关键词： Python  

摘要： We introduce drexml, a command line tool and Python package for rational data-driven drug repurposing. The package employs machine learning and mechanistic signal transduction modeling to identify drug targets capable of regulating a particular disease. In addition, it employs explainability tools to contextualize potential drug targets within the functional landscape of the disease. The methodology is validated in Fanconi Anemia and Familial Melanoma, two distinct rare diseases where there is a pressing need for solutions. In the Fanconi Anemia case, the model successfully predicts previously validated repurposed drugs, while in the Familial Melanoma case, it identifies a promising set of drugs for further investigation. © 2024 The Author(s)

关键词： Python  

摘要： With the planned future reliance on variable renewable energy, the ability to store energy for prolonged time periods will be required to reduce the disruption of market fluctuations. This paper presents a method to analyze a hybrid liquid-oxygen (LOx) storage/direct-fired supercritical carbon dioxide (sCO2) power cycle and optimize the economic performance over a diverse range of scenarios. The system utilizes a modified version of the NET Power process to produce energy when energy demand exceeds the supply while displacing much of the cost of the air separation unit (ASU) energy requirements through cryogenic storage of oxygen. The model uses marginal cost of energy data to determine the optimal times to charge and discharge the system over a given scenario. The model then applies ramp rates and other time-dependent factors to generate an economic model for the system without storage considerations. The size of the storage system is then applied to create a realistic model of the plant operation. From the real plant operation model, the amount of energy charged and discharged, the capital expenditures (CAPEX) of each system, energy costs and revenue and other parameters can be calculated. The economic parameters are then combined to calculate the net present value (NPV) of the system for the given scenario. The model was then run through the SMPSO genetic algorithm in Python for a variety of geographic regions and large-scale scenarios (high solar penetration) to maximize the NPV based on multiple parameters for each subsystem. The LOx storage requirements will also be discussed. © 2024 by ASME.

关键词： Python   Magnetohydrodynamics   Porous medium   Heat transfer   Concentration  

摘要： The flow phenomena become more significant and varied when an extra heat source, a chemical reaction, and thermal and solutal buoyancy forces exist. Transport property optimization results from the current demand in several sectors dependent on the solutal reactant and the heat source given. Partial differential equations are transformed into ordinary equations using appropriate similarity transformations. Python was used as an efficient tool to solve equations in this research. According to the available facilities and libraries, Python can efficiently solve various physics and engineering problems. The homotopy perturbation method (HPM) and Akbari-Ganji Method (AGM) have been used to solve differential equations. The results calculated in Python indicate the high accuracy of calculations in Python. The effect of different parameters on the profile of velocity, temperature, and concentration has been investigated graphically. Also, the friction coefficient ( C f ), Nusselt number ( N u ), and Sherwood number ( S h ) have also been investigated in this study. The results show that the velocity profile decreases with increased magnetic field intensity. Also, the porosity parameter has an inverse effect on the velocity and temperature profile, so the growth of the porosity parameter reduces the fluid velocity while improving the temperature profile.

关键词： Python  

摘要： Python package installation is far from trivial. Users encounter a variety of errors when installing Python libraries, including dependency conflicts and incompatibilities. Existing solutions focus on parsing third-party dependencies but ignore the impact of local settings and user requirements. In this paper, we propose a novel approach, HELP, to help Python users tackle installation errors. We first establish a local knowledge base by extracting the PyPI database and dependency analysis. When the user provides the installation requirements, HELP extracts multiple constraints including user requirements, Python version constraints, and dependency constraints, and models them into SMT expressions. Then HELP solves the installation problem by using the SMT solver. To understand the status of Python version compatibility, we conduct an empirical study on Python version compatibility on 8,502 libraries. The study reveals that 80% of Python libraries do not declare Python version constraints in most versions. We also find that installation errors are strongly related to Python versions. To evaluate HELP, we conduct the experiment on 495 sampled installation failures. The results show that HELP can effectively resolve 263 installation failures, 42% more than the baseline approach. Especially, HELP provides a more comprehensive diagnosis when encountering configuration-related installation failures. Besides, HELP is more efficient than pip at predicting installation failures (30X speedups), which may save much effort if the installation cannot succeed. © 2024 Elsevier B.V.

