Traditional Analysis Techniques Research Articles

Correlation between air pollution and prevalence of conjunctivitis in South Korea using analysis of public big data

This study investigated how changes in weather factors affect the prevalence of conjunctivitis using public big data in South Korea. A total of 1,428 public big data entries from January 2013 to December 2019 were collected. Disease data and basic climate/air pollutant concentration records were collected from nationally provided big data. Meteorological factors affecting eye diseases were identified using multiple linear regression and machine learning analysis methods such as extreme gradient boosting (XGBoost), decision tree, and random forest. The prediction model with the best performance was XGBoost (1.180), followed by multiple regression (1.195), random forest (1.206), and decision tree (1.544) when using root mean square error (RMSE) values. With the XGBoost model, province was the most important variable (0.352), followed by month (0.289) and carbon monoxide exposure (0.133). Other air pollutants including sulfur dioxide, PM10, nitrogen dioxides, and ozone showed low associations with conjunctivitis. We identified factors associated with conjunctivitis using traditional multiple regression analysis and machine learning techniques. Regional factors were important for the prevalence of conjunctivitis as well as the atmosphere and air quality factors.

New HS-SPME-GC-BID method for the determination of volatile constituents in distilled beverages

To maintain quality control in the production of distilled beverages, it is necessary to perform several types of chromatographic analysis, where gas chromatography (GC) coupled with mass spectrometry (GC/MS) and flame ionization detector (GC-FID) have been used as traditional analysis techniques for volatile organic compounds. More recently the dielectric barrier discharge ionization detector (BID) has been coupled to GC (GC-BID) which is more sensitive than the FID and the thermal conductivity detector (TCD) for analyzing various volatile compounds. This study aims to develop a new method for determining higher alcohols, n-butanol, and ethyl acetate in cachaça and whisky samples using headspace solid-phase microextraction (HS-SPME) followed by GC-BID. The HS-SPME method was optimization with multivariate analysis. The analytical results were provided by the weighted linear Square (WLS) method for linearity correction, internal standard addition (ISA) as a calibration method for matrix effect correction, reproducibility, recovery and accuracy. The best conditions for extraction of the compounds by HS-SPME were obtained for 50 °C, 20 min and without addition of NaCl. The WLS regression allowed to obtaining satisfactory correlation coefficients (0.9632 to 0.9992). The matrix effect (ME) was verified for n-propanol, isobutanol and isoamyl in both matrices. The recovery values of the cachaça matrix ranged from 81.24 to 106.94 %, except for isoamyl alcohol, and ranged from 82.06 to 106.25 % for the whisky matrix, except for n-butane. The precision ranged from 0.66 to 7.38 % and 0.62 to 6.77 % for the cachaça and whisky samples, respectively. Limit of detection (LOD) values ranged from 0.26 to 0.51 mg L-1, while limit of quantification (LOQ) values ranged from 0.78 to 1.55 mg L-1. The validation process and statistical methods were successfully applied to quality assurance of the analysis of cachaça and whiskey samples, where n-propanol, ethyl acetate, isobutanol and isoamyl were detected for all samples within the maximum content for higher alcohols and ethyl acetate, except n-butanol. The method can be satisfactorily applied to quantify higher alcohols and esters in samples of alcoholic beverages.

Active mobility-oriented urban development: a morpho-syntactic scenario for a mid-sized town

ABSTRACT This work is based on the analysis and the assessment of morphological and syntactic characteristics of the street network of a mid-sized city: Potenza (County Seat of Basilicata region – Italy). Based on the identification of sustainable mobility options related to the selected case study, the research identifies an active mobility infrastructure framework to improve sustainable mobility policies as a component of urban planning. This research presents the methodology used for creating a ‘morpho-syntactic’ scenario for the case study town. Traditional techniques for morphological analysis (including terrain and street slope) have been integrated by mathematical graph theory-based spatial configuration measurements using the Space Syntax Analysis and Place Syntax Analysis as an extended framework oriented to assess ‘urban places’. The results obtained by this approach are useful for planning a more sustainable urban mobility scenario based on the pedestrian route network for Potenza town and represent a transferable analytical framework for similar cases.

