Analysis Package Research Articles

ЕКОНОМІКО-СТАТИСТИЧНЕ МОДЕЛЮВАННЯ В АНАЛІТИЦІ І ПРОГНОЗУВАННІ БЮДЖЕТУ МАРКЕТИНГОВИХ КОМУНІКАЦІЙ: МЕТОДИЧНИЙ АСПЕКТ

In the conditions of global challenges, the problem of justified management decision-making, information and analytical support for forecasting marketing results is becoming increasingly relevant for enterprises operating in the conditions of a market economy. The purpose of the article is the methodical justification of building a model that allows forecasting the costs of marketing communication appeals of an enterprise to achieve the desired market share based on data on the market share of the totality of enterprises in the industry, activity costs and the specific weight of the costs of communication appeals in the total amount of expenses. According to the well-known Weinberg model in the theory of marketing analysis and forecasting, as a factor characteristic that should determine the size of the marketing communications budget, an indicator reflecting the ratio of the share of marketing communications costs in the total amount of costs of the investigated company and the company of the competitor, which is the leader in terms of market share, was chosen. The company's share on the market was chosen as an effective indicator. The presented model building and forecasting algorithm was developed on the basis of the application of a formalized method and regression analysis, which made it possible to analytically describe the statistical correlation dependence between the variation of the resulting and factor characteristics and to evaluate the adequacy of the obtained linear pair model on the basis of variance analysis. Regression analysis was carried out using the available toolkit of the Excel spreadsheet analysis package. The approach to building the Weinberg model substantiated in the article can be easily implemented in practice to analyze the results of the company's marketing activities and develop and forecast the marketing communications budget to achieve the desired market share. The practical significance of using the proposed model in practice is to ensure an increase in the efficiency of costs directed to communication activities in marketing in order to achieve the desired marketing results and strengthen the company's position on the market.

Analysis Tools in Geant4

Analysis Tools in Geant4

Assessment of hydrophobic properties of oil-contaminated soils using the «Water Spot» method

The aim of the study was to assess the hydrophobicity degree of oil-contaminated soils using the Water Spot method. Location and time of the study. The study was carried out in 2024 in laboratory conditions with typical chernozem and gray forest soil. Methods. The sieved (2 mm, air-dried and milled soil samples were amended with oil in amounts needed to established the chosen concentration (0, 0,2, 0,5, 1, 3 and 5% on the mass basis). Then hexane and distilled water were added to reduce oil viscosity and increase soil moisture, respectively. After two days of incubating soil, sodium fluorescein solution was added onto the sample surface to visualize spot boundaries. The images were taken in a dark place under the ultraviolet light. To determine the effectiveness of chitosan (as a natural sorbent), a second series of experiments was performed in the same setup, but with chitosan addition, followed with two days incubation. Sodium fluorescein was then applied, and the area of the resulting spot was imaged. The resulted images were processed using the CorelDRAW software and the Microsoft Excel analysis package. Results. Oil contamination of soils usually disturbs the water regime. Increase in petroleum products content from 0,5 to 5% leads to decrease in absorbency and the area of the water spot on the soil surface. After treating the contaminated soil samples with 0,1% chitosan solution, the water spot area increased on average by 43% on the gray forest soil, and by 6% on typical chernozem. With a twofold increase in the concentration of chitosan solution, the water spot area increased on the gray forest soil by an average of 48% and by 46% on typical chernozem. Thus chitosan solution can be used as a sorbent when soil is contaminated with oil products. Conclusions. The water spot method is reproducible, simple, and can be used to assess soil contamination with petroleum products. The results show that with an increase in the degree of pollution, a decrease in the area of the water spot on the soil is observed.

