Dynamic Database Research Articles

МОДЕЛИРОВАНИЕ РАСПРОСТРАНЕНИЯ ЛЕСНОГО ПОЖАРА ПРИ НЕСТАЦИОНАРНОСТИ И НЕОПРЕДЕЛЕННОСТИ ПОСРЕДСТВОМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА И ГЛУБОКОГО МАШИННОГО ОБУЧЕНИЯ

The article describes the results of increasing the efficiency of operational forecast of the forest fire dynamics under nonstationarity and uncertainty through the fire dynamics modeling based on artificial intelligence and deep machine learning. To achieve the goal there were used following methods: system analysis method, theory of neural networks, deep machine learning method, method of operational forecasting of the forest fire dynamics, method of filtering images (modified median filter), MoSCoW method, and ER-method. In the course of study there have been developed forest fire forecasting models (models of treetop and ground fires) using artificial neural networks. The developed models solve the recognition and forecasting problems in order to determine the dynamics of forest fires in successive images and generating images with a forecast of fire spread. There has been given the general logical scheme of the proposed forest fire forecasting models involving five stages: stage 1 - data input; stage 2 - preprocessing of input data (format check; size check; noise removal); stage 3 - object recognition using Convolutional Neural Networks (recognition of fire data; recognition of data on environmental factors; recognition of data on the nature of forest plantations); stage 4 - development of forest fire forecasting; stage 5 - output of the generated image with the operational forecast. To build and train artificial neural networks, a visual forest fire dynamics database was proposed to use. The developed forest fire forecasting models are based on a tree of artificial neural networks in the form of an acyclic graph and identify dependencies between the dynamics of a forest fire and the characteristics of the external and internal environment.

Estimation of growth regulation in natural populations by extended family of growth curve models with fractional order derivative: Case studies from the global population dynamics database

Estimating the trend in population time series data using growth curve models is a central idea in population ecology. Several models, mainly governed by differential or difference equations, have been applied to real data sets to identify general growth pattern and make predictions. In this article, we analyze ecological time series data by fitting mathematical models governed by fractional differential equations (FDE). The order of the FDE (α) is used to quantify the evidence of memory in the population processes. The application of FDE is exemplified by analyzing time series data on two bird species Phalacrocorax carbo (Great cormorant) and Parus bicolor (Tufted titmouse) and two mammal species Castor canadensis (Beaver) and Ursus americanus (American black bear) extracted from the global population dynamics database. Five different population growth models were fitted to these data; density-independent exponential, negative density-dependent logistic and θ-logistic model, positive density-dependent exponential Allee and strong Allee model. Both ordinary and fractional derivative representations of these models were fitted to the time series data. Markov chain Monte Carlo (MCMC) method was used to estimate the model parameters and Akaike information criterion was used to select the best model. By estimating the return rate for each of the time series, we have shown that populations governed by FDE with a small value of α (high level of memory) return to the stable equilibrium faster. This demonstrates a synergistic interplay between memory and stability in natural populations.

Dynamic Texture Classification Using Unsupervised 3D Filter Learning and Local Binary Encoding

Local binary descriptors, such as local binary pattern (LBP) and its various variants, have been studied extensively in texture and dynamic texture analysis due to their outstanding characteristics, such as grayscale invariance, low computational complexity and good discriminability. Most existing local binary feature extraction methods extract spatio-temporal features from three orthogonal planes of a spatio-temporal volume by viewing a dynamic texture in 3D space. For a given pixel in a video, only a proportion of its surrounding pixels is incorporated in the local binary feature extraction process. We argue that the ignored pixels contain discriminative information that should be explored. To fully utilize the information conveyed by all the pixels in a local neighborhood, we propose extracting local binary features from the spatio-temporal domain with 3D filters that are learned in an unsupervised manner so that the discriminative features along both the spatial and temporal dimensions are captured simultaneously. The proposed approach consists of three components: 1) 3D filtering; 2) binary hashing; and 3) joint histogramming. Densely sampled 3D blocks of a dynamic texture are first normalized to have zero mean and are then filtered by 3D filters that are learned in advance. To preserve more of the structure information, the filter response vectors are decomposed into two complementary components, namely, the signs and the magnitudes, which are further encoded separately into binary codes. The local mean pixels of the 3D blocks are also converted into binary codes. Finally, three types of binary codes are combined via joint or hybrid histograms for the final feature representation. Extensive experiments are conducted on three commonly used dynamic texture databases: 1) UCLA; 2) DynTex; and 3) YUVL. The proposed method provides comparable results to, and even outperforms, many state-of-the-art methods.

