This research presents the design and implementation of an Internet of Things (IoT)-based solution to measure the percentage of Liquefied Petroleum Gas (LPG) inside domestic stationary tanks. The IoT-based sensor, in addition to displaying the percentage of the LPG level in the tank to the user through a mobile application (app), has the advantage of simultaneously sharing the acquired data with an LPG filling plant via the Internet. The design process and calculations for the selection of the electronic components of the IoT-based sensor are presented. The methodology for obtaining and calibrating the measurement of the tank filling percentage from the magnetic level measurement system is explained in detail. The operation of the developed software, and the communication protocols used are also explained so that the data can be queried both in the user’s app and on the gas company’s web platform safely. The use of the Clark and Wright savings algorithm is proposed to sufficiently optimize the distribution routes that tank trucks should follow when serving different home refill requests from customers located in different places in a city. The experimental results confirm the functionality and viability of the hardware and software developed. In addition, by having the precise location of the tank, the generation of optimized gas refill routes for thirty customers using the heuristic algorithm and a visualization of them on Google Maps is demonstrated. This can lead to competitive advantages for home gas distribution companies.

Recently, all inorganic perovskite solar cells have triggered great attention thanks to the rising performance during their development in solid state photovoltaics showing enhanced characteristics, such as: good stability, high photoluminescence quantum yield, tunable size, and morphology. In this work, a high open-circuit voltage solar cell based on all-inorganic perovskite through SCAPS simulator program is presented by analysing electron transport layer (ETL), perovskite layer, hole transport layer (HTL) thickness and doping density from a FTO/TiO2/CsPbBr3/Spiro-OMeTAD/Au structure were modified to observe its influence on solar cell performance. Therefore, simulation results show that a thicker ETL hinders carrier transport towards the FTO layer due to larger distance which leads to higher recombination rate, reducing carrier’s lifetime. Albeit high doping density values in ETL enhances the overall solar cell performance. As for the absorber layer, while its thickness increases, carrier collection rate decreases due to recombination impacting Voc, which results from thickness increase. Based on the results, solar cell efficiency improvement is attributed to the built-in electric field as absorber layer doping density increases. While HTL thickness has minimum impact on the solar cell output, doping density enhances device parameters significantly. Summarising the results obtained from thickness and doping density simulations, the optimal solar cell operation was obtained at 10 nm, 600 nm, and 100 nm layer thickness as well as 1020 cm-3, 1016 cm-3, and 1020 cm-3 doping density (TiO2, CsPbBr3 and Spiro-OMeTAD). Results from three different sources, collected from literature, were used to compare, and fitting them along with simulation results.

Installation of flat solar collectors (FSCs) has been increasing due to the zero cost of renewable energy. However, the performance of this equipment is limited by the area, the material and the thermophysical properties of the working fluid. To improve the properties of the fluid, metal and metal oxide nanoparticles have mainly been used. This paper presents the performance assessment of the FSCs using simple and hybrid carbon nanofluids of low thermal capacity. Energy and mass balance modeling was performed for this study. A parametric analysis was conducted to examine the impact of key variables on the performance of the solar collectors using simple graphite and fullerene nanofluids, as well as hybrid metal–oxide–carbon nanofluids. From the results of heat transfer in FSCs, using graphite and fullerene nanofluids, it can be concluded that adding these nanoparticles improves the convection coefficient by 40% and 30%, respectively, with 10% nanoparticles. The graphite and fullerene nanoparticles can enhance the efficiency of FSCs by 2% and 1.5% more than base fluid. As the decrease in efficiency using fullerene with magnesium oxide is less than 0.2%, fullerene hybrid nanofluids could still be used in FSCs.

El cultivo de magueyes pulqueros ha disminuido drásticamente ya que su principal uso económico es la producción de bebidas fermentadas de bajo costo, por lo que el objetivo del trabajo es generar un producto con mayor valor comercial que sirva como una opción remunerable a los productores magueyeros, por lo que se elaboró una mermelada a base de ciruela e higo, endulzada con “aguamiel”, las frutas base son endémicas de la región de Jalacingo, Veracruz. También se elaboró una mermelada endulzada con azúcar y en los análisis bromatológicos se incluyó a una mermelada comercial para realizar la comparación de los parámetros. Los resultados del análisis de la mermelada endulzada con aguamiel fueron humedad (53.04 %), cenizas (1.82 %), pH (3.77 %), acidez titulable (1.13 %), grasa (0.28 %) y fibra (1.21 %). El producto es recomendable para personas que cuidan su salud ya que está hecha a base de frutas, aguamiel como edulcorante y conservadores naturales.

