In this research article, a python-based machine learning model prediction study was conducted based on the study results obtained from sudden expansion tubes containing different expansion angles, dimpled fin structu...
详细信息
In this research article, a python-based machine learning model prediction study was conducted based on the study results obtained from sudden expansion tubes containing different expansion angles, dimpled fin structures and nanofluids, whose thermo-hydraulic performance was previously examined. In the study, Artificial Neural Network and Ridge regression models were used to make predictions on the average Nusselt number (Nu), average Darcy friction factor (f) and performance evaluation criteria (PEC). Physical variations of the sudden expansion tube were taken into account and a detailed comparison of the results was made. A superior average Nu was acquired as 172.45 %, 22.05 %, 17.18 %, 13.65 %, and 7.76 % compared to Ag-MgO/H2O, Al2O3/H2O (blade), CoFe2O4/H2O, Al2O3/H2O (cylindrical), and Al2O3/H2O (platelet), respectively. The highest Performance Evaluation Criteria (PEC) for Re= 2000 based on Al2O3/H2O (platelet) shows an increase of 4.84%, 12.08 %, 11.76 %, 66.05 %, and 148.94 % compared to Al2O3/H2O (cylindrical), Al2O3/H2O (blade), CoFe2O4/H2O, Fe3O4/H2O, and Ag-MgO/H2O, respectively. From the results obtained, it was determined that python-based Machine Learning approach which facilitates custom optimizations showed a significant performance with small margins of error in predicting the heat transfer parameters. The lowest error rates of machine learning and polynomial ridge regression models ranged from 0.2 % to 5.4 % for the unseen test set and the application of python-based algorithms provided considerable savings in calculation time compared to conventional methods. On the other hand, using machine learning models with feature engineering has been found to increase model performance by at least 30%. In these years when studies on the predictions of thermo-hydraulic studies are very rare in the literature, this study is intended to facilitate scientists, engineers and academicians who will further study on this subject.
Developing a just-in-time (JIT) compiler can be a daunting task, especially for a language as flexible as python. While PyPy, powered with JIT compilation, can often outperform the official pure interpreter, Cpython, ...
详细信息
Developing a just-in-time (JIT) compiler can be a daunting task, especially for a language as flexible as python. While PyPy, powered with JIT compilation, can often outperform the official pure interpreter, Cpython, by a noteworthy margin, its popularity remains far from comparable to that of Cpython due to some issues. Given that an easier-to-deploy and better-compatible JIT compiler would benefit more python users, we have developed comPyler, a simple JIT compiler functioning as a Cpython extension and intended to convert frequently interpreted Cpython bytecode into equivalent machine code. Designed with good compatibility in mind, it does not alter Cpython's internal data structures or external interfaces. Based on LLVM's mature infrastructure, it can be readily ported to almost all platforms. Compared with Cpython, it achieved the highest speedup of 2.205, with an average of 1.093. Despite its relatively limited effect, comPyler incurs low development costs. As a baseline compiler, it also sheds light on the improvement attainable by optimizing solely the overhead of bytecode interpretation. Furthermore, as there is still a dearth of empirical research covering the multitude of JIT compilers available for python, we have conducted a performance study that examines Jython, Ironpython, PyPy, GraalPy, Pyston, Pyjion, and our comPyler. Our research takes into account not only the benchmark speed for various time windows but also the boot latency and memory footprint. Through this comprehensive study, our objective is to assist developers in gaining a better understanding of the effects of distinct JIT compilation techniques and to aid users in making informed decisions when choosing among different python implementations.
The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse ...
详细信息
The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.
We present the python package CELL,which provides a modular approach to the cluster expansion(CE)*** can treat a wide variety of substitutional systems,including one-,two-,and threedimensional alloys,in a general mult...
详细信息
We present the python package CELL,which provides a modular approach to the cluster expansion(CE)*** can treat a wide variety of substitutional systems,including one-,two-,and threedimensional alloys,in a general multi-component and multi-sublattice *** is capable of dealing with complex materials comprising several atoms in their parent lattice.
This paper proposes a reinforcement learning-based control education platform utilizing python and light-weight rapid control prototyping (LW-RCP). The platform employs the Sim-to-Real technique, in which neural netwo...
详细信息
We present a wrapper for the Springer Nature RESTful API written for python 3. The wrapper allows users to access their free-to-use endpoints via user-friendly interfaces and can be used without prior knowledge of RES...
详细信息
We present a wrapper for the Springer Nature RESTful API written for python 3. The wrapper allows users to access their free-to-use endpoints via user-friendly interfaces and can be used without prior knowledge of RESTful APIs. The APIs allow users to query metadata and open-access full-text of articles, conference proceedings and books published with Springer Nature. Files are cached to speed up subsequent analysis. The package addresses all users of scientific full-text, such as researchers working in Science of Science or evaluators. It facilitates reproducibility of research projects and enhances data integrity for researchers using Springer Nature data.
