Computational Analysis Methods Research Articles

Precedent Names and their Functions in Umberto Eco's Novel "The Name of the Rose"

The subject of this study is the precedent names in Umberto Eco's novel "The Name of the Rose". The author relies on the theoretical provisions developed in relation to the precedent names of E. A. Nakhimova. With the help of computational methods of analysis and subsequent manual verification of the results, a list of all precedent names present in the novel was obtained. The names invented by Eco itself were also analyzed, precedent components were found in them, which were also analyzed. As a result, a list of 544 names was obtained, which the author classified into several groups. Next, the functions were defined in which the existing precedent names are used. The scientific novelty of this study is due to two factors. On the one hand, the author contributes to the development of the concept of "precedent name", which entered science quite recently (in the 2000s). On the other hand, the novelty is determined by the very material on which the research is carried out, since the texts of Umberto Eco until today are more often the subject of literary research, while their linguistic aspects remain out of the field of view of specialists. The author discovers the predominance of pragmatic and modeling functions of precedent names in the text of the novel. There are also examples of the use of case names in the human function and the evaluation function. At the same time, due to the theoretical provisions of postmodernism, in accordance with which the novel was developed, it can be said that the password function is concomitant with everything else. In addition, we can say that the massive use of precedent names in the text of the novel is used as a technique. Although science has not yet developed a single term for this technique, following the Danish researcher Jon Kyust, the author suggests using the term "neimdropping" for this.

Capnography derived breath variability analysis feasibility and its importance for pulmonary embolism prediction

Capnography is a method that monitors the concentration of Carbon Dioxide (CO2) in patients’ respiratory gases. Its outcome, i.e., the capnographic curve shows CO2 levels plotted against time. The measurement of the carbon dioxide that is released at the end of an exhaled breath is called end-tidal carbon dioxide (etCO2). Such monitoring provides information about CO2 production and clearance. This paper hypothesizes that the capnographic curve could be useful in everyday clinical practice for differential diagnosis. Specifically, a computational analysis method was developed and a pilot study was conducted to determine if capnography can be used as a predictive indicator for Pulmonary Embolism (PE). To verify whether the respiratory pattern may have clinical significance, an assessment was carried out in patients in whom PE was diagnosed and those in whom PE was suspected but ultimately excluded. The age, gender, end-tidal-CO2 (etCO2), respiratory rate, and symptoms at the admission of patients who had a chest CT angiography were noted. Classification and regression tree analysis were designed and used to identify variables predictive for PE. A total of 120 patients (60 female, 60 males) aged 64.3 +/- 18.0 admitted to the Emergency Department had the computed tomography pulmonary angiography imaging study: 60 with positive PE and 60 with negative PE results. In patients with etCO2>=32 mmHg was a negative predictor for PE. In those with the etCO2 < 32 mmHg was predictive for PE. The developed model indicates that it could assist in predicting PE based on the etCO2 values.

REFUGEE OR EXPAT, HERO OR THREAT: MIGRANT QUERIES IN GOOGLE NEWS SEARCH RESULTS

Search engines play a gatekeeper role in current high-choice information environments. Considered a form of new media, users are still more likely to find and trust news found through search than social media sites. Indeed, search engines are one of the most utilised technologies to find political information, despite audits uncovering biases in their results, for example, towards national outlets over local ones. It is therefore important to keep in mind the potential of search results to affect public opinion. With this study, we investigate how Google search news headlines and snippets differ when varying migrant search terms (e.g., immigrant, refugee, expat). We employ computational text analysis methods as well as qualitative content analysis. Specifically, we employ an automated framework for detecting media frames, originally trained on Twitter data, and attempt to transfer it to news data; this framework allows for a categorization of data to frames of a generic-issue (economy, safety, health) and specific (hero:diversity, threat:jobs) nature. We evaluate its applicability for this novel data source and find that it performs well for frames related economy and security. Our next steps include analysing the results of other computational measures, namely, sentiment, agency and political outlet of the news item. We expect that sentiment and agency will complement the initial results we see based on media frames.

Nociception in fruit fly larvae.

