Reference Graph Research Articles

Quality Evaluation of Argan Oil as a Food Product Using Ultrasonic Characterization

Abstract Start This study focuses on evaluating the quality of food-grade argan oil, an essential component of Moroccan cuisine, using experimental ultrasonic characterization. Variations in ultrasonic velocity and attenuation with temperature were exploited as significant parameters for quality control of argan oil. The results reveal an inverse correlation between ultrasonic velocity and temperature, providing a unique model for the studied samples of argan oil. The measurements also show that the acoustic attenuation of argan oil decreases with temperature, while it increases with the volumetric fractions of sunflower oil added to argan oil. The data collected during this experimental study demonstrate that the velocity of the mixture of argan oil and sunflower oil increases with rising temperature. It was observed that both ultrasonic parameters of this mixture are challenging to detect by ultrasound. Ensuring the reproducibility of measurements under the same conditions, reference graphs are established to detect adulteration, especially when considering blends of argan oil with sunflower oil.

Application of artificial intelligence in rheumatic disease: a bibliometric analysis

The utilization of artificial intelligence (AI) in rheumatic diseases has enhanced the diagnostic accuracy of rheumatic diseases, enabled the prediction of patient outcomes, expanded treatment options, and facilitated the provision of individualized medical solutions. The research in this field has been progressively growing in recent years. Consequently, there is a need for bibliometric analysis to elucidate the current state of advancement and predominant research foci in AI applications within rheumatic diseases. Additionally, it is crucial to identify key contributors and their interrelations in this field. This study aimed to conduct a bibliometric analysis to investigate the current research hotspots and collaborative networks in the application of AI in rheumatic disease in recent years. A comprehensive search was conducted in Web of Science for articles on artificial intelligence in rheumatic diseases, published in SSCI and SCI-EXPANDED until January 1, 2024. Utilizing software tools like VOSviewers and CiteSpace, we analyzed various parameters including publication year, journal, country, institution, and authorship. This analysis extended to examining cited authors, generating reference and citation network graphs, and creating co-citation network and keyword maps. Additionally, research hotspots and trends in this domain were evaluated. As of January 1, 2024, a total of 3508 articles have been published on the application of artificial intelligence (AI) in rheumatic disease, exhibiting a steady rise in both the annual publication frequency and rate. “Scientific Reports” emerged as the leading journal in terms of relevant publications. The United States stood out as the predominant country in terms of the volume of published papers, with the University of California, San Francisco (UCSF) being the most prolific and frequently cited institution. Among authors, Young Ho Lee and Valentina Pedoia were noted for their significant contributions, with Pedoia achieving the highest average citation count per publication. Machine learning emerged as a prominent and central keyword. The trend indicates a growing interest in AI research within rheumatologic diseases, with its role expected to become increasingly pivotal in the field. This study presents a comprehensive summary of research trends and developments in the application of artificial intelligence (AI) in rheumatic diseases. It offers insights into potential collaborations and prospects for future research, clarifying the research frontiers and emerging directions in recent years. The findings of this study serve as a valuable reference for scholars studying rheumatology and immunology.

An ultrasound approach to characterize mixtures of vegetable oils with the same type of dominant chemical compositions

The non-destructive characterization of ultrasonic properties in mixtures of vegetable oils with similar dominant chemical compositions is becoming increasingly important for various applications. The combination of ultrasonic wave reflection as an experimental method and the partial least squares statistical method has been employed to establish reference graphs and models for detecting adulterated oils. This experimental study focuses precisely on measuring the attenuation and ultrasonic velocity of mixtures of organic argan oil with volumetric fractions of sesame oil, peanut oil or argan oil extracted from kernels depulped by goats. The measurements exhibit distinct behaviors manifested by electrical signals for the mixture obtained after the addition of each volumetric fraction, reflecting the capability of the adopted method to detect this difference. A notable decrease in ultrasonic velocity is observed in the mixtures as the quantity of added oil increases, with a maximum variation of 11 m/s for the argan/peanut oil mixture. Conversely, The attenuation of ultrasonic waves increases proportionally with the added volumetric fractions, with the argan/peanut oil mixture exhibiting an attenuation variation range of 3.57 Np/m. Prediction models for the added volumetric fractions to organic argan oil based on attenuation and ultrasonic velocity, showed a weak correlation between the predicted quantity of added oil and the actual quantity added to organic argan oil, with determination coefficients (r2) not exceeding 65%. The weak correlation is due to the similar chemical compositions of the oils. These results underscore the value of ultrasonic-statistical analysis for authenticating and ensuring the quality of vegetable oils. However, the limitations highlight the need to refine models for better accuracy. This method can verify oil authenticity in the food industry, ensuring consumer protection and quality standards. Additionally, it offers a quick and simple alternative to traditional methods, reducing costs and improving efficiency in quality control processes.

