Random Fragments Research Articles

Constructing phylogenetic trees for microbiome data analysis: A mini-review

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes. In contrast, metagenomic and whole genome shotgun (WGS) sequencing involve sequencing from random fragments of the entire gene, making identification of consistent marker genes challenging owing to the vast diversity of genomic regions, resulting in a scarcity of robust tools for constructing phylogenetic trees. Although bacterial sequence tree construction tools exist for upstream bioinformatics, many downstream researchers—those integrating these trees into statistical models or machine learning—are either unaware of these tools or find them difficult to use due to the steep learning curve of processing raw sequences. This is compounded by the fact that public datasets often lack phylogenetic trees, providing only abundance tables and taxonomic classifications. To address this, we present a comprehensive review of phylogenetic tree construction techniques for microbiome data (16S rRNA or whole-genome shotgun sequencing). We outline the strengths and limitations of current methods, offering expert insights and step-by-step guidance to make these tools more accessible and widely applicable in quantitative microbiome data analysis.

New evidence supports the prophage origin of RcGTA.

Gene transfer agents (GTAs) are phage-like entities that package and transfer random host genome fragments between prokaryotes. RcGTA, produced by Rhodobacter capsulatus, is hypothesized to originate from a prophage ancestor. Most of the evidence supporting this hypothesis came from the finding of RcGTA-like genes in phages. More than 75% of the RcGTA genes have a phage homolog. However, only a few RcGTA homologs have been identified in a (pro)phage genome, leaving the hypothesis that GTAs evolved from prophages through gene loss with only weak evidence. We herein report the discovery of an inducible prophage (vB_MseS-P1) from a Mesorhizobium sediminum strain that contains the largest number (12) of RcGTA homologs found in a phage genome to date. We also identified three putative prophages and two prophage remnants harboring 12-14 RcGTA homologs in a Methylobacterium nodulans strain. The protein remote homology detection also revealed more RcGTA homologs from other phages than we previously thought. Moreover, the head-tail gene architecture of these newly discovered prophage-related elements closely resembles that of RcGTA. Furthermore, vB_MseS-P1 virions have structural proteins similar to RcGTA particles. Close phylogenetic relationships between certain prophage genes and RcGTA-like genes in Alphaproteobacteria further support the shared ancestry between RcGTA and prophages. Our findings provide new relatively direct evidence of the origin of RcGTA from a prophage progenitor.IMPORTANCEGTAs are important genetic elements in certain groups of bacteria and contribute to the genetic diversification, evolution, and ecological adaptation of bacteria. RcGTA, a common type of GTA, is known to package and transfer random fragments of the bacterial genome to recipient cells. However, the origin of RcGTA is still elusive. It has been hypothesized that RcGTA evolved from a prophage ancestor through gene loss. However, the few RcGTA homologs identified in a (pro)phage genome leave the hypothesis lacking direct evidence. This study uncovers the presence of a large number of RcGTA homologs in an inducible prophage and several putative prophages. The similar head-tail gene architecture and structural protein compositions of these newly discovered prophage-related elements and RcGTA further demonstrate an unprecedentedly observed close evolutionary relationship between prophages and RcGTA. Together, our findings provide more direct evidence supporting the origin of RcGTA from prophage.

Resilience of DNA chains to molecular fracture after PCR heating cycles and implications on PCR reliability.

Soon after its introduction in 1987, polymerase chain reaction (PCR) has become a technique widely employed in diagnostic medical devices and forensic science with the intention of amplifying genetic information. PCR prescribes that each of its cycles must include a heating subprocess at 95 °C or more (denominated DNA denaturation and provided for allowing a claimed orderly separation of the two complementary nucleotides strands), which can produce significant damage to DNA, caused by high-speed collisions with surrounding molecules. Since such disruption should be prevented in order to reliably employ PCR, a study of the mechanics of such loss of structural integrity is herein presented, preceded by a review of the fundamental literature which has elucidated the effects of molecular agitation on DNA fragmentation. The main conclusion of this retrospective survey is that the body of examined theoretical and experimental evidence consistently and redundantly confirms scarce resilience and significant loss of structural integrity when DNA is heated at temperatures above 90 °C, even for 1minute. Such conclusion contradicts the claimed paradigm of PCR fidelity and raises the concern that, at least for long sequences, if PCR can amplify some information, such amplified information may be unreliable for diagnostic or forensic applications, since it originates from sequences of nucleotides subjected to random fragmentation and reaggregation. Such a low-reliability scenario should be preventively considered in the various fields where DNA amplification methodologies are employed which provide for high-temperature heating under conditions equal to or similar to those prescribed by the PCR protocols reviewed in this study.