Nociception, the process of encoding and processing noxious or painful stimuli, allows animals to detect and avoid or escape from potentially life-threatening stimuli. Here, we provide a brief overview of recent technical developments and studies that have advanced our understanding of the Drosophila larval nociceptive circuit and demonstrated its potential as a model system to elucidate the mechanistic basis of nociception. The nervous system of a Drosophila larva contains roughly 15,000 neurons, which allows for reconstructing the connectivity among them directly by transmission electron microscopy. In addition, the availability of genetic tools for manipulating the activity of individual neurons and recent advances in computational and high-throughput behavior analysis methods have facilitated the identification of a neural circuit underlying a characteristic nocifensive behavior. We also discuss how neuromodulators may play a key role in modulating the nociceptive circuit and behavioral output. A detailed understanding of the structure and function of Drosophila larval nociceptive neural circuit could provide insights into the organization and operation of pain circuits in mammals and generate new knowledge to advance the development of treatment options for pain in humans.

Effect of Unbalanced Magnetic Pull of Generator Rotor on the Dynamic Characteristics of a Pump—Turbine Rotor System

In pumped storage units, the rotor-bearing electromagnetic system is under the joint influence of hydraulics, mechanics, and electromagnetics, and the mechanism of unit vibration problems is very complex to investigate. ANSYS software is used to establish a three-dimensional model of a pumped storage power plant’s rotor-bearing electromagnetic system, and the stiffness coefficient of the unbalanced magnetic traction forces is calculated using the Fourier series of the magnetic conductivity of the air gap. This shows that the nonequilibrium magnetic attraction increases non-linearly with increasing excitation current and eccentricity of the rotor. At each order, the critical velocity of the rotor system increases as the stiffness factor of the bearing increases, with the greatest increase in critical velocity at the third and fourth orders. In the first-order mode-oscillation pattern, the unbalanced magnetic attraction has an effect on the intrinsic frequency of the transverse oscillation, with a reduction in the amplitude of the intrinsic frequency by 34.65%. Axial and transverse modal vibrations manifest themselves as upward and downward motions and transverse oscillations in different portions of the rotor system, respectively, whereas torsional modal vibrations manifest as a radial broadening or reduction in the generator rotor, runner, and coupling portions of the rotor system. The results of the study provide a theoretical foundation and a computational method for the dynamic analysis and design of the rotor system of pumped storage power stations.

Human papilomaviru-related oropharyngeal squamous cell carcinoma and radiomics: A new era?

The increase of the incidence of human papillomavirus dependent oropharyngeal squamous cell carcinoma is alarming, although we have greatly progressed in the classification and staging of this disease. We now know that human papillomavirus related oropharyngeal squamous cell carcinoma is a sub-type of head and neck squamous cell carcinoma with favourable prognosis and good response to therapy that needs a proper system of classification and staging. Thus, in routine practice it is essential to test patients for the presence of human papillomavirus. The most popular technique to assess human papillomavirus status is immunohistochemistry on biopsy samples with p16, which is an excellent surrogate for high-risk human papillomavirus infection. Another highly sensitive and specific tissue-based technique for the detection of human papillomavirus is RNAscope In situ hybridization that has a prohibitive cost, limiting its use in routine practice. Radiomics is an artificial intelligence based non-invasive method of computational analysis of computed tomography, magnetic resonance imaging, positron emission tomography, and ultrasound images. In this review, we summarise the last findings of radiomics applied to human papillomavirus associated oropharyngeal squamous cell carcinoma. A growing body of evidence suggest that radiomics is able to characterise and detect early relapse after treatment, and enable development of tailored therapy of human papillomavirus positive oropharyngeal squamous cell carcinoma.

Gut Microbes Meet Machine Learning: The Next Step towards Advancing Our Understanding of the Gut Microbiome in Health and Disease

The human gut microbiome plays a crucial role in human health and has been a focus of increasing research in recent years. Omics-based methods, such as metagenomics, metatranscriptomics, and metabolomics, are commonly used to study the gut microbiome because they provide high-throughput and high-resolution data. The vast amount of data generated by these methods has led to the development of computational methods for data processing and analysis, with machine learning becoming a powerful and widely used tool in this field. Despite the promising results of machine learning-based approaches for analyzing the association between microbiota and disease, there are several unmet challenges. Small sample sizes, disproportionate label distribution, inconsistent experimental protocols, or a lack of access to relevant metadata can all contribute to a lack of reproducibility and translational application into everyday clinical practice. These pitfalls can lead to false models, resulting in misinterpretation biases for microbe-disease correlations. Recent efforts to address these challenges include the construction of human gut microbiota data repositories, improved data transparency guidelines, and more accessible machine learning frameworks; implementation of these efforts has facilitated a shift in the field from observational association studies to experimental causal inference and clinical intervention.

