Python Code Research Articles

Reducing the Constrained Multibody Dynamics Problem to the Solution of a System of Ordinary Differential Equations Via Velocity Partitioning and Lie Group Integration

Abstract In multibody dynamics, formulating the equations of motion in absolute Cartesian coordinates results in a set of index-3 differential algebraic equations (DAEs). In this work, we present an approach that bypasses the DAE problem by partitioning the velocities in the system into dependent and independent coordinates, thereby reducing the task of producing the time evolution of the mechanical system to one of solving a set of ordinary differential equations (ODEs). In this approach, the independent coordinates are integrated directly, while the dependent coordinates are recovered through the kinematic constraint equations at the position and velocity levels. Notably, Lie group integration is employed to directly obtain the orientation matrix A at each time-step of the simulation. This eliminates the need to choose generalized coordinates to capture the orientation of a body, as the matrix A is a by-product of the solution algorithm. Herein, we outline the new approach and demonstrate it in conjunction with four mechanisms: a single pendulum, a double pendulum, a four-link mechanism, and a slider crank. We report on the convergence order behavior of the proposed method and compare its performance with an established method that combines coordinate partitioning with an Euler parameter formulation. The Python code developed to generate the reported results is open-source and available in a public repository for reproducibility studies.

The PDI model system for parameterizing soil hydraulic properties

AbstractThe Peters–Durner–Iden (PDI) model system for describing soil hydraulic properties (SHP) has been developed over a decade. Inspired by Rien van Genuchten's seminal work, the PDI system focuses on an efficient and simple parameterization of water retention curves and hydraulic conductivity curves (HCC) across the entire soil moisture spectrum. By combining capillary and non‐capillary components for water retention and conductivity, it aims to reconcile mathematical simplicity and insights on water adsorption and film flow in soils. Recent developments have reduced the number of free parameters of the conductivity model to zero, enhancing the model's applicability in cases of limited data availability. The first reduction was achieved by a prediction of absolute non‐capillary conductivity based on the consideration of film and corner flow on the pore scale, and the second by a prediction of absolute capillary conductivity by a capillary bundle model. This allows a complete characterization of SHP over the entire moisture range with only four retention curve parameters. The inclusion of a maximum pore size in the capillary conductivity model prevents an unrealistic drop of the HCC near saturation. This paper provides a comprehensive overview of the PDI model system, emphasizing its conceptual features and mathematical details. An Excel sheet and a Python code stored in a repository are provided for accessibility.

Equivalent sentiment measures for cross-language analysis of corporate communications

This paper presents a technique for sentiment measurement in many languages. The method allows researchers to efficiently analyze corporate documents, management reports, and financial statements using python. When the texts are written in many languages, the method extracts equivalent cross-linguistic sentiment features that can be used for statistical analysis or machine learning. We use Open Multilingual WordNet, a large lexicon organizing words into semantic groups, as the knowledge base about word equivalence in more than 200 languages. We experiment with a parallel English-French corpus and find that our senitment measures across the two languages are comparable. The method produces a consistent classification of positive and negative texts in two languages, and sentiment measure values correlate. The paper provides a detailed account of the method and python code, So that it can be applied to other languages, text mining, quantitative communication studies, and management research.•Method to create equivalent sentiment measures in multiple languages•Based on established lexicons and WordNet•Validated for English and French

Evaluating the Quality of Serial EM Sections with Deep Learning.

Automated image acquisition can significantly improve the throughput of serial section scanning electron microscopy (ssSEM). However, image quality can vary from image to image depending on autofocusing and beam stigmation. Automatically evaluating the quality of images is, therefore, important for efficiently generating high-quality serial section scanning electron microscopy (ssSEM) datasets. We tested several convolutional neural networks for their ability to reproduce user-generated evaluations of ssSEM image quality. We found that a modification of ResNet-50 that we term quality evaluation Network (QEN) reliably predicts user-generated quality scores. Running QEN in parallel to ssSEM image acquisition therefore allows users to quickly identify imaging problems and flag images for retaking. We have publicly shared the Python code for evaluating images with QEN, the code for training QEN, and the training dataset.

