Cochlear implantation is the common treatment for severe to profound sensorineural hearing loss if there is no benefit from hearing aids. Measuring the electrode impedance along the electrode array at different time points after surgery is crucial in verifying the electrodes’ status, determining the compliance levels, and helping to identify the electric dynamic range. Increased impedance values without proper reprogramming can affect the patient’s performance. The prediction of acceptable levels of electrode impedance at different time points after the surgery could help clinicians during the fitting sessions through a comparison of the predicted with the measured levels. Accordingly, clinicians can decide if the measured levels are within the predicted normal range or not. In this work, we used a dataset of 80 pediatric patients who had received cochlear implants with the MED-EL FLEX 28 electrode array. We predicted the impedance of the electrode arrays in each channel at different time points: at one month, three months, six months, and one year after the date of surgery. We used different machine learning algorithms such as linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, and neural networks. The used features include the patient’s age and the intra-operative electrode impedance at different electrodes. Our results indicated that the best algorithm varies depending on the channel, while the Bayesian linear regression and neural networks provide the best results for 75% of the channels. Furthermore, the accuracy level ranges between 83% and 100% in half of the channels one year after the surgery, when an error range between 0 and 3 KΩ is defined as an acceptable threshold. Moreover, the use of the patient’s age alone can provide the best prediction results for 50% of the channels at six months or one year after surgery. This reflects that the patient’s age could be a predictor of the electrode impedance after the surgery.

Visual programming languages (VPLs) play a significant role in simplifying the process of learning to program and reducing development time. Most VPLs are developed for use in education or specific domains. Recently, some projects have aimed to provide general-purpose VPLs. Among these projects is the Programming Without Coding Technology (PWCT) project, which has been used for several years to develop and maintain the compiler and virtual machine for the Ring programming language. However, PWCT faces several issues related to code generation performance and the operating systems it supports. Additionally, its visual editor lacks many features, such as rich comments, auto-run, and the ability to import textual code, which are highly important in the era of using large language models for generating textual code. In this research, we present the PWCT2 visual programming language, which is distributed on the Steam platform. On Steam, 1772 users have launched the software, and the total usage time recorded exceeds 17,000 h. This generation provides approximately 36 times faster code generation and 20 times lower storage requirements for visual source files. It also allows for the conversion of Ring code into visual code, enabling the creation of a self-hosting VPL. It consists of approximately 92,000 lines of Ring code and comes with 394 visual components. Moreover, using Ring in this project demonstrates the feasibility of utilizing the language for projects of this scale. Ring compiles PWCT2 in less than one second, and the generated bytecode consists of approximately 724,000 instructions.

New programming languages are often designed to keep up with technological advancements and project requirements while also learning from previous attempts and introducing more powerful expression mechanisms. However, most existing dynamic programming languages rely on English keywords and lack features that facilitate easy translation of language syntax. Additionally, maintaining multiple implementations of the same language for different platforms, such as desktops and microcontrollers, can lead to inconsistencies and fragmented features. Furthermore, they usually do not use visual programming to fully implement the compiler and virtual machine. In this research paper, we introduce Ring—a dynamically-typed language with a lightweight implementation. However, it boasts several advantages, including a rich and versatile standard library and direct support for classes and object-oriented programming. The Ring language offers customization features. For instance, it allows easy modification of the language syntax multiple times, enabling programming by writing code using Arabic, English, or other keywords. Additionally, the language permits the creation of domain-specific languages through new features that extend object-oriented programming, allowing for specialized languages resembling CSS or Supernova. In the era of the Internet of Things, instead of creating another language implementation to support microcontrollers, the same Ring implementation allows us to create projects and applications for desktops, the web, WebAssembly, Android, or Raspberry Pi Pico. The Ring Compiler and Virtual Machine are designed using the PWCT Visual Programming language based on ANSI C. The visual implementation is composed of 18,945 components that generate 24,743 lines of code, which increases the abstraction level by approximately 23.5% and hides unnecessary details.

Large language models (LLMs) are increasingly used in software development, yet their ability to generate and maintain large, multi-module systems through natural language interaction remains insufficiently characterized. This study presents an empirical analysis of developing a 7420-line Terminal User Interface (TUI) framework for the Ring programming language using a prompt-driven workflow with Claude Code (Opus 4.5), employing an iterative testing and corrective feedback. The system was produced through 107 prompts: 21 feature requests, 72 bug fix prompts, 9 prompts sharing information from Ring documentation, 4 prompts providing architectural guidance, and 1 prompt dedicated to generating documentation. Development progressed across five phases, with the Window Manager phase requiring the most interaction (35 prompts), followed by complex UI systems (25 prompts) and control expansion (20 prompts). Bug-related prompts covered redraw issues, event-handling faults, runtime errors, and layout inconsistencies, while feature requests focused primarily on new widgets, window-manager capabilities, and advanced UI components. Most prompts were brief (mean ≈ 258 characters; median = 207 characters), reflecting a highly iterative workflow in which the human role was limited to specifying requirements, validating behavior, and issuing corrective prompts—without writing any code manually. The resulting framework contains 28 classes, 334 methods and includes a windowing subsystem, event-driven architecture, interactive widgets, hierarchical menus, grid and tree components, tab controls, and a multi-window desktop environment. By combining quantitative prompt analysis with qualitative assessment of model behavior, this study provides empirical evidence that modern LLMs can preserve architectural coherence across iterations and support the construction of new libraries and tools for emerging programming languages, highlighting prompt-driven development as a viable methodology within software-engineering practice.

Sensorineural hearing loss can be treated using Cochlear implantation. After this surgery using the electrode array impedance measurements, we can check the stability of the impedance value and the dynamic range. Deterioration of speech recognition scores could happen because of increased impedance values. Medicines used to do these measures many times during a year after the surgery. Predicting the electrode impedance could help in taking decisions to help the patient get better hearing. In this research we used a dataset of 80 patients of children who did cochlear implantation using MED-EL FLEX28 electrode array of 12 channels. We predicted the electrode impedance on each channel after 1 month from the surgery date. We used different machine learning algorithms like neural networks and decision trees. Our results indicates that the electrode impedance can be predicted, and the best algorithm is different based on the electrode channel. Also, the accuracy level varies between 66% and 100% based on the electrode channel when accepting an error range between 0 and 3 KO. Further research is required to predict the electrode impedance after three months, six months and one year.

Networks exist all around on the planet, and inside the brain of every living organism. On the city streets we see transport networks, inside homes and organizations we see water piping networks. And in the digital information age we see the rise of the computer networks and the mobile networks and their upgrade from generation to generation. We need networks for efficient sharing of resources and as human beings the most valuable thing that we can share along the time from age to age is our data that could represent our knowledge and information represented through text, voice, image, and video. In this paper we provide a quick overview on network generation and IoT applications. Also, we point to the security threats in these generations.