Effect of Airport Efficiency Dimensions on International Tourists’ Satisfaction in Sri Lanka; with Reference to Bandaranaike International Airport

Bandaranaike International Airport plays a significant role rather than other international airports in Sri Lanka. Maintaining service efficiency and quality of the service have been challenged when it comes to passenger service delivery for last few years. It was a well-known fact that Bandaranaike International Airport has been listed out to the most inefficient international airports in South Asian Region. By using quantitative research approach and deductive reasoning the researcher has incorporated SPSS with Smart PLS statistical packages for the data analysis. Using simple and multiple linear regression analysis, the nature of the relationships has been measured between airport efficiency dimensions and international tourist satisfaction. 150 foreign tourists have participated for the sample and as for the findings, the researcher has found that, all the hypotheses are accepted denoting that relationships between independent variables and dependent variable are positive relationships and security is the most influential airport efficiency dimension.

SATLAS2: An update to the package for analysis of counting data

SATLAS2 is a Python library that enables the user to fit counting data from laser spectroscopy experiments, in particular those that measure atomic hyperfine structures. In this analysis, the user can choose how the uncertainties are treated and can also opt to generate a random walk in order to present a fuller picture of the parameter space. The major upgrade compared to the previous version of SATLAS [1] is the different architecture of the codebase, which enabled a performance boost, with speed-up factors ranging from 20 to 300 times for various use cases. For backward compatibility, a translation layer between the two architectures is available, implementing only the core functionality of SATLAS. New version program summaryProgram Title: SATLAS2CPC Library link to program files:https://doi.org/10.17632/3hr8f5nkhb.2Developer's repository link:https://github.com/IKS-nm/satlas2Licensing provisions: MITProgramming language: PythonJournal reference of previous version: Computer Physics Communications, Volume 222, 2018, Pages 286-294, ISSN 0010-4655, https://doi.org/10.1016/j.cpc.2017.09.012Does the new version supersede the previous version?: YesReasons for the new version: Improved and more stable performanceSummary of revisions: The architecture of the SATLAS package has been completely changed. Instead of operating on Models, using SumModel and LinkedModel for various analysis options, SATLAS2 works with a central Fitter object to which Sources get assigned, which themselves get assigned Models. This structure improves the performance by streamlining the code and benefits from using a pass by reference approach for changing the parameters, rather than a pass by value. Most of the functionality is maintained, with a small interface for most basic things available so SATLAS2 can be used as a drop-in replacement.Nature of problem: The analysis of specifically counting data has some special considerations compared to more common datasets with Gaussian-distributed uncertainties. Application of Bayesian inference through random walk exploration is useful for more accurate exploration of parameter space. The fitting of multiple models with shared parameters can be desirable for either fitting multiple datasets with the same parameters or imposing additional restrictions on the parameters.Solution method: SATLAS2 implements the correct statistical costs for fitting of both counting data and Gaussian distributed uncertainties, and allows an easy implementation of custom cost functions.. Through the implementation of multiple Sources and Models, a natural extension of the fitting is made, so multiple models can be both summed together to fit to the same data and multiple fits can be linked together through parameter linking, using the underlying LMFIT [2] library. The ability to fit simultaneously increases the possibilities for extensive statistical analyses, and the interface through LMFIT with the emcee [3] library enables Bayesian exploration of the parameter space.

THE GREEN PORTS FUTURE KNOWLEDGE MAP AND TREND

The purpose of this study is to track the emergence of research related to green ports over time and determine the knowledge map and trends of green port research topics in the future using thematic map bibliometric analysis. This study also provides an overview of the researchers and contributors of research countries in green port research globally. This research provides a comprehensive bibliometric analysis of historical developments and current trends in green port research, by examining 462 articles published from 1991-2023 on the Scopus database. This research uses R studio Cloud bibliometric analysis and biblioshiny package. It was found that there are three phases in green port research, namely the initial stagnation phase (1991-2006), infant growth (2007-2015), and expanding phase (2016-2022), the number of articles increased quite rapidly in the third phase until now. This research also highlights related to authors, contributor countries and it was found that the country of China contributed the most contributor authors compared to other countries, this is because China is a country that places many container ports as the top 10 largest container ports in the world, with a total market share reaching 70% of the global. From this analysis, it is found that the knowledge map and the direction of future topics related to green ports are “sustainable development”, “port development”, “port operation”, “carbon emissions” and “renewable energy resources”, besides that it is also found that there is a direction of the current green port and “smart port” research trends. So, it can be recommended that in the future these topics will be very relevant to develop, the themes of research themes needed include alternative strategies in net zero emission (NZE) in ports in the operational and development aspects of the port.

