A statistical software framework for marketing analytics: methodology, simulations, case study, and a python library

Abstract. The flow (flux) of climate-critical gases, such as carbon dioxide (CO2), between the ocean and the atmosphere is a fundamental component of our climate and an important driver of the biogeochemical systems within the oceans. Therefore, the accurate calculation of these air–sea gas fluxes is critical if we are to monitor the oceans and assess the impact that these gases are having on Earth's climate and ecosystems. FluxEngine is an open-source software toolbox that allows users to easily perform calculations of air–sea gas fluxes from model, in situ, and Earth observation data. The original development and verification of the toolbox was described in a previous publication. The toolbox has now been considerably updated to allow for its use as a Python library, to enable simplified installation, to ensure verification of its installation, to enable the handling of multiple sparingly soluble gases, and to enable the greatly expanded functionality for supporting in situ dataset analyses. This new functionality for supporting in situ analyses includes user-defined grids, time periods and projections, the ability to reanalyse in situ CO2 data to a common temperature dataset, and the ability to easily calculate gas fluxes using in situ data from drifting buoys, fixed moorings, and research cruises. Here we describe these new capabilities and demonstrate their application through illustrative case studies. The first case study demonstrates the workflow for accurately calculating CO2 fluxes using in situ data from four research cruises from the Surface Ocean CO2 ATlas (SOCAT) database. The second case study calculates air–sea CO2 fluxes using in situ data from a fixed monitoring station in the Baltic Sea. The third case study focuses on nitrous oxide (N2O) and, through a user-defined gas transfer parameterisation, identifies that biological surfactants in the North Atlantic could suppress individual N2O sea–air gas fluxes by up to 13 %. The fourth and final case study illustrates how a dissipation-based gas transfer parameterisation can be implemented and used. The updated version of the toolbox (version 3) and all documentation is now freely available.

In the dynamic field of programming education, integrating artificial intelligence (AI) tools has started to play a significant role in enhancing learning experiences. This paper presents a case study conducted during a foundational programming course for first-year students in higher education, where students were encouraged to utilize generative artificial intelligence programming copilot extensions in their programming IDE and browser-based generative AI tools as supportive AI tools. The primary objective was to observe the impact of AI on the learning curve and the overall educational experience.Key findings suggest that the introduction of AI tools significantly altered the learning experience for students. Many who initially struggled with grasping elementary programming concepts found that AI support made understanding basic programming concepts much easier, enhancing their confidence and skills. This was particularly evident in the reduced levels of anxiety typically associated with early programming learning, as the AI copilot provided a non-judgmental, always-available source for clarifying doubts, including queries that students might hesitate to ask in a traditional classroom setting.Notably, some students leveraged the AI to generate similar exercise problems, reinforcing their understanding and skills. The AI's capability to address basic queries also freed up the instructor's time, allowing for more personalized student guidance in more advanced problems. This shift in the instructional dynamic further contributed to a learning environment where students felt more comfortable engaging with complex topics, thereby reducing the psychological barriers often linked with early-stage programming education.The course's structure, enriched by AI, enabled students to delve into more complex programming constructs earlier than traditional curricula would allow. For instance, students were tasked with simulating basic e-commerce operations, such as user registration, product browsing, and cart functionalities. These practical challenges naturally introduced advanced concepts like external data storage, unit testing, and user interface design, which are typically reserved for more advanced courses. With the help of generative AI programming copilot tools, students at any programming skill level were able to develop nearly functional complex structures. Interestingly, even when their projects were not fully functional, students remained motivated. Instead of feeling discouraged by these imperfect outcomes, they showed resilience and a keen interest in understanding and improving their code. This reaction is a significant shift from traditional learning settings, where unfinished or flawed projects often lead to increased anxiety or a drop in motivation.Furthermore, the AI's proactive suggestions inspired students to explore beyond the curriculum. Advanced learners delved into databases, cryptography libraries in Python, and even more advanced user interface design, ensuring that they remained engaged and challenged. This elementary course, enhanced by generative AI tools, also inspired students to learn other programming languages since they now learned that individual learning is more available with the aid of generative AI.In conclusion, the integration of AI in programming education offers a promising avenue for enhancing both the learning experience and outcomes. This case study underscores the potential of AI to revolutionize traditional teaching methodologies, fostering a more dynamic, responsive, and inclusive learning environment.This paper handles the results, possibilities and challenges of AI empowered education in programming. It also gives practical examples as well as future research perspectives.