Matching area selection for arctic gravity matching navigation based on adaptive all‐field extended extremum algorithm

AbstractSuitable and effective matching area selection is crucial for gravity matching‐aided navigation. In this paper, an all‐field extended extremum algorithm based on an adaptive threshold (AT‐AEE) is proposed for matching area selection in the Arctic Sea. The gradient data is obtained by using the convolution of gravity reference graph data and all‐field extended extremum parameters. Then, the adaptive threshold method was employed to determine the optimal gradient threshold based on gravity anomaly data across various test areas. Data points with gradients exceeding the specified threshold are identified as local candidate points for matching areas. The test areas containing a certain proportion of local candidate points are designated as the suitable matching areas. Nine test areas in the Arctic Sea with different gravity change characteristics were chosen for simulation experiments to verify the performance of the proposed algorithm. Simulation experiments showed that superior navigation positioning results could be obtained in the matching areas selected by the AT‐AEE algorithm. Compared to traditional algorithm, the matching areas derived from the AT‐AEE algorithm performed with a better consistency in the gravity matching navigation results. In suitable matching areas with the proportion of local candidate points reaching 70%, the average positioning errors could be reduced to less than 1.5 n miles.

Structured discriminative Gaussian graph learning for multimode process monitoring

AbstractAiming at the actual industrial process background that different modes share the same system configurations and control structure, this article proposes a novel structured discriminant Gaussian graph learning for multimode process monitoring. The proposed method considers not only the sparsity of graph model but also the measurement of data variation based on a mismatched graph and the common node support between different graphical structures. The objective function involves two sets of regularization terms: the trace terms for mismatched measurements and the ‐norm imposed on the union of decomposed graph matrices. Due to the introduced mismatched trace terms, the cost of matching the data points and graph models that have inconsistent class labels can be expanded, which brings more discrimination for the graph‐based mode identification. While the common structure extracted by the ‐norm forces the estimated graph models to have structural similarities, thus alleviating the negative influence caused by graph discrimination. Once a relatively accurate and discriminative reference graph model is obtained, the downstream test graph learning and analysis can be conducted online by employing the moving window techniques. By comparing the matched and mismatched graph‐based measurements, the process mode can be identified correctly and stably. To grasp the abnormal process changes, the ‐norm for the row sparsity is again applied to the graph difference matrices, the sensitive monitoring statistics and the fault isolation results can be obtained effectively. All the optimization problems in this paper can be solved using the alternating direction multiplier (ADMM) algorithm. The effectiveness of our proposed approach is illustrated by the application to a real blast furnace iron‐making production process.

Semantic and style based multiple reference learning for artistic and general image aesthetic assessment

Artistic Image Aesthetic Assessment (AIAA) is an emerging paradigm that predicts the aesthetic score as the popular aesthetic taste for an artistic image. Previous AIAA takes a single image as input to predict the aesthetic score of the image. However, most existing AIAA methods fail dramatically to predict the artistic images with a large variance of artistic subjective voting with only a single image. People are good at employing multiple similar references for making relative comparisons. Motivated by the practice that people considers similar semantics and specific artistic style to keep the consistency of the voting result, we present a novel Semantic and Style based Multiple Reference learning (SSMR) to mimic this natural process. Our novelty is mainly two-fold: (a) Similar Reference Index Generation (SRIG) module that considers artistic attribution of semantics and style to generate the index of reference images; (b) Multiple Reference Graph Reasoning (MRGR) module that employs graph convolutional network (GCN) to initialize and reason by adjusting the weight of edges with intrinsic relationships among multiple images. Our evaluation with the benchmark BAID, VAPS and TAD66K artistic aesthetic datasets demonstrates that the proposed SSMR outperforms state-of-the-art AIAA methods, and verifies the comparable to the SOTA IAA methods on the AVA general aesthetic dataset.