Study on damage characteristics of sandwich plate under the combined action of explosion shock wave and randomly distributed fragments

The load resulting from the explosion of a missile warhead comprises high-intensity shock waves and a random distribution of fragment loads characterized by diverse shapes, sizes, and distributions. Complex coupling between shock waves and fragments are necessary to comprehensively evaluate the blast-resistance performance of U-typed sandwich panels. A numerical model based on the finite-element software was established to investigate the dynamic response of U-typed sandwich-panel structures under the combined action of shock waves and randomly distributed fragment groups. The effectiveness of the numerical model was validated by comparing results with experimental values. A random fragment field-generation model that conforms to the characteristics of a power-law distribution is constructed through the implementation of the Voronoi segmentation algorithm and a Python-based computational module. Building on this framework, this study explored the complex coupling process between shock waves and randomly distributed fragments. Furthermore, an in-depth analysis was performed to examine the structural damage characteristics with various fragment distributions and fragment densities. Diverse perforation patterns were observed in the upper plate of the U-typed sandwich panel for different fragment densities. Furthermore, the perforation area of the upper plate initially increased, followed by a decrease with increasing fragment density.

An Explainable and Generalizable Recurrent Neural Network Approach for Differentiating Human Brain States on EEG Dataset.

Electroencephalogram (EEG) is one of the most widely used brain computer interface (BCI) approaches. Despite the success of existing EEG approaches in brain state recognition studies, it is still challenging to differentiate brain states via explainable and generalizable deep learning approaches. In other words, how to explore meaningful and distinguishing features and how to overcome the huge variability and overfitting problem still need to be further studied. To alleviate these challenges, in this work, a multiple random fragment search-based multilayer recurrent neural network (MRFS-MRNN) is proposed to improve the differentiating performance and explore meaningful patterns. Specifically, an explainable MRNN module is proposed to capture the temporal dependences preserved in EEG time series. Besides, a MRFS module is designed to cut multiple random fragments from the entire EEG signal time course to improve the effectiveness of brain state differentiating ability. MRFS-MRNN is concatenatedto effectively overcome the huge variabilities and overfitting problems. Experiment results demonstrate that the proposed MRFS-MRNN model not only has excellent differentiating performance, but also has good explanation and generalization ability. The classification accuracies reach as high as 95.18% for binary classification and 89.19% for four-category classification on the individual level. Similarly, 95.53% and 85.84% classification accuracies are obtained for the binary and four-category classification on the group level. What's more, 94.28% and 85.43% classification accuracies of binary and four-category classifications are achieved for predicting brand new subjects. The experiment results showed that the proposed method outperformed other state-of-the-art (SOTA) models on the same underlying data and improved the explanation and generalization ability.

Analysis of the quality of tunnel roof topography by automatic cutting control under the coupling of multiple factors.

The automatic cutting of coal and rock surface morphology modeling based on the actual geological environment of coal mine underground excavation and mining is of great significance for improving the surface quality of coal and rock after cutting and enhancing the safety and stability of advanced support. To this end, using the principle of coordinate transformation, the kinematic trajectory of the cutting head of the tunneling machine is established, and the contour morphology of the cutting head under variable cutting technology is obtained. Then, based on the regenerative vibration theory of the cutting head, a dynamic model of the cutting head coal wall is established, and the coordinate relationship of the cutting head in the tunnel coordinate system under vibration induction is analyzed. Based on fractal theory and Z-MAP method, a simulation method for the surface morphology of coal and rock after cutting is proposed, which is driven by the cutting trajectory Under the coupling effect of cutting vibration induction and random fragmentation of coal and rock, simulation of the surface morphology of comprehensive excavation tunnels was conducted, and relevant experiments were conducted to verify the results. A 1:3 similarity experimental model of EBZ160 tunneling machine was used to build a cutting head coal and rock system cutting experimental platform for comparative experiments of cutting morphology. Furthermore, statistical methods were used to compare and evaluate the simulated roof with the actual roof. The results show that the relative errors between the maximum range of peaks and valleys, the peak skewness coefficient of height standard deviation, and the kurtosis coefficient of the actual roof are 1.3%, 24.5%, 16%, and 2.9%, respectively. Overall, this indicates that the surface morphology distribution characteristics of the simulated roof and the actual roof are similar, verifying the effectiveness of the modeling and simulation method proposed in this paper, and providing theoretical support for the design and optimization of advanced support in the future.

