Using Deep Learning to Recognize Therapeutic Effects of Music Based on Emotions.

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.

Reconfigurable Wireless Sensor Node Remote Laboratory Platform with Cloud Connectivity.

Thanks to the recent rapid technological advancement in IoT usage, there is a need for students to learn IoT-based concepts using a dedicated experimental platform. Furthermore, being forced into remote learning due to the ongoing COVID-19 pandemic, there is an urgent need for innovative learning methods. From our perspective, a learning platform should be reconfigurable to accommodate multiple applications and remotely accessible at any time, from anywhere, and on any connected device. Considering that many of the university courses are now held online, the reliability and scalability of the system become critical. This paper presents the design and development of a wireless configurable myRIO-based sensor node that connects to SystemLink Cloud. The sensors that were used are for ambient light, temperature, and proximity. A graphical programming environment (G-LabVIEW) and related APIs were used for rapid concept-to-development process. Distinct applications have been developed for the instructor and students, respectively. The students can select which sensor and application to run on the system and observe the measurements on the local student's application or the cloud platform at a specific moment. They can also read the data on the cloud platform and use them in their LabVIEW application. In the context of remote education, we strongly believe that this platform is and will be suitable for the COVID and Post-COVID eras as well because it creates a much better remote laboratory experience for students. In conclusion, the system that was developed is innovative because it is software reconfigurable from the device, from the instructor's application and cloud via a web browser, it is intuitive, and it has a user-friendly interface. It meets most of the necessary requirements in the current era, being also highly available and scalable in the cloud.

Nanograting layers of Si.

The paper presents research regarding the complex behavior of materials based on Si and SiO2, geometrically processed at nano-scale. The geometry, which induces the doping effect (G-doping), occurred when it was possible to fabricate nanograting structures. Studies on the influence of nanograting structures on the properties of materials have shown that this process may lead to effects similar to those created by doping with donors. The resistivity values measured in Si-based nanograting layers, for example, were approximately 10-2 Ω cm, similar to those of Si semiconductors doped with phosphorus 'impurities' having a volume concentration of 1018 cm-3. This increase in electronic properties, as a result of the nanograting structure, seems to appear due to the fact that the electrons rejected in the process are placed in the structure vacancies. It has been experimentally proven that, in the case of semiconductors, the nanograting structuring makes the rejected electrons from the valence band to be placed in the conduction band. In the paper, after samples fabrication with nanograting structures on Si films placed on SiO2 support, the I-V curves of the obtained layers were drawn, both by measuring in four points and also in two points. The resistivity measurements were made in two directions: along and perpendicular to the strips of the structure and showed the existence of an anisotropy.

Influence of Heat and Thermochemical Treatment Parameters on C75 Steel Fatigue Resistance.

The paper presents the results of the fatigue testing of heat-treated and thermochemically treated C75 steel with different process parameters in terms of working medium (gas, salt bath), temperature, and time. The experimental program aims to analyze the changes in microstructure under the influence of heat treatment and fatigue resistance. The relationships between the structural changes, the internal stress, and the heat-treated material's mechanical and physical properties can determine the first nano cracks leading to rupture propagation. Based on the experimental values of this paper, we highlight the dependence between the nature of the cracks and the stress to which the specimen was subjected. The paper presents a brief introduction to the fatigue test and the experimental tests performed to determine the fatigue resistance characteristics, the macroscopic analysis of the material, and the crystallographic analysis. The results obtained allow a comparison between the fatigue limits of heat-treated and thermochemically treated C75 steel in gas and salt baths.

Investigations Regarding the Addition of ZnO and Li2O-TiO2 to Phosphate-Tellurite Glasses: Structural, Chemical, and Mechanical Properties.