Finite Element Analysis (FEA) is a prevalent tool for the integrity assessment of pipelines in the industry and is widely acknowledged by industrial standards such as CSA Z662 and CFR 192.712. Previous research has established the capability of FEA to accurately simulate the response of pipelines under various loading scenarios, demonstrating high fidelity in processes like dent simulation and predicting the failure of corroded pipelines. The Finite Element method is a complex numerical technique that involves discretizing a continuous pipeline into small finite elements. While the accessibility of commercial FEA software tools has facilitated the application of this method, challenges and potential issues persist. Firstly, FEA simulations generate substantial amounts of data, including nodal solutions and stiffness matrices. Secondly, debugging can be intricate, necessitating a combination of experience and specialized knowledge. Thirdly, postprocessing can be time-consuming; for instance, calculating the Ductile Failure Damage Indicator (DFDI) involves integrating stress components across all time steps. Lastly, commercial FEA software typically operates as closed source, limiting customization options to the features provided by the software. This paper introduces the use of a Python-based FEA library, PyAnsys, offering a more accessible and streamlined approach to managing the extensive data generated by FEA simulations. The integration of PyAnsys with Python enhances the debugging process by providing an intuitive and user-friendly scripting interface. Python’s readability and extensive debugging tools contribute to a smoother debugging experience, reducing the dependency on specialized knowledge. Additionally, PyAnsys facilitates efficient pre-processing and post-processing by leveraging the power of Python. The open-source nature of PyAnsys enhances customization options, allowing integration with other Python libraries, such as the visualization package PyVista, making PyAnsys a versatile and powerful tool for FEA applications. Two case studies are presented in the paper to demonstrate the effectiveness of PyAnsys. The first case study utilizes PyAnsys to analyze a dented pipe in structural analysis, while the second case study involves study simulating a corroded pipeline to evaluate the load carrying capacity of a pipeline containing metal loss corrosions.

Station–city integration cyberspace behaves as an interdisciplinary field of intelligent transportation and smart city, also a representative scenario in the Architecture, Engineering and Construction (AEC) sector. Due to the growing demands in data integration research, the intelligent operation and maintenance (O&M) of the station–city integration cyberspace needs to implement semantic ontology, which is suitable for semantic web construction. To achieve semantic information fusion of multi-source heterogeneous data, and clarify the decision-making role of various types of data on specific operational goals, this article proposed a framework for semantic ontology model construction, based on the deployed sensor network. Specifically, an ontology model for station–city integration cyberspace O&M was constructed, incorporating sensor data mainly from five categories, named structure, environment, crowd flow, emergency events, and energy consumption, respectively. Subsequently, the r d f l i b library in Python was utilized to assign data flow to static semantic models. Furthermore, a semantic web inference engine was generated using decision rules, ultimately completing risk early warning and equipment maintenance for the sensor network. Finally, a case study was conducted for the station–city integration O&M scenario in Shenzhen North Station, and the experimental results demonstrated the applicability and effectiveness, providing robust, intelligent data support for intelligent O&M.

With the increasing popularity of electric vehicles (EVs), the demands for rechargeable and high-performance batteries like lithium-ion (Li-ion) batteries have soared. Li-ion battery systems require the use of a battery management system (BMS) to perform safely and efficiently. Accurate and reliable battery modeling is important for the BMS to function properly. Currently, many BMS applications use the equivalent circuit model due to its simplicity. However, with the development of a cloud BMS, machine learning battery models can be utilized, which can potentially improve the accuracy and reliability of the BMS. This work investigates the performance of four different machine learning models used to predict the thermal (temperature) and electrical (voltage) behaviors of Li-ion battery cells. A prismatic Li-ion battery cell with a capacity of 25 Ah was cycled under a constant current profile at three different ambient temperatures, and the surface temperature and voltage of the battery were measured. The four machine learning regression models—linear regression, k-nearest neighbors, random forest, and decision tree—were developed using the scikit-learn library in Python and validated with experimental data. The results of their performance were reported and compared using the R2 metric. The decision tree-based model, with an R2 score of 0.99, was determined to be the best model in this case study.

With the rise of inverter-based resources (IBRs) within the power system, the control of grid-connected converters (GCCs) has become pertinent due to the fact they interface IBRs to the grid. The conventional method of control for a GCC such as the voltage-sourced converter (VSC) is through a decoupled control loop in the synchronous reference frame. However, this model-based control method is sensitive to parameter changes causing deterioration in controller performance. Data-driven approaches such as machine learning can be utilized to design controllers that are capable of operating GCCs in various system conditions. This work explores a deep learning-based control method for a three-phase grid-connected VSC, specifically utilizing a long short-term memory (LSTM) network for robust control. Simulations of a conventional controlled VSC are conducted using Simulink to collect data for training the LSTM-based controller. The LSTM model is built and trained using the Keras and TensorFlow libraries in Python and tested in Simulink. The performance of the LSTM-based controller is evaluated under different case studies and compared to the conventional method of control. Simulation results demonstrate the effectiveness of this approach by outperforming the conventional controller and maintaining stability under different system parameter changes.

