Information Content Of Data Research Articles

Transdimensional Inversion on the New England Mud Patch Using High-Order Modes

This article presents geoacoustic inversion results for modal-dispersion data collected during the 2017 Seabed Characterization Experiment on the New England Mud Patch, an area where the seabed is characterized by an upper layer of mud. The experiment utilized a combustive sound source and a vertical line array of receivers at 5.4-km range. Using a careful combination of source deconvolution and warping time–frequency analysis, modal dispersion data (arrival time as a function of frequency) are estimated for 18 modes between modes 1 and 21. The modal dispersion data are then used to estimate seabed geoacoustic profiles and uncertainties via transdimensional Bayesian inversion. This article demonstrates the capacity to estimate high-order modes using warping. Comparing inversion results obtained with subsets of (lower order) modes to those obtained with the full set of available modes highlights the rich data information content carried by high-order modes. The results suggest a small sound-speed increase over the first 8 m of the seabed, the upper portion of the mud layer, which some earlier studies found to be isospeed. Overall, the inversion results are consistent with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements, as well as with previous geoacoustic inversion results.

The local structure of water from combining diffraction and X-ray spectroscopy

We build a large (~3400 structures) library of X-ray absorption (XAS) and X-ray emission (XES) spectra computed at high level, which together with their associated structures form the basis for a Monte Carlo fit to the experimental oxygen‑oxygen pair-distribution, XAS and XES spectra for ambient liquid water. The procedure results in weights giving the relative importance of each structure to reproduce the experimental properties. We show that the information content in the X-ray spectroscopic data is strongly complementary to that of X-ray diffraction, and dependent on specific structures that are still consistent with the diffraction data. We fit simultaneously to the X-ray spectroscopy and diffraction experimental data and obtain weights on each structure that give a structural distribution that is consistent with the three experimental datasets. These weights are applied to structural parameters characterizing the local environment of each structure and result in the emergence of more preferred values of these parameters.

Improving the information content of seismic data and increasing the depth of investigation by choosing the optimal length of the amplitude adjustment operator

The article is devoted to the role of digital automatic amplitude control in increasing the depth of seismic exploration. It is noted that the depth of productive strata is gradually increasing, at the same time, more powerful explosive sources are being replaced by relatively weak non-explosive ones. Naturally, during processing, it is necessary to pay close attention to the choice of parameters for adjusting the amplitudes. The article provides basic information about the form and amplitude of seismic vibrations, lists the main empirical formulas that characterize the dependence of the amplitudes of seismic vibrations on time. It is noted that when registering seismic vibrations, one has to deal with minimal soil displacements caused by the arrival of a seismic wave at the observation point, which must be amplified millions of times and their adjustment. In this regard, in the digital processing of seismic data, a technique was developed for recovering the true amplitudes, more precisely, the true amplitude ratio. Adjustment while maintaining the true ratio of amplitudes is used only when it is required to study the dynamics of the wave field (dynamic digital processing). The optimal choice of the length of the adjustment operator (or window) is of paramount importance, since with a small adjustment interval (less than 0.1 s), a loss of dynamic expressiveness of the recording may occur, and this is clearly shown in model studies. The article considers the efficiency of choosing the length of the optimal adjustment operator on the example of the Kurovdag area, which has complex both surface and deep seismogeological conditions. A summary of the Kurovdag field is given. An amplitude compensation function for spherical divergence is given, and to compensate for the effects associated with changes in reception and excitation conditions, a surface-matched amplitude adjustment was performed after removing amplitude bursts. A fragment of the time section is given before and after the optimal adjustment of the amplitudes according to the seismograms of the Kurovdag area, which clearly demonstrates how the information content of the time section increases and, thereby, the depth of the seismic exploration: before the optimal digital automatic adjustment of the amplitudes at times of 3.25 - 3.5 sec, no seismic horizons, while after this procedure, dynamically well-defined seismic horizons appear at the same times, reflecting the structure of the medium at great depths.