Message Deletion on Telegram: Affected Data Types and Implications for Computational Analysis

ABSTRACT Ephemeral digital trace data can decrease the completeness, reproducibility, and reliability of social media datasets. Systematic post deletions thus potentially bias the results of computational methods used to map actors, content, and online information diffusion. Therefore, the aim of this study was to assess the extent and distribution of message deletion across different data types using data from the hybrid messenger service Telegram, which has experienced an influx of deplatformed users from mainstream social media platforms. A repeatedly scraped sample of messages from public Telegram groups and channels was used to investigate the effect of message ephemerality on the consistency of Telegram datasets. The findings revealed that message deletion introduces biases to the computational collection and analysis of Telegram data. Further, message ephemerality reduces dataset consistency, the quality of social network analyses, and the results of computational content analysis methods, such as topic modeling or dictionaries. The implications of these findings for scholars aiming to use Telegram data for computational research, possible solutions, and contributions to the methodological advancement of studying online political communication are discussed further in this article.

Sampling constrained continuous probability distributions: A review

AbstractThe problem of sampling constrained continuous distributions has frequently appeared in many machine/statistical learning models. Many Markov Chain Monte Carlo (MCMC) sampling methods have been adapted to handle different types of constraints on random variables. Among these methods, Hamilton Monte Carlo (HMC) and the related approaches have shown significant advantages in terms of computational efficiency compared with other counterparts. In this article, we first review HMC and some extended sampling methods, and then we concretely explain three constrained HMC‐based sampling methods, reflection, reformulation, and spherical HMC. For illustration, we apply these methods to solve three well‐known constrained sampling problems, truncated multivariate normal distributions, Bayesian regularized regression, and nonparametric density estimation. In this review, we also connect constrained sampling with another similar problem in the statistical design of experiments with constrained design space.This article is categorized under: Applications of Computational Statistics &gt; Computational Mathematics Statistical and Graphical Methods of Data Analysis &gt; Bayesian Methods and Theory Statistical and Graphical Methods of Data Analysis &gt; Markov Chain Monte Carlo (MCMC) Statistical and Graphical Methods of Data Analysis &gt; Sampling

What Keeps Teachers Engaged during Professional Development? The Role of Interest Development

Interest influences adults’ and young learners’ learning in formal and informal contexts. Although interest and interest development frameworks have been used in research on student-learning, they are not used in teacher-focused research, especially as “outcomes” of teacher professional development (PD) activities. In this study, we used interest development as the outcome of PD in computer science (CS) and investigated the factors that influenced teachers’ (n = 5) interest development toward CS using various data sources and analysis methods. We found that interest development is (a) varied, (b) influenced by self-relation, knowledge, and affect, (c) associated with reengagement with PD activities, and (d) it can be captured using computational text analysis methods and online log data.

Analisis Bibliometrik Penggunaan Video Pembelajaran di Sekolah Dasar Tahun 2013-2022 Menggunakan Aplikasi VOSViewer

This study aims to analyze the use of instructional videos in elementary schools between 2013 and 2022 using the bibliometric computational mapping analysis method. The Publish or Perish software was used to collect publications data from the Google Scholar database using the keywords "Instructional Video" AND "Elementary School" OR "ES". This search yielded 282 articles. These results indicated that the number of publications related to this topic from 2013-2022 was still small, with an average number of publications of 28.2. Then, it was found that the highest number of publications was in 2021, with a total of 102 articles, while the lowest one was in 2014, with three articles. Analysis of the co-occurrence of keywords using VOSViewer resulted in 15 terms grouped into 5 clusters. Network and density visualization showed several keywords that often appear, namely, Elementary School (ES), instructional video, and video. In addition, the overlay visualization depicted keywords that have become a trend in recent years, namely, video, instructional media, animated video, mathematics learning, and teacher. Thus, future research needs to focus on various types of instructional videos and various subjects in elementary schools.&#x0D; Keywords: Bibliometric Analysis, Computational Mapping Analysis, Instructional Video, Elementary School, VOSViewer

A review on recent machine learning applications for imaging mass spectrometry studies

