Statistical Graphics Research Articles

Empirical analysis on the impact of labour market regulations on uninsured employment in Greece

The impact of labour market regulations on uninsured employment in Greece between 2014 and 2019 is a crucial exploration in response to the pervasive challenge of undeclared work. With the rise of globalization and evolving labour dynamics, understanding and addressing the complexities of uninsured employment by analysing data from the Greek Ministry of Labour and Social Affairs was the main purpose of this study. Employing a comprehensive examination of key indicators, the study sought to evaluate the effectiveness of measures in curbing undeclared work. The pivotal strategies implemented by the Ministry of Labour and Social Affairs during the specified period were analysed using statistical analysis, focusing on high fines, reduced non-salary costs, the ERGANI information system, and a modernized fines framework. Statistical graphs were utilized to examine trends, providing a solid foundation for evaluating the impact of the implemented strategies. Qualitative assessments were employed to offer a nuanced understanding of the contextual factors influencing the outcomes. The findings reveal a positive trend, showcasing a significant reduction in undeclared workers and non-compliant businesses, underscoring the effectiveness of the implemented measures. An examination of 237,455 businesses in Greece over the period 2014-2019 revealed that 28,044 entities (11.81%) employed undeclared workers. Among 812,460 scrutinized employees, 44,642 (5.49%) were identified as undeclared, leading to fines totalling €468,997,663 in efforts to address non-compliance with labour regulations. This study not only sheds light on the successful strategies adopted by the Greek government but also offers valuable practical lessons for policymakers globally

Effect of Binder on Oxidation Properties of Tungsten Carbides: A Review by a Conceptual Classification Approach

This study presents a conceptual classification scheme to review the literature on improving the oxidation resistance of tungsten carbide by modifying the binder. The first parts of the article are dedicated to the specification of the databases, the search method, and the description of the criteria chosen to classify the articles. Then, the data collected are presented in statistical graphs according to the proposed classification scheme. The data analyzed show that most of the significant improvements in oxidation resistance are achieved with advanced production processes, especially HIP and SPS, which eliminate porosity to a very high degree. In addition, statistical studies showed that the use of new replacement binders, Ni3Al, Fe–based alloys, FeAl, and Al2O3, improved the oxidation properties in 75–100% of cases. Meanwhile, the use of high–entropy alloys (HEAs) as cermet binders may be the subject of future research for oxidation, given the recently published results of good mechanical properties.

Comparison and Enhancement of Design Procedures for Guideline Expressions to Predict the Compressive Capacity of FRP-Wrapped Concrete Members

The FRP wrapping of reinforced concrete columns is a simple way to increase the axial load capacity of such members with substandard concrete quality. A total of 2197 specimens were collected as a database to discuss the efficiency of predicting axial compressive strength of FRP-wrapped concrete columns according to several national and international guidelines. Various aspects of the database, including fiber type, unconfined strength and specimen geometry used to investigate the guidelines, and results are presented in terms of averages, standard deviations, and ratio of overestimated specimens. The performance of these expressions was compared with one another based on these statistical results and graphs, and those that captured the behavior best were identified for specific subsets. Recognizing the expressions’ inadequacy in capturing efficiency improvements related to radius to area and confinement ratios, some improvements in the form of coefficients for lateral confinement stress in the Turkish Building Earthquake Code were introduced, specifically for certain subsets.

The timeless beauty of data: inventing educational pasts, presents and futures through data visualisation

ABSTRACT This article explores the complex interplay between the visual, numerical and verbal elements of data visualisation and their role in shaping policy concerns. Focusing on the aesthetic and temporal dimensions of statistical graphics and drawing on the notion of diagram in the Deleuzian sense, the article emphasises the performative nature of data visualisation. More specifically, it explores how data visualisation suggests, rather than reveals, particular visions of educational pasts, presents and futures. Based on an analysis of graphs and charts selected from recent UNESCO and OECD reports, the article discusses the practices of the datafication of time and temporalisation and the beautification of data, which together produce ‘beautiful evidence’. This evidence informs education policies and practices and affects the way education can be seen, known and acted upon.

