Analysis Code Research Articles

The Predictability of Naturalistic Evaluation of All-Day Recordings for Speech and Language Development.

The study aimed to investigate the predictive potential of language environment and vocal development status measures obtained through integrated analysis of Language ENvironment Analysis (LENA) recordings during the prelinguistic stage for subsequent speech and language development in Korean-acquiring children. Specifically, this study explored whether measures from both LENA-automated analysis and human coding at 6-8 months and 12-14 months of age predict vocabulary and phonological development at 18-20 months. One-day home recordings from 20 children were collected using a LENA recorder at 6-8 months, 12-14 months, and 18-20 months. Both LENA-automated measures and measures from human coding were obtained from recordings at 6-8 months and 12-14 months. The number of different words, consonant inventory, and utterance structure inventory were identified from recordings of 18-20 months. Correlation and multiple regression analyses were performed to investigate whether measures related to early language environment and child vocalization at 6-8 months and 12-14 months were predictive of vocabulary and phonological measures at 18-20 months. The results showed that the two main LENA-automated measures, conversational turn count (CTC) and child vocalization count, were positively correlated with all vocabulary and phonological measures at 18-20 months. Multiple regression analysis revealed that CTC during the prelinguistic stages was the most significant predictor of a number of different words, consonant inventory, and utterance structure inventory at 18-20 months. Also, adult word count in LENA-automated measures, child-directed speech ratio, and canonical babbling ratio measured by human coding significantly predicted some vocabulary and phonological measures at 18-20 months. This study highlights the multifaceted nature of language acquisition and collectively emphasizes the value of considering both quantitative and qualitative aspects of language input to understand early language development in children.

Designing A Model for Production of Children's Health Information Content in Radiology Centers: A Case Study

Background: In contemporary healthcare systems, there is a growing emphasis on promoting self-care, patient empowerment, and informed decision-making. The creation of health information content is recognized as a vital tool for enhancing the health literacy of patients. Objectives: This study aimed to explore the components of children's health information in radiology centers to develop a model for producing health information content tailored to their needs. Methods: This qualitative exploratory case study involved 71 participants, including 25 children aged 6 - 14 years, 25 parents of children aged 0 - 14 years, and 23 staff members (radiology technicians and doctors) from the Radiology Department at Children's Medical Center Hospital in Tehran, Iran. Semi-structured and in-depth interviews were conducted to collect data, which were then analyzed using qualitative content analysis and selective-axial coding. Results: The developed model comprised two main dimensions: Health information content and health information format. The dimension of health information content included process information, emotional information, and self-regulating information. The dimension of health information format encompassed various formats such as informational brochures, books, educational animations, applications, and oral explanations. Conclusions: Creating health information content that aligns with children's interests and preferences can enhance their cooperation and participation in radiology examinations.

Unveiling ethical challenges: An in-depth analysis of unethical behavior in Albanian preschool education

Background: The study delves into the prevalence and severity of unethical behavior among educators and parents in Albanian early childhood education institutions, a topic previously unexplored. Objective: The research aims to identify the most common unethical behaviors among educators and parents, understand the primary determinants of unethical behavior in preschool settings, and provide recommendations for policy development in Albanian public preschools to improve ethical norms. Settings: The study was conducted in Albanian early childhood education institutions, focusing on both educators and parents as key stakeholders. Methods: A comprehensive analysis was undertaken, utilizing questionnaires administered to educators and parents, drawing on codes of ethical behavior and thematic analysis to identify and categorize unethical behaviors. Statistical analysis using SPSS was employed to derive insights from the data. Results: Findings revealed a notable discrepancy between educators' and parents' perceptions of unethical behavior, with guardians reporting higher frequencies of unethical conduct. The most prevalent unethical behaviors included unequal treatment of children, favoritism, and use of punishment by educators, while parents commonly violated norms such as disrespecting educators and encouraging physical aggression in children. Conclusion: The study underscores the importance of addressing unethical behavior in Albanian preschool settings through enhanced awareness, training, and policy interventions. Strategies to foster better communication between educators and parents, improve professional standards, and combat discrimination are imperative for creating a more ethical environment conducive to positive child development.