Layered and gradient model parameterizations in geoacoustic inversion

This paper considers the importance of model parameterization in geoacoustic inversion and uncertainty estimation, including quantitative approaches to model selection as well as potential limitations of the information content of acoustic data to determine the form of geoacoustic profiles, e.g., differentiating between layered and gradient structures. In particular, general parameterizations are considered based on trans-dimensional (trans-D) inversion, which represents profiles as an unknown number of uniform layers, and Bernstein polynomial (BP) inversion, which represents smooth gradients as polynomials of unknown order. These approaches are illustrated and compared for the inversion of high-order modal-dispersion data collected at the New England Mud Patch. It is shown that while the data constrain the sound-speed profile in the mud layer to reasonably high precision, the data cannot differentiate between trans-D layered or BP gradient representations. However, simpler (fixed) parameterizations, such as a homogeneous layer or linear gradient, can be ruled out based on the Bayesian information criterion. Furthermore, the prior choice of parameterization (layers or gradient) has implications on whether the sound-speed ratio at the water–sediment interface is estimated to be less or greater than one with high probability (an issue other acoustic datasets may share). [Work supported by the Office of Naval Research]

Sustainable Development Goals and Problems of Measuring Poverty and Extreme Poverty

The article presents the results of the study on improving the methodology for measuring poverty and extreme poverty in accordance with the fundamental UN document – the UN Sustainable Development Goals (SDGs). The author argues his position that when assessing the achievement of targets for the eradication of extreme poverty within the framework of achieving Goal 1 (SDG 1) at the national level, one should not be guided by the average threshold value of extreme poverty for all countries. It is necessary to focus on the gross national income (GNI) per capita, an indicator developed by the World Bank (according to the Atlas method). For the first time, a comparative analysis of the interpretations of SDG 1 by Russian and European researchers, as well as experts of international organizations is carried out, and threshold values of global poverty for groups of countries with different levels of GNI per capita are analyzed. It allows us to identify the main problem of monitoring the eradication of extreme poverty in Russia: the low information content of data on socio-demographic groups experiencing extreme poverty, which, in turn, hinders the formation of effective public policy measures to reduce poverty and eradicate extreme poverty. The paper shows that ignoring the indicator of extreme poverty, calculated depending on the size of GNI per capita using the Atlas method, can lead to erroneous conclusions in the Voluntary National Review of the Russian Federation on achieving the UN Sustainable Development Goals. The author proposes to select indicators of global extreme poverty in accordance with the level of socio-economic development of Russia and to assess the eradication of extreme poverty, taking them into account on a par with the national poverty line. In this context, the author draws attention to inaccuracies in domestic publications related to the interpretation of the concept of «extreme poverty», which can lead to distortion of estimates.

Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) Based on Temporal, Spatial and Spectral Measurements

Geostationary satellites observe the earth surface and atmosphere with a short repeat time, thus, providing aerosol parameters with high temporal resolution, which contributes to the air quality monitoring. Due to the limited information content in satellite data, and the coupling between the signals received from the surface and the atmosphere, the accurate retrieval of multiple aerosol parameters over land is difficult. With the strategy of taking full advantage of satellite measurement information, here we propose a neural network AEROsol retrieval framework for geostationary satellite (NNAeroG), which can potentially be applied to different instruments to obtain various aerosol parameters. NNAeroG was applied to the Advanced Himawari Imager on Himawari-8 and the results were evaluated versus independent ground-based sun photometer reference data. The aerosol optical depth, Ångström exponent and fine mode fraction produced by the NNAeroG method are significantly better than the official JAXA aerosol products. With spectral bands selection, the use of thermal infrared bands is meaningful for aerosol retrieval.

