Programming Environment Research Articles

How Gaussian mixture modelling can help to verify reference intervals from laboratory data with a high proportion of pathological values

Abstract Objectives Although there are several indirect methods that can be used to verify reference limits, they have a common weakness in that they assume a low proportion of pathological values. This paper investigates whether a Gaussian decomposition algorithm can identify the non-pathological fraction even if it is not the main subset of mixed data. Methods All investigations are carried out in the R programming environment. The mclust package is used for Gaussian mixture modelling via the expectation maximization (EM) algorithm. For right-skewed distributions, logarithms of the original values are taken to approximate the Gaussian model. We use the Bayesian information criterion (BIC) for evaluation of the results. The reflimR and refineR packages serve as comparison procedures. Results We generate synthetic data mixtures with known normal distributions to demonstrate the feasibility and reliability of our approach. Application of the algorithm to real data from a Nigerian and a German population produces results, which help to interpret reference intervals of reflimR and refineR that are obviously too wide. In the first example, the mclust analysis of hemoglobin in Nigerian women supports the medical hypothesis that an anemia rate of more than 50 % leads to falsely low reference limits. Our algorithm proposes various scenarios based on the BIC values, one of which suggests reference limits that are close to published data for Nigeria but significantly lower than those established for the Caucasian population. In the second example, the standard statistical analysis of creatine kinase in German patients with predominantly cardiac diseases yields a reference interval that is clearly too wide. With mclust we identify overlapping fractions that explain this false result. Conclusions Gaussian mixture modelling does not replace standard methods for reference interval estimation but is a valuable adjunct when these methods produce discrepant or implausible results.

COMPARING SCIENTIFIC COMPUTING ENVIRONMENTS FOR SIMULATING 2D NON-BUOYANT FLUID PARCEL TRAJECTORY UNDER INERTIAL OSCILLATION: A PRELIMINARY EDUCATIONAL STUDY

This study presents a preliminary numerical investigation of the two-dimensional trajectory of a non-buoyant fluid parcel subjected to inertial oscillations and abrupt external forcing events. The simulations were implemented using Python, GNU Octave, R, Julia, and Fortran open-source scientific computing environments. By running 1,000 iterations in each environment, we evaluated the computational performance of these languages in tackling this idealized problem. The results, visualized through static plots and animation, validate the numerical model's ability to represent the fundamental physics governing fluid motion. Statistical analysis using the Kruskal-Wallis test and Dunn's post-hoc test with Bonferroni correction revealed that Fortran exhibits significantly faster execution times than other environments. However, the choice of programming language should also consider factors such as coding expertise, library availability, and scalability requirements. This study focuses on the performance of scientific computing environments within each language rather than the languages themselves. The observed execution times should be interpreted in the context of the specific environments used, as they often leverage optimized libraries written in lower-level languages. Despite the limitations of this work, such as the simplified 2D model and the use of a single hardware configuration, this study provides valuable insights into selecting appropriate computational tools. It contributes to educational resources for teaching idealized fluid dynamics models. Future studies could explore more complex scenarios, a more comprehensive range of programming environments, and the impact of different numerical schemes and physical parameterizations.

Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

Investigation of the accuracy of integral models of the oculo-motor system

For mathematical modeling of the human oculomotor system (OMS), integral nonlinear models are used, which simultaneously take into account the nonlinear and inertial properties of the research object. Based on the data of experimental studies of the OMS "input-output", transient and diagonal intersections of transient functions of the second and third orders are determined. To obtain experimental data, an innovative eye tracking technology is used, which allows recording eye responses to test visual stimuli. Thus test signals are displayed on the computer monitor at different distances from the starting position in the horizontal direction. The aim of the research is to study the accuracy of OMS identification using eye-tracking data by evaluating the calculation errors of multidimensional transient functions when using methods of nonlinear dynamic identification based on models in the form of Volterra series and polynomials. The object of the study is the process of nonparametric identification of the OMS based on Volterra models in the time domain. The subject of the research is algorithmic and software tools for calculating the dynamic characteristics of OMS based on eye-tracking data, analyzing the accuracy of the obtained models using two identification methods: the approximation method and the least squares method (LSM). The means of nonlinear dynamic identification of the human OMS based on Volterra series and polynomials were developed in the Python programming environment. The accuracy estimates of various OMS (linear, quadratic, and cubic) models were obtained based on data from three responses to test signals of different amplitudes. For the same test signals, the same models in the form of the Volterra series and polynomials were obtained, as these models coincide in the region of convergence of the Volterra series. The analysis of errors in the assessment of the dynamic characteristics of the OMS demonstrated that the model in the form of an integral polynomial of the second degree, which was built using the LSM based on three responses, has an accuracy twice as high as the accuracy of similar models built using the data of two responses. Thus, in further studies of the psychophysiological state of a person based on nonlinear dynamic models of the OMS according to the data of three responses, it is advisable to use a model in the form of a quadratic Volterra polynomial. Figs.: 17. Tabl. 3. Refs.: 10 titles.