关键词： Python  

摘要： This paper is going to present the advanced feature in Plaxis using remote scripting with Python wrapper. Python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making. © 2024 Elsevier Ltd

关键词： Python  

摘要： The Python functions distributed with this article can be used for calculating the parameters of multichannel quantum defect theory models describing excited bound states of complex atoms. These parameters are obtained by fitting a model to experimental data provided by the user. The two main formulations of the theory are supported, namely the one in which the parameters of the model are a set of eigen channel quantum defects and a transformation matrix, and the one where these parameters are the elements of a reactance matrix. The distribution includes programs for calculating theoretical energy levels, calculating mixing coefficients and channel fractions and producing Lu-Fano plots. Program summary: Program Title: mqdtfit CPC Library link to program files: https://***/10.17632/nzgygzd96c.1 Developer's repository link: https://***/durham-qlm/mqdtfit Licensing provisions: BSD 3-clause. Programming language: Python 3. Nature of problem: Multichannel quantum defect theory aims at giving a unified description of the excited states of multielectron systems whereby the properties of entire series can be predicted from models involving a relatively small number of parameters. These parameters are obtained by fitting theory to experimental data, in the empirical version of the theory. The present programs specifically concern the application of this theory to the bound states of complex atomic systems, such as divalent atoms, for which spectroscopic series are often perturbed by isolated states of a different symmetry, making a multichannel description necessary. Solution method: The functions forming this library are grouped and linked to each other into a single Python module. They can be used to calculate the parameters of a given multichannel quantum defect theory model by fitting the model to experimental data provided by the user. These parameters are either a set of eigen channel quantum defects and a transformation matrix, in the eigenchannel parametrization of the theory, or the elements of an orthogonal matr

关键词： Python  

摘要： This paper introduces PolyWeight, a Python software featuring a user-friendly graphical user interface (GUI), which offers two distinct approaches for MWD determination: an analytical relation-based method and a parametric model-based method. By utilizing dynamic moduli, users can calculate MWD as well as molecular weight averages such as Mn, Mw, and Mz. The functionality of PolyWeight is validated using synthetic data and real data obtained from the literature, exhibiting good agreement. Log files and datasets used in this work are available on GitHub. Program summary: Program Title: PolyWeight CPC Library link to program files: https://***/10.17632/rn8t4mmvyd.1 Developer's repository link: https://***/a-minotto/PolyWeight Licensing provisions: GPLv3 Programming language: Python 3.x Nature of problem: Analytical rheology enables the determination of molecular weight distribution (MWD) by solving inverse problems. The reptation theory establishes a liaison between the viscoelastic behavior and MWD, permitting the application of mathematical resolution methods like regularization and parameterization. PolyWeight offers two solutions: a parameterization-based method that assumes a generalized exponential distribution (GEX) for the MWD, and an analytical relationship-based method that utilizes the relaxation spectrum. Solution method: The analytical relationship-based solution incorporates a mathematical formulation that considers the relaxation spectrum obtained from the dynamic moduli using the NLREG software. The numerical integration of the relaxation spectrum is performed utilizing the integrate.quad function from the SciPy library. An optimization process is employed in the parametric solution to perform a multiobjective fit of the viscoelastic models to the dynamic moduli. The integrals in this case are also calculated using the integrate.quad package from the SciPy library. The optimization procedure utilizes the lmfit library, where a Minimizer object is created based on the optimization function and subseq