Determining the ability of artificial intelligence to establish authorship of artistic ukrainian texts using significant fragments

Artificial intelligence is becoming an integral part of everyday life and profes-sional activity of a person. Bing, as an intelligent search system, can serve as a tool for determining the authorship of artistic text in Ukrainian. Bing helps to uncover in-formation about a text fragment and its author, although the search results may be inaccurate or incomplete. This work aims to explore the effectiveness of determining the authorship of ar-tistic texts using modern artificial intelligence tools based on significant fragments of works. Ten Ukrainian authors with a rich body of artistic works, reflecting various as-pects of Ukrainian culture and history, were selected for the experiment. Random fragments up to 500 words in length were selected from various works of these au-thors. An experiment was conducted to determine the authorship of 360 fragments. Using the Python programming language and the skpy package, software was created that sends queries and receives responses from the Bing bot embedded in Microsoft Skype. The presence of the author’s name and the corresponding title of the work were checked in the response texts. This work introduces, for the first time, a method of verifying the authorship of Ukrainian-language text fragments using the Bing bot equipped with artificial intelli-gence. A comparative analysis was conducted and experiments were carried out to identify the authorship of significant long fragments. It was found that long fragments allow the author of the artistic Ukrainian text to be determined with high accuracy. Ivan Franko has the highest percentage of re-sponses where the author’s name and the title of the work were mentioned - 87%. The proposed hypothesis regarding the effectiveness of artificial intelligence in establishing authorship of works has not been confirmed. Artificial intelligence has slightly lower efficiency than expected, which indirectly exposes its means of opera-tion. Namely, when establishing authorship, a sequential research comparison of the proposed fragment with a bank of works that are widespread in the Internet environ-ment is not performed.

Task sub-type states decoding via group deep bidirectional recurrent neural network

Decoding brain states under different cognitive tasks from functional magnetic resonance imaging (fMRI) data has attracted great attention in the neuroimaging filed. However, the well-known temporal dependency in fMRI sequences has not been fully exploited in existing studies, due to the limited temporal-modeling capacity of the backbone machine learning algorithms and rigid training sample organization strategies upon which the brain decoding methods are built. To address these limitations, we propose a novel method for fine-grain brain state decoding, namely, group deep bidirectional recurrent neural network (Group-DBRNN) model. We first propose a training sample organization strategy that consists of a group-task sample generation module and a multiple-scale random fragment strategy (MRFS) module to collect training samples that contain rich task-relevant brain activity contrast (i.e., the comparison of neural activity patterns between different tasks) and maintain the temporal dependency. We then develop a novel decoding model by replacing the unidirectional RNNs that are widely used in existing brain state decoding studies with bidirectional stacked RNNs to better capture the temporal dependency, and by introducing a multi-task interaction layer (MTIL) module to effectively model the task-relevant brain activity contrast. Our experimental results on the Human Connectome Project task fMRI dataset (7 tasks consisting of 23 task sub-type states) show that the proposed model achieves an average decoding accuracy of 94.7% over the 23 fine-grain sub-type states. Meanwhile, our extensive interpretations of the intermediate features learned in the proposed model via visualizations and quantitative assessments of their discriminability and inter-subject alignment evidence that the proposed model can effectively capture the temporal dependency and task-relevant contrast.

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Lithium-ion battery capacity estimation based on fragment charging data using deep residual shrinkage networks and uncertainty evaluation

Accurate and reliable capacity estimation is crucial for lithium-ion batteries to operate safely and stably. However, the extraction steps of health indicators (HIs) limit the feasibility and applicability of data-driven methods. This study proposes a novel estimation framework using deep residual shrinkage network (DRSN) and uncertainty evaluation to estimate the lithium-ion battery capacity directly; model inputs are only random fragment charging data. Results on three datasets confirm that accurate capacity estimation is achieved by DRSN through integrated attention mechanisms and soft thresholding (the mean absolute percentage error is below 2 %). Meanwhile, the inherent noise sensitivity in data-driven methods is alleviated. Additionally, uncertainty evaluation implemented by Bayesian neural networks provides valuable metrics for the reliability of estimated results from different voltage ranges. The significant correlation between uncertainty and error proves the potential of uncertainty to assist battery management systems for control and decision-making. The experimental study demonstrates the high accuracy, adaptability, and robustness of the proposed framework, especially in coping with the frequent occurrence of random incomplete charging scenarios.