Co-linear chaining on pangenome graphs

Pangenome reference graphs are useful in genomics because they compactly represent the genetic diversity within a species, a capability that linear references lack. However, efficiently aligning sequences to these graphs with complex topology and cycles can be challenging. The seed-chain-extend based alignment algorithms use co-linear chaining as a standard technique to identify a good cluster of exact seed matches that can be combined to form an alignment. Recent works show how the co-linear chaining problem can be efficiently solved for acyclic pangenome graphs by exploiting their small width and how incorporating gap cost in the scoring function improves alignment accuracy. However, it remains open on how to effectively generalize these techniques for general pangenome graphs which contain cycles. Here we present the first practical formulation and an exact algorithm for co-linear chaining on cyclic pangenome graphs. We rigorously prove the correctness and computational complexity of the proposed algorithm. We evaluate the empirical performance of our algorithm by aligning simulated long reads from the human genome to a cyclic pangenome graph constructed from 95 publicly available haplotype-resolved human genome assemblies. While the existing heuristic-based algorithms are faster, the proposed algorithm provides a significant advantage in terms of accuracy. Implementation (https://github.com/at-cg/PanAligner).

Cardiovascular magnetic resonance reference values of right ventricular volumetric variables in patients with hypoplastic left heart syndrome

Cardiovascular magnetic resonance (CMR) has established itself as the gold standard for serial assessment of systemic right ventricular (RV) performance but due to the lack of standardized RV reference values for hypoplastic left heart syndrome (HLHS) patients, the interpretation of RV volumetric data in HLHS remains difficult. Therefore, this study aimed to close this gap by providing CMR reference values for the systemic RV in HLHS patients. CMR scans of 160 children, adolescents, and young adults (age range 2.2-25.2years, 106 males) with HLHS were retrospectively evaluated. All patients were studied following total cavopulmonary connection. Short-axis stacks were used to measure RV end-diastolic and end-systolic volumes (RVEDV, RVESV), RV stroke volume (RVSV), RV ejection fraction (RVEF), and RV end-diastolic myocardial mass (RVEDMM). Univariable and multiple linear regression analyses were performed to assess associations between RV parameters and demographic and anthropometric characteristics. Following the results of the regression analysis, reference graphs and tables were created with the Lambda-Mu-Sigma method. Multiple linear regression analysis showed strong associations between body height and RVEDV, RVESV as well as RVSV. Age was highly associated with RVEDMM. Therefore, percentile curves and tables were created with respect to body height (RVEDV, RVESV, RVSV) and age (RVEDMM). The influence of demographic and anthropometric parameters on RVEF was mild, thus no percentile curves and tables for RVEF are provided. We were able to define CMR reference values for RV volumetric variables for HLHS patients. These data might be useful for the assessment and interpretation of CMR scans in these patients and for research in this field.

Single-Cell Genomics Details the Maturation Block in BCP-ALL and Identifies Therapeutic Vulnerabilities in DUX4-Rearranged Cases

B cell progenitor acute lymphoblastic leukemia (BCP-ALL) is the most common childhood malignancy. It is initiated by multiple genetic alterations, causing a maturation arrest and accumulation of abnormal progenitor B cells. Current treatment protocols with chemotherapy have led to favorable outcomes, but are associated with significant toxicity and risk of side effects, highlighting the necessity for highly effective, less toxic, targeted drugs that also show efficacy in children experiencing relapse. We have used multimodal single-cell sequencing to delineate the transcriptional, epigenetic, and immunophenotypic characteristics of 23 childhood BCP-ALLs, including the BCR::ABL1-positive, ETV6::RUNX1-positive, high hyperdiploid, and recently discovered DUX4-rearranged subtypes. In total, 188,546 single cells derived from 23 BCP-ALLs and 9 normal bone marrow samples were profiled using the 10X Genomics platform. Cellranger multi and atac count (10X Genomics) were used to generate read count matrices. Further analysis was performed using Seurat and Signac. The in-house developed program SingleCellProjections was used to create a normal bone marrow reference graph onto which the BCP-ALL data was projected. Projection of the ALL cells along the normal B cell differentiation axis, revealed a diversity in the maturation block between the different BCP-ALL subtypes. Whereas the BCR::ABL1-, ETV6::RUNX1-positive and high hyperdiploidy cells mainly showed similarities to normal pro-B cells, the DUX4-rearranged ALL cells also displayed a transcriptional signature resembling mature B cells. In addition, the blast population in DUX4-rearranged ALLs showed multilineage priming toward non-hematopoietic cells, myeloid and T cell lineages, but also an activation of PI3K/AKT signaling that sensitized the cells to PI3K inhibition. Finally, we show that chimeric antigen receptor T cell therapy targeting the upregulated myeloid receptor CD371 (CLL-1), effectively eliminates DUX4-rearranged ALL cells. Our results provide a detailed characterization of BCP-ALL at the single-cell level and reveal therapeutic vulnerabilities in the DUX4-rearranged subtype with important implications for the understanding of ALL biology and new therapeutic strategies.