Occupational Stress and Coping Strategies among Self Financing Arts and Science College Faculties in Erode

The aim of the study is to find out the stress management level of arts and science college teachers in Erode City. The teachers are the assets of any country. They are burdened with the greatest responsibility of ‘Nation Building’. The teaching profession has an esteemed place among all professions. However, teaching as a profession at any level is becoming more demanding day by day. Teaching has also become more like a managerial job and is having multi-dimensional job responsibilities. Stress can’t be avoided, but one can learn how to manage it. Policy makers are advised to analyze the college teacher training and assessment system with the assumption that personal and social characteristics and working conditions may have an effect on college teacher stress. The present paper discusses the various emerging issues of occupational stress among faculties of higher education in India and required management strategies to handle them. Sample teachers were selected for simple random sampling by using lottery method because of easy accessibility and affordability analyzed by using statistical package of simple percentage and chi square analysis.

STUDY OF THE RISK OF PARENTS’ DISSATISFACTION WITH THE QUALITY OF PROVIDING MEDICAL AID TO CHILDREN WITH RESPIRATORY DISEASES

Introduction. Reforming the health care system requires defining criteria for assessing patients’ dissatisfaction with the quality of medical care. It is known that the previous experience of communication with a doctor affects the quality of further interaction and compliance with medical recommendations.
 The aim was to predict the risk of parents’ dissatisfaction with the quality of medical care for children with respiratory diseases, depending on the number and reasons for visits to pediatricians and family general practitioners (GP), by the results of the questionnaire.
 Materials and methods. Overall, 171 parents were questioned (87.72 %). The analysis was by logistic regression models using the statistical analysis package MedCalc v.19.4.1 (MedCalc Software Inc, Broekstraat, Belgium, 1993-2020).
 Results. The results of the study show that 125 (83.3 %) parents rated satisfaction with the quality of medical care for children with respiratory diseases (RD) in health care facilities as 5 points by a five-point scale.
 The univariate analysis showed a significant relationship between the risk of parents’ dissatisfaction with the quality of medical care for RD children and the number of ARI visits to the GPs per year (p<0.05), namely: the increase in the number of visits is associated with significant decrease in parental dissatisfaction with quality of medical care, for every next visit (p = 0.040), OR = 0.31 (95 % CI 0.10-0.95). The area under the curve of operational characteristics AUC = 0.66 (95 % CI 0.58-0.74) indicates a weak relationship between the risk of parental dissatisfaction and the number of ARI visits to the GPs per year. When using the stepwise regression, 4 factor features were selected for multivariate analysis, namely: the number of cases of acute respiratory viral infections in a child per year, the number of pneumonias in a child during life, the number of visits to the PHC (primary health care) physician, and to the PHC pediatrician per year. According to this model, an increase in the number of visits to the GP is associated with decrease in parents’ dissatisfaction with the quality of medical care, for each repeated visit (when standardizing by the number of ARI cases in a child per year, the number of pneumonia cases in a child, and the number of visits to the PHC pediatrician) (p = 0.026), OR = 0.25 (95 % CI 0.08-0.85).
 Conclusions. The multifactorial factor logistic regression of the parental dissatisfaction with the quality of medical care for ARI children included 4 factors, including the number of cases of acute respiratory viral infections in a child per year, the number of pneumonias in a child during life, the number of visits to the GP, and to the PCH pediatrician per year. For every next RD-associated visit to the physician, the risk of dissatisfaction with the medical care quality significantly reduces. Positive previous experience of communication between parents and the GP leads to further satisfaction with the quality of medical care for RD children.