Python code for modeling ARIMA-LSTM architecture with random forest algorithm

Over conventional statistical models, machine learning mechanisms are establishing themselves as a potential area for modeling and forecasting complex time series. Because it can integrate several forecasting methodologies’ capabilities, hybrid time series models are fundamental in data science. Here, we present a Python script that builds a combined architecture of the ARIMA-LSTM model with random forest technique to generate a high-accuracy prediction. This script is a step-by-step process to create a statistical and then machine learning model through statistical assumption.

First Light Curve Analysis of NSVS 8294044, V1023 Her, and V1397 Her Contact Binary Systems

The first photometric light curve investigation of the NSVS 8294044, V1023 Her, and V1397 Her binary systems is presented. We used ground-based observations for the NSVS 8294044 system and Transiting Exoplanet Survey Satellite data for V1023 Her and V1397 Her. The primary and secondary times of minima were extracted from all the data, and, by collecting the literature, a new ephemeris was computed for each system. Linear fits for the O − C diagrams were conducted using the Markov Chain Monte Carlo (MCMC) method. Light curve solutions were performed using the PHysics Of Eclipsing BinariEs Python code and the MCMC approach. The systems were found to be contact binary stars based on the fillout factor and mass ratio. V1023 Her showed the O’Connell effect, and a cold starspot on the secondary component was required for the light curve solution. The absolute parameters of the system were estimated based on an empirical relationship between orbital period and mass. We presented a new T–M equation based on a sample of 428 contact binary systems and found that our three target systems were in good agreement with the fit. The positions of the systems were also depicted on the M–L, M–R, q–L ratio, and M tot–J 0 diagrams in the logarithmic scales.

An LLM-based vision and language cobot navigation approach for Human-centric Smart Manufacturing

Industry 5.0 prioritizes Human-centric Smart Manufacturing (HSM), aiming to enhance human operators’ well-being and needs. This necessitates collaborative robots with advanced natural interaction capabilities and improved perception, cognition, and action intelligence. The Large Language Model (LLM) exhibits strong reasoning abilities and generalization capabilities, which can significantly advance the development of HSM once integrated into human–robot interaction and collaboration. Accordingly, this paper explores part of LLM’s ability in the context of smart manufacturing, focusing on addressing interruptions in the manufacturing process caused by repetitive tool fetching. To alleviate this issue, a vision and language cobot navigation approach is innovatively adopted in the manufacturing environment that could be further used to assist operators in retrieving tools. Specifically, a real Human–Robot Collaboration (HRC) manufacturing scene is first reconstructed and annotated using Three-Dimensional (3D) point cloud techniques. Then the LLM is utilized for the Automated Guided Vehicle (AGV) to comprehend natural language commands and generate Python code to triger navigation actions. Finally, the Pathfinder algorithm is applied for corresponding path planning. The framework is implemented in the Artificial Intelligence (AI) Habitat simulator, and the case studies demonstrate that the AGV can accurately comprehend complex language instructions, empowering human operators to complete manufacturing tasks efficiently.

Detection of Breast Cancer Cells Using Deep Learning with Convolution Neural Network

Abstract: Breast cancer, a pervasive global health concern, necessitates timely and precise diagnosis to ensure effective treatment and enhance patient outcomes. This comprehensive review critically examines a Convolutional Neural Network (CNN) model specifically crafted for the detection of breast cancer utilizing histopathological images. The Python code, developed with the powerful TensorFlow and Keras libraries, strategically incorporates advanced methodologies such as transfer learning and data augmentation to optimize the model's diagnostic performance. The study meticulously delves into crucial aspects, encompassing data preprocessing techniques, intricate model architecture, rigorous training procedures, and thorough evaluation methodologies. The culmination of these efforts is a practical demonstration showcasing the model's capability in accurately detecting breast cancer. The integration of machine learning techniques with Python implementation underscores a sophisticated approach to navigating the complex terrain of breast cancer prediction. By offering a detailed exploration of the intricate landscape surrounding breast cancer detection, this review provides valuable insights into the potential of such machine learning models. The amalgamation of advanced technologies, thoughtful implementation, and holistic approaches signifies a significant step forward in advancing diagnostic capabilities for this critical health issue, ultimately contributing to improved patient care and outcomes.