Graph signal recovery using variational Bayes in Fourier pairs with Cramér–Rao bounds

In this paper, the graph signal recovery problem is addressed by employing an aggregation of samples in the vertex domain and the Fourier graph transform domain. The statistical graph signal is modeled using a Gaussian Markov Random Field (GMRF). The reconstruction process involves employing a variational Bayes (VB) algorithm, which is a fully Bayesian method that iteratively estimates all unknown parameters by computing the posteriors in a closed-form. Furthermore, the closed-form of the Cramér–Rao lower bound (CRLB) for graph signal estimation is also derived. Simulation results demonstrate the superiority of the proposed algorithm over some of state-of-the-art algorithms in the literature.

Statistical Analysis by Linear Regression of the Prevalence of Flatfoot in Preschool Children of a Mexican Community

Context: In Mexico's rural towns, it is essential to generate a culture of studying the prevalence of flat feet in children aged 3 to 5, whose arch is still in development and can be corrected. By means of a computer application, statistical bar graph and correlation studies via linear regression can validate the results obtained regarding the categorization of infants' footprint type, which are acquired through the Hernández Corvo index (HCI), Clarke’s angle protocol (CA), the Staheli index (SI), the Chippaux index (CI), and the body mass index (BMI). Methods: A statistical analysis of the plantar footprint of 95 infants in a rural region of Mexico was carried out, employing a computational technique together with a photo-podoscope. Footprint images were acquired, processed, and classified. The footprint type was categorized with respect to the HCI, CA, and the Staheli-Chippaux index (SCI). The footprint distribution was validated via the linear regression method. Results: We evidenced a prevalence of flat foot of 54,7% in relation to HCI, 58,9% in relation to CA, and 61,05% in relation to SCI, where the male gender was shown to be more susceptible (up to 28, 32, and 33 cases, respectively). The best prediction was obtained using the SI and the CI: 90,7 and 87,0% for the right and left feet, with a positive increase. No dependence on body composition was observed. Conclusions: The diagnosis of the type of footstep, in its normal, cavus, and flat categories, shows the prevalence of flat feet among infants aged 3 to 5, with at least 28 cases, mostly male and without dependence on weight. Although it is difficult to perform plantar footprint diagnoses in the rural communities of Mexico, this statistical study highlights the importance of monitoring foot development in preschool infants with the advantages and practicality of computational techniques.

Neutrosophic Delphi method to analyze the impact of Internships on the comprehensive development of university students

Internships play a crucial role in the comprehensive education of university students as they provide practical experience and promote the development of technical and soft skills. These practices not only promote personal development but also ease the transition into the world of work. The study aims to use a Neutrosophic Delphi method to analyze the extent to which work practices influence the comprehensive education of university students in Ecuador in 2023. A descriptive study was conducted with a sample of 410 students from academies and universities in Ecuador. Country Ecuador. Center of the country This method uses structured surveys to collect qualitative and quantitative data about the experiences, advantages, and skills acquired during internships. The results are presented in the form of data tables and statistical graphics that illustrate the close connection between professional experience and the overall educational level of students. Emphasis was placed on acquiring skills such as teamwork, leadership, and problem-solving. In summary, internships are a valuable learning tool for university students as they provide the opportunity to apply knowledge, develop skills, and improve their employability.

