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The classification of surface myoelectric signals (sEMG) remains a great challenge when focused on its implementation in an electromechanical hand prosthesis, due to its nonlinear and stochastic nature, as well as the great difference between models applied offline and online. In this work, the selection of the set of the features that allowed us to obtain the best results for the classification of this type of signals is presented. In order to compare the results obtained, the Nina PRO DB2 and DB3 databases were used, which contain information on 50 different movements of 40 healthy subjects and 11 amputated subjects, respectively. The sEMG of each subject was acquired through 12 channels in a bipolar configuration. To carry out the classification, a convolutional neural network (CNN) was used and a comparison of four sets of features extracted in the time domain was made, three of which have shown good performance in previous works and one more that was used for the first time to train this type of network. Set one is composed of six features in the time domain (TD1), Set two has 10 features also in the time domain (TD2) including the autoregression model (AR), the third set has two features in the time domain derived from spectral moments (TD-PSD1), and finally, a set of five features also has information on the power spectrum of the signal obtained in the time domain (TD-PSD2). The selected features in each set were organized in four different ways for the formation of the training images. The results obtained show that the set of features TD-PSD2 obtained the best performance for all cases. With the set of features and the formation of images proposed, an increase in the accuracies of the models of 8.16% and 8.56% was obtained for the DB2 and DB3 databases, respectively, compared to the current state of the art that has used these databases.

This article solves the problem of detecting water leaks with a minimum size of down to 1 mm in diameter. Two new mathematical tools are used to solve this problem: the first one is the Te cross-spectral density and the second is Te coherence. These mathematical tools provide the possibility of discriminating spurious frequency components, making use of the property of multi-sensitivity. This advantage makes it possible to maximize the sensitivity of the frequency spectrum. The wavelet function used was Daubechies 45, because it provides an attenuation of 150 dB in the rejection band. The tools were validated with two scenarios. For the first scenario, a synthetic signal was analyzed. In the second scenario, two types of background leakage were analyzed: the first one has a diameter of 1 mm with a signal-to-noise ratio of 2.82 dB and flow rate of 33.7 mL/s, and the second one has a diameter of 4 mm with a signal-to-noise ratio of 9.73 dB with a flow rate of 125.0 mL/s. The results reported in this paper show that both the Te cross-spectral density and Te coherence are higher than those reported in scientific literature.

Sargassum has become an “ecological disaster” on most beaches in the Caribbean Sea, causing serious health, safety, economic and environmental problems. The objective of this study was to valorize sargassum for the extraction of sodium alginate, cellulose and cellulose nanocrystals. Cellulose was also used in the manufacture of a separator for an energy storage device (ESD) based on expanded graphite and activated carbon. Sargassum was characterized physicochemically; the results of this characterization are very relevant for the generation of bioenergy and removal of toxic contaminants. The extracted materials were identified by FT-IR; the spectra were very similar to the IR spectra of commercial samples, which corroborated that the materials extracted were sodium alginate, cellulose and cellulose nanocrystals. The performance of the ESD/cellulose-based separator was determined using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Additionally, an RC circuit and an oscilloscope were used to perform a charge/discharge test. The voltammograms obtained for the ESD showed a rectangular shape with redox peaks characteristic of a pseudocapacitor and a battery, and the Nyquist plot showed a typical electrochemical performance of such ESD. Likewise, the developed devices have the capacity to store and release energy in seconds.

Quantum computing is a rapidly emerging and promising field with the potential to transform various research domains including drug design, network technologies, and sustainable energy solutions. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries enable quantum simulations on classical computers and execution on corresponding quantum hardware, such as Qiskit programs on IBM quantum computers. Despite the variations among these platforms, the core concepts remain the same. One notable challenge is the absence of a Python-based quantum interpreter to connect these five frameworks, a gap that remains to be fully addressed. In response, our work introduces a tool called Qinterpreter, accessible through a user-friendly web interface, the Quantum Science Gateway QubitHub, which operates alongside Jupyter Notebooks. Built using the Python Object-Oriented Programming System, Qinterpreter unifies the five well-known quantum libraries into a single framework. Designed as an educational tool for students and researchers entering the quantum domain, Qinterpreter enables the straightforward development and execution of quantum circuits across such platforms. This work highlights the quantum programming versatility and accessibility of Qinterpreter and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.

The current global warming is driving the use of clean and renewable energy. Intermittence is a drawback of clean energy. Energy storage is the answer to intermittence. Flow batteries, batteries, capacitors, and supercapacitors are among the most used energy storage devices in the world. In this paper, capacitors made from graphite and commercial graphene were fabricated. The obtained specific capacitances reached 851 F g −1 , indicating that the devices meet the performance standard for supercapacitors. The devices were fabricated using cellulose nanocrystals as binding material in the electrode, thus using an environmentally friendly binding material. Cyclic voltammetry revealed a quasi‐rectangular process. Charge–discharge cycles revealed a storage mechanism of electrical double layer. The Warburg impedance indicates the diffusive impedance of electrolyte ions in the devices.

Based on a V-shaped microactuator with a pair of beams, modifications were made to the length and width of a microactuator to observe the effects. A theoretical approach and numerical characterization of the modified microactuator were performed. Its performance was compared to a similar microactuator with equal beam widths, and a V-shaped microactuator. The proposed microactuator, fed at 2 V, compared to the V-shaped actuator, showed a 370.48% increase in force, but a 29.8% decrease in displacement. The equivalent von Mises stress level increased (until 74.2 MPa), but was below the silicon ultimate stress. When the modified microactuator was applied to the proposed microgripper, compared to the case using a V-shaped actuator, the displacement between the jaws increased from 0.85 µm to 4.85 µm, the force from 42.11 mN to 73.61 mN, and the natural frequency from 11.36 kHz to 37.99 kHz; although the temperature increased, on average, from 42 °C up to 73 °C, it is not a critical value for many microobjects. The maximum equivalent von Mises stress was equal to 68.65 MPa. Therefore, it has been demonstrated that the new modified microactuator with damping elements is useful for the proposed microgripper of novel geometry, while a reduced area is maintained.

Quantum computing is a rapidly emerging and promising field that has the potential to revolutionize numerous research domains, including drug design, network technologies and sustainable energy. Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. These libraries allow for quantum simulations on classical computers and facilitate program execution on corresponding quantum hardware, e.g., Qiskit programs on IBM quantum computers. While all platforms have some differences, the main concepts are the same. QInterpreter is a tool embedded in the Quantum Science Gateway QubitHub using Jupyter Notebooks that translates seamlessly programs from one library to the other and visualizes the results. It combines the five well-known quantum libraries: into a unified framework. Designed as an educational tool for beginners, Qinterpreter enables the development and execution of quantum circuits across various platforms in a straightforward way. The work highlights the versatility and accessibility of Qinterpreter in quantum programming and underscores our ultimate goal of pervading Quantum Computing through younger, less specialized, and diverse cultural and national communities.