Imaging mass spectrometry (IMS) is a powerful analytical technique widely used in biology, chemistry, and materials science fields that continue to expand. IMS provides a qualitative compositional analysis and spatial mapping with high chemical specificity. The spatial mapping information can be 2D or 3D depending on the analysis technique employed. Due to the combination of complex mass spectra coupled with spatial information, large high-dimensional datasets (hyperspectral) are often produced. Therefore, the use of automated computational methods for an exploratory analysis is highly beneficial. The fast-paced development of artificial intelligence (AI) and machine learning (ML) tools has received significant attention in recent years. These tools, in principle, can enable the unification of data collection and analysis into a single pipeline to make sampling and analysis decisions on the go. There are various ML approaches that have been applied to IMS data over the last decade. In this review, we discuss recent examples of the common unsupervised (principal component analysis, non-negative matrix factorization, k-means clustering, uniform manifold approximation and projection), supervised (random forest, logistic regression, XGboost, support vector machine), and other methods applied to various IMS datasets in the past five years. The information from this review will be useful for specialists from both IMS and ML fields since it summarizes current and representative studies of computational ML-based exploratory methods for IMS.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

About Some Features of the Existence of the Humanities in the Digital Age

In the digital age, the humanities have undergone changes as new technologies have opened up new opportunities for research and interaction. Scientists now have access to huge amounts of data and can use computational methods for analysis, leading to new discoveries. However, the digital age also poses challenges, such as the need for long-term data retention. As the field of humanities continues to evolve, scientists must cope with these challenges and use the opportunities provided by the digital age to advance their research and interact with society in new and innovative ways.

Картографирование растительности южной части острова Сахалин по данным дешифрирования снимков спутниковой системы Landsat

The purpose of the research is to determine the factors that have a significant impact on the distribution structure of plant communities over the territory and to draw up a vegetation map. The article pre-sents the analytical results of the vegetation cover diversity and geobotanical mapping of the territory based on the data of decoded satellite images. Materials and methods. The main sources of information for the study are materials of field geobotanical studies and satellite images of medium spatial resolution obtained from the Landsat-8 satellite, which provided an assessment of the current state of forest communities. Modern compilation and design of maps is characterized by the widespread use of GIS technologies and computer analysis methods. To decrypt and process the images, the ENVI, ArcGIS, and QGIS software complexes were used. The final processing and design of the cartographic material was carried out using the Adobe Illustrator program. Results. The southern part of the Sakhalin Island territory was chosen as the object of research, on which a large number of east-asian species, including tree and shrub forms, grow. In the Yuzhno-Sakhalinsk floristic region, the flora of the North-Japanese type prevails, and the presence of northern species characteristic of the dark coniferous taiga zone is noticeably reduced in its composition. Krillionsky district is distinguished by the presence of species common with the Southern Kuril Islands and the Hokkaido Island. The re-sults of studying the spatial distribution of forest landscapes served as a basis for identifying the most resistant to climate change plant communities, as well as those capable of rapid recovery after forest fires. Climatic conditions have been analyzed, which made it possible to identify trends in the long-term dynamics of plant communities in the territory. The analysis of long-term dynamics made it possible to identify the current state of forest communities, predict certain negative natural phenomena (fires, waterlogging, etc.). Conclusions. The dynamics of the state of forest plant communities is clearly determined by the data of satellite surveys. The map, compiled on the basis of the results of space images interpretation, reflects the spatial distribution and state of the vegetation cover of the territory, enables to make an inventory of plant associations, to establish the boundaries of geobotanical regions in their current state.

Retracted: Early Physical Linear Growth of Small-for-Gestational-Age Infants Based on Computer Analysis Method.

[This retracts the article DOI: 10.1155/2021/7227928.].

Computer modeling of robotic systems made of composite materials, analysis of the stress-strain state under dynamic influences

In the present study, a robotic system is considered: a multi-stage semi-natural simulation stand. The stand is designed to simulate the flight characteristics of aircraft instruments in laboratory conditions. The use of such stands can significantly reduce the cost of developing and testing aircraft instruments and contains all the attributes of robotic systems, which allows the developed method of computer modeling and analysis of stands to be applied in the field of robotic systems. The problem is solved by the finite element method. As a result of the study, a technique has been developed for approximating parts that carry out movements: gear rims, bearings, gearboxes, motors in the finite element method. Comparison with the available experimental studies showed the effectiveness of the developed methodology. As the stand material, a composite material is used, which has a high specific strength, which makes it possible to change the physical and mechanical characteristics depending on the location of the composite base in the layers of the multilayer composite structure. A technique has been developed for arranging the base layers in a multilayer composite to obtain maximum strength and rigidity of the stand, which is one of the main factors in the efficiency of the stand. The analysis of the behavior and stress-strain state of the bench under dynamic impact has been carried out.