Semiclassical vibrational spectroscopy with Hessian databases.

We report on a new approach to ease the computational overhead of ab initio "on-the-fly" semiclassical dynamics simulations for vibrational spectroscopy. The well known bottleneck of such computations lies in the necessity to estimate the Hessian matrix for propagating the semiclassical pre-exponential factor at each step along the dynamics. The procedure proposed here is based on the creation of a dynamical database of Hessians and associated molecular geometries able to speed up calculations while preserving the accuracy of results at a satisfactory level. This new approach can be interfaced to both analytical potential energy surfaces and on-the-fly dynamics, allowing one to study even large systems previously not achievable. We present results obtained for semiclassical vibrational power spectra of methane, glycine, and N-acetyl-L-phenylalaninyl-L-methionine-amide, a molecule of biological interest made of 46 atoms.

Building a dynamic searchable encrypted medical database for multi-client

E-medical record is an emerging health information exchange model based on cloud computing. As cloud computing allows companies and individuals to outsource their data and computation, the medical data is always stored at a third party such as cloud, which brings a variety of risks, such as data leakage to the untrusted cloud server, unauthorized access or modification operations. To assure the confidentiality of the data, the data owner needs to encrypt the sensitive data before uploading to the third party. Yet, issues like encrypted data search, flexible access and control on sensitive data have also remained the most significant challenges. In this paper, we investigate a novel searchable encrypted e-medical framework for multi-client which provides both confidentiality and searchability. Different from previous privacy protecting works in secure data outsourcing, we focus on providing a fine-grained access control encrypted data search scheme including clients and data. Our scheme also enables secure data update of the encrypted database by leveraging a secure dynamic searchable encryption. Furthermore, we implement the proposed scheme based on some existed cryptography library, and conduct several experiments on a selected dataset to evaluate its performance. The results demonstrate that our scheme provides a balance between security and efficiency.

Rapid Performance Evaluation of Water Supply Services for Strategic Planning

The assessment of existing water supply services was carried out through selected performance indicators with the aim of using that data in future for strategic planning of urban Mardan. The key performance indictors studied were selected to assess both the quantity and quality of water. The quality of water was assessed by turbidity, pH, and E-coli tests for samples collected at the start, middle, and tail end of the distribution system. The quantity of water supplied was measured by calculating discharges from water tapes at the three selected locations in the distribution system. A total of thirty samples were collected from ten union councils out of fourteen covering urban Mardan. A number of issues are highlighted in the overall water supply infrastructure and short, mid, and long term remedial measures are recommended. The results are presented in the form of an interactive map using Google Earth and VBA based dynamic database. It was found that the overall quality of water is generally acceptable for drinking. However, the presence of bacteria is an issue in many cases which needs to be resolved. A significant decrease in discharge is observed in the distribution systems away from the source due to leakages and illegal connections. A comprehensive overhaul of both management and infrastructure is required for sustainable and satisfactory level of services.

Retrospective study of patients radiation dose during cardiac catheterization procedures.