This study employs three advanced gradient boosting machine learning algorithms to assess potential disparities in healthcare delivery. We specifically investigate which factors contribute to a patient’s timely diagnosis of metastatic breast cancer using a public healthcare dataset. Our approach involves training and testing three separate models, as well as an ensemble model with automatically optimized weights. The models try to predict whether patients received a diagnosis of metastatic breast cancer within 90 days. Each model has different preprocessing and feature selection steps. The hyperparameter optimization is performed using the Optuna library in Python. Models are evaluated on the Kaggle platform, with our metrics indicating strong predictive performance; Categorical Boosting achieved an Area Under the Receiver Operating Characteristic Curve score of 0.813, Extreme Gradient Boosting reached 0.808, Light Gradient Boosting Machine scored 0.805, and the ensemble model culminated at 0.808. Additionally, we analyze the set of features being used by all the best models and examine the impact of hyperparameters on the models’ overall performance.

This research paper seeks to offer a whole knowledge of Matplotlib through exploring its structure and format principles. An in-depth examination of its scripting, artist, and backend layers lays the inspiration for the next discussions on simple and advanced plotting techniques. The paper emphasizes practical insights into customization options, empowering customers to tailor visualizations for maximum impact. Furthermore, a critical element of this exploration is the mixing of Matplotlib with vital facts science libraries like Pandas and NumPy. Through realistic examples, the paper demonstrates how seamless integration complements statistics manipulation and visualization workflows, showcasing Matplotlib's synergy with those foundational tools. Real-global packages function a highlight, illustrating Matplotlib's versatility across domains. Whether visualizing economic traits, organic phenomena, or social dynamics, Matplotlib proves instrumental in distilling complicated facts into meaningful visible narratives. The paper delves into demanding situations encountered in those applications, offering valuable insights and ability answers. In the ever-increasing realm of information technological know-how, powerful visualization and evaluation are paramount for extracting meaningful insights from complicated datasets. This research paper, titled "Exploring Data Visualization and Analysis with Matplotlib," delves into the flexible competencies of Matplotlib, a distinguished information visualization library in Python. With its comprehensive suite of gear, Matplotlib empowers researchers, analysts, and developers to create visually compelling representations of information. This summary presents a succinct overview of the paper, emphasizing its recognition on Matplotlib's functionalities, packages, and satisfactory practices. The subsequent exploration covers essential elements, together with the library's architecture and simple plotting techniques, before advancing to more complex topics like interactive visualization, overall performance optimization, and real-world programs. Through case research and examples, the paper showcases Matplotlib's versatility throughout various domain names, shedding light on its position in enhancing data analysis workflows. As Matplotlib continues to evolve, the paper additionally examines destiny traits and community contributions, positioning itself as a treasured useful resource for both beginners and seasoned practitioners seeking to harness the strength of Matplotlib of their statistics visualization endeavors.

Microsoft Excel is essential for the End-User Approach (EUA), offering versatility in data organization, analysis, and visualization, as well as widespread accessibility. It fosters collaboration and informed decision-making across diverse domains. Conversely, Python is indispensable for professional programming due to its versatility, readability, extensive libraries, and robust community support. It enables efficient development, advanced data analysis, data mining, and automation, catering to diverse industries and applications. However, one primary issue when using Microsoft Excel with Python libraries is compatibility and interoperability. While Excel is a widely used tool for data storage and analysis, it may not seamlessly integrate with Python libraries, leading to challenges in reading and writing data, especially in complex or large datasets. Additionally, manipulating Excel files with Python may not always preserve formatting or formulas accurately, potentially affecting data integrity. Moreover, dependency on Excel’s graphical user interface (GUI) for automation can limit scalability and reproducibility compared to Python’s scripting capabilities. This paper covers the integration solution of empowering non-programmers to leverage Python’s capabilities within the familiar Excel environment. This enables users to perform advanced data analysis and automation tasks without requiring extensive programming knowledge. Based on Soliciting feedback from non-programmers who have tested the integration solution, the case study shows how the solution evaluates the ease of implementation, performance, and compatibility of Python with Excel versions.

