Effect in variation of the cationic precursor temperature on the electrical and crystalline properties of MnS growth by SILAR.

The crystallographic, optical, and electrical properties of manganese sulfide thin films depend on the control of the temperature precursors in the synthesis process, as shown by the results of this work. MnS thin films were deposited on glass substrates using the SILAR method and over an additional layer of CdS synthesized by chemical bath deposition (CBD) to acquire a p-n heterojunction. SILAR is an inexpensive method performed with a homemade robot in this case. Temperature in the solution precursors varied from 20 to 80 °C in four experiments. The morphology and structure of MnS and FTO/CdS/MnS thin films were studied through scanning electron microscopy (SEM) and grazing-incidence X-ray diffraction (GIXRD); the results indicate that materials showed a polycrystalline behavior, a diffraction peak of α- MnS cubic phase was observed with lattice constants values, ranging from 4.74 to 4.75 Å. Additionally, Raman spectroscopy showed a signal corresponding to the transversal optical phonons of MnS at a wavenumber near 300 cm-1. UV-vis spectroscopy showed optical bandgap values of 3.94, 4.0, 4.09, and 4.26 eV for thin films obtained at 20°, 40°, 60°, and 80 °C. respectively. Results indicated 80 °C as an optimal cationic precursor process temperature, achieving optical transmittance T% and good film quality according to SEM and GIXRD for the synthetization of MnS. The current-voltage (I-V) characterization in the heterojunction showed a characteristic diode curve with an open circuit voltage (VOC) of 300 mV under illumination, which indicated that the manganese sulfide behaves as p-type material contributing with positive charge carriers, while CdS behaves as n-type material.

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature.

The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

Chameleon: a cloud computing Industry 4.0 neutron spectrum unfolding code.

This work presents Chameleon, a cloud computing (CC) Industry 4.0 (I4) neutron spectrum unfolding code. The code was designed under the Python programming language, using Streamlit framework®, and it is executed on the cloud, as I4 CC technology through internet, by using mobile devices with internet connectivity and a web navigator. In its first version, as a proof of concept, the SPUNIT algorithm was implemented. The main functionalities and the preliminary tests performed to validate the code are presented. Chameleon solves the neutron spectrum unfolding problem and it is easy, friendly and intuitive. It can be applied with success in various workplaces. More validation tests are in progress. Future implementations will include improving the graphical user interface, inserting other algorithms, such as GRAVEL, MAXED and neural networks, and implementing an algorithm to estimate uncertainties in the calculated integral quantities.

Current Therapeutic Strategies in Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) are the fastest growing chronic complication of diabetes mellitus, with more than 400 million people diagnosed globally, and the condition is responsible for lower extremity amputation in 85% of people affected, leading to high-cost hospital care and increased mortality risk. Neuropathy and peripheral arterial disease trigger deformities or trauma, and aggravating factors such as infection and edema are the etiological factors for the development of DFUs. DFUs require identifying the etiology and assessing the co-morbidities to provide the correct therapeutic approach, essential to reducing lower-extremity amputation risk. This review focuses on the current treatment strategies for DFUs with a special emphasis on tissue engineering techniques and regenerative medicine that collectively target all components of chronic wound pathology.

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry.

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN.