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 this work we try to address the large interest existing nowadays in the better understanding of the interaction between microbial biofilms and metallic implants. Our aimed was to identify a new preventive strategy to control drug release, biofilm formation and contamination of medical devices with microbes. The transfer and printing of novel bioactive glass-polymer-antibiotic composites by Matrix-Assisted Pulsed Laser Evaporation into uniform thin films onto 316 L stainless steel substrates of the type used in implants are reported. The targets were prepared by freezing in liquid nitrogen mixtures containing polymer and antibiotic reinforced with bioglass powder. The cryogenic targets were submitted to multipulse evaporation by irradiation with an UV KrF* (λ = 248 nm, τ_FWHM ≤ 25 ns) excimer laser source. The prepared structures were analyzed by infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy and profilometry, before and after immersion in physiological fluids. The bioactivity and the release of the antibiotic have been evaluated. We showed that the incorporated antibiotic underwent a gradually dissolution in physiological fluids thus supporting a high local treatment efficiency. Electrochemical measurements including linear sweep voltammetry and impedance spectroscopy studies were carried out to investigate the corrosion resistance of the coatings in physiological environments. The in vitro biocompatibility assay using the MG63 mammalian cell line revealed that the obtained nanostructured composite films are non-cytotoxic. The antimicrobial effect of the coatings was tested against Staphylococcus aureus and Escherichia coli strains, usually present in implant-associated infections. An anti-biofilm activity was evidenced, stronger against E. coli than the S. aureus strain. The results proved that the applied method allows for the fabrication of implantable biomaterials which shield metal ion release and possess increased biocompatibility and resistance to microbial colonization and biofilm growth.

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.