Using Immersive Environments in E-Mental Health Rehabilitation Programs Directed to Future Health Professionals to Promote Empathy and Health Literacy about Schizophrenia.

Rehabilitation involves all types of patients, including people with schizophrenia. Schizophrenia is considered a complex syndrome characterized in general by fundamental and characteristic distortions of thinking and perception. The quality of life of a person with schizophrenia can be compromised by difficulty in carrying out their daily tasks and by the social stigma of their condition. The importance of training and sensitizing students in rehabilitation areas to this type of problem to improve the rehabilitation processes in which they will participate as future professionals involves empathy and the ability to communicate with these populations. It is possible through virtual reality to create immersive environments to simulate some psychotic symptoms characteristic of people with schizophrenia, such as visual hallucinations and hearing voices. The aim of this study was to test the effect of exposure to experiences characteristic of schizophrenia through two different types of immersive environments, graphical computational virtual reality and 360° video, on students from areas of social rehabilitation regarding empathy, social distance, and attitudes towards people with schizophrenia. Although the results were positive for the three parameters under study, no significant differences were found for each of them between the environments to which the participants were exposed. This study concluded that the choice between the two types of immersive environments should be based on the project's objectives, the target audience's needs, and available resources, rather than the type of environment itself, as their impact was similar.

A practical applications guide to machine learning regression models in psychology with Python

This guide presents a detailed overview of the most used machine learning (ML) techniques for psychologists who may not be familiar with advanced statistical methods, algorithms, or programming. Recognizing the growing interest in using data-driven approaches within psychological research, this guide describes applying ML techniques to investigate complex psychological phenomena. The paper covers the spectrum of algorithms, including decision trees, random forests, gradient boosting, stochastic gradient boosting, and XGBoost, highlighting their concepts and practical applications in psychology. Aiming to bridge the gap between theoretical understanding and practical performance, this paper offers step-by-step instructions on data preprocessing, correlation exploration, feature selection, and model evaluation within the Python programming environment. Readers are offered the necessary tools to apply ML in their research through explanations, examples, and visualization.

Bridging the theory-practice divide: Reflections of school-based student teachers

Background: Teacher education programmes often prioritise theory over practical experience, leaving students struggling to connect classroom learning with real-world application. Addressing this gap, a proposed model integrates online coursework with mentored practice-based experiences through full-time placements in selected schools.Aim: The study delves into the experiences of first-year school-based student teachers (SBST) enrolled in a full-time undergraduate online programme aimed at bridging the theory-practice divide.Setting: First-year student teachers in the 2021 and 2022 cohorts (N = 30) placed full time in selected schools participated in the study.Methods: Using an interpretive qualitative research design, data were collected via focus group interviews with first-year student cohorts. Data was analysed using the cultural-historical activity theory combined with the constant comparative method.Results: Two primary findings emerged. Firstly, SBSTs faced difficulties balancing academic requirements between the school and the programme. Secondly, the varying degrees of support from stakeholders within both the programme and school environment influenced the quality and meaningfulness of SBSTs’ learning experiences.Conclusion: The convergence of university and school activity systems revealed tensions and contradictions yet also presented opportunities for expanded learning when coursework and classroom practices aligned. Despite criticisms of universities being overly theoretical, the research indicates that SBSTs prioritise practical experience in school settings, potentially overshadowing the significance of theoretical knowledge.Contribution: This study contributes to knowledge of the challenges faced by student teachers and highlights the need to address these contradictions through open dialogue within teacher education programmes.