Euka: Robust tetrapodic and arthropodic taxa detection from modern and ancient environmental DNA using pangenomic reference graphs

Abstract Ancient environmental DNA (aeDNA) is a crucial source of information for past environmental reconstruction. However, the computational analysis of aeDNA involves the inherited challenges of ancient DNA (aDNA) and the typical difficulties of eDNA samples, such as taxonomic identification and abundance estimation of identified taxonomic groups. Current methods for aeDNA fall into those that only perform mapping followed by taxonomic identification and those that purport to do abundance estimation. The former leaves abundance estimates to users, while methods for the latter are not designed for large metagenomic datasets and are often imprecise and challenging to use. Here, we introduce euka, a tool designed for rapid and accurate characterisation of aeDNA samples. We use a taxonomy‐based pangenome graph of reference genomes for robustly assigning DNA sequences and use a maximum‐likelihood framework for abundance estimation. At the present time, our database is restricted to mitochondrial genomes of tetrapods and arthropods but can be expanded in future versions. We find euka to outperform current taxonomic profiling tools and their abundance estimates. Crucially, we show that regardless of the filtering threshold set by existing methods, euka demonstrates higher accuracy. Furthermore, our approach is robust to sparse data, which is idiosyncratic of aeDNA, detecting a taxon with an average of 50 reads aligning. We also show that euka is consistent with competing tools on empirical samples. euka's features are fine‐tuned to deal with the challenges of aeDNA, making it a simple‐to‐use, all‐in‐one tool. It is available on GitHub: https://github.com/grenaud/vgan. euka enables researchers to quickly assess and characterise their sample, thus allowing it to be used as a routine screening tool for aeDNA.

ПРОГРАМНА АНАЛІТИЧНА СТОХАСТИЧНА МОДЕЛЬ ЕКСПЛУАТАЦІЙНОЇ ФУНКЦІЙНОЇ ПОВЕДІНКИ РАДІОЕЛЕКТРОННОГО КОМПЛЕКСУ ВИЯВЛЕННЯ БЕЗПІЛОТНИХ ЛІТАЛЬНИХ АПАРАТІВ

The article considers an electronic system for detecting unmanned aerial vehicles, which includes a radar system, an optoelectronic system, a thermal imaging system, and an acoustic system. In information technology, the development of a software discrete-continuous stochastic model of the operational behavior of an electronic complex is an important stage in the creation of a structural-automatic model. The creation of a structural-automatic model is described in the article in the following sequence: description of the selected algorithm for the functioning of the electronic complex; verbal model of the operational functional behavior of the electronic complex; according to the verbal model, the development of a reference graph of states and transitions is described; on the basis of the reference graph of states and transitions, a structural-automatic model of operational behavior is formed; the need to verify the developed structural-automatic model is indicated. The combination of the structural-automatic model of operational behavior with the ASNA-2 software module forms a software stochastic model. The software stochastic model is designed to solve the problems of system engineering design (analysis and synthesis) of an electronic complex. The structural-automatic model allows the Designer to set any values of the performance indicators of the systems that are included in the electronic complex. The ASNA-2 software module automates the construction of state graphs based on the structural automated model; according to the state graph, it generates and solves the Kolmogorov-Chopman system of differential equations. By validating the software stochastic model, the reliability of the results to be obtained by the Projector was verified.