Regulation of PhoB on biofilm formation and hemolysin gene hlyA and ciaR of Streptococcus agalactiae

PhoB is a response regulator protein that plays a key role in the PhoBR two-component signal transduction system. In this study, we used transcriptome and proteomics techniques to evaluate the detect the gene network regulated by PhoB of Streptococcus agalactiae. The results showed that expression of biofilm formation and virulence-related genes were changed after phoB deficiency. Crystal violet and CLSM assay confirmed that the deletion of the phoB increased the thickness of S. agalactiae biofilm. The results of lacZ reporter and the bacterial one-hybridization method showed that PhoB could directly bind to the promoter regions of hemolysin A and ciaR genes but not to the promoter regions of cylE and hemolysin III. Through the construction of an 18-base pair deoxyribose nucleic acid (DNA) random fragment library and the bacterial one-hybridization system, it was found that the conservative sequence of PhoB binding was TTGGAGAA(G/T). Our research has uncovered the virulence potential of the PhoBR two-component system of S. agalactiae. The findings of this study provide the theoretical foundation for in-depth research on the pathogenic mechanism of S. agalactiae.

A Simple Model Generator for Radar Sounding Applications Based on Stochastic Media Model and gprMax

Ground penetrating radar (GPR), or more generically called radar sounding, is an established technique for probing subsurface objects and sublayer structures. Simulating pre-constructed models and comparing with the real-world observations can contribute to retrieve the geometrical and physical properties of subsurface objects. However, most GPR simulations are limited to geometrically simple models with homogeneous physical properties of subsurface media which are generally unable to represent the real-world situations. In this paper, we develop a simple model generator by combining the stochastic media model and gprMax, an open-source software for electromagnetic simulations, to jointly generate models with geometrically random fragments, which are forward models with minimum human intervention. These easily prepared and simulated models can enrich the model training datasets which can be further utilized in machine learning for GPR retrieval algorithms.

Application of Fragmented Extracellular Self-DNA (esDNA) Concept as an Alternative Prophylactic Approach against Vibrio parahaemolyticus and Vibrio harveyi Infection in Brine Shrimp Artemia

The purpose of this study was to investigate species specific inhibitory effects of esDNA isolated from two conspecific organisms: Vibrio parahaemolyticus (VP) and Vibrio harveyi (VH), and to assess the functional role of esDNA to enhance the survival rate of Artemia sp. In an in vitro study, nine doses of Extracellular self-DNA of Vibrio parahaemolyticus (esDNAVP) and Vibrio harveyi (esDNAVH) were used as the target for the challenge test with the conspecific bacteria. In an in vivo study, the protective effect of esDNA was then tested in nauplii of the brine shrimp Artemia at various priming times and concentrations of esDNA under gnotobiotic conditions prior to challenge with VP and VH at the concentration of 5 × 105 CFU mL-1. The results from in vitro study showed that the use of esDNAVP at levels of 24.02 and 48.05 ng µl-1 and esDNAVH at concentrations of 13.33 and 26.67 ng µl-1 were able to inhibit the growth of the conspecific species when added to the culture medium at the concentration level of 5 × 105 CFU mL-1. The results from in vivo study showed that the use of 24.02; 48.05 and 72.07 ng µl-1 of esDNAVP as well as the use of 13.33; 26.67 and 40.00 ng µl-1 of esDNAVH inhibited the growth of VP and VH and enhanced the survival rate of Artemia sp compared to the control treatment (P<0.05). Taken together, we confirmed that esDNA obtained from the extraction and random fragmentation from esDNAVP and esDNAVH, produces a species-specific inhibitory effect on the same species and can serve as a potential alternative strategy for disease control to deliver the functionality of esDNA to the fish and shrimp.