The article presents an open-access code, written in CUDA® and C++ programming language, applicable for generating computational models of nanostructured surface coatings deposited by electrodeposition. The code uses the Schrödinger equation, energy potentials, and electrochemistry as a theoretical basis to determine the deposition and electrodeposition energies, allowing the prediction of the formation and growth of these coatings. Likewise, the parameter variation enabled within the code provides for determining the main electrodeposition parameters (voltage, current, concentration, and residence time) for experimental depositions. The code can be easily implemented for any metallic coating-substrate arrangement where the filler material is nanomaterials.

This paper presents a finite-control-set model predictive control (FCS-MPC) with fractionalorder proportional-integral (FO-PI) controller to regulate the dc-link voltage of a single-phase cascaded H-bridge (CHB) 5-level inverter working as a shunt active power filter (APF). By using the FCS-MPC scheme in a single-phase CHB multilevel inverter as an APF, it is possible to generate a compensation current that tracks its reference with maximum precision, in addition, it presents a quick dynamic reaction to disturbances. When the compensation current is injected into the electrical system, current harmonic distortions are effectively reduced. To simplify the number of evaluations in the algorithm, the voltages on the dc-link of each H-bridge are equalized by a redundant selection of switching states of the multilevel inverter. The discrete-time model of the single-phase shunt APF, the technique to generate the compensation current reference signal, and the developed FCS-MPC algorithm are explained in detail. Furthermore, to keep the dc-link voltage steady, with minimal disturbance, it is proposed to use a discrete-time FO-PI controller. The effectiveness of the proposed control scheme is investigated in steady-state, as well as dynamic transients caused by sudden load changes. The developed control algorithm is verified through experimental tests conducted on a 5 kVA single-phase CHB 5-level inverter-based shunt APF. INDEX TERMS Discrete-time control systems, finite control set, fractional order control, model predictive control, multilevel converter, shunt active power filter.

Dysphonia is a prevalent symptom of some respiratory diseases that affects voice quality, even for prolonged periods. For its diagnosis, speech-language pathologists make use of different acoustic parameters to perform objective evaluations on patients and determine the type of dysphonia that affects them, such as hyperfunctional and hypofunctional dysphonia, which is important because each type requires a different treatment. In the field of artificial intelligence this problem has been addressed through the use of acoustic parameters that are used as input data to train machine learning and deep learning models. However, its purpose is usually to identify whether a patient is ill or not, making binary classifications between healthy voices and voices with dysphonia, but not between dysphonias. In this paper, harmonic-to-noise ratio, cepstral peak prominence-smoothed, zero crossing rate and the means of the Mel frequency cepstral coefficients (2-19) are used to make multiclass classification of voices with euphony, hyperfunction and hypofunction by means of six machine learning algorithms, which are: Random Forest, K nearest neighbors, Logistic regression, Decision trees, Support vector machines and Naive Bayes. In order to evaluate which of them presents a better performance to identify the three voice classes, bootstrap.632 was used. It is concluded that the best confidence interval ranges from 87% to 92%, in terms of accuracy for the K Nearest Neighbors model. Results can be implemented in the development of a complementary application for the clinical diagnosis or monitoring of a patient under the supervision of a specialist.

This article presents a computational numerical model for the simulation and analysis of quantum chemistry and Gibbs free energy theory using static (ANNS), dynamic (DANN), and chaotic neural networks (CANN). The model calculates the physical-surface mechanics of hardness, adhesion, and strength. They resulted in nanostructured metal coatings with electrodeposited chromium nanoparticles on low-carbon steel. The ANNS, DANN, and CANN simulations showed that model-obtained values for analyzed properties presented an approximation of 99 % concerning theoretical matters taken as base. Likewise, model accuracy was verified by comparison with reference data (datasheet). The proposed model is not limited to the analyzed case and provides consistent results for predicting surface physical-mechanical properties of electrodeposited coating-substrate arrangements, with a minimum error percentage of 1–1.5 % over learning.