Perception of pregnant individuals, health providers and decision makers on interventions to cease substance consumption during pregnancy: a qualitative study

BackgroundDespite multiple recommendations and strategies implemented at a national and international level, cigarette smoking, alcohol consumption, and cannabis use during pregnancy remains high in most countries. The objective of this study was to examine key stakeholders’ perception of the treatment interventions adopted in Spain, to identify political, organizational and personal factors associated with successful implementation, and to propose strategies for improvement.MethodsA qualitative study with a phenomenological approach was conducted in 2022. The target groups were: (1) clinical decision makers in the field of addiction science, (2) health professionals who carry out treatment interventions, and (3) pregnant individuals who use tobacco, alcohol or cannabis. Two focus groups and eight in-depth interviews were conducted, recorded, and transcribed. Exploratory analysis and inductive open coding was performed, codes were merged into categories, and subcategories were identified.ResultsThe analysis resulted in 10 subcategories which were further merged into three main categories: (1) Degree of adoption and utility of treatment interventions implemented; (2) Needs and demands with respect to the organization of treatment interventions; and, (3) Personal barriers to and facilitators for treatment. Respondents reported that despite multiple national and regional cessation initiatives, treatment interventions were rarely adopted in clinical practice. Health care administrators demanded reliable records to quantify substance use for better planning of activities. Health care professionals advocated for additional time and training and both echoed the importance of integrating cessation interventions into routine prenatal care and creating in-house specialized units. The difficulty in quitting, lack of awareness of risk for foetus and child and the controversial advice were identified as barriers by pregnant individuals.ConclusionsConsistent with previous work, this study found that cessation strategies implemented by the health authorities are not effective if they are not accompanied by organizational and behavioral changes. The current study identifies a set of factors that could be pivotal in ensuring the success of treatment interventions targeting tobacco, alcohol and cannabis use among pregnant individuals.

Effect of Cooling Pump Transient Power Supply on ETRR-2 Safety Parameters

The primary goals of the engineering design for nuclear reactors involve safeguarding the integrity of the reactor core. Preserving the integrity of the cladding material is especially crucial, as it serves as the initial defense against the potential dangers posed by radioactive materials. In this work, an accident analysis of core cooling pump power transients of different ratios of the nominal pump power in a typical material test reactor is conducted. Phase failure is a very common electrical fault experienced by three-phase motors. Pump power reduction can be initiated due to several causes, like phase failure, voltage reduction, winding failure, and other causes. The nuclear reactor analysis code PARET/ANL version 7.6 is used to carry out these calculations. The accident scenario began with the reactor operating steadily, then experiencing a transient in the core pump power. This caused the core flow rate to decrease and eventually stabilize at a lower level as the pump power decreased. Core cooling pump power variations ratios of 20%, 33.3%, 50%, and 70% of the nominal pump power are considered in this work. The accident analysis is conducted under the availability and unavailability of reactor safety systems. Reactor safety parameters are reported for all cases of the core pump power variations.

System code validation in periodic two-phase flow from low-pressure oscillations

Low-pressure two-phase flow instabilities can potentially challenge start-up transients of water-cooled nuclear reactors. Predicting oscillations caused by flow instability is therefore paramount to reactor safety. While many thermal hydraulics system codes are developed as general-purpose tools, their performance is not guaranteed outside their optimized application ranges. The current study therefore performs validations of two thermal hydraulics system analysis codes, the Adaptive SYStem Thermal-hydraulics Version 3 (ASYST VER3) and Reactor Excursion and Leak Analysis Program MOD3.3 (RELAP5/MOD3.3), in reproducing oscillatory two-phase flow induced by low-pressure flashing instability. Benchmark conditions are selected from a novel dataset resolving void fraction with radial resolution and quantifying periodic behavior with ensemble averaging. Focusing on the prediction of transient two-phase phenomena rather than the determination of stability, validations are conducted by simulating a single channel under prescribed periodic boundary conditions. ASYST exhibits a systematic underprediction of void fraction in an adiabatic chimney downstream of a heated section. The prominent causal discrepancy is identified as a lost travelling void wave due to overpredicted condensation. RELAP5 noticeably overpredicts subcooled boiling and underpredicts flashing, which is consistent with existing validations against steady-state separate-effect tests under low pressure. This degraded code performance also suggests that in low-pressure transients prone to flashing instability, the confidence in RELAP5 derived from existing validations against integral-effect tests shall be carefully limited to validated capabilities in integral systems. In general, the current study fills the previous gap of knowledge about the performance of ASYST and RELAP5 in predicting detailed transient two-phase phenomena in low-pressure flashing-induced oscillations. The identified code defects reveal future directions for code improvements eventually towards reliable application of ASYST and RELAP5 under low pressure beyond their original calibration.