Computational thinking in primary mathematics classroom activities

The integration of computational thinking (CT) into primary education is often facilitated using one or more CT tools, such as block-based programming environments and educational robotics. A major concern is that these CT tools often are used to design mathematics classroom activities that focus on CT at the expense of mathematics. Hence, there is a need to investigate more closely how CT tools can be used in primary mathematics classroom activities in ways that enable a stronger focus on the learning of mathematics. Using information ecology as a theoretical lens, this study aims to understand how and why CT tools are integrated into primary mathematics classrooms, and how teachers value the possible contributions of such tools. We draw on multiple interviews with two primary teachers, recordings of planning sessions where classroom activities that include CT were designed, the classroom implementations themselves, and reflective conversations with the teachers after the CT tools were integrated in their mathematics classrooms. A deductive analytical approach to our data revealed that (1) CT tools, to varying degrees, facilitate the learning of mathematics; (2) some CT tools were valued by teachers as a better ‘fit’ than others; and (3) CT tools are primarily used to support the learning of geometry, excluding other mathematical domains. Based on these findings, we suggest that there is a need for more research on the use of different CT tools and their role in the learning of primary mathematics. Moreover, more research is needed to understand how CT tools can be used in topics other than geometry.

Use of the Alice Visual Programming Environment in Programming Education : A Descriptive Case Study

The process of learning a programming language is seen as complicated, especially for those who first start to learn programming language. For this reason, visual programming tools have begun to be used in educational environments. Visual programming tools purpose to concretize abstract programming language concepts. Alice program is one of the visual programming tools. The aim of this study is to determine the effects of the Alice program on programming learning in the education environment. The study group was 18 freshman students enrolled in the Object Based Programming I course at the vocational school computer technology department. The study lasted 8 weeks, with 4 hours each week. The teaching was realized on Gagne’s nine events of instruction model. At the end of the teaching process, two focus group interviews were conducted with the students to determine the obstructing and facilitating factors in using the Alice program. This study was utilized a descriptive case study. The data were analyzed by using the content analysis. When the results were examined; It was seen that the Alice program makes learning the basic code concepts easy and understanding programming logic, and increases the desire to learn programming. However, it was stated that the practicality of writing code could not be improved because code could not be written in the Alice program. It is thought that employing the Alice program is beneficial for enhancing comprehension of programming logic.

A rapid realist review on leadership and career advancement interventions for women in healthcare

Women represent 70% of the global health workforce but only occupy 25% of health and social care leadership positions. Gender-based stereotypes, discrimination, family responsibilities, and self-perceived deficiencies in efficacy and confidence inhibit the seniority and leadership of women. The leadership inequality is often compounded by the intersection of race and socio-economic identities. Resolving gender inequalities in healthcare leadership brings women’s expertise to healthcare decision making, which can lead to equity of healthcare access and improve healthcare services. With the aim of enhancing women’s advancement to leadership positions, a rapid realist review (RRR) was conducted to identify the leadership and career advancement interventions that work for women in healthcare, why these interventions are effective, for whom they are effective, and within which contexts these interventions work. A RRR ultimately articulates this knowledge through a theory describing an intervention’s generative causation. The Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) for conducting a realist synthesis guided the methodology. Preliminary theories on leadership and career advancement interventions for women in healthcare were constructed based on an appraisal of key reviews and consultation with an expert panel, which guided the systematic searching and initial theory refinement. Following the literature search, 22 studies met inclusion criteria and underwent data extraction. The review process and consultation with the expert panel yielded nine final programme theories. Theories on programmes which enhanced leadership outcomes among women in health services or professional associations centred on organisational and management involvement; mentorship of women; delivering leadership education; and development of key leadership skills. The success of these strategies was facilitated by accommodating programme environments, adequacy and relevance of support provided and programme accessibility. The relationship between underlying intervention entities, stakeholder responses, contexts and leadership outcomes, provides a basis for underpinning the design for leadership and career advancement interventions for women in healthcare.

