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This paper presents the implementation of an intelligent real-time single-channel electromyography (EMG) signal classifier based on open-source hardware. The article shows the experimental design, analysis, and implementation of a solution to identify four muscle movements from the forearm (extension, pronation, supination, and flexion), for future applications in transradial active prostheses. An EMG signal acquisition instrument was developed, with a 20–450 Hz bandwidth and 2 kHz sampling rate. The signals were stored in a Database, as a multidimensional array, using a desktop application. Numerical and graphic analysis approaches for discriminative capacity were proposed for feature analysis and four feature sets were used to feed the classifier. Artificial Neural Networks (ANN) were implemented for time-domain EMG pattern recognition (PR). The system obtained a classification accuracy of 98.44% and response times per signal of 8.522 ms. Results suggest these methods allow us to understand, intuitively, the behavior of user information.

Aims: Lumen-apposing metal stents (LAMSs) in ultrasonography-guided gallbladder drainage (EUS-GBD) have become increasingly important for high-risk surgical patients. Our study aims to evaluate the technical and clinical success, safety, and feasibility of endoscopic ultrasonography-guided gallbladder drainage using a new dedicated LAMS. Methods: This is a retrospective multicenter study that included all consecutive patients not suitable for surgery who were referred to a tertiary center for EUS-GBD using a new dedicated electrocautery LAMS for acute cholecystitis at eight different centers. Results: Our study included 54 patients with a mean age of 76.48 years (standard deviation: 12.6 years). Out of the 54 endoscopic gallbladder drainages performed, 24 (44.4%) were cholecysto-gastrostomy, and 30 (55.4%) were cholecysto-duodenostomy. The technical success of LAMS placement was 100%, and clinical success was achieved in 23 out of 30 patients (76.67%). Adverse events were observed in two patients (5.6%). Patients were discharged after a median of 5 days post-stenting. Conclusions: EUS-GBD represents a valuable option for high-surgical-risk patients with acute cholecystitis. This new dedicated LAMS has demonstrated a high rate of technical and clinical success, along with a high level of safety.

Background: Despite weak evidence, antibiotic prophylaxis prior to endoscopic ultrasound-guided through-the-needle biopsy (EUS-TTNB) of pancreatic cystic lesions (PCLs) is routinely used in clinical practice. We aim to compare a group of patients treated with antibiotics before EUS-TTNB of PCLs and a group who did not undergo antimicrobial prophylaxis. Methods: Out of 236 patients with pancreatic cystic lesions referred to two high-volume centers between 2016 and 2021, after propensity score matching, two groups were compared: 98 subjects who underwent EUS-TTNB under antibiotic prophylaxis and 49 subjects without prophylaxis. Results: There was no difference in terms of baseline parameters between groups. Final diagnosis was serous cystadenoma in 36.7% of patients in the group not treated with prophylaxis and in 37.7% of patients in the control group, whereas IPMN and mucinous cystadenoma were diagnosed in 3 (6.1%) and 16 (32.6%) versus 6 (6.1%) and 32 (32.6%) patients in the two groups, respectively (p = 0.23). Overall, the adverse event rate was 6.1% in the group not treated with antibiotic prophylaxis and 5.1% in the control group (p = 0.49). Only a single infectious adverse event occurred in each group (p = 0.48). The diagnostic yields were 89.7% and 90.8% in the two groups (p = 0.7), and the diagnostic accuracy rate was 81.6% in both groups (p = 1.0). Conclusions: Prophylactic antibiotics do not seem to influence the risk of infection, and their routine use should be discouraged.

This paper presents the computational modeling of three cooling systems based on three different methods (passive, active, and hybrid), to improve the efficiency of PV panels when operating beyond the recommended temperature under standard test conditions. All simulations were implemented using the COMSOL Multiphysics software. In the passive method, through-holes were made in the solar panel, to allow the transfer of heat by convection due to the air flow. In the active method, water was used to cool the solar panel, spraying it on the front when the operating temperature reaches a threshold value. The analysis includes both fluid dynamics and heat transfer effects. In addition, a hybrid method that uses both passive and active cooling methods simultaneously was implemented. Finally, a mathematical model for the PV panel is presented, which allowed obtaining the changes in the output power from the reduction in the operating temperature. Results demonstrated improvements in the performance of the solar panel with the implementation of the three cooling systems, showing better performance in the active and hybrid methods compared to the passive method.

In the field of engineering when a situation is not resolved analytically, efforts are made to develop methods that approximate a possible solution. These efforts have originated the numerical methods known at present, which allow formulating mathematical problems that can be solved using logical and arithmetic operations. This paper presents a comparison between the numerical optimization algorithms golden section search and simulated annealing, which are tested in four different scenarios. These scenarios are functions implemented with a feedforward neural network, which emulate a partial shading behavior in photovoltaic modules with local and global maxima. The presence of the local maxima makes it difficult to track the maximum power point, necessary to obtain the highest possible performance of the photovoltaic module. The programming of the algorithms was performed in C language. The results demonstrate the effectiveness of the algorithms to find global maxima. However, the golden section search method showed a better performance in terms of percentage of error, computation time and number of iterations, except in test scenario number three, where a better percentage of error was obtained with the simulated annealing algorithm for a computational temperature of 1000.

The Real-Time Recurrent Learning Gradient (RTRL) algorithm is characterized by being an online learning method for training dynamic recurrent neural networks, which makes it ideal for working with non-linear control systems. For this reason, this paper presents the design of a novel Maximum Power Point Tracking (MPPT) controller with an artificial neural network type Adaptive Linear Neuron (ADALINE), with Finite Impulse Response (FIR) architecture, trained with the RTRL algorithm. With this same network architecture, the Least Mean Square (LMS) algorithm was developed to evaluate the results obtained with the RTRL controller and then make comparisons with the Perturb and Observe (P&O) algorithm. This control method receives as input signals the current and voltage of a photovoltaic module under sudden changes in operating conditions. Additionally, the efficiency of the controllers was appraised with a fuzzy controller and a Nonlinear Autoregressive Network with Exogenous Inputs (NARX) controller, which were developed in previous investigations. It was concluded that the RTRL controller with adaptive training has better results, a faster response, and fewer bifurcations due to sudden changes in the input signals, being the ideal control method for systems that require a real-time response.

This paper addresses the situation of an industrial park in Mexico from the perspective of its community. It exposes a case study approach with the points of view of residents, businesses, and authorities to improve the socioeconomic conditions of the region: Sahagun, Hidalgo. The result of this article comes from a project ran by the Ministry of Economy of the state, to trigger the development of this area. The background of the topic is presented, its theoretical basis, and the methodological design. Finally, an integrative model is exposed to show the necessary actions to articulate enterprises—the society and the government to develop the town.

The output power of a photovoltaic (PV) module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT) to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O) algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA) was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.

Endoscopic submucosal dissection (ESD) is a minimally invasive technique developed by Japanese endoscopists that allows one-piece endoscopic removal of early malignant lesions in the gastrointestinal tract. There is now a large and ever-increasing number of reports worldwide, and in the West use of this technique is on the increase. New materials and techniques have been developed to improve the technical aspects of ESD. Recent reports show successful clinical outcomes and improved overall 5-year survival rates for this technique. This review aims to offer an update of ESD based on the latest reports in the literature.