Drug resistance prediction for Mycobacterium tuberculosis with reference graphs.

Tuberculosis is a global pandemic disease with a rising burden of antimicrobial resistance. As a result, the World Health Organization (WHO) has a goal of enabling universal access to drug susceptibility testing (DST). Given the slowness of and infrastructure requirements for phenotypic DST, whole-genome sequencing, followed by genotype-based prediction of DST, now provides a route to achieving this. Since a central component of genotypic DST is to detect the presence of any known resistance-causing mutations, a natural approach is to use a reference graph that allows encoding of known variation. We have developed DrPRG (Drug resistance Prediction with Reference Graphs) using the bacterial reference graph method Pandora. First, we outline the construction of a Mycobacterium tuberculosis drug resistance reference graph. The graph is built from a global dataset of isolates with varying drug susceptibility profiles, thus capturing common and rare resistance- and susceptible-associated haplotypes. We benchmark DrPRG against the existing graph-based tool Mykrobe and the haplotype-based approach of TBProfiler using 44 709 and 138 publicly available Illumina and Nanopore samples with associated phenotypes. We find that DrPRG has significantly improved sensitivity and specificity for some drugs compared to these tools, with no significant decreases. It uses significantly less computational memory than both tools, and provides significantly faster runtimes, except when runtime is compared to Mykrobe with Nanopore data. We discover and discuss novel insights into resistance-conferring variation for M. tuberculosis - including deletion of genes katG and pncA - and suggest mutations that may warrant reclassification as associated with resistance.

Enhanced subgraph matching for large graphs using candidate region-based decomposition and ordering

The subgraph matching problem associated with large graphs is an emerging research challenge in graph search due to the growing size of the web, social, and metabolic graphs, and the wide availability of graph databases. Such problems involve finding all instances (aka embedding) of the small-sized query graph in the associated large-sized reference graph. Many state-of-the-art algorithms, including VF3, RI, CFL-Match, and Glasgow, exist to solve subgraph matching problem. RI is one of the fastest subgraph matching algorithms focusing mainly on time efficiency performance measures. However, other performance measures, such as the number of found instances of the query graph (embedding count), the method of ordering the query graph’s vertices, and the number of recursive calls, are crucial for the efficiency and effectiveness of the subgraph matching. In this paper, the RI+ algorithm is proposed as an enhanced version of RI, which has been designed using candidate region-based decomposition and ordering. Three novel candidate region orderings have been introduced, namely vertex-count, density, and average-path-length, based on the structural properties of the candidate regions. On empirical analysis of RI+ on real-world data sets, it was observed that RI+ shows significant improvement in efficiency and effectiveness over RI on both performance evaluation measures, namely, embedding count and search time. The influence of the proposed candidate region orderings on the search time of RI+ was also analyzed, revealing that a suitable candidate region ordering has the potential to improve the search time of the proposed algorithm.

HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph.

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment.

Change in prefilter pressure as a key determinant in the decision to return blood in continuous renal replacement therapy: An observational study.

During continuous renal replacement therapy (CRRT), circuit coagulation is an important event that can result in suboptimal outcomes. Nurses must remain alert throughout the treatment and observe machine pressures. Transmembrane pressure (TMP) is commonly used for monitoring but it is sometimes too late to return blood to the patient. To compare the capacity of prefilter pressure (FP) versus TMP to predict the risk of circuit coagulation in adult patients with acute renal failure on CRRT. An observational, longitudinal, prospective study. This study was carried out in a tertiary referral hospital over 2 years. Data collected included the following variables: TMP, filter or FP, effluent pressure, venous and arterial pressure, filtration fraction, and ultrafiltration constant of each circuit. Means and their trends over time were collected, for both diffusive and convective therapy and for two membrane types. A total of 151 circuits (24 polysulfone and 127 acrylonitrile) were analysed, from 71 patients (n = 22 [34%] women; mean age, 66.5 [36-84] years). Of the total treatments, 80 were diffusive, and the rest were convective or mixed. In the diffusive circuits, a progressive rise in FP was observed without an increase in TMP and with an increasing trend in effluent pressure. Circuit lifespan was between 2 and 90 h. In 11% (n = 17) of the cases, the blood could not be returned to the patient. These findings allowed the creation of graphs that indicate the appropriate point to return blood to the patient. FP was a major determinant in this decision; in most cases, TMP was not a reliable parameter. Our findings are applicable to convective, diffusive, and mixed treatments as well as both types of membranes used in this acute setting. This study provides two clear reference graphs showing risk scales for the assessment of circuit pressures in CRRT. The graphs proposed here can be used to evaluate any machine on the market and the two types of membranes used in this acute setting. Both convective and diffusive circuits can be assessed, allowing safer evaluation in patients who change treatment.

Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes

ObjectiveTo accurately estimate liver PDFF from chemical shift-encoded (CSE) MRI using a deep learning (DL)-based Multi-Decoder Water-Fat separation Network (MDWF-Net), that operates over complex-valued CSE-MR images with only 3 echoes.MethodsThe proposed MDWF-Net and a U-Net model were independently trained using the first 3 echoes of MRI data from 134 subjects, acquired with conventional 6-echoes abdomen protocol at 1.5 T. Resulting models were then evaluated using unseen CSE-MR images obtained from 14 subjects that were acquired with a 3-echoes CSE-MR pulse sequence with a shorter duration compared to the standard protocol. Resulting PDFF maps were qualitatively assessed by two radiologists, and quantitatively assessed at two corresponding liver ROIs, using Bland Altman and regression analysis for mean values, and ANOVA testing for standard deviation (STD) (significance level: .05). A 6-echo graph cut was considered ground truth.ResultsAssessment of radiologists demonstrated that, unlike U-Net, MDWF-Net had a similar quality to the ground truth, despite it considered half of the information. Regarding PDFF mean values at ROIs, MDWF-Net showed a better agreement with ground truth (regression slope = 0.94, R2 = 0.97) than U-Net (regression slope = 0.86, R2 = 0.93). Moreover, ANOVA post hoc analysis of STDs showed a statistical difference between graph cuts and U-Net (p < .05), unlike MDWF-Net (p = .53).ConclusionMDWF-Net showed a liver PDFF accuracy comparable to the reference graph cut method, using only 3 echoes and thus allowing a reduction in the acquisition times.Clinical relevance statementWe have prospectively validated that the use of a multi-decoder convolutional neural network to estimate liver proton density fat fraction allows a significant reduction in MR scan time by reducing the number of echoes required by 50%.Key Points• Novel water-fat separation neural network allows for liver PDFF estimation by using multi-echo MR images with a reduced number of echoes.• Prospective single-center validation demonstrated that echo reduction leads to a significant shortening of the scan time, compared to standard 6-echo acquisition.• Qualitative and quantitative performance of the proposed method showed no significant differences in PDFF estimation with respect to the reference technique.

An approach to pseudocoloring of grey scale image using deep learning technique

Image pseudo colorization is the process of adding RGB colours to grayscale images to make them more appealing. Deep learning technology has made progress in the field of automatic colouring. In general, we divide automatic colouring methods into three groups based on where the colour information comes from: colouring based on what you already know and on reference pictures. The colouring method can meet the needs of most users, but there are some drawbacks. For example, users can’t colour different reference graphs for the different things in a picture. In order to solve this problem by recognising several objects and background regions in a picture and combine the final colouring results, the proposed method uses the deep learning approach that regional mixed colours be used more and the method be mastered by using deep learning. Qualitative results (visual perception) validate the effectiveness of pseudocolorisation which split into foreground colour based on a reference picture and background colour based on prior knowledge. Quantitative results such as Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), Image Matching Error and Entropy validates the effectiveness of strong edge strength, visually appealing quality and retention of maximum information without disturbing quality of image.