Hazard risk awareness and disaster management: Extracting the information content of twitter data

• This study conducts a comprehensive spatiotemporal analysis on Twitter data. • We provide Hazard Risk Awareness (HRA) index for effective disaster management. • We utilize Statistical techniques along with Natural Language Processing (NLP). The use of social media platforms such as Twitter significantly increases during natural hazards. With the emergence of several social media platforms over the past decade, many studies have investigated the applications of these platforms during calamities. This study presents a comprehensive spatiotemporal analysis of textual content from millions of tweets shared on Twitter during Hurricane Harvey (2017) across several affected counties in southeast Texas. We propose a new Hazard Risk Awareness (HRA) Index, which considers multiple factors, including the number of tweets, population, internet use rate, and natural hazard characteristics per geographic location. We then map the HRA Index across southeast Texas. Utilizing a dataset of 18 million tweets, we employ Natural Language Processing (NLP) along with a set of statistical techniques to perform analysis on the textual data generated by Twitter users during Hurricane Harvey. This enables us to subdivide the tweet contents into several categories per county that would inform crisis management during the event. In all, our study provides valuable information at the county level before, during, and after Harvey that could significantly help disaster managers and responders to minimize the consequences of the event and improve the preparedness of the residents for it. Since HRA is derived based on the meteorological observations and some demographic information, depending on the availability of such dataset and the nature of the hazard (i.e., flood, wildfire, hurricane, and earthquake), this index can be calculated and employed for assessing the risk awareness of a community exposed to either of these natural hazards.

Comparing SAXS modelling approaches on structural transitions in calmodulin protein measured at high-throughput LIX beamline

Small-angle X-ray scattering is an increasingly popular technique used to detect protein structures and ensembles in solution. However, the refinement of structures and ensembles against SAXS data is often ambiguous due to the low information content of SAXS data, unknown systematic errors, and unknown scattering contributions from the solvent. At LIX beamline we can perform high throughput measurements reliably and collect data at wider angle (∼3 Å−1) thus accessing the water peak. We use water peak as an internal reference to perform sample to buffer subtraction.

Precautionary Principle or Evidence-Based Conservation? Assessing the Information Content of Threat Data for the Yangtze Finless Porpoise

Conservation management requires evidence, but robust data on key parameters such as threats are often unavailable. Conservation-relevant insights might be available within datasets collected for other reasons, making it important to determine the information content of available data for threatened species and identify remaining data-gaps before investing time and resources in novel data collection. The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) has declined severely across the middle-lower Yangtze, but multiple threats exist in this system and the relative impact of different anthropogenic activities is unclear, preventing identification of appropriate mitigation strategies. Several datasets containing information on porpoises or potential threats are available from past boat-based and fishing community surveys, which might provide novel insights into causes of porpoise mortality and decline. We employed multiple analytical approaches to investigate spatial relationships between live and dead porpoises and different threats, reproductive trends over time, and sustainable offtake levels, to assess whether evidence-based conservation is feasible under current data availability. Our combined analyses provide new evidence that mortality is spatially associated with increased cargo traffic; observed mortality levels (probably a substantial underestimate of true levels) are unsustainable; and population recruitment is decreasing, although multiple factors could be responsible (pollutants, declining fish stocks, anthropogenic noise, reduced genetic diversity). Available data show little correlation between patterns of mortality and fishing activity even when analyzed across multiple spatial scales; however, interview data can be affected by multiple biases that potentially complicate attempts to reconstruct levels of bycatch, and new data are required to understand dynamics and sustainability of porpoise-fisheries interactions. This critical assessment of existing data thus suggests thatin situporpoise conservation management must target multiple co-occurring threats. Even limited available datasets can provide new insights for understanding declines, and we demonstrate the importance of an integrative approach for investigating complex conservation problems and maximizing evidence in conservation planning for poorly known taxa.

Phenotyping Complex Plant Structures with a Large Format Industrial Scale High-Resolution X-Ray Tomography Instrument.