An iterative frequency-domain envelope-tracking filter for dispersive signal decomposition in structural health monitoring

Dispersive signals are omnipresent in structural health monitoring and nondestructive testing. Due to the strong dispersion and multimodal characteristics, such signals often contain multiple components with frequency-varying group delays (GDs) overlapping in time–frequency (TF) domain, bringing great challenges to the existing signal processing methods. This paper presents a novel dispersive signal decomposition method named iterative frequency-domain envelope-tracking filter (IFETF) to accurately estimate the dispersion curves and separate overlapped components. Specifically, an envelope tracking issue is introduced in frequency domain based on dispersive signal model. Then, GDs are refined by recovered frequency envelope, and an iterative processing procedure is designed to ameliorate the TF resolution. To initialize the IFETF, an adaptive ridge detection technique is introduced to automatically estimate the number of components and their GDs with little prior knowledge. In addition, a high-quality TF representation can be constructed according to the results of IFETF, unambiguously revealing TF patterns of a multi-component dispersive signal. The proposed method was demonstrated by numerical analysis and engineering applications on broadband Lamb wave inspection and rail damage detection. Compared with traditional TF analysis techniques, the results show that the IFETF provides higher accuracy and efficiency in signal reconstruction and GD estimation.

Integration of SOM and PCA for Analyzing Sports Economic Data and Designing a Management System

Currently, sports economic data have attracted more attention because they normally exist with a high dimensionality manner that reflects the historical behavior and the potential decision trend of users or players. Traditional analysis techniques dealing with such kinds of data rely heavily on the empirical knowledge of the manager. With the development of data science, traditional experience-based knowledge barely meets the requirements of multiple features and high-dimensional data analysis. In this regard, machine learning-based data analysis techniques nowadays can give more importance to the process of extracting latent information hidden in chaotic data, which helps users make decisions and take appropriate actions. In this paper, we integrate principal component analysis (PCA) and a self-organizing map (SOM) to exploit the hidden features in the high-dimensional data. Specifically, PCA considers an orthogonal transformation operation to linearly transform the observed data to the low-dimensional one. SOM clusters the data by constructing a two-layered neural network without manual intervention and knowing the category in the training stage. The integration of PCA and SOM helps promote the research on pattern recognition and visualization of high-dimensional data. The experimental results obtained from economic data indicate the effectiveness of the combination of PCA and SOM.

Comparison of anti-peptide and anti-protein antibody-based purification techniques for detection of SARS-CoV-2 by targeted LC-MS/MS

The COVID-19 pandemic has necessitated exploration of alternative testing methods for detection of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) to ensure clinical laboratories can continue to provide critical testing results. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is established in many clinical laboratories due its high specificity and sensitivity, making it a logical alternative methodology. However, matching the sensitivity of quantitative reverse transcription-polymerase chain reaction (qRT-PCR) remains challenging, which forced utilization of antibody-based enrichment prior to targeted LC-MS/MS analysis. When utilizing antibody purification techniques, investigators must decide whether to enrich the target protein or peptides, but there are few studies comparing the two approaches to assist in this decision-making process. In this work, we present a comparison of intact protein and peptide antibody-based purification for LC-MS/MS based detection of SARS-CoV-2. We have found that protein purification yields more intense LC-MS/MS signals, but is also less specific, yielding higher noise and more background when compared to peptide purification techniques. Therefore, when using traditional data analysis techniques, the enrichment technique that provides superior sensitivity varies for individual peptides and no definitive overall conclusion can be made. These observations are corroborated when using a novel machine learning approach to determine positive/negative test results, which yielded superior sensitivity when using protein purification, but better specificity and area under the ROC curve when performing peptide purification.

“Should I stay or should I go?” Nurses' perspectives about working during the Covid-19 pandemic's first wave in the United States: A summative content analysis combined with topic modeling