Macro, meso, micro and nanoplastics in horticultural soils in Argentina: Abundance, size distribution and fragmentation mechanism

Soil contamination with plastics is a major worldwide concern. However, data on plastic pollution in horticultural soils from Latin America is scarce. Furthermore, there is limited information on the fragmentation process that plastics undergo in environmental conditions. In this study, we investigated the abundance of macro, meso, micro and nano plastics in a previously studied horticultural soil (2015) from Buenos Aires, that has not been used for any productive activity since. Although the mass of macroplastics was conserved, the number of plastic fragments per square meter increased significantly, indicating a possible natural fragmentation process. Black polyethylene (PE) mulch film was the most abundant plastic found. For this material, when considering the mass of plastic fragments per square meter, the relative abundance was, in decreasing order: macroplastics (65.1–79.1 %) > mesoplastics (15.6–24.8 %) > microplastics (5.3–12.4 %) > nanoplastics (0.1 %). However, when considering the number of plastic items per square meter, the order was: microplastics (2383–3815) > mesoplastics (1019–1076) > nanoplastics (509–550) > macroplastics (25–46). The size distribution of plastic debris was analyzed using the natural logarithm of abundance versus the square root of the mean decile area, with good linear correlations (0.7749 < R2 < 0.9785). These results provide evidence for an ongoing dynamic fragmentation process (Mott model). We hypothesize that the breakdown of plastic into smaller pieces could be explained by a random fragmentation process based on soil volume changes between natural hydration/dehydration states. These data suggest that soil under natural conditions could act as an ‘environmental plastic grinder’.

A Novel Experiment Approach for Measurement Breakup Length, Cone Angle, Sheet Velocity, and Film Thickness in Swirl Air-Blast Atomizers

&lt;div&gt;Measuring the dynamic parameters of liquid fragments generated in the near-field of atomizing sprays poses a significant challenge due to the random nature of the fragments, the instability of the spray, and the limitations of current measuring technology. Precise determination of these parameters can aid in improving the control of the atomization process, which is necessary for providing suitable spray structures with appropriate flow rates and droplet size distributions for various applications such as those used in heat engines. In piston and gas turbine engines, controlling spray characteristics such as penetration, cone angle, particle size, and droplet size distribution is crucial to improve combustion efficiency and decrease exhaust emissions. This can be accomplished by adjusting the structural and/or operating parameters of the fuel supply system. This article aims to measure the breakup length, spray cone angle, axial velocity, breakup time, and liquid sheet film thickness for a swirl air-blast atomizer used in a gas-steam engine. The measurement was conducted using a shadowgraph imaging system developed specifically for this study, consisting of a high-speed camera, a lens, and a light source. While lasers are commonly used as light sources in the literature, this study utilized a special LED high-speed pulse light generator, which is cheaper, easier to handle, and provides a more uniform background. Images were processed using a MATLAB code developed for this study. Although the breakup zone is naturally random and the breakup location significantly varies with time, the novel method developed in this study helps quantify critical parameters under different operating conditions.&lt;/div&gt;

Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network

EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-invasiveness and low costs. Specifically EEG can be applied to differentiate brain states, which is important for better understanding the working mechanisms of the brain. Recurrent neural network (RNN)-based learning strategy has been widely utilized to differentiate brain states, because its optimization architectures improve the classification performance for differentiating brain states at the group level. However, present classification performance is still far from satisfactory. We have identified two major focal points for improvements: one is about organizing the input EEG signals, and the other is related to the design of the RNN architecture. To optimize the above-mentioned issues and achieve better brain state classification performance, we propose a novel multi-clip random fragment strategy-based interactive bidirectional recurrent neural network (McRFS-IBiRNN) model in this work. This model has two advantages over previous methods. First, the McRFS component is designed to re-organize the input EEG signals to make them more suitable for the RNN architecture. Second, the IBiRNN component is an innovative design to model the RNN layers with interaction connections to enhance the fusion of bidirectional features. By adopting the proposed model, promising brain states classification performances are obtained. For example, 96.97% and 99.34% of individual and group level four-category classification accuracies are successfully obtained on the EEG motor/imagery dataset, respectively. A 99.01% accuracy can be observed for four-category classification tasks with new subjects not seen before, which demonstrates the generalization of our proposed method. Compared with existing methods, our model outperforms them with superior results. Overall, the proposed McRFS-IBiRNN model demonstrates great superiority in differentiating brain states on EEG signals

Holes on surfaces of the weathered plastic fragments from coastal beaches

The surface morphology of weathered plastics undergoes a variety of changes. In this study, 3950 plastic fragments from 26 beaches around the world, were assessed to identify holes. Holes were identified on 123 fragments on 20 beaches, with the highest frequency (10.3 %) being identified at Qesm AL Gomrok Beach in Egypt. The distribution of holes could be divided into even, single-sided, and random types. The external and internal holes were similar in size (37 ± 15 μm) of even type fragments. The external holes were larger than the internal holes in single-sided (516 ± 259 μm and 383 ± 161 μm) and random (588 ± 262 μm and 454 ± 210 μm) fragment types. The external hole sizes were positively correlated with the internal hole sizes for each type. This study reports a novel deformation phenomenon on the surface of weathered plastics and highlights their potential effects on plastics.