Plasma profile reconstruction supported by kinetic modeling

Combining the analysis of multiple diagnostics and well-chosen prior information in the framework of Bayesian probability theory, the Integrated Data Analysis code (IDA Fischer et al 2010 Fusion Sci. Technol. 58 675–84) can provide density and temperature radial profiles of fusion plasmas. These IDA-fitted measurements are then used for further analysis, such as discharge simulations and other experimental data analysis. Since IDA considers measurement data, which is frequently fragmentary, with statistical and systematic uncertainties, which are often difficult to quantify, from a heterogeneous set of diagnostics, the fitted profiles and their gradients may be in contradiction to well-established expectations from transport theory. Using the modeling suite ASTRA coupled with the quasi-linear transport solver TGLF, we have created a loop in which simulated profiles and their uncertainties are fed back into IDA as an additional prior, thus providing constraints about the physically reasonable parameter space. We apply this physics-motivated prior to several different plasma scenarios and find improved heat flux match, while still matching the experimental data. This work feeds into a broader effort to make IDA more robust against measurement uncertainties or lack of measurements by combining multiple transport solvers with different levels of complexity and computing costs in a multi-fidelity approach.

Platform Design of Passive Target Perception and Localization Based on Sensor Networks

With the rapid development, extensive knowledge, and diverse application scenarios of target perception and positioning technology in sensor networks, a passive target perception and localization platform based on Wireless Sensor Networks (WSN) has been designed. The platform is mainly applied for the teaching of electronic information, communication, and Internet of Things (IoT) engineering. The platform follows the teaching concept of “developing students’ ability to solve complex engineering problems” in the emerging engineering discipline and combines experimental simulation with real-world testing, as well as integrating scientific research and teaching. It encompasses technical elements such as perception and localization theory, sparse representation classification modeling, solving underdetermined equations, data analysis, and sparse coding, as well as nontechnical aspects such as team collaboration and cost budgeting. The platform boasts high fidelity and scalability, providing students with the opportunity to develop comprehensive practical and innovative skills in solving complex engineering problems.

Development and benchmarking of space-time kinetics solver in REDAC

A space–time kinetics solver based on finite volume approach has been developed for REactor Dynamics Analysis Code (REDAC). It solves 3D multi-group neutron diffusion equation, coupled with the heat transport equations. Code offers flexibility in terms of reactor geometry, boundary conditions and discretization. Efficient numerical methods have been employed for solving the governing equations. The performance of the code has been demonstrated by solving diverse benchmark problems. It is shown that REDAC is able to predict important static and dynamic core parameters with good accuracy. The framework of REDAC easily lends itself for sensitivity studies with respect to spatial discretization and temporal integration schemes. From these studies, it is observed that spatial resolution could have significant influence on accuracy and the semi-implicit method performs better than the implicit method. Improved quasi-static method requires less computational time compared to the direct method, however, its accuracy depends on the time step size.

Thermal and hydraulic characteristics analysis of horizontal lead-bismuth reactor assembly based on sub-channel analysis code

In this paper, the self-developed subchannel analysis program SACOS-PB was used to analyze the thermal hydraulic characteristics of 127 rods in horizontal lead-bismuth reactor under steady state and typical ocean transient conditions. The results of the steady-state analysis showed that the coolant flow and temperature of the vertical assembly were symmetrically distributed, the coolant flow of the horizontal assembly gradually increased along the gravity direction, the temperature rose first and then fell, and the peak coolant temperature of the cross-sectional channel was about 365 °C. Three typical ocean transient operating conditions, inclined, undulating and swing, were also carried out. The results showed that the maximum temperature of the coolant at the exit of the assembly increased with the increase of the transverse heeling angle and the decrease of the trim angle after the incline motion changes the gravity component. Under the undulating and swing conditions, the coolant hydrothermal parameters fluctuated periodically, and the parameter fluctuation period is 10s in line with the oceanic conditions. In the above transient conditions, the peak coolant temperature at the assembly outlet increases and exceeds 365 °C. Compared with the pitching motion on the Y-axis, the rolling and tumbling motion on the X-axis had a significant effect on the coolant flow rate and temperature distribution. The calculation results can provide support for the analysis study of the safety characteristics of lead-bismuth power units under marine conditions.

