Enhancing Critical Path Problem in Neutrosophic Environment Using Python

In the real world,one of the most common problems in project management is the unpredictability of resources and timelines.An efficient way to resolve uncertainty problems and overcome such obstacles is through an extended fuzzy approach,often known as neutrosophic logic.Our rigorous proposed model has led to the creation of an advanced technique for computing the triangular single-valued neutrosophic number.This innovative approach evaluates the inherent uncertainty in project durations of the planning phase,which enhances the potential significance of the decision-making process in the project.Our proposed method,for the first time in the neutrosophic set literature,not only solves existing problems but also introduces a new set of problems not yet explored in previous research.A comparative study using Python programming was conducted to examine the effectiveness of responsive and adaptive planning,as well as their differences from other existing models such as the classical critical path problem and the fuzzy critical path problem.The study highlights the use of neutrosophic logic in handling complex projects by illustrating an innovative dynamic programming framework that is robust and flexible,according to the derived results,and sets the stage for future discussions on its scalability and application across different industries.

Design and Implementation of Wuzhou Meteorological Statistical Yearbook System Based on Python

Based on Python language, this system implements the Wuzhou Meteorological Statistical Yearbook system, which automatically calculates and generates Excel table. This system can accurately and quickly collect annual meteorological data, form meteorological yearbook reports for government departments, and is of great significance for the development of meteorological data business by the Wuzhou Meteorological Bureau.

GPPre:A Python⁃Based Tool in Grasshopper for Office Building Performance Optimization

With the development of the economic and low⁃carbon society,high⁃performance building(HPB)design plays an increasingly important role in the architectural area.The performance of buildings usually includes the building energy consumption,building interior natural daylighting,building surface solar radiation,and so on.Building performance simulation(BPS)and multiple objective optimizations(MOO)are becoming the main methods for obtaining a high performance building in the design process.Correspondingly,the BPS and MOO are based on the parametric tools,like Grasshopper and Dynamo.However,these tools are lacking the data analysis module for designers to select the high⁃performance building more conveniently.This paper proposes a toolkit“GPPre”developed based on the Grasshopper platform and Python language.At the end of this paper,a case study was conducted to verify the function of GPPre,which shows that the combination of the sensitivity analysis(SA)and MOO module in the GPPre could aid architects to design the buildings with better performance.

Local Radial Basis Function Methods: Comparison, Improvements, and Implementation

Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.