Phenotyping specific plant traits is difficult when the samples to be measured are architecturally complex. Inflorescence and root system traits are of great biological interest, but these structures present unique phenotyping challenges due to their often complicated and three-dimensional (3D) forms. We describe how a large industrial scale X-ray tomography (XRT) instrument can be used to scan architecturally complex plant structures for the goal of rapid and accurate measurement of traits that are otherwise cumbersome or not possible to capture by other means. The combination of a large imaging cabinet that can accommodate a wide range of sample size geometries and a variable microfocus reflection X-ray source allows noninvasive X-ray imaging and 3D volume generation of diverse sample types. Specific sample fixturing (mounting) and scanning conditions are presented. These techniques can be moderate to high throughput and still provide unprecedented levels of accuracy and information content in the 3D volume data they generate.

Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data

We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence image content or structure. Including such data in the synthesis process increases the quality of the generated images and exerts more control on their characteristics. Spatially adaptive normalization layers fuse both inputs and are applied to a full-blown generator architecture consisting of encoder and decoder to take full advantage of the information content in the auxiliary raster data. Our method successfully synthesizes medium (10 m) and high (1 m) resolution images when trained with the corresponding data set. We show the advantage of data fusion of land cover maps and auxiliary information using mean intersection over unions (mIoUs), pixel accuracy, and Fr\'echet inception distances (FIDs) using pretrained U-Net segmentation models. Handpicked images exemplify how fusing information avoids ambiguities in the synthesized images. By slightly editing the input, our method can be used to synthesize realistic changes, i.e., raising the water levels. The source code is available at https://github.com/gbaier/rs_img_synth and we published the newly created high-resolution dataset at https://ieee-dataport.org/open-access/geonrw.

Model predictive control guided with optimal experimental design for pulse-based parallel cultivation

Optimal experimental design for parameter precision attempts to maximize the information content in experimental data for a most effective identification of parametric model. With the recent developments in miniaturization and parallelization of cultivation platforms for high-throughput screening of optimal growth conditions massive amounts of informative data can be generated with few experiments. Increasing the quantity of the data means to increase the number of parameters and experimental design variables which might deteriorate the identifiability and hamper the online computation of optimal inputs. To reduce the problem complexity, in this work, we introduce an auxiliary controller at a lower level that tracks the optimal feeding strategy computed by a high-level optimizer in an online fashion. The hierarchical framework is especially interesting for the operation under constraints. The key aspect of this method are discussed together with an in silico study considering parallel glucose limited bacterial fed batch cultivations.

Information content in 4D seismic data: Effect of correlated noise

Several types of data used in history matching for subsurface reservoir characterization have errors that are spatially or temporally correlated. Although it is often assumed that correlated observation errors decrease information content, using a simplified flow problem, we show that for data that are spatially dense (such as 4D seismic data), correlated observation errors result in higher information content than data with similar levels of errors, but without correlation. Unfortunately, correlations in the observation error are often unrecognized and difficult to estimate, especially if the correlation scale of the errors is similar to a characteristic length of the signal. In addition, many history matching algorithms are incapable of accounting properly for correlated observation error, so data are either thinned or the observation error is inflated to partially account for the lack of proper treatment. We show that neglecting correlations in the observation errors or inflating the variance, result in loss of information content. Finally, we show that it is possible to iteratively estimate the correlated observation errors through analysis of residuals after history matching. • Measurement errors in seismic data are generally correlated. • Correlated errors can increase information content when measurements are dense. • Neglecting correlations during model calibration results in information loss. • Correlations in seismic data can be estimated from data mismatch after calibration.

Genetic diversity and population structure analysis of rice false smut pathogen in North India using molecular markers

AbstractFalse smut, caused by Ustilaginoidea virens, is one of the emerging diseases of rice in the world. The fungi show a high degree of morphological and cultural variability and produce diverse symptoms. India is a vast country with 44‐million‐ha rice production area and has only one report on genetic diversity and population analysis of U. virens from the eastern and north‐eastern region. Ten polymorphic RAPD and 21 SSR markers were used to analyse the genetic diversity and population structure of 81 isolates of U. virens from northern India, the second largest rice‐growing region. In this study, higher Polymorphic Information Content (PIC) and Quality Nature of Data (QND) data in simple‐sequence repeats (SSR) markers over random amplified polymorphic DNA (RAPD) revealed its higher efficacy; thus, SSR markers are highly informative and can help to dissect the genetic structures of U. virens. All markers yielded a total of 180 alleles with 1.0 effective alleles per loci. Genetic diversity ranged from 0.1 to 0.39. The principal coordinate analysis (PCoA), and structure analysis identified two genetic clusters of U. virens isolates with some degree of distinctness according to locations. But clusters, PCoA and structure analysis did not group the isolates according to their geographical origin and this may be due to high gene flow (Nm = 1.14).

