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This paper introduces PyGAD, an open-source easy-to-use Python library for building the genetic algorithm (GA) and solving multi-objective optimization problems. PyGAD is designed as a general-purpose optimization library with the support of a wide range of parameters to give the user control over its life cycle. This includes, but not limited to, the population, fitness function, gene value space, gene data type, parent selection, crossover, and mutation. Its usage consists of 3 main steps: build the fitness function, create an instance of the pygad.GA class, and call the pygad.GA.run() method. The library supports training deep learning models created either with PyGAD itself or with frameworks such as Keras and PyTorch. Given its stable state, PyGAD is also in active development to respond to the user’s requested features and enhancements received on GitHub.

An individual experiencing hearing impairment faces a persistent challenge when it comes to person-to-person interactions. Sign language has unquestionably emerged as a highly effective solution for those with both hearing and speech disabilities, providing a practical and successful means to convey their thoughts and emotions to the wider world, further developed for facilitating the integration procedure for simplification among individuals. This difficulty of sign language development will be feasible based on specific constraints. The movement of different sign languages supports the one that can ever learn. Besides, these languages commonly turn out to be cluttered and ambiguous. The study fills the communication gap for automating the sign motions identified with the challenging approach. Therefore, utilize the web camera for capturing the pictures associated with hand gestures, integrating the developed system for anticipating and demonstrating the output image. Images underwent several quantified phases processed for the involvement of supervised technology. For attaining the classification of the picture within the means of training and testing of the network, the progressed heuristic approach like convolutional neural network has been employed. It is observed from the resources that the accuracy and loss evolution are reached at the equivalent rate under exponential growth within the action distributed, which has been distinguished with 24 American Sign Language gesture alphabets. This approach exhibits complete characterization performance with around 96%.

This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques.

Background As molecular biology is creating an increasing amount of sequence and structure data, the multitude of software to analyze this data is also rising. Most of the programs are made for a specific task, hence the user often needs to combine multiple programs in order to reach a goal. This can make the data processing unhandy, inflexible and even inefficient due to an overhead of read/write operations. Therefore, it is crucial to have a comprehensive, accessible and efficient computational biology framework in a scripting language to overcome these limitations. Results We have developed the Python package Biotite : a general computational biology framework, that represents sequence and structure data based on NumPy ndarrays . Furthermore the package contains seamless interfaces to biological databases and external software. The source code is freely accessible at https://github.com/biotite-dev/biotite . Conclusions Biotite is unifying in two ways: At first it bundles popular tasks in sequence analysis and structural bioinformatics in a consistently structured package. Secondly it adresses two groups of users: novice programmers get an easy access to Biotite due to its simplicity and the comprehensive documentation. On the other hand, advanced users can profit from its high performance and extensibility. They can implement their algorithms upon Biotite , so they can skip writing code for general functionality (like file parsers) and can focus on what their software makes unique.

Background Biomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines. Results pyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook. Conclusions We introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

The quality assessment of water is the need of the hour as water pollution has reached to an alarming level. The pollution of natural water bodies due to mine drainage system and mining activities is a major environmental concern worldwide. There are many potential reasons of water pollution such as agricultural, sewage, oil, radioactive materials, dumping & mining activities etc. Mining activities are responsible for the contamination of watercourses with metal and increment of sediment levels in it, however acid mine drainage can be viewed as the most lethal means of polluting watercourse. In this study an analysis was done on the water samples collected from different coal mines of Jharkhand and Telangana State. The WQI for each sample were calculated and correlated with their physico-chemical parameters. The lowest grades of the water samples are mainly due to the presence of the strongest correlated parameters. It was observed that the iron content in the samples has the strongest correlation with a Pearson coefficient of 0.9977 and highest significance with a P value lower than 0.001.

The inability to control the limbs is the main reason that affects the daily activities of the disabled which causes social restrictions and isolation. More studies were performed to help disabilities for easy communication with the outside world and others. Various techniques are designed to help the disabled in carrying out daily activities easily. Among these technologies is the Smart Wheelchair. This research aims to develop a smart eye-controlled wheelchair whose movement depends on eye movement tracking. The proposed Wheelchair is simple in design and easy to use with low cost compared with previous Wheelchairs. The eye movement was detected through a camera fixed on the chair. The user's gaze direction is obtained from the captured image after some processing and analysis. The order is sent to the Arduino Uno board which controls the wheelchair movement. The Wheelchair performance was checked using different volunteers and its accuracy reached 94.4% with a very short response time compared with the other existing chairs.

Occupational health and safety has top priority within the cement industry. The preheating tower with its series of installed cyclones is essential in the cement kiln production process and it is considered among the most dangerous places in a cement plant. Coatings and blockages can often occur in the cyclone preheaters of rotary kiln plants for burning cement clinker. These wall build-ups disturb and/or block the process downward flow of hot kiln feed and the upward flow of hot kiln exhaust gases. Actually, our research aims to use process prediction by operating the digital transformation through a 4.0 tool for monitoring and analyzing temperature and pressure in real time. This tool monitors temperature and pressure using sensors that transform the data into a computer platform for real-time analysis and predicts failures according to a predictive model to prevent the occurrence of preheater cyclone blockages. This new technology will help to further improve occupational safety, increases the efficiency of industrial processes, and increases productivity.

Background Cell-based models are becoming increasingly popular for applications in developmental biology. However, the impact of numerical choices on the accuracy and efficiency of the simulation of these models is rarely meticulously tested. Without concrete studies to differentiate between solid model conclusions and numerical artifacts, modelers are at risk of being misled by their experiments’ results. Most cell-based modeling frameworks offer a feature-rich environment, providing a wide range of biological components, but are less suitable for numerical studies. There is thus a need for software specifically targeted at this use case. Results We present CBMOS, a Python framework for the simulation of the center-based or cell-centered model. Contrary to other implementations, CBMOS’ focus is on facilitating numerical study of center-based models by providing access to multiple ordinary differential equation solvers and force functions through a flexible, user-friendly interface and by enabling rapid testing through graphics processing unit (GPU) acceleration. We show-case its potential by illustrating two common workflows: (1) comparison of the numerical properties of two solvers within a Jupyter notebook and (2) measuring average wall times of both solvers on a high performance computing cluster. More specifically, we confirm that although for moderate accuracy levels the backward Euler method allows for larger time step sizes than the commonly used forward Euler method, its additional computational cost due to being an implicit method prohibits its use for practical test cases. Conclusions CBMOS is a flexible, easy-to-use Python implementation of the center-based model, exposing both basic model assumptions and numerical components to the user. It is available on GitHub and PyPI under an MIT license. CBMOS allows for fast prototyping on a central processing unit for small systems through the use of NumPy. Using CuPy on a GPU, cell populations of up to 10,000 cells can be simulated within a few seconds. As such, it will substantially lower the time investment for any modeler to check the crucial assumption that model conclusions are independent of numerical issues.

Python is a robust, procedural, object-oriented, and functional language. The features of the language make it valuable for web development, game development, business, and scientific programming. This book deals with problem-solving and programming in Python. It concentrates on the development of efficient algorithms, the syntax of the language, and the ability to design programs in order to solve problems. In addition to standard Python topics, the book has extensive coverage of NumPy, data visualization, and Matplotlib. Numerous types of exercises, including theoretical, programming, and multiple-choice, reinforce the concepts covered in each chapter.