Computational methods for the enhancement of energy efficiency rely on a measurement process with sufficient accuracy and number of measurements. Networked energy meters, energy monitors, serve as vital link between energy consumption of households and key insights that reveal strategies to achieve significant energy savings. During the design of such an energy monitor, several aspects such as data update rate or variety of measured physical quantities have to be considered. This paper introduces YoMoPie, a user-oriented energy monitor based on the Raspberry Pi platform that aims to enable intelligent energy services in households. YoMoPie measures active as well as apparent power, stores data locally, and integrates an easy to use Python library. Furthermore, the presented energy monitor comes with a Python API enabling the execution of user-designed services to enhance energy efficiency in buildings and households. Along with the presented design, possible applications that could run on top of this system such as residential demand response, immediate user feedback, smart meter data analytics, or energy disaggregation are discussed. Finally, a case study is presented, which compares the measurement accuracy of YoMoPie to a certified energy analyser for a selection of common household appliances.

Subject. The Russian automotive market, which has undergone a structural transformation after 2022. Objectives. Development and testing of a predictive machine learning model (binary logistic regression) to assess the probability of a successful entry of a new car model into the Russian market based on its technical and operational characteristics (using the example of Haval Xialong Max). Methods. The research is based on econometric modeling: the construction and evaluation of a binary logistic regression model based on a representative sample of 70 models sold in 2024 using Python libraries (Statsmodels, Pandas). ROC analysis and metrics Accuracy, Precision, Recall, and F1-score were used to evaluate the model. Results. The evaluation of the model showed high predictive quality (Accuracy = 85%, AUC = 0.94). It has been established that the maximum speed and average cost of a car are the most statistically significant factors of demand. Based on the model, the probability of high sales of the new Haval Xialong Max model is 79%. A targeted marketing strategy has been developed for this model. Conclusions. After 2022, the Russian car market has radically transformed with the dominance of Chinese brands. The created logistic regression model is an effective analytical tool for predicting the success of new models, demonstrating high accuracy. The model confirmed the high potential of the Haval Xialong Max crossover in the Russian market. The research results have practical value for manufacturers and dealers in making informed decisions about entering the market and developing marketing strategies.

ANDROMEDE — A software platform for optical surface velocity measurements

This study aims to investigate translator competence desired in the Tur-kish market, and to compare them to the competences determined by the European Union in order to provide an insight regarding the employability of Turkish translators in the global market.The first section presents the notion of competence in general while the second section handles translator competence from various perspectives, with specific emphasis on competences suggested by the EU. Later on, the method for the analysis has been elaborated. Finally, the findings have been presented with graphics and then discussed. This study has been carried out utilising content analysis in order to identify the differences between the competences listed by EMT expert group (the EU) and those demanded in the market. The analysis has been performed manually since it was not possible to create codes suitable for computerised processing. The job advertisements posted on the well-known job-search engine, www.kariyer.net, between November and December 2021 were chosen as the data to be examined. The ads were retrieved via Beatifulsoup which is a Python library used for Web scraping. Then the retrieved ads were coded as per the competence criteria listed by the European Union. As a result of the research, it has been seen that the translator qualifications requested in the advertisements broadcast on the internet for the Turkish market do not meet most of the EMT competence criteria. Turkish translators may not be regarded employable in the global market in the event that they shape their career paths by based on the job ads for national market.

Abstract. The increasing amount of data in meteorological science requires effective data-reduction methods. Our study demonstrates the use of advanced scientific lossy compression techniques to significantly reduce the size of these large datasets, achieving reductions ranging from 5× to over 150×, while ensuring data integrity is maintained. A key aspect of our work is the development of the “enstools-compression” Python library. This user-friendly tool simplifies the application of lossy compression for Earth scientists and is integrated into the commonly used NetCDF file format workflows in atmospheric sciences. Based on the HDF5 compression filter architecture, enstools-compression is easily used in Python scripts or via command line, enhancing its accessibility for the scientific community. A series of examples, drawn from current atmospheric science research, shows how lossy compression can efficiently manage large meteorological datasets while maintaining a balance between reducing data size and preserving scientific accuracy. This work addresses the challenge of making lossy compression more accessible, marking a significant step forward in efficient data handling in Earth sciences.

Language is a fundamental component of human communication. African low-resourced languages have recently been a major subject of research in machine translation, and other text-based areas of NLP. However, there is still very little comparable research in speech recognition for African languages. OkwuGbé is a step towards building speech recognition systems for African low-resourced languages. Using Fon and Igbo as our case study, we build two end-to-end deep neural network-based speech recognition models. We present a state-of-the-art automatic speech recognition (ASR) model for Fon, and a benchmark ASR model result for Igbo. Our findings serve both as a guide for future NLP research for Fon and Igbo in particular, and the creation of speech recognition models for other African low-resourced languages in general. The Fon and Igbo models source code have been made publicly available. Moreover, Okwugbe, a python library has been created to make easier the process of ASR model building and training.