'We are all bringing, like a unique sort of perspective': The core elements of a strengths-based digital arts mentoring program for autistic adolescents from the perspective of their mentors.

While the adoption of strengths-based approaches to supporting autistic adolescents is growing in popularity, the application of this approach to a digital arts mentoring program has yet to be explored. This study reports on the core elements contributing to the success of a community digital arts mentoring program for autistic adolescents from the mentors' perspective. This paper presents an in-depth exploration of mentors' experiences, comprising a component of a broader line of research investigating a digital arts mentoring program for autistic adolescents emphasising positive youth development. The digital arts mentoring program spanned 20 weeks across two Australian school terms and was attended by two groups of autistic adolescents (N = 18) aged between 11 and 17 years. A qualitative approach was utilised in exploring the perspective of their mentors (N = 6). Qualitative data were collected at the end of each school term for each group with the mentors using an interpretive phenomenological approach and Colaizzi's seven-step analysis method. Thirteen individual interviews were conducted with six mentors. This research was conducted with a disability arts provider to provide a digital arts mentoring program to autistic adolescents. The mentors employed have lived experience with disorders such as attention deficit hyperactivity disorder (ADHD) and anxiety. Five primary themes emerged from the data: positive connections, mentor knowledge and experience, mentoring approaches, autism education, program organisation, resources and environment. Subthemes underpinned the primary themes related to positive connections (three subthemes), mentoring approaches (four subthemes) and program organisation, resources and environment (three subthemes). The findings suggest that prior experience, sufficient training, a supportive environment and a flexible and adaptable approach were essential for success. Understanding the core elements of a strengths-based digitalarts program in occupational therapy provides a comprehensive framework for utilising clients' inherent strengths and creativity as therapeutic tool, creating an empowering environment, fostering meaningful outcomes for clients.

An economic-environmental energy management system design for MT-HVDC networks via a semi-definite programming approximation with robust analysis

This research deals with the economic-environmental dispatch problem (EEDP) in multi-terminal high-voltage direct-current (MT-HVDC) systems by proposing a convex approximation based on semi-definite programming (SDP). The exact formulation of the EEDP corresponds to a non-convex, nonlinear programming problem due to the presence of a nonlinear quadratic constraint regarding the products between voltage variables. The thermal plants' economic and objective functions are modeled using typical quadratic functions. The SDP approach allows reaching a convex approximation that ensures the global optimal solution for each objective function independently or the construction of the optimal Pareto front via the weighting-factor optimization methodology. The proposed SDP approach also considers uncertainties in the demand and in the available power of renewable sources, which makes it robust. The main contribution of this research is a multi-period analysis that includes large-scale renewable generation sources and a robust analysis regarding demand and variations in renewable generation. Numerical results in two MT-HVDC systems demonstrate the effectiveness of the proposed SDP approach when compared to combinatorial optimization algorithms. All numerical simulations were carried out using the CVX convex disciplined tool, with the help of the SEDUMI and SDPT solvers, in the MATLAB programming environment.

Accuracy evaluation of dental CBCT and scanned model registration method based on pulp horn mapping surface: an in vitro proof-of-concept