BackgroundThe COVID-19 pandemic had its first peak in the United States between April and July of 2020, with incidence and prevalence rates of the virus the greatest in the northeastern coast of the country. At the time of study implementation, there were few studies capturing the perspectives of nurses working the frontlines of the pandemic in any setting as research output in the United States focused largely on treating the disease. ObjectiveThe purpose of this study was to capture the perspectives of nurses in the United States working the frontlines of the COVID-19 pandemic's first wave. We were specifically interested in examining the impact of the pandemic on nurses' roles, professional relationships, and the organizational cultures of their employers. DesignWe conducted an online qualitative study with a pragmatic design to capture the perspectives of nurses working during the first wave of the United States COVID-19 pandemic. Through social networking recruitment, frontline nurses from across the country were invited to participate. Participants provided long form, text-based responses to four questions designed to capture their experiences. A combination of Latent Dirichlet Allocation--a natural language processing technique--along with traditional summative content analysis techniques were used to analyze the data. SettingThe United States during the COVID-19 pandemic's first wave between May and July of 2020. ResultsA total of 318 nurses participated from 29 out of 50 states, with 242 fully completing all questions. Findings suggested that the place of work mattered significantly in terms of the frontline working experience. It influenced role changes, risk assumption, interprofessional teamwork experiences, and ultimately, likelihood to leave their jobs or the profession altogether. Organizational culture and its influence on pandemic response implementation was a critical feature of their experiences. ConclusionsFindings suggest that organizational performance during the pandemic may be reflected in nursing workforce retention as the risk for workforce attrition appears high. It was also clear from the reports that nurses appear to have assumed higher occupational risks during the pandemic when compared to other providers. The 2020 data from this study also offered a number of signals about potential threats to the stability and sustainability of the US nursing workforce that are now manifesting. The findings underscore the importance of conducting health workforce research during a crisis in order to discern the signals of future problems or for long-term crisis response. Tweetable abstractHealthcare leaders made the difference for nurses during the pandemic. How many nurses leave their employer in the next year will tell you who was good, who wasn't.

Classification of Alzheimer’s Disease and Mild-Cognitive Impairment Base on High-Order Dynamic Functional Connectivity at Different Frequency Band

Functional brain connectivity networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI) have been extensively utilized for the diagnosis of Alzheimer’s disease (AD). However, the traditional correlation analysis technique only explores the pairwise relation, which may not be suitable for revealing sufficient and proper functional connectivity links among brain regions. Additionally, previous literature typically focuses on only lower-order dynamics, without considering higher-order dynamic networks properties, and they particularly focus on single frequency range time series of rs-fMRI. To solve these problems, in this article, a new diagnosis scheme is proposed by constructing a high-order dynamic functional network at different frequency level time series (full-band (0.01–0.08 Hz); slow-4 (0.027–0.08 Hz); and slow-5 (0.01–0.027 Hz)) data obtained from rs-fMRI to build the functional brain network for all brain regions. Especially, to tune the precise analysis of the regularized parameters in the Support Vector Machine (SVM), a nested leave-one-out cross-validation (LOOCV) technique is adopted. Finally, the SVM classifier is trained to classify AD from HC based on these higher-order dynamic functional brain networks at different frequency ranges. The experiment results illustrate that for all bands with a LOOCV classification accuracy of 94.10% with a 90.95% of sensitivity, and a 96.75% of specificity outperforms the individual networks. Utilization of the given technique for the identification of AD from HC compete for the most state-of-the-art technology in terms of the diagnosis accuracy. Additionally, results obtained for the all-band shows performance further suggest that our proposed scheme has a high-rate accuracy. These results have validated the effectiveness of the proposed methods for clinical value to the identification of AD.

Lexical semantics of the concept “Steppe” in the artwork

The article discusses the cognitive-semantic level of a literary work that is verbalized in the author’s text through semantic content, which represents the concept under consideration. Idiostylistic features of the concept “Steppe” in the classic trilogy of Ilyas Esenberlin “The Nomads” (the article analyzes the first book “The Charmed Sword”) reflect the special ethnic worldview of nomads, representing an inseparable connection to the environment. The article is conceived as an effort to expand current approaches to analyzing the literary composition concept, the concept semantics correlation with denotative-referential meaning and conceptual spirit of the text. Collectively it helps to understand the universal and unique world of a literary text as a whole. The article considers expression features, lexical semantics and cognitive interpretation of the concept within the text semantic space through the cognitive-semantic approach, combining traditional semantic and conceptual analysis techniques. The conducted content analysis allows to highlight specific thematic aspects of the semantic space conceptualization, identify significant categories and phenomena of reality expressed by means of language. The results and conclusions proposed in the scientific article can serve as additional material in the field of linguocognitive research for further development of methods for studying the semantic space of a literary text. Key words: Conceptual analysis, concept, reflective semantics, verbalization, means of expression.

A Novel Approach for Band Selection Using Virtual Dimensionality Estimate and Principal Component Analysis for Satellite Image Classification

Images, being around us in every aspect of life, have become an emerging field of research. Extensive image analysis has been done on binary as well as coloured images, which has led various researchers to explore images having deep spectral knowledge about a particular area of interest. High resolution images, having more than three spectral bands, capture minute details of an object in various spectral bands resulting in high computational complexity. In this paper, the authors have tried to reduce the complexity of multispectral image by selecting only the relevant bands need to reconstruct an image. Traditional principal component analysis technique is used for band selection of true color bands and classification-assessed results of both the images; original and dimensionality reduced images are compared using partitioning clustering technique. Experimental results show that compressed image after reduction of bands by PCA yields better classification results than the original image.

Molecular Diversity Assessment using Chemotypes.

Many techniques to design chemical libraries for screening have been put forward over time. General use libraries are still important when screening against novel targets, and their design has relied on the use of molecular descriptors. In contrast, chemotype or scaffold analysis has been used less often. We describe a simple method to assess chemical diversity based on counts of the chemotypes that offers an alternative to model chemical diversity. We describe a simple method to assess chemical diversity based on counts of the chemotypes that offers an alternative to model chemical diversity based on computed molecular properties. We show how chemotype counts can be used to evaluate the diversity of a library and compare diversity selection algorithms. We demonstrate an efficient compound selection algorithm based on chemotype analysis. We use automated chemotype perception algorithms and compare them to traditional techniques for diversity analysis to check their effectiveness in designing diverse libraries for screening. The best type of molecular fingerprints for diversity selection in our analysis are extended circular fingerprints, but they can be outperformed by the use of a chemotype diversity algorithm, which can be more intuitive than traditional techniques based on molecular descriptors. Chemotype- -based algorithms retrieve a larger share of the chemotypes contained in a library when picking a subset of the chemicals in a collection. Chemotype analysis offers an alternative for the generation of a general-purpose screening library as it maximizes the number of chemotypes present in a subset with the smallest number of compounds. The applications of methods based on chemotype analysis that does not resort to the use of molecular descriptors are a very promising but seldom explored area of chemoinformatics.

Risk Assessment of Port Congestion Risk during the COVID-19 Pandemic

With the COVID-19 outbreak hitting the world, the frequency and severity of port congestion caused by various factors are increasing, challenging the stability of international supply chains. Thus, it is necessary to conduct an in-depth study on congestion risks to reduce their adverse impacts on congestion. Although traditional criticality analysis techniques may be capable of ranking port congestion risk in common scenarios, new risk analysis methods are urgently required to tackle uncertainty along with the COVID-19 pandemic. This paper develops a methodology designed for the identification and prioritization of port congestion risk during the pandemic. First, a novel congestion risk assessment model is established by extending the risk prioritization index (RPI) suggested by failure mode and effects analysis (FMEA). Next, the combination of fuzzy Bayesian reasoning, AHP and the variation coefficient method is incorporated into the model in a complementary way to facilitate the treatment of uncertainty and quantitative analysis of the congestion under the different influence of risk factors in ports. Finally, the mode introduces a set of risk utility values for calculating the RPI for prioritization. A real case study and a sensitivity analysis were carried out to illustrate and validate the proposed model. The results proved that the applied method is feasible and functional. In the illustrative example, the top three risk factors are “Interruption of railways/barges services”, “Skilled labor shortage” and “Shortage of truck-drivers/drayage truck”. The findings obtained from this paper could provide useful insights for risk prevention and mitigation.

CiftiTools: A package for reading, writing, visualizing, and manipulating CIFTI files in R

There is significant interest in adopting surface- and grayordinate-based analysis of MR data for a number of reasons, including improved whole-cortex visualization, the ability to perform surface smoothing to avoid issues associated with volumetric smoothing, improved inter-subject alignment, and reduced dimensionality. The CIFTI grayordinate file format introduced by the Human Connectome Project further advances grayordinate-based analysis by combining gray matter data from the left and right cortical hemispheres with gray matter data from the subcortex and cerebellum into a single file. Analyses performed in grayordinate space are well-suited to leverage information shared across the brain and across subjects through both traditional analysis techniques and more advanced statistical methods, including Bayesian methods. The R statistical environment facilitates use of advanced statistical techniques, yet little support for grayordinates analysis has been previously available in R. Indeed, few comprehensive programmatic tools for working with CIFTI files have been available in any language. Here, we present the ciftiTools R package, which provides a unified environment for reading, writing, visualizing, and manipulating CIFTI files and related data formats. We illustrate ciftiTools’ convenient and user-friendly suite of tools for working with grayordinates and surface geometry data in R, and we describe how ciftiTools is being utilized to advance the statistical analysis of grayordinate-based functional MRI data.