The method of increasing the information content of microfocus X-ray images

A method for processing microfocus X-ray images is described. It is based on high-frequency filtration and morphological image processing, which increases the contrast of the X-ray details. One of the most informative X-ray techniques is microfocus X-ray. In some cases, microfocus X-ray images cannot be reliably analyzed due to the peculiarities of the shooting method. So, the main disadvantages of microfocus X-ray images are most often an uneven background, distorted brightness characteristics and the presence of noise. The proposed method for enhancing the contrast of fine image details is based on the idea of combining high-frequency filtering and morphological image processing. The method consists of the following steps: noise suppression in the image, high-frequency filtering, morphological image processing, obtaining the resulting image. As a result of applying the method, the brightness of the contours in the image is enhanced. In the resulting image, all objects will have double outlines. The method was tested in the processing of 50 chest radiographs of patients with various pathologies. Radiographs were performed at the Mariinsky Hospital of St. Petersburg using digital stationary and mobile X-ray machines. In most of the radiographs, it was possible to improve the images contrast, to highlight the objects boundaries. Besides, the method was applied in microfocus X-ray tomography to improve the information content of projection data and improve the reconstruction of the 3D image of the research object. In both the first and second cases, the method showed satisfactory results. The developed method makes it possible to significantly increase the information content of microfocus X-ray images. The obtained practical results make it possible to count on broad prospects for the method application, especially in microfocus X-ray.

Quantization Algorithms in Healthcare Information Telematics Using Wireless Interactive Services of Digital Video Broadcasting

Background: In the new era of wireless communications new challenges emerge including the provision of various services over the digital television network. In particular, such services become more important when referring to the tele-medical applications through terrestrial Digital Video Broadcasting (DVB). Objective: One of the most significant aspects of video broadcasting is the quality and information content of data. Towards that end several algorithms have been proposed for image processing in order to achieve the most convenient data compression. Methods: Given that medical video and data are highly demanding in terms of resources it is imperative to find methods and algorithms that will facilitate medical data transmission with ordinary infrastructure such as DVB. Results: In the present work we have utilized a quantization algorithm for data compression and we have attempted to transform video signal in such a way that would transmit information and data with a minimum loss in quality and succeed a near maximum End-user approval. Conclusions: Such approaches are proven to be of great significance in emergency handling situations, which also include health care and emergency care applications.

Nonlinear reconfiguration of network edges, topology and information content during an artificial learning task

Here, we combine network neuroscience and machine learning to reveal connections between the brain’s network structure and the emerging network structure of an artificial neural network. Specifically, we train a shallow, feedforward neural network to classify hand-written digits and then used a combination of systems neuroscience and information-theoretic tools to perform ‘virtual brain analytics’ on the resultant edge weights and activity patterns of each node. We identify three distinct phases of network reconfiguration across learning, each of which are characterized by unique topological and information-theoretic signatures. Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding layers (as relevant). We also observe a process of low-dimensional category separation in the network as a function of learning. Our results offer a systems-level perspective of how artificial neural networks function—in terms of multi-stage reorganization of edge weights and activity patterns to effectively exploit the information content of input data during edge-weight training—while simultaneously enriching our understanding of the methods used by systems neuroscience.

Model-Free or Not?