Background and aim3D fusion model of cone-beam computed tomography (CBCT) and oral scanned data can be used for the accurate design of root canal access and guide plates in root canal therapy (RCT). However, the pose accuracy of the dental pulp and crown in data registration has not been investigated, which affects the precise implementation of clinical planning goals. We aimed to establish a novel registration method based on pulp horn mapping surface (PHMSR), to evaluate the accuracy of PHMSR versus traditional methods for crown-pulp registration of CBCT and oral scan data.Materials and methodsThis vitro study collected 8 groups of oral scanned and CBCT data in which the left mandibular teeth were not missing, No. 35 and No. 36 teeth were selected as the target teeth. The CBCT and scanned model were processed to generate equivalent point clouds. For the PHMSR method, the similarity between the feature directions of the pulp horn and the surface normal vectors of the crown were used to determine the mapping points in the CBCT point cloud that have a great influence on the pulp pose. The small surface with adjustable parameters is reconstructed near the mapping point of the crown, and the new matching point pairs between the point and the mapping surface are searched. The sparse iterative closest point (ICP) algorithm is used to solve the new matching point pairs. Then, in the C + + programming environment with a point cloud library (PCL), the PHMSR, the traditional sparse ICP, ICP, and coherent point drift (CPD) algorithms are used to register the point clouds under two different initial deviations. The root square mean error (RSME) of the crown, crown-pulp orientation deviation (CPOD), and position deviation (CPPD) were calculated to evaluate the registration accuracy. The significance between the groups was tested by a two-tailed paired t-test (p < 0.05).ResultsThe crown RSME values of the sparse ICP method (0.257), the ICP method (0.217), and the CPD method (0.209) were not significantly different from the PHMSR method (0.250). The CPOD and CPPD values of the sparse ICP method (4.089 and 0.133), the ICP method (1.787 and 0.700), and the CPD method (1.665 and 0.718) than for the PHMSR method, which suggests that the accuracy of crown-pulp registration is higher with the PHMSR method.ConclusionCompared with the traditional method, the PHMSR method has a smaller crown-pulp registration accuracy and a clinically acceptable deviation range, these results support the use of PHMSR method instead of the traditional method for clinical planning of root canal therapy.

Timing Side-Channel Mitigation via Automated Program Repair

Side-channel vulnerability detection has gained prominence recently due to Spectre and Meltdown attacks. Techniques for side-channel detection range from fuzz testing to program analysis and program composition. Existing side-channel mitigation techniques repair the vulnerability at the IR/binary level or use runtime monitoring solutions. In both cases, the source code itself is not modified, can evolve while keeping the vulnerability, and the developer would get no feedback on how to develop secure applications in the first place. Thus, these solutions do not help the developer understand the side-channel risks in her code and do not provide guidance to avoid code patterns with side-channel risks. In this paper, we present Pendulum , the first approach for automatically locating and repairing side-channel vulnerabilities in the source code, specifically for timing side channels. Our approach uses a quantitative estimation of found vulnerabilities to guide the fix localization, which goes hand-in-hand with a pattern-guided repair. Our evaluation shows that Pendulum can repair a large number of side-channel vulnerabilities in real-world applications. Overall, our approach integrates vulnerability detection, quantization, localization, and repair into one unified process. This also enhances the possibility of our side-channel mitigation approach being adopted into programming environments.

Computing irregular hypar-based quad-mesh patterns for segmented timber shells

Hyperbolic paraboloids, “hypars,” are special types of ruled surfaces. Their geometric properties provide them with loadbearing and stabilizing capacities, as well as distinct esthetic qualities. These attributes become evident in numerous applications in buildings, in many of which concrete or timber is used for the construction of the hypars. Hypars could also be relevant in the context of circular construction and design for disassembly, and the upcycling of construction waste. Due to the geometric simplicity of straight lines, which generate ruled surfaces, hypar-based structures can be designed and built with relatively simple means. They can consist of self-similar or even identical elements, which could facilitate their reuse.Compared to other types of ruled surfaces, such as conoids, hypars have the advantage of being doubly ruled, meaning that structural grids of straight elements can be formed. This paper investigates another interesting property, which is the possibility of creating flat-quad meshes by diagonally connecting the intersection points of the generatrices. This property has been previously described by other scholars, some of which explored its applicability for glass-clad steel grid shells. In this research, we focus on its potential for segmented timber shells that can serve as stand-alone structures, or as modular and reusable building parts, such as façade or roof components. The reusability of such modular units could be achieved by using reversible joints between them.More specifically, our research investigates the design space of construction systems based on such components via computational design and optimization algorithms, such as the memory limited Broyden–Fletcher–Goldfarb–Shanno (LBFGS) algorithm with automatic computation of the gradient, within the Julia programming environment. By applying principles and methods of differential geometry, we study hypars with irregular tilings, enabling the integration of panels with diverse proportions, shapes and sizes, as they can occur in wood production waste. By reducing construction waste, the work aims at reducing the negative environmental impact of the building construction sector. Moreover, irregular tilings could enable a more customized design of acoustic qualities and offer visual variety in segmented hypar based timber structures.The here presented studies show that the proposed optimization method provides a good fit of many tiles to rhombi, particularly when the steepness is not too large. We also show that optimizing towards rectangles provides better results. Overall, the results support the initial assumption that irregular rulings could be a means of adapting to both homogeneous and diverse material stocks.

