This study develops the idea that by visualizing an artistic performance through motion and sound graphs, it is possible to identify errors or tenden-cies that negatively affect the sound and the exact moments when they occur. This provides students with a potential way to reduce practice time and maintain a more balanced and healthier schedule. For this study, recordings from the side-positioned camera were utilized, focusing on the arm handling the bow. The video recordings were analyzed using a video analysis software called Kinovea, commonly used by athletes to evaluate their techniques and movements to enhance performance. However, this software also has appli-cations in biomechanics, as it allows for a detailed analysis of body kinemat-ics to correct movement errors and prevent injuries or medical conditions. The goal was to extract the x and y coordinates of the shoulder, elbow, and wrist throughout the entire movement. This was achieved by placing markers on the respective areas. After obtaining the data, it will be processed in MATLAB to generate the graphs. MATLAB is a widely used programming and numerical computation software, commonly applied in engineering as well as other fields such as economics. The graphs were designed to provide as much relevant information as possible to help achieve the study's objective.

Hypertension remains a highly prevalent cardiovascular condition, strongly influenced by autonomic imbalance and chronic stress exposure. Receptive music therapy has gained recognition as a supportive non-pharmacological strategy capable of modulating autonomic and emotional responses, yet ob-jective personalization remains limited. This study investigated the clinical effects of receptive music therapy in adults with hypertension and applied ar-tificial intelligence–based acoustic analysis to identify musical characteristics associated with cardiovascular improvement.

A total of 134 hypertensive adults were followed for six months, engaging in regular home-based listening, while a highly adherent subgroup additionally participated in supervised sessions with physiological monitoring. Blood pressure and metabolic parameters were assessed longitudinally. Musical stimuli were analyzed using a combined AI framework including acoustic feature extraction, unsupervised clustering, and supervised regression model-ing.

Significant reductions in systolic and diastolic blood pressure, alongside helpful lipid profile changes, were observed exclusively among adherent par-ticipants. Therapeutic musical profiles were defined by slow tempo, harmon-ic consonance, rhythmic stability, and reduced timbral complexity, showing strong associations with parasympathetic activation.

These findings support AI-enhanced receptive music therapy as a viable and scalable adjunct in hypertension management, enabling data-driven person-alization of therapeutic listening strategies.

The paper fits into the new concepts for engineering education in higher and vocational education institutions, including emerging technologies in learning. It approaches interdisciplinary and synergistic elements from nature harmoniously combined with modern technologies to highlight some physical and acoustic characteristics of wood. The novelty of paper consists in the use of electronic systems to highlight the chromatic and acoustic resonance properties of the structures of deciduous wood species (oak (Quercus robur Pall.), field elm (Ulmus minor Mill.), downy ash (Fraxinus pallisiae Wilmott), honey locust (Gleditsia triacanthos L.), ash (Ailanthus altissima (Mill.) Swingle), cherry (Prunus avium L.), plum (Prunus domestica L.). The seven rounds obtained from the listed wood species were mechanically processed and protected against humidity and temperature variations by coatings with transparent varnishes and resins. Their exposure and fixation is achieved using a removable metal structure inspired by biomimetics. The chromaticity of the wood was highlighted with high-power LED-COB light sources designed and executed using 3D printing, using a design inspired by nature. The acoustic resonance of each species is highlighted by playing the audio recording of the seven resonance frequencies corresponding to the mentioned species. These frequencies were extracted by processing the acoustic signals with Fast Fourier Transform (FFT) analysis following experimental modal analysis. From a musical point of view, the sounds obtained represent the pentatonic system frequently used in Afro-American music - bluess. The work highlights this aspect by correlating the sound source with the light indicators placed in the proximity of each woody species. The technologies involved in the paper range from CAD design, CNC machining, finishing and stabilization with advanced materials, 3D printing, testing and signal processing, electrical diagram design and incorporation of electronic lighting and acoustic systems.

This study investigates how AI can act as a pedagogical partner in developing the vocal technique, expressive skills, and interpretative autonomy of opera singers. The research seeks to determine whether conversational AI models can assist in structuring vocal study, providing contextual feedback, and supporting artistic reflection. The motivation arises from practical experience in preparing and performing roles such as Caramello (Eine Nacht in Venedig – Johann Strauss), Ruggero (La Rondine – Giacomo Puccini), and Don Carlo (Don Carlo – Giuseppe Verdi). The study adopts a qualitative approach based on self-observation, reflective dialogue with AI models, and iterative documentation of the vocal study process. Conversations with AI were analyzed to identify patterns of feedback, interpretative guidance, and organizational structuring of practice sessions. The framework also anticipates the use of visual and auditory AI tools, including image and GIF processing, 3D modeling, and audio analysis technologies, to illustrate phonatory mechanisms and to integrate these multimodal resources into future didactic materials.

The paper aims to compare the acoustic and environmental characteristics of anthropogenic and forest areas in order to highlight the parameters that contribute to forest therapy and psychological well-being. Thus, the study was carried out in one of the most famous resorts in Brasov County, with nine measurement points for noise levels, pollution levels, acoustic spectrum and anthropogenic/natural elements in the vicinity being established. All recordings were made on the same day, with weather conditions being approximately constant throughout the study. The results showed that the equivalent noise level decreased by 32% in the forest, compared to the central area of ​​the resort, while the CO2 level was approximately constant, considering that the resort is located in a mountainous area, surrounded by mixed deciduous and coniferous forests. Subsequently, the acoustic recordings were processed in Laboratory Virtual Instrumentation Workbench (LabVIEW) and the frequency spectra were extracted. After listening to the forest acoustic sequences, the level of delta waves increased, and the level of theta and low alpha waves decreased.