ВЫБОР МЕТОДА СТРАТЕГИЧЕСКОГО ПЛАНИРОВАНИЯ ПРОМЫШЛЕННОГО ПРЕДПРИЯТИЯ В УСЛОВИЯХ ВЫСОКОТУРБУЛЕНТНОЙ СРЕДЫ

The increasing speed of changes in the environment of industrial enterprises has led to the more frequent use of the term ‘turbulence’ and to the search for a method of strategic planning in this context. The hypothesis of the study is that this method should be based on two key criteria: firstly, a quantitative assessment of turbulence in the industrial environment of the enterprise and, secondly, the degree of resistance. The study develops a matrix for selecting the most effective method of strategic planning, depending on the selected criteria. The methodological basis of the study was the theory of organization and the theory of strategic management. The study used structural and logical analysis of information, and methods of statistical evaluation, matrix construction, and graphics. A Stacey matrix is developed, in which four possible zones of industrial enterprises are conditionally allocated: the zone of ‘conditional comfort’, the zone of ‘additional opportunities’, the zone of ‘threats’, the zone of ‘main risk’. The higher the level of turbulence in the industry and the lower the level of resistance of the in-dustrial enterprise, the more effective the application of integrated methods of strategic planning. An al-gorithm for assessing the effectiveness of accelerating the response to environmental changes is pro-posed. The key conclusion of the research is the confirmation of the hypothesis. The results can be used in the strategic management systems of industrial enterprises.

Correlation between Ankylosing Spondylitis Disease Activity Score and MRI Scoring in Patients with Ankylosing Spondylitis: A Cross-sectional Study

Introduction: Ankylosing Spondylitis (AS) is an inflammatory disorder of unknown cause that primarily affects the axial skeleton, peripheral joints, and extra-articular structures. Typically, the condition starts in the second or third decade. Lower back pain lasting longer than three months, morning stiffness lasting more than 30 minutes, relieved by daily activities, are signs of disease onset. Aim: To establish a correlation between disease severity score Ankylosing Spondylitis Disease Activity Score (ASDAS) and Magnetic Resonance Imaging (MRI) scoring in patients with AS. Materials and Methods: This cross-sectional study was conducted on 66 patients attending the Medicine Emergency/ Outpatient Department (OPD)/ward of ESIPGIMSR and ESI Hospital, Basaidarapur, New Delhi, India, who were diagonsed with AS based on the modified New York Criteria. Clinical assessments included ASDAS based on Erythrocyte Sedimentation Rate (ESR) and C-reactive Protein (CRP), while MRI disease activity scores were determined using the Spondyloarthritis Research Consortium of Canada (SPARCC) MRI scoring. The sample size was calculated with a coefficient limit of 10% and a confidence level of 95%. Data analysis was performed using Statistical Packages for Social Sciences (SPSS) version 21.0, a widely used statistical computing and graphics tool. Pearson’s correlation coefficients were computed to explore relationships between variables, including age, disease duration, ASDAS-ESR, and MRI changes. Results: In present study, a total of 66 patients were included, out of which 51 (77.3%) were males and 15 (22.7%) were females. The age group of patients included in present study was 33 years to 44 years with a mean of 37.96 years. In present study, the disease duration ranged from a minimum of two years to a maximum of 10 years with a mean of 5.4 years. The mean ASDAS CRP was 3.687 (minimum-2.80, maximum-4.60), and the disease activity as assessed by MRI score (SPARCC) had a minimum value of 8.85 and a maximum value of 26.2 with a mean of 16.359. In present study, the X-ray grading of sacroiliitis revealed that 19.7% of the subjects were classified as Grade-2, 60.6% as Grade-3, and 19.7% as Grade-4. Conclusion: The clinical disease activity index may not always reflect active inflammation, which is detectable by MRI. Incorporating MRI into diagnostic and treatment strategies for AS is crucial for the accurate assessment of disease severity and better patient outcomes.

The Impact Of Site Management On The Success Of Construction Project In Abuja

The significance of site management as a powerful tool for enhancing site performance in the construction industry is often undermined due to its inefficient and ineffective implementation. This study aimed to evaluate the impact of proper site management on project completion, delivery, and success in Abuja. The research utilized a survey design involving professionals from the built environment, such as civil engineers, architects, project managers, and more. The cluster sampling technique was employed, selecting 100 respondents from a population of 3,250 in Abuja. The study achieved a 100% response rate. Data analysis involved the Relative Importance Index (RII) and Pearson correlation techniques, presenting results through statistical equations, tables, charts, and graphs. Notable findings included the significance of sound site management principles like planning, professional engagement, funding, technology integration, neighbourhood security, worker motivation, and teamwork in enhancing site management effectiveness. Inadequate site management issues could be alleviated through improved pre-project planning and successful project management, critical for construction project success. Neglecting effective site management practices adversely affected project performance. The research emphasized the importance of enhanced leadership skills, technical knowledge, adoption of new methodologies, and standardized tools in achieving better project performance through optimal site management practices. Improved site management practices are essential to mitigate challenges and ensure successful project outcomes.