What drives the real estate market? Could behavioral indicators be useful in house pricing models?

PurposeThe objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.Design/methodology/approachIn our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.FindingsOur findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.Practical implicationsThe results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.Originality/valueThis is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

VAMPyR-A high-level Python library for mathematical operations in a multiwavelet representation.

Wavelets and multiwavelets have lately been adopted in quantum chemistry to overcome challenges presented by the two main families of basis sets: Gaussian atomic orbitals and plane waves. In addition to their numerical advantages (high precision, locality, fast algorithms for operator application, linear scaling with respect to system size, to mention a few), they provide a framework that narrows the gap between the theoretical formalism of the fundamental equations and the practical implementation in a working code. This realization led us to the development of the Python library called VAMPyR (Very Accurate Multiresolution Python Routines). VAMPyR encodes the binding to a C++ library for multiwavelet calculations (algebra and integral and differential operator application) and exposes the required functionality to write a simple Python code to solve, among others, the Hartree-Fock equations, the generalized Poisson equation, the Dirac equation, and the time-dependent Schrödinger equation up to any predefined precision. In this study, we will outline the main features of multiresolution analysis using multiwavelets and we will describe the design of the code. A few illustrative examples will show the code capabilities and its interoperability with other software platforms.

A Novel Physics-Based Model for Predicting Melt Pool Dimensions in Laser Powder Bed Fusion Process

Abstract This paper employed a scaling analysis to represent the processing parameters, affecting the melting process in the dimensionless numbers, identify the relationships of these dimensionless numbers, and develop semi-empirical correlations to predict the width and depth of the melt pool. To develop the correlations, Ti-6Al-4V powder was used to print 38 tracks at various processing conditions. The correlations were then fit into this experimental data using python code to find the constants of the correlations. The correlations were then used to predict the depth and width of the melt pools. It was found that the mean discrepancy between the predicted melt pool dimensions and the experiment is 7%. To evaluate the accuracy of the correlation in predicting the melt pool dimensions of the materials never used during the development of the correlations, the melt pool depth of some tracks made out of stainless steel 316L printed at various conditions was predicted using the model, and it was found that the mean discrepancy between the predicted melt pool depth and experiment is 11%.

Uniqueness and Hyers–Ulam’s stability for a fractional nonlinear partial integro-differential equation with variable coefficients and a mixed boundary condition

Abstract Introducing a pair-parameter matrix Mittag–Leffler function, we study the uniqueness and Hyers–Ulam stability to a new fractional nonlinear partial integro-differential equation with variable coefficients and a mixed boundary condition using Banach’s contractive principle as well as Babenko’s approach in a Banach space. These investigations have serious applications since uniqueness and stability analysis are essential topics in various research fields. The techniques used also work for different types of differential equations with initial or boundary conditions, as well as integral equations. Moreover, we present a Python code to compute approximate values of our newly established pair-parameter matrix Mittag–Leffler functions, which extend the multivariate Mittag–Leffler function. A few examples are given to show applications of the key results obtained.

EL Greco Platform: A novel Python programming learning platform that uses a real robot

AbstractThis paper introduces the El Greco Platform, a Python programming platform for distance learning that employs an educational robot. This website allows prospective learners to remotely control Greco, a social humanoid robot designed to be cost‐effective, simple to construct, and appropriate for use in education. El Greco is capable of performing multiple tasks, including combined movements. These Robot capabilities can be programmed using either Python code or the Blockly library, which adds an editor to an application that visualizes coding concepts as interlocking blocks. Programming a robot appears to be a significantly more effective and creative method for students to learn a programming language. This educational tool was designed primarily for use by students and allows anyone to learn Python while controlling a robot for free. El Greco Platform features gamification elements that increase the enjoyment and engagement of the learning experience while reinforcing the concepts taught. The survey results on students aged 13–18 revealed that the El Greco Platform captivated the study participants and positively affected their attitudes toward programming and robotics. In addition, it significantly impacted their comprehension of programming and motivated them to seek additional opportunities to expand their knowledge of robotics and programming.