MODELS FOR PREDICTING THE RISK OF DISSATISFACTION OF GENERAL PRACTITIONERS-FAMILY DOCTORS WITH WORK IN PRIMARY HEALTH CARE CENTERS

The aim of the study was to determine the model for predicting the risk of dissatisfaction of general practitioners (GPs) with their work in primary health care centers based on the results of a questionnaire.
 Materials and methods. A survey of 89 GPs was conducted using a questionnaire to determine job satisfaction. Each question of the questionnaire was evaluated by the respondents on the Likert total ratings on a 5-point scale. Job satisfaction was assessed at 4 and 5 points, dissatisfaction at 1-3 points.
 The logistic regression analysis was used to build a model for risk prediction of GPs’ job dissatisfaction. To assess the degree of each factor influence in the logistic regression model, the odds ratio (OR) and its 95 % CI (confidence interval) were calculated. Receiver Operating Characteristic analysis was used to assess the quality of the logistic regression model. The analysis of the research results was carried out using the license package the statistical analysis package MedCalc v.19.4.1 (MedCalc Software Inc, Broekstraat, Belgium, 1993-2020).
 The results show that 76.06 % of GPs are satisfied with their work. The vast majority are satisfied with the attitude of the management staff, fellow GPs, patients, opportunities of professional development and advanced training, work schedule, working conditions, and team spirit. At the same time, more than half of GPs (54.92 %) are dissatisfied with the comfort of business relations with other subspecialists, 36.61 % of doctors are dissatisfied with the salary, 40.84 % believe that the salary does not correspond to the efforts made, and 45.07 % think that additional financial compensation does not affect the quality of medical care provided. The method of stepwise inclusion/exclusion of variables was used to select a set of features for the multivariate logistic regression model. As a result of this method, 2 factors were selected: a sense of pride in work and working conditions. We established that the more a GP is proud of his chosen specialty and the better working conditions he has, the less dissatisfied he feels with his work. We determined the sensitivity of the model as 76.5 % (95 % CI: 50.1 % – 93.2 %), the specificity as 85.2 % (95 % CI: 72.9 % – 93.4 %), and the diagnostic accuracy as 83.1 % (CI: 72.3 % – 91 %).
 Conclusions. In the personnel management system of health care institutions, it is necessary to consider many different factors of GPs’ satisfaction with their work, including salaries, relationships with colleagues, patients, the opportunities of professional development, the attitude of management staff, a sense of respect, and working conditions. Forecasting the risk of GPs’ dissatisfaction with their work must be carried out taking into account the presence of doctors’ subjective sense of pride in their work and working conditions.

Identification of hub genes based on integrated analysis of single-cell and microarray transcriptome in patients with pulmonary arterial hypertension

BackgroundPulmonary arterial hypertension (PAH) is a devastating chronic cardiopulmonary disease without an effective therapeutic approach. The underlying molecular mechanism of PAH remains largely unexplored at single-cell resolution.MethodsSingle-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database (GSE210248) was included and analyzed comprehensively. Additionally, microarray transcriptome data including 15 lung tissue from PAH patients and 11 normal samples (GSE113439) was also obtained. Seurat R package was applied to process scRNA-seq data. Uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification, and the SingleR package was performed for cell annotation. FindAllMarkers analysis and ClusterProfiler package were applied to identify differentially expressed genes (DEGs) for each cluster in GSE210248 and GSE113439, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) were used for functional enrichment analysis of DEGs. Microenvironment Cell Populations counter (MCP counter) was applied to evaluate the immune cell infiltration. STRING was used to construct a protein-protein interaction (PPI) network of DEGs, followed by hub genes selection through Cytoscape software and Veen Diagram.ResultsNineteen thousand five hundred seventy-six cells from 3 donors and 21,896 cells from 3 PAH patients remained for subsequent analysis after filtration. A total of 42 cell clusters were identified through UMAP and annotated by the SingleR package. 10 cell clusters with the top 10 cell amounts were selected for consequent analysis. Compared with the control group, the proportion of adipocytes and fibroblasts was significantly reduced, while CD8+ T cells and macrophages were notably increased in the PAH group. MCP counter revealed decreased distribution of CD8+ T cells, cytotoxic lymphocytes, and NK cells, as well as increased infiltration of monocytic lineage in PAH lung samples. Among 997 DEGs in GSE113439, module 1 with 68 critical genes was screened out through the MCODE plug-in in Cytoscape software. The top 20 DEGs in each cluster of GSE210248 were filtered out by the Cytohubba plug-in using the MCC method. Eventually, WDR43 and GNL2 were found significantly increased in PAH and identified as the hub genes after overlapping these DEGs from GSE210248 and GSE113439.ConclusionWDR43 and GNL2 might provide novel insight into revealing the new molecular mechanisms and potential therapeutic targets for PAH.

TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data

BackgroundPan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME), and methylation becomes available based on the multi-omics data from The Cancer Genome Atlas Program (TCGA). Some online tools provide analysis of gene and protein expression, mutation, methylation, and survival for TCGA data. However, these online tools were either Uni-functional or were not able to perform analysis of user-defined functions. Therefore, we created the TCGAplot R package to facilitate perform pan-cancer analysis and visualization of the built-in multi-omic TCGA data.ResultsTCGAplot provides several functions to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, TIME, and promoter methylation. Functions for visualization include paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot. Moreover, gene set based pan-cancer and tumor specific analyses were also available. Finally, all these built-in multi-omic data could be extracted for implementation for user-defined functions, making the pan-cancer analysis much more convenient.\\ConclusionsWe developed an R-package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. The source code and pre-built package are available at GitHub (https://github.com/tjhwangxiong/TCGAplot).

The Ionospheric Plasma Perturbations before a Sequence of Strong Earthquakes in Southeast Asia and Northern Oceania in 2018

From August to October 2018, a series of strong earthquake (EQ) events occurred in southeast Asia and northern Oceania (22°S to 0°N, 115°E to 170°E) within 50 days. In this paper, we analyze the features of ionospheric plasma perturbations, recorded by the Plasma Analyzer Package (PAP) and Langmuir probe (LAP) onboard the China Seismo-Electromagnetic Satellite (CSES-01), before four EQs with magnitudes of Ms 6.9 to Ms 7.4. The ion parameters such as the oxygen ion density (No+), the ion drift velocity in the vertical direction (Vz) under the conditions of geomagnetic storms, and strong EQs are compared. The results show that within 1 to 15 days before the strong EQs, the No+ and the electron density (Ne) increased while the electron temperature (Te) decreased synchronously. Meanwhile, the Vz significantly increased along the ground-to-space direction. The relative variation of No+ and Vz before the strong EQs is more prominent, and the Vz is not easily influenced by the geomagnetic storm but is susceptible to the seismic activities. Our results suggest that the anomaly of ionospheric plasma perturbations occurring in this area is possibly related to the pre-EQ signatures.

Characteristics of erythropoiesis in sows during different periods of the production cycle