An Iootfuzzer Method for Vulnerability Mining of Internet of Things Devices based on Binary Source Code and Feedback Fuzzing

With the technological progress of the Internet and 5G communication network, more and more Internet of Things devices are used in it. Limited by the cost, power consumption and other factors of Internet of Things devices, the systems carried by the Internet of Things devices often lack the security protection provided by larger equipment systems such as desktop computers. Because the current personal computers and servers mostly use the x86 architecture, and the previous research on security tools or hardware-based security analysis feature support is mostly based on the x86 architecture, the traditional security analysis techniques cannot be applied to the current large-scale ARM-based and MIPS-based Internet of Things devices. Based on this, this paper studies the firmware binary program of common Linux-based Internet of Things devices. A binary static instrumentation technology based on taint information analysis is proposed. The paper also analyzes how to use the binary static instrumentation technology combined with static analysis results to rewrite binary programs and obtain taint path information when binary programs are executed. Firmware binary fuzzing technology based on model constraints and path feedback is studied to cover more dangerous execution paths in the target program. Finally, iootfuzzer, a prototype vulnerability mining system for firmware binaries of Internet of Things devices, is used to test and analyze the two technologies. The results show that its fuzzing efficiency for Internet of Things devices is better than other fuzzing technologies such as boofuzz and Peach 3. It can fill in some gaps in the current security analysis tools for the Internet of Things devices and improve the efficiency of security analysis for Internet of Things devices, which contributes to the field through automated security vulnerability detection systems.

SMTD Co-Simulation Framework With HELICS for Future-Grid Analysis and Synthetic Measurement-Data Generation

The power grid is transforming with large amounts of distributed energy resource (DER) integration that is impacting the bulk power system planning and operations. Grid regulators have identified that traditional power system analysis techniques are challenged under the changing grid conditions. Transmission and distribution (T&D) co-simulation is an effective methodology to accurately account for the changing power-grid. A scalable multitimescale T&D (SMTD) co-simulation framework is developed with commercial transmission system solver that can handle large-scale system models, and distribution system solver that can model the distribution systems in detail with dynamic models of load and DER. In this article, PSS/E and GridLAB-D are combined using a Python interface with Hierarchical Engine for Large-scale Infrastructure Co-Simulation software to enable T&D co-simulation. This framework is scalable and parallel computing compatible that enables large-scale system simulation and adoption of T&D co-simulation by the industry and utilities. The SMTD cosimulator is designed to integrate with existing system models and new ones. The article also highlights functional specifications for the T&D co-simulation solvers and models along with providing the algorithms for the steady state, quasi-steady state, and dynamic T&D co-simulation. In context of changing power grid, future power grid planning, validation of controls under critical contingencies and validation of various wide area monitoring and controls can effectively utilize the proposed framework that can capture the T&D interactions accurately. The SMTD cosimulator can also be used to generate realistic synthetic measurement-data with noise and packet drop for uncommon grid events enabling development and validation of power system data analytics and controls.

Sentiment Analysis of Weibo Comments Based on Graph Neural Network

Weibo is one of the most important online social platforms. Currently, user comments are increasing rapidly, which makes data management difficult. Comments show the non-standardized and colloquial form of expression. Traditional sentiment analysis techniques are no longer applicable to unspecified sentence analysis tasks. To mitigate overreliance on text sequences, ignoring syntactic structure, and the poor interpretability of feature space that are typical of traditional classification models, a sentiment classification model based on a graph neural network (GNN) is developed in this study. For each comment text, the dependency syntax is used to construct the semantic graph of the short text. Aiming at the heterogeneity of the semantic graph, the spatial domain graph filter is designed for feature extraction. Concurrently, long short-term memory (LSTM) is used as a state updater to filter node noise. In this method, a graph neural network is used as a semantic parser to encode the syntactic dependency tree, which can extract the semantic and syntactic features of sentences concurrently. Experimental results show that GNN-LSTM has achieved superior performance in the Weibo comments dataset by achieving 95.25% accuracy and 95.22% F1 score.

Using temporal network methods to reveal the idiographic nature of development.