State estimation of urban air pollution with statistical, physical, and super-learning graph models

State estimation of urban air pollution with statistical, physical, and super-learning graph models

СВЕРТОЧНАЯ НЕЙРОСЕТЕВАЯ МОДЕЛЬ АКУСТИЧЕСКОГО ОБНАРУЖЕНИЯ АВАРИЙНО-СПАСАТЕЛЬНЫХ МАШИН

This article solves the problem of developing a neural network model for the acoustic detection of rescue vehicles. To solve this problem, it is proposed to use the mathematical apparatus of convolutional neural networks. Emergency vehicles such as ambulances, fire engines and police vehicles play an important role in life-threatening situations. Timely response is a key factor in the work of emergency services, which confirms the relevance of developing a neural network to solve the problem. The development of a convolutional neural network model required solving the following problems: searching and preparing initial data for analysis, selecting the necessary libraries for software implementation of the neural network, building an architecture, training, testing and assessing the adequacy of the neural network model. The “Emergency Vehicle Siren” data set from the public Kaggle Datasets database was used as the initial data for building the model. The data is presented in the form of a set of audio recordings of the sound of sirens of ambulances, fire trucks and city traffic noise, with 200 examples of each class. The data has been prepared for analysis. Training, validation and test samples were formed. The volume of the training sample was 384 audio recordings, the validation sample was 96, and the test sample was 120. The following libraries were selected for training the convolutional neural network: Librosa (for analyzing and processing audio data), NumPy (for working with multidimensional data arrays), Matplotlib (for creating graphs, charts, histograms and other forms of data visualization), Seaborn (for creating statistical graphs built on the basis of the Matplotlib library), TensorFlow (for machine learning) and Keras (for interacting with artificial neural networks). The architecture of a neural network was built, consisting of three convolution layers, three subsampling layers and a fully connected part responsible for classification and consisting of two layers. A neural network model was trained in the Kaggle notebook environment. Training was carried out for 50 epochs. The accuracy of the neural network model on the training data set was 99.22%, on the validation set - 93.75%. To assess the adequacy of the constructed neural network model, a test data sample was used. The classification accuracy for data from this sample was 99.17%. The results obtained indicate the effectiveness of the constructed convolutional neural network model and the possibility of its practical use for automating the solution of the problem of acoustic detection of rescue vehicles.

Cholera detection system using CNN machine learning algorithm

A comprehensive cholera detection system leveraging cutting-edge technologies such as neural networks, machine learning, chatbots, live maps, and real-time statistical graphs is proposed. The system integrates a user-friendly chatbot interface to interact with individuals, prompting them to input relevant health information and symptoms. Behind the scenes, neural networks and machine learning algorithms analyze the data to detect potential cholera cases, offering users instant insights into their health status. The system incorporates live maps to track reported cases geographically, enabling a swift response from health authorities. Moreover, real-time statistical graphs provide dynamic visualizations of cholera trends, aiding in the identification of potential outbreak hotspots. By amalgamating these technologies, the cholera detection system not only facilitates early diagnosis and intervention but also enhances public health monitoring and management, contributing to the overall control and prevention of cholera outbreaks.

Analysis of the Statistical Literacy Ability of Sociology Education Students

This research aims to identify students' statistical literacy abilities, and this type of research is descriptive qualitative. The subjects in this research consisted of 24 Sociology Education students from Muhammadiyah University of Kupang taking statistics courses in the 2023/2024 academic year. The instrument used is an assignment consisting of 2 questions. Data analysis was done by analyzing students' answer documents to complete the assignments given. Students' statistical literacy abilities are identified using five statistical literacy competencies: understanding statistical concepts, insight into the application of statistical concepts, calculation and graphing skills, interpretation abilities, and visualization and communication skills. The research results show that more than 50% of students can fulfill statistical literacy competencies such as understanding statistical concepts, numeracy, interpretation, and communication skills. However, less than 50% of students do not meet competency in insight into the application of statistical concepts and visualization skills. Students' abilities in essential statistical literacy competencies still need to be improved because most students still have limitations in these competencies.