Hot Jupiter Diversity and the Onset of TiO/VO Revealed by a Large Grid of Non-Grey Global Circulation Models

Abstract The population of hot Jupiters is extremely diverse, with large variations in their irradiation, period, gravity and chemical composition. To understand the intrinsic planet diversity through the observed population level trends, we explore the a-priori scatter in the population created by the different responses of atmospheric circulation to planetary parameters. We use the SPARC/MITgcm 3D global circulation model to simulate 345 planets spanning a wide range of instellation, metallicity, gravity and rotation periods typical for hot Jupiters, while differentiating between models with and without TiO/VO in their atmosphere. We show that the combined effect of the planetary parameters leads to a large diversity in the ability of atmospheres to transport heat from day-side to night-side at a given equilibrium temperature. We further show that the hot-spot offset is a non-monotonic function of planetary rotation period and explain our findings by a competition between the rotational and divergent parts of the circulation. As a consequence, hot-spot offset and phase curve amplitude are not necessarily correlated. Finally, we compare the observables from our grid to the population of Spitzer and Hubble observations of hot Jupiters. We find that the sudden jump in brightness temperature observed in the Spitzer secondary eclipse measurements can be naturally explained by the cold-trapping of TiO/VO at approximately 1800K. The grid of modelled spectra, phase curves and thermal structures are made available to the community, together with a python code for visualization of the grid properties, at DOI:10.5281/zenodo.10785321 and http://sim3d.oca.eu/.

RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds.

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

Particle dynamics in spherically symmetric electro-vacuum instantons

In this paper, we study the geodesic motion in spherically symmetric electro-vacuum Euclidean solutions of the Einstein equation. There are two kinds of such solutions: the Euclidean Reissner–Nordström (ERN) metrics, and the Bertotti–Robinson-like (BR) metrics, the latter having constant Kretschmann scalar. First, we derive the motion equations for the ERN spacetime and we generalize the results of Battista–Esposito, showing that all orbits in as ERN spacetime are unbounded if and only if it has an event horizon. We also obtain the Weierstrass form of the polar radial motion, providing an efficient tool for numerical computations. We then study the angular deflection of orbits in the Euclidean Schwarzschild spacetime which, in contrast to the Lorentzian background, can be either positive or negative. We observe the presence of a null and a maximal deflection rings for particles with velocity at infinity v>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v>1$$\\end{document} and we give approximate values for their size when v≳1.\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v > rsim 1.$$\\end{document} For BR spacetimes, we obtain analytic solutions for the radial motion in proper length, involving (hyperbolic) trigonometric functions and we deduce that orbits either exponentially go to the singularity or are periodic. Finally, we apply the previous results and use algorithms related to Weierstrass’ elliptic functions to produce a Python code to plot orbits of the spacetimes ERN and BR, and draw “shadows” of the first ones, as it was already done before for classical black holes.

Dominant conditions for strong resonance of an advancing containership under small-amplitude regular waves

ABSTRACT Seakeeping of a ship under waves is vital for safety, with strong resonance occurring when two conditions are met: (1) large wave loads, and (2) wave frequency being close to the natural frequency. Prior research focused on resonant phenomena, nonlinear traits, and failure modes, but the dominant condition for resonance in ocean-going ships is unclear. This study developed a mathematical model using the boundary element method (BEM) for an advancing ship under small-amplitude waves. An open-source solution was proposed, utilizing NEMOH for hydrodynamic coefficients and a Python code for dynamic equation solving. Validated with KRISO Container Ship (KCS) data, the mathematical model accurately predicts ship motions. Findings show that ship heave resonance is influenced by both conditions, but the second condition affects rolling more, and the first condition controls pitch more. This is more evident in high-speed ships and is less affected by ship size variations.

Estimating insulin sensitivity and β-cell function from the oral glucose tolerance test: validation of a new insulin sensitivity and secretion (ISS) model.