The tactics of periodic monitoring of the hematology of sows allows timely measures to prevent anemia, which inevitably occurs, since the targeted screening and selection of industrial breeds are aimed at multiple births, which increases the risks of anemia during pregnancy and suckling period. With periodic and constant monitoring, it is possible to deduce individual reference intervals of hematological indicators for a particular animal and control the response to compensatory measures that directly affects the yield of viable piglets and their survival rate. The purpose of this study was to study the number of red blood cells, hemoglobin level, hematocrit, average volume of red blood cells and erythrocyte indices depending on the production cycle of the sow. The groups were formed from sows on the 80th day of pregnancy (group 1, n=5), after farrowing (group 2, n=5) and before weaning piglets (group 3, n=5). The Vet ABCTM analyzer (Horiba Medical Poland) was used. Counting of reticulocytes by “supravital staining” with a solution of “brilliant cresyl blue”. Statistical processing in the Microsoft Excel program (Data Analysis package), evaluation of the reliability of differences based on the Mann—Whitney U criterion. As a result, it was found that the average volume of red blood cells increases with a decrease in their number and hemoglobin concentration, which is quite natural and indicates a regenerative response with the appearance of reticulocytes in the blood. The correlation coefficient between erythrocytes and the average volume of erythrocytes was r=-0.53 (r2=0.28, p=0.01), between the average volume of erythrocytes and reticulocytes r=-0.50 (r2=0.25, p=0.05). There were no significant differences among the groups in reticulocytes and the average volume of erythrocytes. The dynamics and intensity of regeneration are moderate in all three groups.

Exploration of the role of oxidative stress-related genes in LPS-induced acute lung injury via bioinformatics and experimental studies

During the progression of acute lung injury (ALI), oxidative stress and inflammatory responses always promote each other. The datasets analyzed in this research were acquired from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-expression Network Analysis (WGCNA) and limma package were used to obtain the ALI-related genes (ALIRGs) and differentially expressed genes (DEGs), respectively. In total, two biological markers (Gch1 and Tnfaip3) related to oxidative stress were identified by machine learning algorithms, Receiver Operator Characteristic (ROC), and differential expression analyses. The area under the curve (AUC) value of biological markers was greater than 0.9, indicating an excellent power to distinguish between ALI and control groups. Moreover, 15 differential immune cells were selected between the ALI and control samples, and they were correlated to biological markers. The transcription factor (TF)-microRNA (miRNA)-Target network was constructed to explore the potential regulatory mechanisms. Finally, based on the quantitative reverse transcription polymerase chain reaction (qRT-PCR), the expression of Gch1 and Tnfaip3 was significantly higher in ALI lung tissue than in healthy controls. In conclusion, the differences in expression profiles between ALI and normal controls were found, and two biological markers were identified, providing a research basis for further understanding the pathogenesis of ALI.

Difference spectrum fitting of the ion–neutral collision frequency from dual-frequency EISCAT measurements

Abstract. The plasma–neutral coupling in the mesosphere–lower thermosphere strongly depends on the ion–neutral collision frequency across that region. Most commonly, the collision frequency profile is calculated from the climatologies of atmospheric models. However, previous measurements indicated that the collision frequency can deviate notably from the climatological average. Direct measurement of the ion–neutral collision frequency with multifrequency incoherent scatter radar (ISR) measurements has been discussed before, though actual measurements have been rare. The previously applied multifrequency analysis method requires a special simultaneous fit of the two incoherent scatter spectra, which is not possible with standard ISR analysis software. The difference spectrum method allows us to infer ion–neutral collision frequency profiles from multifrequency ISR measurements based on standard incoherent scatter analysis software, such as the Grand Unified Incoherent Scatter Design and Analysis Package (GUISDAP) software. In this work, we present the first results by applying the difference spectrum method. Ion–neutral collision frequency profiles obtained from several multifrequency EISCAT ISR campaigns are presented. The profiles obtained with the difference spectrum method are compared to previous collision frequency measurements, both from multifrequency ISR and other measurements, as well as results from empirical and comprehensive atmosphere models. Ion–neutral collision frequency measurements can be applied to improve first-principle ionospheric models.