MolSym: A Python package for handling symmetry in molecular quantum chemistry.

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program's environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree-Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. This package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

РЕАЛІЗАЦІЯ ДИФЕРЕНЦІЙОВАНОГО ПІДХОДУ ПРИ НАВЧАННІПРОГРАМУВАННЮ МОВОЮ PYTHON ЗДОБУВАЧІВЗАГАЛЬНОЇ СЕРЕДНЬОЇ ОСВІТИ

Students choose their future path starting from school. The learner can determine their abilities when they genuinely understand their potential and prospects in a specific subject or direction. A differentiated approach helps explore the limits of knowledge and encourages its expansion. The paper discusses the implementation of a differentiated approach in teaching computer science, particularly in programming. Tasks are proposed to students, emphasizing the method of solving. Each learner chooses the method that suits them best. Evaluation criteria are provided in advance, helping students identify the aspects of the task to focus on. An example task demonstrating the implementation of a differentiated approach involves calculating the recommended body weight for a woman and a man using the Broca's Formula. Depending on the complexity chosen by the student, this task can be solved using a linear structure, applying branching, or developing a widget. Widgets allow students to demonstrate the use of libraries and functions in the Python programming language. Various solutions in Python and recommendations for the placement of such tasks in the study of a specific topic are also provided. During the defense, all students become acquainted with different task implementations, allowing each student to understand various execution methods and choose a more challenging one next time. The presented example can be extended to other topics, in the study of other programming languages or programming environments. Prospects for further research include developing a system of multi-level programming tasks for the purpose of implementing differentiated learning

Code Verse

Code Verse features a revolutionary online code editor that aims to revolutionize your coding experience by providing a seamless interface between different programming languages. This innovative platform supports developers working with languages such as JavaScript, Python, Java, php etc. facilitating a versatile programming environment that adapts to the diverse needs of users. Code verse's user-friendly interface removes the traditional barriers associated with language-specific editors and allows developers to seamlessly switch between languages within the same platform.

Διαχείριση ανοιχτών δεδομένων με χρήση της τεχνολογίας API στο περιβάλλον του App Inventor

The contemporary global focus on the evolution and dissemination of the coronavirus (COVID-19) underscores its significance on a worldwide scale. It is imperative to acknowledge the dynamic nature of the pandemic, with ongoing monitoring being crucial for effective response. Our research endeavors to enhance educational approaches in light of these challenges. An innovative educational scenario and learning model have been devised and implemented, rooted in dilemmas and argumentation. This model integrates key educational principles, including a STEM-based interdisciplinary approach, self-regulated learning, and collaborative learning. Furthermore, the study emphasizes the instrumental role of technological tools in fostering active and collaborative participation within the classroom setting. A tangible manifestation of this emphasis is an application developed using the visual programming environment of App Inventor. Students showcased their results in class, engaging in discussions, presenting arguments, and reflecting on the findings that ultimately led to the conclusions presented in class. This multifaceted educational approach seeks to address the challenges posed by the evolving landscape of the COVID-19 pandemic.

Visualization and Segmentation of Lung Tissue in DICOM-format CT Images

Abstract In this project, two tools were developed as a preliminary step towards the creation of future Computer-Aided Diagnosis systems for the automatic detection of lung diseases. The first methodology aimed to extract information from DICOM-format files using Python and the Jupyter Notebook programming environment. The implemented process enables the generation of images in various anatomical planes, using the pixel matrix in Hounsfield units. Additionally, a custom tool was developed for anonymizing and visualizing file information. The techniques employed are independent of the grayscale image generated and are not reliant on the type of window used. The second tool introduces a lung segmentation technique for chest CT images. The segmentation uses the pixel matrix in Hounsfield units to initially identify lung tissue based on its linear X-ray attenuation. Subsequently, the Watershed algorithm based on markers is employed as the primary foundation of the technique. Various morphological operations and transformations on axial slices are applied, resulting in successful lung segmentation.