Double-Relief Silver Coins Minted in the Greek Colonies (444–390/340–280/270 BC) of Southern Italy Analysed by XRF

A sample of 18 double-relief coins from different poleis of Magna Graecia and ancient Italy has been analysed using a handheld XRF spectrometer directly inside the Museo Provinciale Campano (Capua, Italy). The data analysis shows that (i) the main elements are Ag and Cu, indicating that the coins are of high fineness (average Ag 95.7%), (ii) trace elements can help to characterise the coins, (iii) a superficial chemically altered layer (corrosion) is absent, (iv) the values of ratio Ag Kα/Lα evidence the presence of an enrichment layer on the surface of silver or subaerata in some coins. Multivariate statistical analysis and graph analysis allowed the coins to be assigned to different groups with the highest possible accuracy on the basis of the chemical data obtained and models to be constructed to classify the coins according to their historical periods.

Work of the emergency system in polytraumatized patients transferred to the hospital

A descriptive and retrospective observational study of the cases of polytraumatized patients transferred by the emergency medical system to the hospital was carried out, taking into account the work of the emergency system in the transfer of polytraumatized patients in the year 2022. With the aim of highlighting the actions of the emergency system in the face of trauma as a public health problem and its behavior. Percentage analysis was used to express the results, which will be arranged in statistical tables of association of variables and statistical graphs for better analysis and understanding. The main results were that the highest mortality rate was in the series of patients not transferred by the emergency system; of a total of 70 polytraumatized patients, 58 of them were transferred by the emergency system for 82.8% against 12 patients who arrived at the emergency department by other means, of whom the highest percentage corresponded to patients between 40 and 60 years of age, where the main causes were traffic accidents, where the highest number of deaths was in the series of those patients not transferred by the emergency system.

NOAP — SCRIPT PACKAGE FOR PLANNING AN D ANALYSIS OF NEO OBSERVATIONS

We present a package of Python scripts NOAP (NEO Observations Analyzer and Planner) designed for the automatic planning of NEO observations, as well as analysis of already existing observations in the NEODyS-2 database. The package is divided into two parts: analyzer and planner. The analyzer automatically downloads data from the NEODyS-2 database, converts them by adding additional information, including the apparent speed of objects and observation errors along and across the track, and also provides a large amount of statistical data and graphs for the selected period. It can be done for several observatories at once. The planner also selects the objects of observation for the upcoming night and calculates their ephemeris with a given step in a fully automatic mode. The output data format of the scheduler allows its use both on semi-automatic telescopes and for fully robotic observations. NOAP has been successfully used for more than a year for planning and analysis of NEO observations by optical sensors of the National Space Facilities Control and Test Center of the State Space Agency of Ukraine.

CD103+CD8+T cells combined with neutrophil-to-lymphocyte ratio predict response to neoadjuvant chemoimmunotherapy in advanced oral squamous cell carcinoma