This paper presents opstool, a python package designed to enhance the pre- and post-processing capabilities of OpenSees and OpenSeesPy. It simplifies structural analysis workflows by automating tasks such as mesh gene...
详细信息
This paper presents opstool, a python package designed to enhance the pre- and post-processing capabilities of OpenSees and OpenSeesPy. It simplifies structural analysis workflows by automating tasks such as mesh generation, data management, and data visualization. The package efficiently manages large-scale simulation results, enabling the structured extraction of system, nodal, and element responses. In addition, it integrates adaptive iteration algorithms to improve convergence issues in nonlinear static and dynamic response analyses. By reducing manual modeling effort and enhancing model accuracy, opstool improves workflow efficiency and enables researchers and practitioners to conduct more effective computational simulations using OpenSees and OpenSeesPy, which further supports various task forces in earthquake engineering, such as performance-based design of new structures and regional seismic risk assessment of existing infrastructure systems.
In recent years, python has become widely used language for data processing in machine learning and deep learning. However, the dynamic typing of python can lead to errors caused by array shape mismatches that are onl...
详细信息
In recent years, python has become widely used language for data processing in machine learning and deep learning. However, the dynamic typing of python can lead to errors caused by array shape mismatches that are only detected at runtime. To increase development efficiency, we propose a static method to check python code that can detect shape errors before execution. Existing research activities such as Pytropos provide the capability to manually annotate the shape types of arrays and external datasets with python's type hint feature. However, manual annotation decreases code flexibility and can cause problems, such as incorrect marking due to human error, and wasted labor and time costs. To address these issues, we propose a method that leverages abstract interpretation and abstract syntax tree analysis to statically check code for array and dataset shape types, thus reducing the need for manual annotation and improving code flexibility. We give an implementation of the proposed method named ShapeChecker for the widely-used library NumPy as an example. ShapeChecker extracts the shape type of NumPy arrays and automatically reads external datasets to obtain shape type information, accelerating the checking process and outputting the cause of shape errors when detected. We compared ShapeChecker with existing solutions in various scenarios and obtained promising results, demonstrating the tool's usefulness in improving efficiency and reducing runtime errors. To further enhance its functionality, we plan to extend ShapeChecker's support to more packages and address known issues. Overall, our proposed method and ShapeChecker tool provide a static approach to detecting array shape errors that can improve code quality and improve development efficiency.
This article presents the design and development of a dose management system for the generation of dosimetric reports of workers occupationally exposed to radiation from all medical units of the Ecuadorian Social Secu...
详细信息
This article presents the design and development of a dose management system for the generation of dosimetric reports of workers occupationally exposed to radiation from all medical units of the Ecuadorian Social Security (IESS). The system was programmed using free software, python3, ensuring accessibility and sustainability, and uses MySQL as a database for the secure and efficient storage of dosimetric information. The system is aimed at replacing the manual system that had been implemented to guarantee traceability and facilitate the collection, processing and generation of accurate dosimetric reports of radiation exposure of occupationally exposed workers. Its implemen-tation will be carried out in the personal dosimetry laboratory of the Hospital de Especialidades Carlos Andrade Marin, which serves approximately 1,200 users who are provided with the dosimetry service for the whole body, extremities and lens. This system not only optimizes data management processes, but also contributes to compliance with national and international recommendations for improving the control and monitoring of occupational exposure to radiation, promoting a safer work environment for exposed workers.
Federated learning (FL) is a machine learning setting where clients keep the training data decentralized and collaboratively train a model either under the coordination of a central server (centralized FL) or in a pee...
详细信息
Federated learning (FL) is a machine learning setting where clients keep the training data decentralized and collaboratively train a model either under the coordination of a central server (centralized FL) or in a peer-to-peer network (decentralized FL). Correct orchestration is one of the main challenges. In this paper, we formally verify the correctness of two generic FL algorithms, a centralized and a decentralized one, using the Communicating Sequential Processes (CSP) calculus and the Process Analysis Toolkit (PAT) model checker. The CSP models consist of CSP processes corresponding to generic FL algorithm instances. PAT automatically proves the correctness of the two generic FL algorithms by proving their deadlock freedom (safety property) and successful termination (reachability and liveness property). The CSP models are constructed as a faithful representation of the real python code and are expressed directly in CSP# language that PAT uses. Then they are automatically checked top-down by PAT. The python code follows a restricted actor-based programming model, and the construction of CSP# code from such python code is performed systematically. The process is described in detail, ensuring that the models correspond to the actual code. It represents a basis for developing tools for automatic translation of certain classes of python code to CSP models, expressed in CSP#.
暂无评论