Outcomes of Lithotripsy in Severely Calcified Coronary Artery Disease: A Systematic Review and Meta-analysis

BackgroundSeverely calcified coronary artery disease can cause reduced blood flow to the heart, leading to serious cardiac conditions. Intracoronary lithotripsy (ICL) has been developed to improve outcomes for patients with heavily calcified lesions. This study aims to summarize the existing evidence on the effectiveness and safety of intravascular lithotripsy (IVL) for severely calcified coronary artery disease. MethodsWe comprehensively searched PubMed, Cochrane Central Register of Controlled Trials, Web of Science, and Scopus, from their inception up to April 1, 2023, with a restricted search strategy. The R version 4.2.3 was utilized using Metafor package for analysis. To determine the study's heterogeneity, the indices tau2 and I2, as well as Cochran's Q test, were employed. ResultsA total of 17 studies, comprising 2488 observations, were included in the analysis. IVL is a treatment option for patients with severely calcified coronary artery disease. The percentage stenosis reduction showed a mean reduction of 15.0548% (95% CI: 8.310 to 21.79), with high heterogeneity observed among studies. The occurrence of cardiovascular-related events showed a proportion of 0.015 (95% CI: 0.0046-0.0254) and significant heterogeneity. ConclusionThese findings suggest that IVL is an effective treatment option, but further studies are needed to better understand its efficacy and safety.

Fast analysis and prediction approach for geometrically nonlinear bending analysis of plates and shells using artificial neural networks

For the first time, this paper introduces a Fast Analysis and Prediction (FAP) approach for geometrically nonlinear bending analysis of plates and shells. The proposed approach consists of two phases: fast analysis and prediction using artificial neural networks (ANNs) combined with the Bayesian regularization algorithm. In the first phase (fast analysis), geometrically nonlinear bending analysis of a structure is performed using only several load steps. This enables the efficient computation within a short period of time. In the second phase (prediction using ANNs), the deflections obtained in the first phase are utilized as the data for training the ANNs. The trained networks are then used to quickly predict deflections at any load step. The calculation process in the second phase is also very fast. For the analysis package in the first phase, the formulation of this paper is originated from the literature for geometrically nonlinear analysis of composite shells. High accuracy and efficiency of the FAP approach were demonstrated via solving nonlinear bending problems of plates and shells including an isotropic square plate, a composite cylinder subjected to internal pressure, a FG-CNTRC panel and a pinched cylinder with end diaphragms. The proposed approach offers significantly computational savings (up to 70%) compared to the conventional one. ANNs combined with the Bayesian regularization algorithm, which are proposed for the FAP approach in this work, possess the outstanding advantages compared to many existing neural networks. The FAP approach can be applied to simulations, observations, or experiments where nonlinear responses are present or data is limited. Applying the FAP approach to such areas can significantly save labor, time, and money.

FeynMG: A FeynRules extension for scalar-tensor theories of gravity

The ability to represent perturbative expansions of interacting quantum field theories in terms of simple diagrammatic rules has revolutionized calculations in particle physics (and elsewhere). Moreover, these rules are readily automated, a process that has catalyzed the rise of symbolic algebra packages. However, in the case of extended theories of gravity, such as scalar-tensor theories, it is necessary to precondition the Lagrangian to apply this automation or, at the very least, to take advantage of existing software pipelines. We present a Mathematica code FeynMG, which works in conjunction with the well-known package FeynRules, to do just that: FeynMG takes as inputs the FeynRules model file for a non-gravitational theory and a user-supplied gravitational Lagrangian. FeynMG provides functionality that inserts the minimal gravitational couplings of the degrees of freedom specified in the model file, determines the couplings of the additional tensor and scalar degrees of freedom (the metric and the scalar field from the gravitational sector), and preconditions the resulting Lagrangian so that it can be passed to FeynRules, either directly or by outputting an updated FeynRules model file. The Feynman rules can then be determined and output through FeynRules, using existing universal output formats and interfaces to other analysis packages. Program summaryProgram title: FeynMGCPC Library link to program files:https://doi.org/10.17632/bjpfmtt55k.1Developer's repository link:https://gitlab.com/feynmg/FeynMGLicensing provisions: MIT licenseProgramming language:Mathematica 13.2Nature of problem: Determining the additional interactions that scalar-tensor theories of gravity induce between the fields of the Standard Model of particle physics and its extensions is a tedious and time-consuming task that is ripe for automation. FeynMG is a package that provides this automation. It can expand the spacetime metric around a flat background and perform the necessary field redefinitions for a given theory in order to provide a Lagrangian that is in a form that can be processed by FeynRules[1]. The Feynman rules for the theory can then be determined using the existing functionality of FeynRules and output in formats that can be read into other particle-physics analysis packages.Solution method: (1) Load both FeynRules and FeynMG into Mathematica. (2) Load a model file describing a given matter sector and Lagrangian. (3) Use the FeynMG implementation to: (3a) append the gravitational sector and incorporate curvature-dependent objects, such as the metric, curvature scalars and tensors, and covariant derivatives; (3b) effect a Weyl transformation or expand the spacetime metric up to second order in graviton interactions with the matter fields; and (3c) diagonalize mass and kinetic mixings, and canonically normalize dynamical fields. (4) Pass the output directly to FeynRules or output a new model file from FeynMG.