Cardiac catheterization procedures provide tremendous benefits to modern healthcare and the benefit derived by the patient should far outweigh the radiation risk associated with a properly optimized procedure. With increasing utilization of such procedures, there is growing concern regarding the magnitude and variations of dose to patients associated with procedure complexity and techniques parameters. Therefore, this study investigated radiation dose to patients from six cardiac catheterization procedures at our facility and suggest possible initial dose values for benchmark for patient radiation dose from these procedures. This initial benchmark data will be used for clinical radiation dose management which is essential for assessing the impact of any quality improvement initiatives in the cardiac catheterization laboratory. We retrospectively analyzed the dose parameters of 1000 patients who underwent various cardiac catheterization procedures: left heart catheterization (LH), percutaneous coronary intervention (PCI), complex PCI, LH with complex PCI, LH with PCI and cardiac resynchronization therapy (CRT) pacemaker in our cardiac catheterization laboratories. Patient's clinical radiation dose data [kerma-area-product (KAP) and air-kerma at the interventional reference point (Ka,r)] and technique parameters (fluoroscopy time, tube potential, current, pulse width and number of cine images) along with demographic information (age, height and weight) were collected from the hospital's RIS (Synapse), Sensis/Syngo Dynamics and Siemens Sensis Stats Manager electronic database. Statistical analysis was performed with the IBM SPSS Modeler v. 18.1 software. The overall patient median age was 67.0 (range: 26.0-97.0) years and the median body mass index (BMI) was 28.8 (range: 15.9-61.7) kg/m2 . The median KAP for the LH, PCI, LH with complex PCI, complex PCI, LH with PCI and CRT-pacemaker procedures are 44.4 (4.1-203.2), 80.2 (18.9-208.5), 83.7 (48.0-246.1), 113.8 (60.9-284.5), 91.7 (6.0-426.0) and 51.1 (7.0-175.9) Gy-cm2 . The median Ka,r for the LH, PCI, LH with complex PCI, complex PCI, LH with PCI and CRT-pacemaker procedures are 701.0 (35.3-3794.0), 1384.7 (291.7-4021.8), 1607.0 (883.5-4448.3), 2260.2 (867.4-5311.9), 1589.3 (100.2-7237.4) and 463.8 (67.7-1695.9) mGy respectively. We have analyzed patient radiation doses from six commonly used procedures in our cardiac catheterization laboratories and suggested possible initial values for benchmark from these procedures for the fluoroscopy time, KAP and air-kerma at the interventional reference point based on our current practices. Our data compare well with published values reported in the literature by investigators who have also studied patient doses and established benchmark dose levels for their facilities. Procedure-specific benchmark dose data for various groups of patients can provide the motivation for monitoring practices to promote improvements in patient radiation dose optimization in the cardiac catheterization laboratories. We have investigated local patients' radiation doses and established benchmark radiation data which are essential for assessing the impact of any quality improvement initiatives for radiation dose optimization.

Dynamic texture analysis with diffusion in networks

Dynamic texture is a field of research that has gained considerable interest from computer vision community due to the explosive growth of multimedia databases. In addition, dynamic texture is present in a wide range of videos, which makes it very important in expert systems based on videos such as medical systems, traffic monitoring systems, forest fire detection system, among others. In this paper, a new method for dynamic texture characterization based on diffusion in directed networks is proposed. The dynamic texture is modeled as a directed network. The method consists in the analysis of the dynamic of this network after a series of graph cut transformations based on the edge weights. For each network transformation, the activity for each vertex is estimated. The activity is the relative frequency that one vertex is visited by random walks in balance. Then, texture descriptor is constructed by concatenating the activity histograms. The main contributions of this paper are the use of directed network modeling and diffusion in network to dynamic texture characterization. These tend to provide better performance in dynamic textures classification. Experiments with rotation and interference of the motion pattern were conducted in order to demonstrate the robustness of the method. The proposed approach is compared to other dynamic texture methods on two very well known dynamic texture database and on traffic condition classification, and outperform in most of the cases.