Overview of single-cell RNA sequencing analysis and its application to spermatogenesis research.

Single-cell transcriptomics allows parallel analysis of multiple cell types in tissues. Because testes comprise somatic cells and germ cells at various stages of spermatogenesis, single-cell RNA sequencing is a powerful tool for investigating the complex process of spermatogenesis. However, single-cell RNA sequencing analysis needs extensive knowledge of experimental technologies and bioinformatics, making it difficult for many, particularly experimental biologists and clinicians, to use it. Aiming to make single-cell RNA sequencing analysis familiar, this review article presents an overview of experimental and computational methods for single-cell RNA sequencing analysis with a history of transcriptomics. In addition, combining the PubMed search and manual curation, this review also provides a summary of recent novel insights into human and mouse spermatogenesis obtained using single-cell RNA sequencing analyses. Single-cell RNA sequencing identified mesenchymal cells and type II innate lymphoid cells as novel testicular cell types in the adult mouse testes, as well as detailed subtypes of germ cells. This review outlines recent discoveries into germ cell development and subtypes, somatic cell development, and cell-cell interactions. The findings on spermatogenesis obtained using single-cell RNA sequencing may contribute to a deeper understanding of spermatogenesis and provide new directions for male fertility therapy.

Development and Deployment of Web Application Using Machine Learning for Predicting Intraoperative Transfusions in Neurosurgical Operations

Background and Aim: Preoperative blood product preparation is a common practice in neurosurgical patients. However, over-requesting of blood is common and leads to the wastage of blood bank resources. Machine learning (ML) is currently one of the novel computational data analysis methods for assisting neurosurgeons in their decision-making process. The objective of the present study was to use machine learning to predict intraoperative packed red cell transfusion. Additionally, a secondary objective focused on estimating the effectiveness of blood utilization in neurosurgical operations. Methods and Materials/Patients: This was a retrospective cohort study of 3,021 patients who had previously undergone neurosurgical operations. Data from the total cohort were randomly divided into a training dataset (N=2115) and a testing dataset (N=906). The supervised ML models of various algorithms were trained and tested with test data using both classification and regression algorithms. Results: Almost all neurosurgical conditions had a cross-match to transfusion ratio of more than 2.5. Support vector machine (SVM) with linear kernel, SVM radial kernel, and random forest (RF) classification had a performance with good AUC of 0.83,0.82, and 0.82, respectively, while RF regression had the lowest root mean squared error and mean absolute error. Conclusion: In almost all neurosurgical surgeries, preoperative overpreparation of blood products was detected. The ML algorithm was proposed as a high-performance method for optimizing blood preparation and intraoperative consumption. Furthermore, ML has the potential to be incorporated into clinical practice as a calculator for the optimal cross-match to transfusion ratio.

Earthing Resistance and Poldzolic Soil Resistivity at PT. Perta Samtan Gas Field Extraction Plant Prabumulih

It is important to undertake research at the location of PT. Perta Samtan Gas Field Extraction Plant Prabumulih with Podzolik soil type (yellow gravel clay) and analysis in order to prevent work accidents and the risk of fire caused by leakage current at the gas processing extraction installation. Direct measurement of soil resistance value is used as the basis for the methodology, which is followed by literature study as background information and technical comprehension of experimental research. The outcomes of the research study are then compared using Comsol simulation with the computational method of computational finite element analysis of the resulting resistance. According to the data, the resistivity of the podzolic soil was 214.4 Ωm, while the Comsol simulation yielded a resistance value per meter of 209.1 Ω and 196.7Ω, respectively. The Comsol simulation demonstrates that, in order to obtain a ground resistance value of 1 ohm on a Single rod system with Fe material, it is necessary to optimize the depth of the grounding electrode, which can range from 3 meters to 1053 meters d. In the simulation, the results of soil resistance measurements and the Comsol Multiphysics application are obtained, and the difference is calculated with a percentage error of 1.62%.