Engineering Innovations Vol. 15

Abstract: A theoretical analysis of low-temperature plastic deformation processes and acoustic relaxation in the high-entropy alloy Al_0.5CoCrCuFeNi has been conducted. Within the framework of proposed dislocation model it was established the key types of dislocation defects in the alloy's lattice structure; types of barriers that hinder the movement of dislocation lines (strings); and mechanisms of thermally activated movement of various dislocation line elements through these barriers at room and low temperatures. Using this model, quantitative estimates have been derived for significant dislocation characteristics and their interaction with barriers, such as the distance between local obstacles in the slip plane ∼ 4 nm, the Peierls stress for dislocations in an easy slip system 4 · 10⁶ Pa, and more. Additionally, an estimated speed of sound 3.4 · 10³ m/s based on the proposed model aligns well with the direct experimental data. The empirical estimates for the energy per unit length of a dislocation ∼ 10^-8 J/m and the linear mass density ∼ 10^-15 kg/m are consistent with modern continuum dislocation theory. A detailed examination of the structure of the alloy Al_0.5CoCrCuFeNi was carried out using X-ray diffraction and Energy Dispersive Spectroscopy techniques. Numerical estimates of the dislocation density ∼ 5 · 10¹⁵m^-2 were obtained through Williamson-Hall analysis of X-ray diffraction patterns. It was found to correlate with the estimates overall length of dislocation segments per unit volume which effectively interacts with elastic vibrations of the sample ∼ 4 · 10¹³m^-2, as determined from acoustic relaxation measurements.

Abstract: The most vulnerable food products related to halal issues are in the form of mixing beef and chicken meat with pork, which has a physical resemblance if not carefully considered. The rise of meat adulteration is often found due to high demand and high prices. For this reason, a fast, effective, and low-cost meat adulteration detection tool is needed. Detection of beef and chicken adulteration in this study was carried out using a VIS-NIR spectrophotometer from an AS7341 multispectral sensor equipped with an LED light source and 11 channels to read the reflection of meat light in the near light and near infrared ranges, raspberry pi as a microcontroller, data displayed on an LCD stored in CSV form. The results of sensor response patterns formed in beef, chicken, pork, mixed beef-pork, and minced chicken-pork mixed meat show different characteristics. Then to clarify the characteristics of each meat, the results of the sensor response were analyzed using the Principle Componen Analysis (PCA) method. The results of data reduction from PCA projections through Principle Component 1 and Principle Component 2 regions are able to detect the presence of pork mixture in beef and chicken. The results of the PCA score plot on beef, pork and cow-pig mixture the percentage of PC1 is 100% and PC2 is 0% while on chicken, pork and chicken-pig mixture the percentage of PC1 is 100% and PC2 is 0%. The results of this study show the great potential of using a portable spectrophotometer using the AS7341 sensor whose results are analyzed using the PCA method to detect adulteration of minced beef and chicken.

Abstract: This research uses a multi-domain technique to give a thorough analysis of mechanical gears health evaluation that includes time, frequency, and time-frequency signal analysis. The research seeks to discover patterns indicative of healthy, partially damaged, or fully damaged gear states using a variety of graphical representations, including time and frequency plots, the Short-Time Fourier Transform (STFT), and scalograms which are visual representations of the wavelet transform of a signal. Advanced machine learning models are used to improve diagnostic accuracy when manual identification of these trends becomes difficult. The goal is to achieve a validation accuracy greater than 70% a threshold selected based on prior studies indicating that this level ensures reliable fault detection for industrial applications while balancing computational constraints. The reliability and effectiveness of gear monitoring systems can be increased by integrating contemporary signal processing and machine learning approaches, as demonstrated by this research, which also advances the identification of gear faults. Among the conclusions are the outcomes of tests done to identify gear problems in which authors were able to train a model with more than 72% accuracy and able to propose Artificial Intelligence model for classification of faults in gears.

Abstract: The integration of Internet of Things (IoT) technologies is transforming homes by improving energy efficiency, safety, convenience, and even health. This paper presents the development of an IoT-enabled smart home automation system designed to enhance these aspects using cost-effective components and user-friendly control methods. The system utilizes the ESP8266 microcontroller along with KY-018 Light and HC-SR04 Ultrasonic Sensors to collect environmental data. Core features include real-time visual feedback through LEDs and LCD I2C displays, and remote operation via a custom Android mobile application integrated through Firebase. Both automated and manual control modes are provided, ensuring seamless and adaptive responses to real-world conditions. Experimental results confirm accurate detection and reliable control actions, demonstrating the practicality and scalability of this smart home automation system.

Abstract: The growing demand for effective energy management in smart grids necessitates modern monitoring and optimization methods. Traditional energy distribution systems confront numerous issues, including high electricity costs, ineffective load management, and a lack of real-time data analytics. This study describes an IoT-based Smart Energy Monitoring System (SEMS) for continuous real-time energy tracking, with accurate current measurements provided via wireless transmission and predictive analytics. The system is designed to provide reliable and precise current measurements. The suggested system combines wireless data transmission and predictive analytics. The technology enables real-time energy monitoring and assists users in analysing consumption trends to make more informed decisions. It also allows for more efficient load control by identifying peak consumption periods. An energy audit validates the system, indicating considerable reductions in power losses as well as increased economic feasibility. The findings demonstrate that IoT-driven smart energy management improves sustainability, dependability, and cost effectiveness in modern smart grids.

Abstract: Digital transformation has developed significantly in the 21st century, and special attention has been paid to it in business and society. It is becoming more relevant and presents the private and public sectors with new challenges. Computer and mobile devices, information, and communication technologies are actively used in social, economic, industrial, engineering, and other fields, to create and implement their respective new-generation software applications. New paradigms based on the software industry including the development, and creation of new methods and methodologies or perfecting the existing ones are receiving a lot of attention worldwide, on which artificial intelligence (AI) had a significant impact. Regarding the ongoing digital transformation of organizations and business models companies have been facing the challenge for years of digitalizing the processes in management control. These processes have been undergoing fundamental changes, driven by the rapid development of AI in recent years. In this context, one of the most innovative and significant technological breakthroughs was the development of generative AI. AI tool ChatGPT plays an important role in advancing scientific progress by promoting the use of artificial intelligence, improving user interaction and accelerating innovation in various industries. This paper explores how AI can increase the effectiveness and efficiency of decision-making and management control. Efficiency is achieved through strategic decisions and efficiency through operational decisions. By integrating AI technologies, organizations can automate repetitive tasks, streamline data processes, and improve financial reporting and forecasting accuracy. AI-based analytics provide managers with deeper insights that enable more informed decisions about resources, processes, products and services. In addition, the paper examines how AI has shifted organizational focus from operational efficiency to strategic priorities. This change has contributed to a more flexible and responsive management control framework that allows organizations to adapt quickly their control system to changing market conditions and maintain a competitive advantage. In addition, one of the most prominent fields today is nanomanufacturing and the optimization of production processes. Through AI-driven optimization, it becomes possible to refine the synthesis and assembly of nanostructures, significantly improving precision and efficiency in production.