Single Image Super-Resolution via Dynamic Lightweight Database with Local-Feature Based Interpolation

Single image super-resolution is devoted to generating a high-resolution image from a low-resolution one, which has been a research hotspot for its significant applications. A novel method that is totally based on the single input image itself is proposed in this paper. Firstly, a local-feature based interpolation method where both edge pixel property and location information are taken into consideration is presented to obtain a better initialization. Then, a dynamic lightweight database of self-examples is built with the aid of our in-depth study on self-similarity, from which adaptive linear regressions are learned to directly map the low-resolution patch into its high-resolution version. Furthermore, a gradually upscaling strategy accompanied by iterative optimization is employed to enhance the consistency at each step. Even without any external information, extensive experimental comparisons with state-of-the-art methods on standard benchmarks demonstrate the competitive performance of the proposed scheme in both visual effect and objective evaluation.

Preparation of Oxidized Enset (Ensete ventricosum) Starch and Its Characterization

Objectives: In this study, we exhibit the examination of Smalltalk. Our focus in this study isn’t on whether the muchtouted permutable figuring for the examination of dynamic databases by Kumar continues running in O(n) time, yet rather on prodding new adaptable advancement (Hornito). Methods/Statistical Analysis: Our exploration is principled. Proceeding with this justification, Hornito does not require such a shocking investigation to run accurately, however it doesn’t hurt. Moreover, regardless of the outcomes, we can demonstrate that the fundamental arbitrary calculation for the organization of the maker purchaser issue is optimal. Findings: Despite the fact that such a theory at first look appears to be unreasonable, it is upheld by earlier work in the field. We want to clarify that our dispersing the chronicled API of our postfix trees is the way to our execution examination. Finally, we examine the second 50% of our examinations. Bugs in our framework caused the unsteady conduct all through the tests. So also, take note of that wide-territory systems have less spiked mean fame of sensor systems bends than do conveyed passages. Keywords: Hornito, API, RPC’s

Characterizing urban building metabolism with a 4D-GIS model: A case study in China

The continued and rapid urbanization of China has led to a large range of urban renewal activities, which have consumed large amounts of resources and generated a massive amount of waste. Therefore, how to recycle this waste, to alleviate the current shortage of resources, and to realize sustainable economic development has become an important issue. The present study established a ‘4D-GIS’ model of Longwu Village in Shenzhen city, combining material flows and stocks analysis (MFSA) and geographic information systems (GIS) to illustrate the spatio-temporal patterns and material metabolism evolution of buildings. The results show that an accumulated amount of approximately 290 thousand metric tonnes (Kt) of building materials would be consumed, and 125 Kt of C&D waste would be generated from 2006 to 2022. This study also built a dynamic urban database of construction material metabolism and used a Web page as a data carrier and visual model display platform, which can provide a means of data acquisition and mining for planning departments and related stakeholders. Overall, this 4D-GIS model could promote the sound management of urban renewal projects, and then reduce the material consumption and increase the recycling rate of construction debris. In addition, this 4D-GIS model can be extended to the regional or city level to provide a basis for city planning and sustainable development.

Comment on “Korean Unification: Economic Adjustments under German Assumptions”

Comment on “Korean Unification: Economic Adjustments under German Assumptions”

A framework to integrate social media and authoritative data for disaster relief detection and distribution optimization

In this paper, we propose an interdisciplinary approach to (natural) disaster relief management. Our framework combines dynamic and static databases, which consist of social media and authoritative data of an afflicted region, respectively, to model rescue demand during a disaster situation. Using Global Particle Swarm Optimization and Mixed-Integer Linear Programming, we then determine the optimal amount and locations of temporal rescue centers. Furthermore, our disaster relief system identifies an efficient distribution of supplies between hospitals and rescue centers and rescue demand points. By leveraging the temporal dimension of the social media data, our framework manages to iteratively optimize the disaster relief distribution.