MotGen: a closed-loop bacterial motility control framework using generative adversarial networks.

Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks (GANs) to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics. Supplementary data are available at Bioinformatics online.

Volatile Organic Compound-Based Predictive Modeling of Smoke Taint in Wine.

Smoke taint in wine has become a critical issue in the wine industry due to its significant negative impact on wine quality. Data-driven approaches including univariate analysis and predictive modeling are applied to a data set containing concentrations of 20 VOCs in 48 grape samples and 56 corresponding wine samples with a taster-evaluated smoke taint index. The resulting models for predicting the smoke taint index of wines are highly predictive when using as inputs VOC concentrations after log conversion in both grapes and wines (Pearson Correlation Coefficient PCC = 0.82; R2 = 0.68) and less so when only grape VOCs are used (Pearson Correlation Coefficient PCC = 0.76; R2 = 0.56), and the classification models also show the capacity for detecting smoke-tainted wines using both wine and grape VOC concentrations (Recall = 0.76; Precision = 0.92; F1 = 0.82) or using only grape VOC concentrations (Recall = 0.74; Precision = 0.92; F1 = 0.80). The performance of the predictive model shows the possibility of predicting the smoke taint index of the wine and grape samples before fermentation. The corresponding code of data analysis and predictive modeling of smoke taint in wine is available in the Github repository (https://github.com/IBPA/smoke_taint_prediction).

Actionable code smell identification with fusion learning of metrics and semantics

Code smell detection is one of the essential tasks in the field of software engineering. Identifying whether a code snippet has a code smell is subjective and varies by programming language, developer, and development method. Moreover, developers tend to focus on code smells that have a real impact on development and ignore insignificant ones. However, existing static code analysis tools and code smell detection approaches exhibit a high false positive rate in detecting code smells, which makes insignificant smells drown out those smells that developers value. Therefore, accurately reporting those actionable code smells that developers tend to spend energy on refactoring can prevent developers from getting lost in the sea of smells and improve refactoring efficiency. In this paper, we aim to detect actionable code smells that developers tend to refactor. Specifically, we first collect actionable and non-actionable code smells from projects with numerous historical versions to construct our datasets. Then, we propose a dual-stream model for fusion learning of code metrics and code semantics to detect actionable code smells. On the one hand, code metrics quantify the code's structure and even some rules or patterns, providing fundamental information for detecting code smells. On the other hand, code semantics encompass information about developers' refactoring tendencies, which prove valuable in detecting actionable code smells. Extensive experiments show that our approach can detect actionable code smells more accurately compared to existing approaches.

Preliminary analysis of in-orbit operation accidents for an ultra-small Lithium-cooled space reactor power system

To study the safety characteristics of an ultra-small (5.2 MWt) lithium-cooled space reactor power system (ULCR) under postulated accidents, a transient analysis code for the ultra-small space reactor coupled with a closed Brayton cycle dynamic conversion and heat pipe radiator was established. A preliminary analysis of four typical postulated accidents occurring at 100 percent power in-orbit operation of the ULCR system was conducted to define postulated problem areas and establish safety requirements. The postulated accidents mainly include (1) reactivity insertions, (2) coolant partial flow loss, (3) turbomachinery failure, and (4) radiator partial failure. The results showed that: a) since the ULCR system was designed to operate at high-temperature conditions (coolant temperature greater than 1500 K), the safety margin of the ULCR system was small. Emergency shutdown control or reduced power operation action should be taken proactively to ensure ULCR's safety in different accidents. b) When the control rod was misoperated and large reactivity was inserted, the maximum temperature of the lithium coolant was very close to the saturation temperature (the difference was 24 K), and the system was operated at extremely high temperatures (the average temperature of the core coolant was 1750 K); c) In coolant flow loss accident, when the coolant flow of the system dropped below 40% of the rated mass flow, the lithium coolant boiling would occur in the primary loop system, and the emergency shutdown control action was required to ensure the safety of the system.