Objective: To investigate the relationship between the expression of CD103+CD8+T cells in locally advanced oral squamous cell carcinoma (LA-OSCC), and the response to neoadjuvant chemoimmunotherapy (NACI). Methods: Thirty LA-OSCC patients from the Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, who underwent NACI from June 2020 to December 2022 were analyzed, including 16 responders and 14 non-responders. Using multiple immunofluorescence technique to stain sections of patients to verify the correlation between the expression of CD103+CD8+T cells and the efficacy of NACI. CD103+CD8+T cell density was counted using Inform and HALO software. The Spearman correlation coefficient in rank correlation is used to describe the correlation between CD103+CD8+T cell and neutrophil-to-lymphocyte ratio (NLR), derived neutrophil-to-lymphocyte ratio (dNLR), platelet-lymphocyte ratio (PLR), systemic immune inflammation index (SII) It's effectiveness as a predictive marker to NACI was analyzed by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA). Two-tailed t-test or Mann-Whitney U-test was used to compare data between two groups, and one-way ANOVA was used to compare data between multiple groups. SPSS 22.0 and GraphPad prism 9.0 software were used for statistical analysis and plotting of relevant statistical graphs such as histograms. P<0.05 was considered a statistically significant difference. Results: The density of CD103+CD8+T cells has expanded in advanced OSCC patients who are responsive to NACI. The CD103+CD8+T cell densities in the responsive and nonresponsive groups were 118.30(41.92, 197.80) pcs/mm2 and 21.63(4.91, 71.92) pcs/mm2 respectively, with statistically significant differences(U=52.00, P=0.012). CD103+CD8+T cell abundance was negatively correlated with NLR, dNLR, PLR, and SII (P<0.05). ROC curve analysis showed that the AUC for predicting efficacy of NLR, dNLR, PLR, and SII were 0.781 (P=0.009, 95%CI: 0.5715-0.9910), 0.671 (P=0.105, 95%CI: 0.467-0.881), 0.679 (P=0.020 95%CI: 0.549-0.951), 0.750 (P=0.096, 95%CI: 0.461-0.896), respectively. The AUC for CD103+CD8+T cells alone was 0.861 (P=0.013, 95%CI: 0.585-0.950), and the AUC of combining CD103+CD8+T cells with NLR was 0.896 (P=0.025, 95%CI: 0.454-0.938). Conclusions: The density of CD103+CD8+T cells is expanded in advanced OSCC patients who are responsive to NACI. CD103+CD8+T cells positively predict favorable responses as a strong indicator to NACI in advanced OSCC patients. Co-interpretation of CD103+CD8+T cells and NLR value enhances the predictive accuracy of NACI in advanced OSCC patients.

Validación de la escala de conductas laborales contraproducentes de entornos virtuales en México, 2022

Counterproductive work behaviors (CWB) are those behaviors that affect the performance of individuals and the organization in general, so far analyzed in traditional work environments. The objective of the present study was to validate the CLC scale in virtual environments in a Mexican population. The English version was adapted to Spanish using the retranslation method. It was applied to a non-probabilistic sample of 332 teleworkers. By means of exploratory and confirmatory factor analysis, with the help of SPSS Statistics and AMOS Graphics, validity and reliability tests were carried out. The model presents a good fit (χ2/df = 1.957, CFI= 0.960, TLI= 0.950, RMSEA= 0.054, SRMR= 0.0536) with 23 items and 4 dimensions presenting adequate reliability indices (AO=.952, UIT=0.895, DP= 0.866, AI= 0.707). An initial proposal of the factorial structure of the CLC scale in virtual environments is presented for the Mexican population; with 4 factors: organizational sabotage or abuse, misuse of time, production deviation and interpersonal abuse. Further studies are required to validate the scale in specific and general contexts of Mexican teleworkers, as well as to use the present scale to validate the individual job performance variable in Mexican teleworkers.

Analyzing the Sentiment of Tweets Regarding Sustainability Practices to Predict Stock Performance

In recent years, sustainability has entered the mainstream consciousness of citizens, policy makers and companies across the globe. A key factor of sustainability in the investing world is ESG (environmental, social, governance) scores that demonstrate how sustainable the actions and decisions of a company are for employees, shareholders, and its ecosystem. In the United States, there has been a political divide in ESG values as investors seek to balance their drive to maximize shareholder value and promote positive change in society for all stakeholders. One platform that debate has occurred on is Twitter where users express their opinions on various companies. I analyzed the sentiment of users’ tweets on companies that have been regarded as sustainability leaders and companies that are laggards in this area. My analysis was completed through the Python coding language, a programming language designed for statistical computing and graphics. This data was then correlated with the stock performances of these respective companies during specific time frames of high Twitter activity. Ultimately, I sought to determine whether social media has a significant influence on the stock market and whether that will play a large part in driving sustainable change which will improve the environmental and social consciousness in the United States. Future research can identify what specific ideas or principles regarding sustainability are most widely discussed on Twitter and how this in turn relates to the decisions that companies make on key public policy issues.