Phosphate and tellurite glasses can be used in optics, optoelectronics, magneto-optics, and nuclear and medical fields. Two series of phosphate-tellurite glasses, (50-x)ZnO-10Al2O3-40P2O5-xTeO2 and (40-x)Li2O-10Al2O3-5TiO2-45P2O5-xTeO2 (x = 5, 10), were synthesized by a non-conventional wet-route, and the mechanical properties as key performance measures for their application in optoelectronics were investigated. X-ray Diffraction (XRD) measurements revealed the vitreous nature of the investigated materials. Instrumented indentation tests allowed the calculation of hardness (H) and Young's modulus (E) using the Oliver and Pharr model. The influence of increasing the TeO2 content, as well as the substitution of ZnO by Li2O-TiO2, on the variation of hardness, Young's modulus, penetration depth (PD), and fracture toughness (FT) was evaluated in both series. As a general trend, there is a decrease in the hardness and Young's modulus with increasing penetration depth. The addition of Li2O and TiO2 instead of ZnO leads to improved hardness and elastic modulus values. Regarding the H/E ratio, it was found that the samples with lower TeO2 content should be significantly more crack-resistant compared to the higher TeO2 content samples. The H3/E2 ratio, being lower than 0.01, revealed a poor resistance of these glasses to plastic deformation. At the same time, a decrease of the fracture toughness with increasing TeO2 content was noticed for each glass series. Based on dilatometry measurements, the thermal expansion coefficient as well as the characteristic temperatures of the glasses were measured. Field Emission Scanning Electron Microscopy-Energy Dispersive X-ray analysis (FESEM-EDX) revealed a uniform distribution of the elements in the bulk samples. The mechanical properties of these vitreous materials are important in relation to their application as magneto-optical Faraday rotators in laser cavities.

Stainless Steel Surface Nitriding in Open Atmosphere Cold Plasma: Improved Mechanical, Corrosion and Wear Resistance Properties.

This work presents preliminary results regarding improving the mechanical, wear and protective properties (hardness, coefficient of friction, corrosion resistance) of AISI 304 stainless steel surfaces by open atmosphere cold plasma surface treatment method. Comparative evaluations of the morphological, corrosion resistance, mechanical and tribological properties for different periods of treatment (using N2 gas for cold plasma generation in an open atmosphere) were performed. AFM surface analyses have shown significant surface morphology modifications (average roughness, FWHM, surface skewness and kurtosis coefficient) of the treated samples. An improved corrosion resistance of the N2 treated surfaces in open atmosphere cold plasma could be observed using electrochemical corrosion tests. The mechanical tests have shown that the surface hardness (obtained by instrumented indentation) is higher for the 304 stainless steel samples than it is for the un-treated surface, and it decreases gradually for higher penetration depths. The kinetic coefficient of friction (obtained by ball-on-disk wear tests) is significantly lower for the treated samples and increases gradually to the value of the un-treated surface. The low friction regime length is dependent on the surface treatment period, with a longer cold plasma nitriding process leading to a significantly better wear behavior.

Nanocrystallized Ge-Rich SiGe-HfO2 Highly Photosensitive in Short-Wave Infrared.

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.

Using Noise and Fluctuations for In Situ Measurements of Nitrogen Diffusion Depth.

In manufacturing processes involving diffusion (of C, N, S, etc.), the evolution of the layer depth is of the utmost importance: the success of the entire process depends on this parameter. Currently, nitriding is typically either calibrated using a "post process" method or controlled via indirect measurements (H2, O2, H2O + CO2). In the absence of "in situ" monitoring, any variation in the process parameters (gas concentration, temperature, steel composition, distance between sensors and furnace chamber) can cause expensive process inefficiency or failure. Indirect measurements can prevent process failure, but uncertainties and complications may arise in the relationship between the measured parameters and the actual diffusion process. In this paper, a method based on noise and fluctuation measurements is proposed that offers direct control of the layer depth evolution because the parameters of interest are measured in direct contact with the nitrided steel (represented by the active electrode). The paper addresses two related sets of experiments. The first set of experiments consisted of laboratory tests on nitrided samples using Barkhausen noise and yieded a linear relationship between the frequency exponent in the Hooge equation and the nitriding time. For the second set, a specific sensor based on conductivity noise (at the nitriding temperature) was built for shop-floor experiments. Although two different types of noise were measured in these two sets of experiments, the use of the frequency exponent to monitor the process evolution remained valid.