Representation of deterministic graphs by defining pair of words

The paper proposes a representation of deterministic graphs (D-graphs) by sets of words in the alphabet of labels of their vertices. Nowadays graphs are a conceptual tool for analytics, development and testing of various software. Among all graphs, there is a subclass of labeled graphs whose elements have labels from a predefined alphabet. Such graphs are actively used to describe and model computational processes in programming, robotics, model verification and validation, etc. Labeled graphs are an information environment for mobile agents, whose movement along the graph can be represented by sequences of vertex labels - words in the label alphabet. A vertex-labeled graph is said to be D-graph if all vertices in the neighborhood of every its vertex have different labels. For such graphs, in the case when its graph map (i.e., the set of vertices and edges and the labeling function) and the initial vertex from which the agents start their movements are known, there is an unambiguous correspondence between the sequence of labels of the vertices visited by the agent and the trajectory of this agent's movements in the graph. In the case when the map of the studied D-graph is unknown to an external observer, the movements of agents can be organized in such a way that, based on their analysis, the observer receives the desired information about the structure of the graph (for example, the map of the graph, the shortest paths between vertices, comparison of the studied graph with the reference graph). Such an analysis can significantly simplify the linguistic representation of a D-graph - the mapping of a graph to one or more finite sets of words in the alphabet of graph vertex labels, which can be used to reconstruct the graph. In this paper, we propose a representation of deterministic graphs by a defining pair of word sets, the first component of which describes the cycles of the graph, and the second - its leaf vertices. This representation is analogous to the system of defining relations for automata. We proposed the algorithm, that for any pair of sets, builds a D-graph for which this pair is deterministic, or reports that it is impossible to do so is given. The algorithm for constructing a canonical defining pair for a D-graph is also given and numerical estimates of this pair for D-graphs with a known number of vertices and edges are found. Further directions of research on this topic are outlined. The results will allow to use new methods and algorithms for solving problems of analysis of graphs with marked vertices.

Reference Values of the Forearm Tremor Power Spectra for Youth Athletes.

The aim of the study was to determine reference graphs of power spectral density functions of forearm physiological tremor and to compare their parameters in the male and female population of young athletes from various sports. One hundred fifty-nine (159) female (15.7 ± 2.1 years, 59.8 ± 8.1 kg, 169.1 ± 7.5 cm) and 276 male (16.4 ± 1.9 years 72.7 ± 10.3 kg and 180.9 ± 8.7 cm) youth athletes participated in the study. Forearm tremor was measured accelerometrically in a sitting position. Power spectrum density (PSD) function was calculated for each individual tremor waveform. Because of right skewness of power distribution, the PSD functions were subjected to logarithmic transformation. Average log-powers in low (2-4 Hz) and high (8-14 Hz) frequency ranges and mean frequencies in those ranges were analyzed. Tremor log-powers for male were greater than for female athletes (p < 0.001), while frequencies of spectrum maxima did not differ from each other. Frequencies of spectrum maxima correlated (p < 0.001) with age (r = 0.277 and 0.326 for males and females, respectively). The obtained reference functions may be utilized in order to quantify and assess tremor size and its changes evoked by stress and fatigue, which can be applied for selection and training monitoring in sports, but also in medicine for detection and diagnosis of pathologic tremor in young individuals.

Determining the components of the structural-automatic model of firing a single target in armor protection with fragmentation-beam projectiles of directed action in a series of three shots based on the reference graph of states

The problem of determining the components of the structural-automatic model is solved by creating a graph of states and transitions. The purpose of the process is to form a formalized expression of the object of study in the form of a structural-machine model of the process of firing a mobile armored vehicle with a series of three shots with fragmentation-beam projectiles of directional action. This model can be further practically implemented during the development of the latest samples of fire resistance in order to reduce errors at the stage of system design. This approach reduces the cost of design and production of prototypes by up to 25 %. This paper considers the process involving interrelated elements of the components of a system while accounting for all possible variants of its behavior from the moment of detection to the failure of a single target in armor protection to perform tasks as intended. The execution of a fire task is considered as a set of certain procedures characterized by the average value of their duration. The explosive destruction of the hull of each fragmentation-beam projectile is characterized by the self-propagation of the reaction of explosive transformations based on tabular data on an armored combat vehicle. Appropriate procedures (phases) of firing a single target in armor protection are advisable to formalize to create preconditions for obtaining the value of a statistical indicator of the effectiveness of causing damage to the target and to study further alternative options for this process. For the proposed structural-automatic model of the process of firing a single target in armor protection with a series of fragmentation-beam projectiles of directional action, validation and verification were carried out, which demonstrated the convergence of the results that exceeded 60 %. The use of the structural-automatic model's components proposed in this work increases the probability of performing a fire task for the first shot from 0.23 to 0.88, for the second – from 0.35 to 0.95, for the third – from 0.45 to 0.98