low resolution and low detection capability. this study aims to generate an accurate tumor probability map directly from the scattering matrix. This direct conversion makes the probability map independent of specific image formation techniques and thus potentially complementary to any image formation technique. An approach based on a convolutional neural network (CNN) is used to convert the scattering matrix into a tumor probability map. The proposed deep learning model is trained using a large realistic numerical dataset of two-dimensional (2D) breast slices. The performance of the model is assessed through visual inspection and quantitative measures to assess the predictive quality at various levels of detail. the results demonstrate a remarkably high accuracy (0.9995) in classifying profiles as healthy or diseased, and exhibit the model's ability to accurately locate the core of a single tumor (within 0.9 cm for most cases). overall, this research demonstrates that an approach based on neural networks (NN) for direct conversion from scattering matrices to tumor probability maps holds promise in advancing state-of-the-art tumor detection algorithms in the MWI domain.

Automatic Mesh and Shader Level of Detail.

The level of detail (LOD) technique has been widely exploited as a key rendering optimization in many graphics applications. Numerous approaches have been proposed to automatically generate different kinds of LODs, such as geometric LOD or shader LOD. However, none of them have considered simplifying the geometry and shader at the same time. In this paper, we explore the observation that simplifications of geometric and shading details can be combined to provide a greater variety of tradeoffs between performance and quality. We present a new discrete multiresolution representation of objects, which consists of mesh and shader LODs. Each level of the representation could contain both simplified representations of shader and mesh. To create such LODs, we propose two automatic algorithms that pursue the best simplifications of meshes and shaders at adaptively selected distances. The results show that our mesh and shader LOD achieves better performance-quality tradeoffs than prior LOD representations, such as those that only consider simplified meshes or shaders.

Influence of BIM's level of detail on the environmental impact of buildings: Danish context

Integrating a BIM-based life-cycle assessment (LCA) in the early design stages of building projects can promote a reduction of environmental impacts. However, the reliability of the analysis depends on the quality of the model. BIM modelling of timber-frame construction is often simplified, potentially hindering the LCA outcome due to quantity inaccuracy and a failure to consider elements regarded as negligible. This study addresses the methodological problem that influences the calculation of the environmental impact by examining the various environmental impacts caused by inventory data obtained from BIM models with a level of detail of (LOD)350–400 vs LOD200. The environmental impacts obtained from both models of a timber building calculated through an attributional LCA method are compared. Material inventory sourced from the LOD200 model results in a 14.7% lower value of embodied impact. The discrepancies resulted from aluminium, mineral wool and bitumen, which are identified as critical materials requiring quantity adjustment. Relying on simplified BIM models for LCA may lead to the GWP being under-estimated with incorrect identification of hotspots in the early design stages. The results aksi question the cut-off criterion of the current EN15978 norm that serves as a foundation for developing environmental policies within the industry. It recommends exclusion of materials constituting less than 1% of the overall mass from the system boundary. This study underscores the potential significance of materials falling below this threshold, challenging the validity of the criterion, and suggesting that such materials should be carefully evaluated and included in the LCA to ensure a comprehensive assessment of the environmental impacts. The overall objective of the study is to emphasize the importance of employing accurate BIM models in LCAs to make informed decisions that are aligned with the sustainability goals, encouraging practitioners to consider the impact of critical materials, even those with seemingly minimal contributions. With this knowledge, the practitioners are able to take meaningful actions that compensate for the LCA uncertainty, mitigating the environmental burden of the most impactful areas. Most importantly, the findings aim to identify the error in design LCA versus as-built studies, helping to develop design LCA tools that predict the as-built impact more precisely and earlier in the design process. The findings can also improve the expected building model definition in carbon policies.

Below the Surface: Analyzing the Level of Detail and Depth of Coverage in Library and Information Science Syllabi Addressing Disability and Accessibility

ABSTRACTWhile accessibility is a core part of diversity, equity, inclusion, accessibility, and social justice (DEIASJ) considerations, disability and accessibility are rarely centered in Library and Information Science (LIS) curricula. The lack of disability and accessibility coverage is problematic since information professionals must have the required knowledge and skills to effectively serve patrons with disabilities. This paper presents preliminary findings from a content analysis of 39 pre‐filtered syllabi examining how disability and accessibility topics are covered in LIS courses. While nearly all of the syllabi analyzed contained a high level of detail, only 13 were ultimately determined to contain a “partial” depth of coverage of disability‐ and accessibility‐related content and two a “detailed” level of coverage. Even fewer syllabi included a conceptualization of disability or accessibility beyond simply including the words alone. The paper offers suggestions for LIS instructors to better address disability and accessibility within their syllabi and course content.