Mining high-utility itemsets in dynamic profit databases

High-Utility Itemset (HUI) mining is an important data-mining task which has gained popularity in recent years due to its applications in numerous fields. HUI mining aims at discovering itemsets that have high utility (e.g., yield a high profit) in transactional databases. Although several algorithms have been designed to enumerate all HUIs, an important issue is that they assume that the utilities (e.g., unit profits) of items are static. But this simplifying assumption does not hold in real-life situations. For example, the unit profits of items often vary over time in a retail store due to fluctuating supply costs and promotions. Ignoring this important characteristic of real-life transactional databases makes current HUI-mining algorithms inapplicable in many real-world applications. To address this critical limitation of current HUI-mining techniques, this paper studies the novel problem of mining HUIs in databases having dynamic unit profits. To accurately assess the utility of any itemset in this context, a redefined utility measure is introduced. Furthermore, a novel algorithm named MEFIM (Modified EFficient high-utility Itemset Mining), which relies on a novel compact database format to discover the desired itemsets efficiently, is designed. An improved version of the MEFIM algorithm, named iMEFIM, is also introduced. This algorithm employs a novel structure called P-set to reduce the number of transaction scans and to speed up the mining process. Experimental results show that the proposed algorithms considerably outperform the state-of-the-art HUI-mining algorithms on dynamic profit databases in terms of runtime, memory usage, and scalability.

Empirical findings on the transmission of draft tube instabilities along the penstocks of hydraulic plants

On EDF hydraulic production plants equipped with Francis turbines, there have been related some cases of significant pressure fluctuations detected in the upper parts of the penstock when units were operating in the high load pressure surge zone. There was no decrease of the Peak-to-Peak pressure fluctuations along the penstock in the upstream part as it could be expected. To understand the phenomenon, on site tests have been carried out using innovative non-intrusive dynamic pressure sensors in many points along the penstock allowing the analysis of upward and downward waves and their “interferences”. These interferences cause in some cases standing waves for which the envelope of pressure oscillations along the penstock exhibits nodes and anti-nodes whose positions are relatively fixed along the penstock for a given operating point of the turbine. It appears that the ratio [Frequency of surge]/[Natural frequency of the penstock] conditions directly the number of nodes. This point is important because upper zones of the penstock close to the surge tank have generally not been designed to withstand high values of pressure fluctuations. For high number of ratio (3, 5, 7…), long term operation may induce mechanical fatigue of the penstock. A statistical approach was performed based on the dynamic signal databases of EDF and GE comprising data analysis of 21 occurrences of strong axial pressure surges of high load. It seems that certain values of the frequency ratio favour this resonance. This emphasizes the role of water exchanges between the rope and the penstock (complementarily with the draft tube) in establishing the conditions of resonance.

Statistics of natural images as a function of dynamic range.

The statistics of real world images have been extensively investigated, but in virtually all cases using only low dynamic range image databases. The few studies that have considered high dynamic range (HDR) images have performed statistical analyses categorizing images as HDR according to their creation technique, and not to the actual dynamic range of the underlying scene. In this study we demonstrate, using a recent HDR dataset of natural images, that the statistics of the image as received at the camera sensor change dramatically with dynamic range, with particularly strong correlations with dynamic range being observed for the median, standard deviation, skewness, and kurtosis, while the one over frequency relationship for the power spectrum breaks down for images with a very high dynamic range, in practice making HDR images not scale invariant. Effects are also noted in the derivative statistics, the single pixel histograms, and the Haar wavelet analysis. However, we also show that after some basic early transforms occurring within the eye (light scatter, nonlinear photoreceptor response, center-surround modulation) the statistics of the resulting images become virtually independent from the dynamic range, which would allow them to be processed more efficiently by the human visual system.

A systematic review of biomarkers to detect active tuberculosis.

Millions of cases of tuberculosis (TB) go undiagnosed each year. Better diagnostic tools are urgently needed. Biomarker-based or multiple marker biosignature-based tests, ideally performed on blood or urine, for the detection of active TB might help to meet target product profiles proposed by the World Health Organization for point-of-care testing. We conducted a systematic review to summarize evidence on proposed biomarkers and biosignatures and evaluate their quality and level of evidence. We screened the titles and abstracts of 7,631 citations and included 443 publications that fulfilled the inclusion criteria and were published in 2010-2017. The types of biomarkers identified included antibodies, cytokines, metabolic activity markers, mycobacterial antigens and volatile organic compounds. Only 47% of studies reported a culture-based reference standard and diagnostic sensitivity and specificity. Forty-four biomarkers (4%) were identified in high-quality studies and met the target product profile minimum criteria, of which two have been incorporated into commercial assays. Of the 44 highest-quality biomarkers, 24 (55%) were multiple marker biosignatures. No meta-analyses were performed owing to between-study heterogeneity. In conclusion, TB biomarker discovery studies are often poorly designed and findings are rarely confirmed in independent studies. Few markers progress to a further developmental stage. More validation studies that consider the intended diagnostic use cases and apply rigorous design are needed. The extracted data from this review are currently being used by FIND as the foundation of a dynamic database in which biomarker data and developmental status will be presented.