Averages dominate developmental science: There are representative groups, mean trajectories, and generalizations to typical children. Nearly all parents and teachers, however, eagerly proclaim that few youth are average; each child, adolescent, and young adult is unique. Indeed, individual youth are the focus of many eminent developmental theories, yet there is a shocking paucity of developmental methods-including study designs and analysis techniques-that truly afford individual-level inferences. Thus, the goal of this chapter is to explicate the advantages of an idiographic approach to developmental science, that is, an approach that provides insight into individual youth, often by studying within-person variation in intensive longitudinal data, such as densely coded observations, repeated daily or momentary assessments, and functional neuroimages. In three domains across development, the chapter illustrates the benefits of an idiographic approach by comparing empirical conclusions offered by traditional mean-based analysis techniques versus techniques that leverage the temporal and individualized nature of intensive longitudinal data. The chapter then concentrates on group iterative multiple model estimation (GIMME), which is an analysis technique that uses intensive longitudinal data to create youth-specific temporal networks, detailing how brain regions or behaviors are directionally related across time. The promise of GIMME is exemplified by applications to three different domains across development. The chapter closes by encouraging future idiographic developmental science to consider how research questions, study designs, and data analyses can be formed, implemented, and conducted in ways that optimize inferences about individual-not average-youth.

A new methodology for assessing human contributions to occurrences (MAHCO) in Air Traffic Management utilising a Bayesian hierarchical predictive coding approach to the brain, and the benefits for just culture

The traditional approaches to classifying Human Error have provided a strong and beneficial approach to understanding the roles of humans in safety occurrences. However, as the demand for human performance has increased and with the rise of more automation, the line between man and machine continues to become increasingly blurred. Traditional Human Error analysis techniques are no longer sufficient for extracting the most valuable learning data from occurrences. Furthermore, there is a continuing need to strengthen Safety and Just culture. One means to achieving this is through developing ever greater trust in the ability of organisations to accurately investigate and report occurrences. This paper outlines a new 10 step approach to investigating human contribution to occurrences which is based on a version of predictive situational awareness in the brain that is derived from hierarchical predictive coding approaches. Utilising statistically based induction, the steps allow investigators to make an objective reconstruction of sub-conscious human error analysis and integrate it with traditional subjective feedback on conscious decisions made. Examples of system data that can provide further evidence to support the reconstructions is also discussed. This approach has been tested on more than 50 human factors related occurrences in Air Traffic Control in 2021/2022 and the results are introduced. It is further argued that using a refined approach and educating operators on the approach can significantly benefit Just Culture and lead to increases in trust and frequency of reporting because it will allow for the removal of biases by individual investigators. Future research should focus on validating the approach against current methods and testing it for occurrences further away from front line operators such as in non-safety related decision making.

Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome.

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Many HLHS patients develop tricuspid regurgitation and right ventricle enlargement which is associated with heart failure and death without surgical intervention on the valve. Understanding the connections between the geometry of the TV and its function remains extremely challenging and hinders TV repair planning. Traditional analysis methods rely on simple anatomical measures which do not capture information about valve geometry in detail. Recently, surface-based shape representations such as SPHARM-PDM have been shown to be useful for tasks such as discriminating between valves with normal or poor function. In this work we propose to use skeletal representations (s-reps), a more feature-rich geometric representation, for modeling the leaflets of the tricuspid valve. We propose an extension to previous s-rep fitting approaches to incorporate application-specific anatomical landmarks and population information to improve correspondence. We use several traditional statistical shape analysis techniques to evaluate the efficiency of this representation: using principal component analysis (PCA) we observe that it takes fewer modes of variation compared to boundary-based approaches to represent 90% of the population variation, while distance-weighted discrimination (DWD) shows that s-reps provide for more significant classification between valves with less regurgitation and those with more. These results show the power of using s-reps for modeling the relationship between structure and function of the tricuspid valve.

Analysis of SARS-CoV-2 cases in the State of Guanajuato during the third wave of infections using advanced information analysis techniques

The State of Guanajuato, located in the center of Mexico, is one of the regions of the country with a high rate of infections of the SARS-CoV-2 virus in relation to its population size, according to official data provided by the federal government. Motivated by this fact, we undertook to further analyze such data in order to identify correlations between a possible complication of the COVID-19 disease, caused by the SARS-CoV-2 virus, and some non-transmissible chronic diseases and other comorbidities. To carry out our study, we rely on the KDD methodology and specialized machine-learning tools, that allow to extract hidden knowledge in the data, which cannot usually be obtained using traditional information analysis techniques. In this way, initially, the cases infected by the SARS-CoV-2 virus were characterized in a general way and, later, classification models were built to identify some rules among the comorbidity variables.