Relaxation in nuclear magnetic resonance is a powerful method for obtaining spatially resolved, timescale-specific dynamics information about molecular systems. However, dynamics in biomolecular systems are generally too complex to be fully characterized based on NMR data alone. This is a familiar problem, addressed by the Lipari-Szabo model-free analysis, a method that captures the full information content of NMR relaxation data in case all internal motion of a molecule in solution is sufficiently fast. We investigate model-free analysis, as well as several other approaches, and find that model-free, spectral density mapping, LeMaster’s approach, and our detector analysis form a class of analysis methods, for which behavior of the fitted parameters has a well-defined relationship to the distribution of correlation times of motion, independent of the specific form of that distribution. In a sense, they are all “model-free.” Of these methods, only detectors are generally applicable to solid-state NMR relaxation data. We further discuss how detectors may be used for comparison of experimental data to data extracted from molecular dynamics simulation, and how simulation may be used to extract details of the dynamics that are not accessible via NMR, where detector analysis can be used to connect those details to experiments. We expect that combined methodology can eventually provide enough insight into complex dynamics to provide highly accurate models of motion, thus lending deeper insight into the nature of biomolecular dynamics.

EFFECTIVE SEISMIC TECHNOLOGIES FOR NEW GEOLOGICAL DISCOVERIES IN EAST SIBERIA

Currently, East Siberia as a whole and the Republic of Sakha (Yakutia) in particular are the territories of extensive geological exploration. Field geophysical surveys are mainly performed by seismic technologies used to discover new deposits and provide data for detailed appraisal of the known fields. Three-dimensional seismic surveys are effectively used for clarifying the details of geologic structures and obtaining the data for drilling programs. Upon further development of technologies, improvements are introduced in field operations, seismic data processing and interpretation, and drilling programs. As the information content of the survey data increases, it becomes possible to revise and update geological models and/or develop them in a more detail. With the use of modern data processing technologies that preserve the signals and depth migration data, the tectonic model of the Srednebotuobinskoe Oil and Gas Condensate Field was fully modified. Furthermore, additional isolated tectonic blocks were discovered in the eastern part of the Central deposit, and their map was constructed. The geological exploration based on the updated tectonic model is successful – new mineral reserves have been discovered, and the vector of geological exploration has been completely changed.

A new framework for experimental design using Bayesian Evidential Learning: The case of wellhead protection area

Groundwater management practices, such as sustainable drinking water extraction or contamination protection, can have significant socioeconomic impacts. A complete uncertainty analysis should ideally be performed to anticipate all possible outcomes and assess any risk. Uncertainties arise as a result of our limited understanding of the physical processes involved, as well as a scarcity of measurement data, whether directly or indirectly related to the physical parameters of interest. In this paper, we use a small number of tracing experiments (predictor) to predict the wellhead protection area (WHPA, target), the shape and extent of which are influenced by the distribution of hydraulic conductivity (K). Our first goal is to make stochastic predictions of the WHPA within the Bayesian Evidential Learning (BEL) framework, which uses machine learning to find a direct relationship between predictor and target. This relationship is learned using a small number of training models (400) drawn from the prior distribution of K. Forward modelling is used to obtain the 400 pairs of simulated predictors and targets. Newly collected field data can then be used directly to predict the approximate posterior distribution of the corresponding WHPA, obviating the need for the traditional step of data inversion. The number and location of data sources (injection wells) influence the posterior WHPA distribution's uncertainty range. Our second goal is to extend BEL to determine the optimal design of data source locations that minimises the WHPA's posterior uncertainty. Because the BEL model, once trained, allows the computation of the posterior uncertainty corresponding to any new input data, experimental design can be done explicitly, without averaging or approximating. We estimate the WHPA's posterior uncertainty range using the Modified Hausdorff Distance (MHD) and Structural Similarity (SSIM) index metrics. Because the breakthrough curves store information on a large area of the K field surrounding the pumping well, increasing the number of injection wells reduces the derived posterior WHPA uncertainty. Our method can also estimate which injection wells are more informative than others, as demonstrated by a k-fold cross-validation procedure. Overall, the application of BEL to experimental design allows for identifying data sources that maximise the information content of any measurement data while keeping budget constraints and computational costs to a minimum.