La complexité médicale appliquée à l’allergologie : causes et solutions

Medical complexity and uncertainty constitute a source of diagnostic delays and poor therapeutic strategies, and thus of increased costs and medical errors. Multiple mechanisms are involved and are related to patients and their pathology as well as to treatment methods. Among the many possible solutions, use of dynamic databases and collaboration between professionals are particularly appreciated by practitioners. This is what we have put in place with the AdviceMedica consultative platform, which was initially developed in an associative and academic way. It is perfectly functional and is used daily. It has recently begun to utilise natural language processing tools. Allergology is the first specialty in France to have a network of collaborative help, which will soon be further strengthened by automated analysis methods at the very forefront of artificial intelligence.

Uncovering a multitude of human glucocorticoid receptor variants: an expansive survey of a single gene

Background Glucocorticoids are commonly used in the clinical setting for their potent anti-inflammatory effects; however, significant variations in response to treatment have been demonstrated. Although the underlying mechanisms have yet to be fully understood, this variable response may be a result of alterations in human glucocorticoid receptor (hGR) expression and function. In addition to hGRα, the biologically active isoform, a screening of current databases and publications revealed five alternative splice isoforms and hundreds of variants that have been reported to date. Many of these changes in the hGR-coding gene, NR3C1, have been linked to pathophysiology. However, many studies focus on evaluating hGR expression in vitro or detecting previously reported variants.ResultsIn this study, blood from healthy volunteers, burn and asthma patients, as well as from peripheral blood mononuclear cells isolated from leukoreduced donor whole blood, were screened for NR3C1 isoforms. We identified more than 1500 variants, including an additional 21 unique splice isoforms which contain 15 new cryptic exons. A dynamic database, named the Universal hGR (UhGR), was created to annotate and visualize the variants.ConclusionThis identification of naturally occurring and stress-induced hGR isoforms, as well as the establishment of an hGR-specific database, may reveal new patterns or suggest areas of interest that will lead to the improved understanding of the human stress response system.

Global distribution of coastal cliffs

AbstractPrevious studies have estimated that coastal cliffs exist on about 80% of the global shoreline, but have not been validated on a global scale. This study uses two approaches to capture information on the worldwide existence and erosion of coastal cliffs: a detailed literature survey and imagery search, and a GIS‐based global mapping analysis. The literature and imagery review show coastal cliffs exist in 93% of the combined recognized independent coastal states and non‐independent coastal regions worldwide (total of 213 geographic units). Additionally, cliff retreat rates have been quantified in at least one location within 33% of independent coastal states and 15% of non‐independent regions. The GIS‐based mapping used the near‐global Shuttle Radar Topography Mission 3 arc second digital elevation model and Arctic Coastal Dynamics Database to obtain near‐global backshore coastal elevations at 1 km alongshore intervals comprising about 1,340,000 locations (81% of the world vector shoreline). Backshore coastal elevations were compared with the mapped distribution of European coastal cliffs to produce a model training set, and this relationship was extended globally to map the likelihood of coastal cliff locations. About 21% of the transects (17% of the world vector shoreline) were identified as mangroves and eliminated as potential cliff locations. The results were combined with estimates of cliff percentages for Greenland and Antarctica from the literature, extending the global coverage to estimate cliff occurrence across 89% of the world vector shoreline. The results suggest coastal cliffs likely exist on about 52% of the global shoreline. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.