关键词： Python  

摘要： The rate of discovery of near-Earth asteroids currently outpaces our ability to analyze them. Knowledge of an asteroid's physical properties is essential to deflect them. I developed free and open-source algorithms and training modules. These tools utilize images from robotic telescopes, open-source data, and high school-level mathematics to determine asteroids' size, rotation period, and strength. I took observations of the Didymos binary asteroid, and my algorithm determined its size to be 850 ± 0.04 m, with a 2.261 ± 0.018 h rotation period and rubble-pile strength. I measured a decrease in the mutual orbital period after impact by NASA's 2022 DART Mission, confirming successful deflection. My findings matched closely with those obtained by NASA and external sources (Daly et al., 2023; Thomas et al., 2023; Talbert, 2022, October 11; Nakanoet al., 2022) [1,2,3,4]. Every young person and citizen scientist can now be a planetary defender. © 2024 IAA

关键词： Python  

摘要： Every day, many short text messages on social media are generated in response to real-world events, providing a valuable resource for various domains such as emergency response and traffic management. Since exact coordinates of social media posts are rarely attached by users, accurately recognizing and resolving fine-grained place names, such as home addresses and Points of Interest, from these posts is crucial for understanding the precise locations of critical events, such as rescue requests. This task, known as geoparsing, involves toponym recognition and toponym resolution or geocoding. However, existing social media datasets for evaluating geoparsing approaches often lack sufficient fine-grained place names with associated geo-coordinates or linked to gazetteers, making evaluating, comparing, and training geocoding methods for such locations challenging. Moreover, the absence of supportive annotation tools compounds this challenge. To address these gaps, we implemented a lightweight Python tool leveraging Nominatim. Using this tool, we annotated a comprehensive X (formerly Twitter) geocoding corpus called DLRGeoTweet. The corpus underwent a rigorous cross-validation process to guarantee its quality. This corpus includes a total of 7,364 tweets and 12,510 places, of which 6,012 are fine-grained. It comprises two global datasets encompassing worldwide events and three local datasets related to local events such as the 2017 Hurricane Harvey. The annotation process spanned over ten months and required approximately 1000 person-hours to complete. We then evaluate 15 latest and representative geocoding approaches, including many deep learning-based, on DLRGeoTweet. The results highlight the inherent challenges in resolving fine-grained places accurately. Despite increasing access constraints to Twitter data, our corpus's focus on short, informal text makes it a valuable resource for geocoding across multiple social media platforms. © 2024 The Author(s)

关键词： Python  

摘要： Solar photovoltaic thermal (PVT) integrates photovoltaic panels and solar thermal collectors in one system for simultaneous electricity and heat production in a reduced area. The absorber type used for heat extraction significantly affects PVT performance. This work examines the performance of an extruded absorber PVT under varying solar irradiance, ambient temperature, and wind speed. A detailed dynamic mathematical model is developed from energy balance equations and heat transfer correlations for each layer of the PVT collector. Using orthogonal collocation on the finite difference method, the system of differential equations is discretized and solved numerically with Pyomo, a Python optimization modeling tool. Following validation against literature data, the model is used to predict the temperature of each layer and the thermal and electrical output of the PVT collector. The effect of cooling fluid mass flow rate, solar irradiance intensity, ambient temperature, and wind speed on PVT performance is analyzed. This design provides a maximum electrical efficiency of 15.47 % at a wind velocity of 3 m/s, regardless of the cooling fluid flow rate. Peak electrical power of 212.26 W is achieved at the highest flow rate (14.14 kg/hr) and with the highest wind velocity (3 m/s), while the peak thermal power of 348.03 W occurs at a wind velocity of 1 m/s and a flow rate of 11.31 kg/hr. Compared to the conventional sheet-and-tube design at 1 m/s wind speed and 5.65 kg/hr cooling fluid flow rate, the extruded absorber PVT delivers 203.18 W electrical and 189.27 W thermal power, outperforming the 207.46 W electrical and 128.9 W thermal power of the conventional design, demonstrating the potential of the extruded absorber design for enhancing heat generation. © 2024 Elsevier Ltd