Determining the Authorship of a Ukrainian-Language Literary Text by Means of Artificial Intelligence from Ultra-Short Excerpts

Purpose. The intelligent search engine Bing can be used as a method and a means of determining the author of a Ukrainian-language test. Bing helps to find information about a text fragment and its author, but the search results may be inaccurate or incomplete. The main purpose of the paper is to study the effectiveness of establishing the authorship of literary texts by state-of-the-art artificial intelligence tools based on ultra-short excerpts. Methodology. Ten Ukrainian authors with a rich body of fiction reflecting various aspects of Ukrainian culture and history were selected, as well as random fragments of 3–7 words each from different works of these authors. An experiment was conducted to determine the authorship of 2,000 fragments. Findings. Using the Python programming language and the skpy package, we developed software that sends questions and receives answers from the Bing bot built into Microsoft Skype. The answers were checked for the name of the author of the phrase and the corresponding title of the work. According to the results, Ivan Franko has the highest percentage of answers where the author's name was mentioned (65%), and Oleksandr Dovzhenko has the lowest result (23%). The answers were analyzed by the length of the fragments. Of course, the longer the length of a text fragment, the greater the likelihood of accurately identifying its authorship. Features of the author's style are manifested in 20–40 % of short fragments. The remaining 60–80% may be commonly used language constructions that the author relayed from the external environment. Originality. In this work, for the first time, the method of checking the authorship of fragments of Ukrainian-language text using the Bing bot with artificial intelligence is presented. A comparative analysis was performed and experiments were given to determine the authorship of short fragments of 3–7 words. It has been established that even quite small fragments of the text have signs characteristic of the original style of the author of artistic works. Practical value. It has been determined to what extent experts in determining the authorship of natural language texts can rely on existing state-of-the-art artificial intelligence tools in combination with an extensive database of texts in the Internet space.

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

High‐throughput genotyping in estimating genetic resources and detecting pathogens in aquaculture

ANALYSIS OF METHODS FOR DIGITAL TERRAIN MODELING BASED ON SPATIAL INTERPOLATION (Part 1)

Digital Elevation Model (DEM) is an important component of geodetic works, which allows reproducing the earth's surface in digital form with a certain level of detail. Various methods are used to construct DEMs, among which it is worth noting the methods based on spatial interpolation. One advantage of spatial interpolation is the ability to take into account the uneven distribution of points on the earth's surface, which provides a more accurate DEM. With the spread of high-precision geodetic instruments and technologies such as GPS and LiDAR, the methods of constructing DEMs have significantly improved. Modern methods include interpolation of curved surfaces, smoothing methods that reduce noise and random errors, and adaptive filtering methods that can detect and correct data anomalies. In the field of geodesy, digital elevation models are an essential element in performing measurements and creating project documentation for construction. The purpose of this article is to analyze existing methods for constructing digital elevation models and to compare them to choose the best one. For the analysis, the widely known geoinformation system package Surfer was selected, which contains a sufficiently large number of deterministic methods and a geostatistical method based on spatial interpolation. The Golden Software Surfer geoinformation system is currently the industry standard for constructing graphical representations of two-variable functions. An unbeatable advantage of the program is its built-in spatial interpolation algorithms, which allow creating digital surface models with high quality for spatially unevenly distributed data. The Surfer program provides 12 different methods of spatial interpolation. For the analysis, a random fragment of a topographic map was taken, which was previously digitized and exported in ASCII format with ready-made coordinates for constructing a DEM. However, only 6 out of the 12 methods that could more accurately reflect the real relief situation were further analyzed based on these topographic data, namely Kriging, Triangulation with Linear Interpolation, Radial Basic Function, Natural Neighbor, Modified Shepard`s Method and Minimum Curvature. Keywords: digital elevation model, construction methods, spatial interpolation, geographic information system, Surfer.