Providing acoustical advice for the purpose of strategic land-use planning

Strategic land-use planning is the process where stakeholders work together to design future land-use to achieve more desirable economic, social or environmental outcomes. There is often uncertainty around the type of acoustical advice required for the purpose of informing strategic land-use planning decisions. There are also limitations to the level of technical detail that can be made available when little is known about the nature or locations of potential future development. EMM Consulting Pty Limited’s experience in recent years has provided opportunities to work on various strategic land-use planning projects. This experience has enabled an understanding of what is required by relevant stakeholders to appropriately inform decisions as well as what is possible from a technical perspective. The importance of establishing clear expectations around technical outcomes and uncertainties is highlighted, as well as the importance of collaboration with all relevant stakeholders. A strong understanding of the planning framework the project will be approved and operate within is critical. The importance of having an appropriate authority and/or framework to facilitate the implementation of required noise measures and a plan to transition existing land use is discussed. The benefits and limitations of a Noise Management Precinct, as described in the Noise Policy for Industry (NSW EPA, 2017), are identified. Noise modeling methods and technical assumptions that could be utilized to predict and manage likely impacts are described.

Regionalism indicators and assessment approach of recent trends in Saudi Arabia’s architecture: The Salmaniah architectural style and the King Salman Charter initiatives as a case study

This study explores recent regionalism trends in the Kingdom of Saudi Arabia. The research positions the argument within a broader context of critical regionalism concepts and theories. It draws insights from the Saudi case on how regionalism approaches holistic yet applicable criteria. The literature review discusses the broader perspectives of critical regionalism and the history of regionalism progress in Saudi Arabia. The review positions the research's framework on how the global and local nexus are compared. A contribution of research methodology is provided on how to study regionalism architecture in societies and to provide regionalism indicators to compare with and conduct an assessment. This research employed a methodological approach using key expert interviews and surveys (building assessments and evaluations) applied to three cases that represent the six core values of the King Salman Charter. The study found that although the King Salman Charter for Architecture and Urbanism demonstrates six core values for contemporary, traditional architecture in Saudi Arabia, it is not at the level of detail of providing detailed implementation criteria. Rather than responding to a global regionalism trend or deriving core values from a selective but not exhaustive list, the Charter should craft its principles and methodological criteria based on studying Saudi Arabia’s historic, traditional settlements, and the Kingdom’s extant built heritage.

Multivariate Emulation of Kilometer-Scale Numerical Weather Predictions with Generative Adversarial Networks: A Proof of Concept

Abstract Emulating numerical weather prediction (NWP) model outputs is important to compute large datasets of weather fields in an efficient way. The purpose of the present paper is to investigate the ability of generative adversarial networks (GANs) to emulate distributions of multivariate outputs (10-m wind and 2-m temperature) of a kilometer-scale NWP model. For that purpose, a residual GAN architecture, regularized with spectral normalization, is trained against a kilometer-scale dataset from the AROME Ensemble Prediction System (AROME-EPS). A wide range of metrics is used for quality assessment, including pixelwise and multiscale Earth-mover distances, spectral analysis, and correlation length scales. The use of wavelet-based scattering coefficients as meaningful metrics is also presented. The GAN generates samples with good distribution recovery and good skill in average spectrum reconstruction. Important local weather patterns are reproduced with a high level of detail, while the joint generation of multivariate samples matches the underlying AROME-EPS distribution. The different metrics introduced describe the GAN’s behavior in a complementary manner, highlighting the need to go beyond spectral analysis in generation quality assessment. An ablation study then shows that removing variables from the generation process is globally beneficial, pointing at the GAN limitations to leverage cross-variable correlations. The role of absolute positional bias in the training process is also characterized, explaining both accelerated learning and quality-diversity trade-off in the multivariate emulation. These results open perspectives about the use of GAN to enrich NWP ensemble approaches, provided that the aforementioned positional bias is properly controlled.

867P Predicting HPV-association using regular H&E slides can identify subgroups of patients with favorable prognosis at a highly detailed level

867P Predicting HPV-association using regular H&E slides can identify subgroups of patients with favorable prognosis at a highly detailed level

THE MODEL OF INFORMATION MESSAGE OF MONITORING RESULTS IN CASE EMERGENCIES OR EMERGENCY THREATS IN THE BORDER ZONES OF RADIOACTIVE CONTAMINATION IN RUSSIA AND BELARUS

The article describes model of the structure of the interstate information message is presented task is prompt and detailed transfer of information on emergency situations and their consequences through the automated systems of data exchange between the regional departments of the EMERCOM of Russia (Bryansk region) and the EMERCOM of the Republic of Belarus of border areas (Gomel and Mogilev regions).The digital representation of the data in the message is based on the use of joint electronic directories: on the settlements in Gomel and Mogilev regions of the Republic of Belarus, Bryansk region of the Russian Federation; on interstate standards on radiation remedial measures and electronic exchange of information; on emergencies and their scale; on weather conditions. The structure of an electronic message has been developed on the basis of a number of joint electronic directories designed to support information exchange procedures, provided that the volume of transmitted data is minimized and the level of detail about emergencies and/or their threats in the zones of radioactive contamination of the border areas of Russia and Belarus is expanded.