Efficient and accurate methods to estimate insulin sensitivity (SI) and β-cell function (BCF) are of great importance for studying the pathogenesis and treatment effectiveness of type 2 diabetes (T2D). Existing methods range in sensitivity, input data, and technical requirements. Oral glucose tolerance tests (OGTTs) are preferred because they are simpler and more physiological than intravenous methods. However, current analytical methods for OGTT-derived SI and BCF also range in complexity; the oral minimal models require mathematical expertise for deconvolution and fitting differential equations, and simple algebraic surrogate indices (e.g., Matsuda index, insulinogenic index) may produce unphysiological values. We developed a new insulin secretion and sensitivity (ISS) model for clinical research that provides precise and accurate estimates of SI and BCF from a standard OGTT, focusing on effectiveness, ease of implementation, and pragmatism. This model was developed by fitting a pair of differential equations to glucose and insulin without need of deconvolution or C-peptide data. This model is derived from a published model for longitudinal simulation of T2D progression that represents glucose-insulin homeostasis, including postchallenge suppression of hepatic glucose production and first- and second-phase insulin secretion. The ISS model was evaluated in three diverse cohorts across the lifespan. The new model had a strong correlation with gold-standard estimates from intravenous glucose tolerance tests and insulin clamps. The ISS model has broad applicability among diverse populations because it balances performance, fidelity, and complexity to provide a reliable phenotype of T2D risk.NEW & NOTEWORTHY The pathogenesis of type 2 diabetes (T2D) is determined by a balance between insulin sensitivity (SI) and β-cell function (BCF), which can be determined by gold standard direct measurements or estimated by fitting differential equation models to oral glucose tolerance tests (OGTTs). We propose and validate a new differential equation model that is simpler to use than current models and requires less data while maintaining good correlation and agreement with gold standards. Matlab and Python code is freely available.

Analytical methodology for determining the extent of pumped freshwater lenses in recharge-limited, circular islands

Island freshwater lenses are often subjected to pumping to support the freshwater needs of isolated communities. Investigations of the lens extent commonly involve the application of analytical solutions, although the effects of pumping from circular island aquifers have not been assessed in previous analytical methodologies. In this study, a new analytical solution is derived based on Green's functions that accommodate freshwater inputs (recharge) and outputs (multiple wells) when obtaining the head distribution and sharp-interface lens shape. The solution is verified using a finite-element model for an illustrative pumping scenario. The solution is also differentiated with respect to pumping rates, recharge, and island radius to obtain vulnerability indices to characterize the lens sensitivity to changes in stresses. Finally, the analytical methodology is modified to examine the effects of non-uniform recharge. Python code incorporating the new solution is included to enable users to investigate a range of recharge and pumping scenarios.

BSN: Photometric Light Curve Analysis of Two Contact Binary Systems LS Del and V997 Cyg

The light curve analyses and orbital period variations for two contact binary stars, LS Del and V997 Cyg, are presented in this work which was conducted in the frame of the Binary Systems of South and North project. Ground-based photometric observations were performed at two observatories in France. We used the Transiting Exoplanet Survey Satellite (TESS) data for extracting times of minima and light curve analysis of the target systems. The O − C diagram for both systems displays a parabolic trend. LS Del and V997 Cyg’s orbital periods are increasing at rates of dP/dt = 7.20 × 10−08 days yr−1 and dP/dt = 2.54 × 10−08 days yr−1, respectively. Therefore, it can be concluded that mass is being transferred from the less massive star to the more massive component with a rate of dM/dt = –1.96 × 10−7 M ⊙ yr−1 for the LS Del system, and dM/dt =–3.83 × 10−7 M ⊙ yr−1 for V997 Cyg. The parameters of a third possible object in the system are also considered. The PHysics Of Eclipsing BinariEs Python code was used to analyze the light curves. The light curve solutions needed a cold starspot due to the asymmetry in the LS Del system’s light curve maxima. The mass ratio, fill-out factor, and star temperature all indicate that both systems are contact binary types in this investigation. Two methods were applied to estimate the absolute parameters of the systems: one method relied on the parallax of Gaia DR3, and the other used a P − M relationship. The positions of the systems are also depicted on the M − L, M − R, q − L ratio, and logMtot−logJ0 diagrams. We recommend that further observations and investigations be done on the existence of a fourth body in this system.