Quantifying power system frequency quality and extracting typical patterns within short time scales below one hour

This paper addresses the lack of consideration of short time scales, below one hour, such as sub-15-min and sub-1-hr, in grid codes for frequency quality analysis. These time scales are becoming increasingly important due to the flexible market-based operation of power systems as well as the rising penetration of renewable energy sources and battery energy storage systems. For this, firstly, a set of frequency-quality indices is considered, complementing established statistical indices commonly used in power-quality standards. These indices provide valuable insights for quantifying variations, events, fluctuations, and outliers specific to the discussed time scales. Among all the implemented indices, the proposed indices are based on over/under frequency events (6 indices), fast frequency rise/drop events (6 indices), and summation of positive and negative peaks (1 index), of which the 5 with the lowest thresholds are identified as the most dominant. Secondly, k-means and k-medoids clustering methods in a learning scheme are employed to identify typical patterns within the discussed time windows, in which the number of clusters is determined based on prior knowledge linked to reality. In order to clarify the frequency variations and patterns, three frequency case studies are analyzed: case 1 (sub-15-min scale, 10-s values, 6 months), case 2 (sub-1-hr scale, 10-s values, 6 months), and case 3 (sub-1-hr, 3-min values, the year 2021). Results obtained from the indices and learning methods demonstrate a full picture of the information within the windows. The maximum value of the highest frequency value minus the lowest one over the windows is about 0.35 Hz for cases 1 and 2 and 0.25 Hz for case 3. Over-frequency values (with a typical 0.1% threshold) slightly dominates under-frequency values in cases 1 and 2, while the opposite is observed in case 3. Medium fluctuations occur in 35% of windows for cases 1 and 2 and 41% for case 3. Outlier values are detected using the quartile method in 70% of windows for case 2, surpassing the other two cases. About six or seven typical patterns are also extracted using the presented learning scheme, revealing the frequency trends within the short time windows. The proposed approaches offer a simpler alternative than tracking frequency single values and also capture more comprehensive information than existing approaches that analyze the aggregated frequency values at the end of the specific time windows without considering the frequency trends. In this way, the network operators have the possibility to monitor the frequency quality and trends within short time scales using the most dominant indices and typical patterns.

Tsunami hazard evaluation of river embankment structures incorporating their vulnerability to seismic strong motion

Development of coastal areas in Japan for various land uses since the 1960s has contributed to industrial upgrades and improved the efficiency of transportation networks. However, there are concerns about the vulnerability of developments on alluvial plains and reclaimed lands to geological events, like ground subsidence due to liquefaction during large earthquakes. Realistic assessment of earthquake and tsunami hazards and evaluation of possible countermeasures require accurate estimation of the amount of subsidence that can be expected from liquefaction at coastal and riverside sites supporting various structures. In this study, to evaluate the amount a river embankment structure might be expected to settle as a result of strong motion from an assumed Nankai Trough great earthquake, we conducted a numerical simulation using the soil–water coupled finite deformation analysis code GEOASIA. We then investigated the effect of the estimated embankment subsidence on tsunami inundation, which was simulated by using nonlinear shallow-water equations and a grid spacing as fine as 3.3 m. The influence of urban structures on the inundated area was taken into account by using a structure-embedded elevation model (SEM). The results showed that subsidence of river embankments and the collapse of parapet walls on top of them would increase both the depth and area of inundation caused by a tsunami triggered by a Nankai Trough scenario earthquake. Our findings underscore the importance of evaluating not only earthquake resistance but also vulnerability of coastal and riverside structures to strong motion in tsunami hazard analyses. Furthermore, the importance of tsunami inundation analysis using a SEM for predicting the behavior of tsunami flotsam in urban areas was demonstrated.

