Music is important in everyday life, and music therapy can help treat a variety of health issues. Music listening is a technique used by music therapists in various clinical treatments. As a result, music therapists must have an intelligent system at their disposal to assist and support them in selecting the most appropriate music for each patient. Previous research has not thoroughly addressed the relationship between music features and their effects on patients. The current paper focuses on identifying and predicting whether music has therapeutic benefits. A machine learning model is developed, using a multi-class neural network to classify emotions into four categories and then predict the output. The neural network developed has three layers: (i) an input layer with multiple features; (ii) a deep connected hidden layer; (iii) an output layer. K-Fold Cross Validation was used to assess the estimator. The experiment aims to create a machine-learning model that can predict whether a specific song has therapeutic effects on a specific person. The model considers a person’s musical and emotional characteristics but is also trained to consider solfeggio frequencies. During the training phase, a subset of the Million Dataset is used. The user selects their favorite type of music and their current mood to allow the model to make a prediction. If the selected song is inappropriate, the application, using Machine Learning, recommends another type of music that may be useful for that specific user. An ongoing study is underway to validate the Machine Learning model. The developed system has been tested on many individuals. Because it achieved very good performance indicators, the proposed solution can be used by music therapists or even patients to select the appropriate song for their treatment.

In the dynamic landscape of modern education, characterized by an increasingly active involvement of IT technologies in learning, the imperative to transfer to university students the skills necessary to integrate Artificial Intelligence (AI) into the process represents an important goal. This paper presents a novel framework for knowledge transfer, diverging from traditional programming language-centric approaches by integrating PSoC 6 microcontroller technology. This framework proposes a cyclical learning cycle encompassing theoretical fundamentals and practical experimentation, fostering AI literacy at the edge. Through a structured combination of theoretical instruction and hands-on experimentation, students develop proficiency in understanding and harnessing AI capabilities. Emphasizing critical thinking, problem-solving, and creativity, this approach equips students with the tools to navigate the complexities of real-world AI applications effectively. By leveraging PSoC 6 as an educational tool, a new generation of individuals is efficiently cultivated with essential AI skills. These individuals are adept at leveraging AI technologies to address societal challenges and drive innovation, thereby contributing to long-term sustainability initiatives. Specific strategies for experiential learning, curriculum recommendations, and the results of knowledge application are presented, aimed at preparing university students to excel in a future where AI will be omnipresent and indispensable.

This article describes the design and development of the NI myRIO device-based remote laboratory. The cloud-based, expandable, and reconfigurable remote laboratory is intended to give students access to an online web-based user interface to perform experiments. Multiple myRIO devices are programmed to host several experiments each. A finite state machine is used to select specific experiments, while a single state can contain several. The laboratory web virtual instruments interfaces are hosted on the SystemLink cloud and SystemLink server. A user-friendly interface has been designed to help students to understand important electronic concepts. Virtual and real experiments were fused to give students a wide range of experiments they can access online. The instructor can check outputs of an experiment being executed on the device. Achieving connection between myRIO and SystemLink through global variables and SystemLink ensured that the low-cost device was utilized, and this is suitable for third-world countries’ universities that cannot afford expensive equipment. Students can perform the experiments which have some resemblance to physical execution. The system is expandable in that the number of myRIO devices or number of experiments can be increased to suit changing requirements. The reconfigurability of the system is such that the finite state machine-based coding technique permits only one experiment to be selected, configure the system, and run while other experiments are idle.

Group IV nanocrystals (NCs), in particular from the Si–Ge system, are of high interest for Si photonics applications. Ge-rich SiGe NCs embedded in nanocrystallized HfO₂ were obtained by magnetron sputtering deposition followed by rapid thermal annealing at 600 °C for nanostructuring. The complex characterization of morphology and crystalline structure by X-ray diffraction, μ-Raman spectroscopy, and cross-section transmission electron microscopy evidenced the formation of Ge-rich SiGe NCs (3–7 nm diameter) in a matrix of nanocrystallized HfO₂. For avoiding the fast diffusion of Ge, the layer containing SiGe NCs was cladded by very thin top and bottom pure HfO₂ layers. Nanocrystallized HfO₂ with tetragonal/orthorhombic structure was revealed beside the monoclinic phase in both buffer HfO₂ and SiGe NCs–HfO₂ layers. In the top part, the film is mainly crystallized in the monoclinic phase. High efficiency of the photocurrent was obtained in a broad spectral range of curves of 600–2000 nm at low temperatures. The high-quality SiGe NC/HfO₂ matrix interface together with the strain induced in SiGe NCs by nanocrystallization of both HfO₂ matrix and SiGe nanoparticles explain the unexpectedly extended photoelectric sensitivity in short-wave infrared up to about 2000 nm that is more than the sensitivity limit for Ge, in spite of the increase of bandgap by well-known quantum confinement effect in SiGe NCs.