Evolution of digital transformation in SMEs management through a bibliometric analysis

This document performs a systematic review of the literature and bibliometrics on the orientation of digital transformation (DT) in the organizational and management processes of small and medium-sized enterprises (SMEs) in the 2015–2022 period. For this analysis, the bibliometrix and biblioshiny software packages have been used to perform the data extraction and quantitative analysis of the research articles, obtained from the Web of Science (WOS) and Scopus databases. 311 articles related to DT in SMEs were identified as the most representative publications of the subject to be analyzed. The results show that there is a clear orientation of DT to modify organizational processes in SMEs so that they are more agile and are thus able to respond to the sudden changes that originate in the markets. The elements that delimit DT in this study are the digital technologies to implement, the strategies to follow and the digital capabilities. Finally, the main limitations, problems and gaps in the research are presented.

Using Spontaneous Speech Recognition as a Biomarker to Distinguish Dementia Patients

AbstractBackgroundDigital biomarkers such as individual speech recordings, are a cost‐effective tool for assessing cognitive status using linguistic and acoustic content. The purpose of this study is to investigate whether Natural Language Processing (NLP) methods and audio feature extraction can distinguish spontaneous speech outputs in dementia patients from a healthy population.Method240 audio samples and transcripts of clinician‐participant dialogues involving the Cookie‐Theft visual description were acquired from the DementiaBank Pitt corpus (Dementia group (DG) n = 169; healthy controls (CG) n = 71). Lexical variety, syntactic complexity, salience of information, disfluency patterns, and sentiment analysis were evaluated. Sound synthesis and an acoustic R analysis package were used to filter and examine acoustic properties of recorded audio such as silence and speech segmentation, pitch tracking, and frequency modulation of vibrato or jitter. DG and CG were compared using non‐parametric Kruskal‐Wallis tests, Dunn’s multiple comparison, and Mann‐Whitney U‐test in the statistical analysis. In comparison to CG, we hypothesized that DG would differ in levels of lexical diversity, grammatical complexity, and overall salience.ResultOverall, text analysis showed no variations in disfluencies between groups, but there were significant differences in lexical diversity, syntactic complexity and reference rate to reality (p<0.0001). Additionally, there were no appreciable differences in the frequency of salient information between the two groups, however the frequency of non‐salient information and disfluencies was considerably higher in the DG (p<0.05). Analysis of acoustic features revealed that DG had longer average speech duration and speech segment durations compared to CG (p<0.001).. On the other hand, the rate of change of acoustic characteristics including pitch, mean entropy, and harmonics‐to‐noise ratios did not significantly differ between DG and CG.ConclusionIn conclusion, this paradigm reveals that, in comparison to healthy controls, dementia patients exhibited increased lexical diversity yet lower linguistic complexity, and a shift towards characterizing non‐salient objects in given stimuli, as well as longer segments of speech. Further examination of the rhythmic and acoustic characteristics of speech may provide a comprehensive framework towards defining speech as a potential digital biomarker for dementia risk, and provide applications to regions experiencing increasing socioeconomic disparities.