Research on thermal hydraulic characteristics of reflooding process based on core multi-channel model in system analysis code

There are four main stages in PWR Loss of Coolant Accident (LOCA): blowdown, re-irrigation, re-flooding and long-term cooling. The re-flooding stage is a critical phase to avoid core meltdown and achieve long-term cooling. RBHT reflooding experiment was simulated with the RELAP5 and COSINE program respectively.Thermal-hydraulic characteristics in re-flooding phase were analyzed using multi-channel model. Quench time is mainly influenced by system pressure and coolant subcooling. Later quench will result in a higher cladding temperature. Comparatively, the peak cladding temperature is less affected by the power of the heating rod and the re-flooding rate, which fluctuates within 7 %. The higher re-flooding rate allows quenching to occur as fast as possible and improves cooling efficiency. However, higher coolant sub-cooling and re-flooding rates may cause greater thermal stresses on the core and endanger core integrity. Based on the analysis of the re-flooding rate on the peak cladding temperature and quench time, the optimal re-flooding rate interval of 0.099–0.105 m/s was obtained.

Technology Adoption as a Factor for Financial Performance in the Banking Sector Using UTAUT Model

This study investigated the complex relationship between technology adoption and the overall performance of selected Zambian banks. The analysis focuses on four prominent banks in Lusaka, namely Zanaco, Atlasmara, Stanbic, and FNB, which were selected based on characteristics such as footprint and income. The time frame is from 2015 to 2020, allowing for a complete examination of trends in the ever-changing banking business. The study used a mixed-methods approach, combining quantitative financial data analysis with qualitative analysis. Data was gathered through the examination of financial reports, industry databases, interviews with banking specialists, and surveys. For the quantitative and qualitative data, statistical analysis and theme coding were used, respectively. The study aimed to provide significant insights to academia, the banking sector, and industry stakeholders by providing a comprehensive understanding of how technology adoption impacts the performance landscape of banks in Zambia. The findings revealed a positive correlation between technology adoption and financial performance with a Pearson’s two-tailed coefficient of 0.6. The study also investigated perceptions around IT platform stability and total bank efficiency, demonstrating a strong belief in the performance-enhancing potential of a reliable IT infrastructure.

Using Interpretable Machine Learning for Differential Item Functioning Detection in Psychometric Tests

This study presents a novel method to investigate test fairness and differential item functioning combining psychometrics and machine learning. Test unfairness manifests itself in systematic and demographically imbalanced influences of confounding constructs on residual variances in psychometric modeling. Our method aims to account for resulting complex relationships between response patterns and demographic attributes. Specifically, it measures the importance of individual test items, and latent ability scores in comparison to a random baseline variable when predicting demographic characteristics. We conducted a simulation study to examine the functionality of our method under various conditions such as linear and complex impact, unfairness and varying number of factors, unfair items, and varying test length. We found that our method detects unfair items as reliably as Mantel–Haenszel statistics or logistic regression analyses but generalizes to multidimensional scales in a straight forward manner. To apply the method, we used random forests to predict migration backgrounds from ability scores and single items of an elementary school reading comprehension test. One item was found to be unfair according to all proposed decision criteria. Further analysis of the item’s content provided plausible explanations for this finding. Analysis code is available at: https://osf.io/s57rw/?view_only=47a3564028d64758982730c6d9c6c547 .

ORTEGA v1.0: an open-source Python package for context-aware interaction analysis using movement data

BackgroundInteraction analysis via movement in space and time contributes to understanding social relationships among individuals and their dynamics in ecological systems. While there is an exciting growth in research in computational methods for interaction analysis using movement data, there remain challenges regarding reproducibility and replicability of the existing approaches. The current movement interaction analysis tools are often less accessible or tested for broader use in ecological research.ResultsTo address these challenges, this paper presents ORTEGA, an Object-oRiented TimE-Geographic Analytical tool, as an open-source Python package for analyzing potential interactions between pairs of moving entities based on the observation of their movement. ORTEGA is developed based on one of the newly emerged time-geographic approaches for quantifying space-time interaction patterns among animals. A case study is presented to demonstrate and evaluate the functionalities of ORTEGA in tracing dynamic interaction patterns in animal movement data. Besides making the analytical code and data freely available to the community, the developed package also offers an extension of the existing theoretical development of ORTEGA for incorporating a context-aware ability to inform interaction analysis.ConclusionsORTEGA contributes two significant capabilities: (1) the functions to identify potential interactions (e.g., encounters, concurrent interactions, delayed interactions) from movement data of two or more entities using a time-geographic-based approach; and (2) the capacity to compute attributes of potential interaction events including start time, end time, interaction duration, and difference in movement parameters such as speed and moving direction, and also contextualize the identified potential interaction events.