The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

Recent technological advances in the fields of biomaterials and tissue engineering have spurred interest in biopolymers for various biomedical applications. The advantage of biopolymers is their favorable characteristics for these applications, among which proteins are of particular importance. Proteins are explored widely for 3D bioprinting and tissue engineering applications, wound healing, drug delivery systems, implants, etc., and the proteins mainly available include collagen, gelatin, albumin, zein, etc. Zein is a plant protein abundantly present in corn endosperm, and it is about 80% of total corn protein. It is a highly renewable source, and zein has been reported to be applicable in different industrial applications. Lately, it has gained attention in biomedical applications. This research interest in zein is on account of its biocompatibility, non-toxicity, and certain unique physico-chemical properties. Zein comes under the GRAS category and is considered safe for biomedical applications. The hydrophobic nature of this protein gives it an added advantage and has wider applications in drug delivery. This review focuses on details about zein protein, its properties, and potential applications in biomedical sectors.

The rapid shift to online learning during COVID-19 posed challenges for students. This investigation explored these hurdles and suggested effective solutions using mixed methods. By combining a literature review, interviews, surveys, and the analytic hierarchy process (AHP), the study identified five key challenges: lack of practical experience, disruptions in learning environments, condensed assessments, technology and financial constraints, and health and mental well-being concerns. Notably, it found differences in priorities among students across academic years. Freshmen struggled with the absence of hands-on courses, sophomores with workload demands, and upperclassmen with mental health challenges. The research also discussed preferred strategies for resolution, emphasizing independent learning methods, managing distractions, and adjusting assessments. By providing tailored insights, this study aimed to enhance online learning. Governments and universities should support practical work, prioritize student well-being, improve digital infrastructure, adapt assessments, foster innovation, and ensure resilience.

Cysteine is one of the body’s essential amino acids to build proteins. For the early diagnosis of a number of diseases and biological issues, L-cysteine (L-Cys) is essential. Our study presents an electrochemical sensor that detects L-cysteine by immobilizing the horseradish peroxidase (HRP) enzyme on a reduced graphene oxide (GCE) modified glassy carbon electrode. The morphologies and chemical compositions of synthesized materials were examined using Fourier transform infrared spectroscopy (FTIR) and field-emission scanning electron microscopy (FESEM). The modified electrode’s electrochemical behavior was investigated using cyclic voltammetry (CV). Cyclic voltammetry demonstrated HRP/rGO/GCE has better electrocatalytic activity than bare GCE in the oxidation of L-cysteine oxidation in a solution of acetate buffer. The electrochemical sensor had a broad linear range of 0 µM to 1 mM, a 0.32 µM detection limit, and a sensitivity of 6.08 μA μM⁻¹ cm⁻². The developed sensor was successfully used for the L-cysteine detection in a real blood sample with good results.

The objective of this work was to analyze the effect of the use of ChatGPT in the teaching-learning process of scientific research in engineering. Artificial intelligence (AI) is a topic of great interest in higher education, as it combines hardware, software and programming languages to implement deep learning procedures. We focused on a specific course on scientific research in engineering, in which we measured the competencies, expressed in terms of the indicators, mastery, comprehension and synthesis capacity, in students who decided to use or not ChatGPT for the development and fulfillment of their activities. The data were processed through the statistical T-Student test and box-and-whisker plots were constructed. The results show that students’ reliance on ChatGPT limits their engagement in acquiring knowledge related to scientific research. This research presents evidence indicating that engineering science research students rely on ChatGPT to replace their academic work and consequently, they do not act dynamically in the teaching-learning process, assuming a static role.