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Abstract Harvesting renewable mechanical energy is envisioned as a promising and sustainable way for power generation. Many recent mechanical energy harvesters are able to produce instantaneous (pulsed) electricity with a high peak voltage of over 100 V. However, directly storing such irregular high‐voltage pulse electricity remains a great challenge. The use of extra power management components can boost storage efficiency but increase system complexity. Here utilizing the conducting polymer PEDOT:PSS, high‐rate metal‐free micro‐supercapacitor (MSC) arrays are successfully fabricated for direct high‐efficiency storage of high‐voltage pulse electricity. Within an area of 2.4 × 3.4 cm2 on various paper substrates, large‐scale MSC arrays (comprising up to 100 cells) can be printed to deliver a working voltage window of 160 V at an ultrahigh scan rate up to 30 V s−1. The ultrahigh rate capability enables the MSC arrays to quickly capture and efficiently store the high‐voltage (≈150 V) pulse electricity produced by a droplet‐based electricity generator at a high efficiency of 62%, significantly higher than that (

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

Abstract Intensive in-store use of smartphones has driven ethically questionable behaviors with significant economic repercussions for the survival of brick-and-mortar retailers. The mobile-assisted showroomer’s dilemma refers to the dilemma such shoppers experience at the moment of decision in a brick-and-mortar store, when they are holding an item in their hands, check their phone, and hesitate between buying it (a) at the physical store (loyal behavior, LB) or (b) through a cheaper online retailer (competitive behavior, CB). Using the theoretical framework of the Composite MES, this research proves that the dilemma exists: in a sample of 648 mobile-assisted showroomers, 44.91% would engage in CB vs. 55.09% in LB. Furthermore, 50.6% of the CB is explained by two dimensions of ethical judgment: relativism and egoism. To prevent sales leakage at brick-and-mortar stores, ethical judgment must be considered. The greater the weight of the relativism dimension, the less predisposed customers are toward CB. The egoism dimension is positively associated with engaging in CB. LB is a major opportunity for independent brick-and-mortar retailers.

Over 750 million cases of COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), have been reported since the onset of the global outbreak. The need for effective treatments has spurred intensive research for therapeutic agents based on pharmaceutical repositioning or natural products. In light of prior studies asserting the bioactivity of natural compounds of the autochthonous Peruvian flora, the present study focuses on the identification SARS-CoV-2 Mpro main protease dimer inhibitors. To this end, a target-based virtual screening was performed over a representative set of Peruvian flora-derived natural compounds. The best poses obtained from the ensemble molecular docking process were selected. These structures were subjected to extensive molecular dynamics steps for the computation of binding free energies along the trajectory and evaluation of the stability of the complexes. The compounds exhibiting the best free energy behaviors were selected for in vitro testing, confirming the inhibitory activity of Hyperoside against Mpro, with a Ki value lower than 20 µM, presumably through allosteric modulation.

Harvesting renewable mechanical energy is envisioned as a promising and sustainable way for power generation. Many recent mechanical energy harvesters are able to produce instantaneous (pulsed) electricity with a high peak voltage of over 100 V. However, directly storing such irregular high‐voltage pulse electricity remains a great challenge. The use of extra power management components can boost storage efficiency but increase system complexity. Here utilizing the conducting polymer PEDOT:PSS, high‐rate metal‐free micro‐supercapacitor (MSC) arrays are successfully fabricated for direct high‐efficiency storage of high‐voltage pulse electricity. Within an area of 2.4 × 3.4 cm2 on various paper substrates, large‐scale MSC arrays (comprising up to 100 cells) can be printed to deliver a working voltage window of 160 V at an ultrahigh scan rate up to 30 V s−1. The ultrahigh rate capability enables the MSC arrays to quickly capture and efficiently store the high‐voltage (≈150 V) pulse electricity produced by a droplet‐based electricity generator at a high efficiency of 62%, significantly higher than that (<2%) of the batteries or capacitors demonstrated in the literature. Moreover, the compact and metal‐free features make these MSC arrays excellent candidates for sustainable high‐performance energy storage in self‐charging power systems. [ABSTRACT FROM AUTHOR]

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds. [ABSTRACT FROM AUTHOR]

Cybersecurity is one of the great challenges of today’s world. Rapid technological development has allowed society to prosper and improve the quality of life and the world is more dependent on new technologies. Managing security risks quickly and effectively, preventing, identifying, or mitigating them is a great challenge. The appearance of new attacks, and with more frequency, requires a constant update of threat detection methods. Traditional signature-based techniques are effective for known attacks, but they are not able to detect a new attack. For this reason, intrusion detection systems (IDS) that apply machine learning (ML) techniques represent an alternative that is gaining importance today. In this work, we have analyzed different machine learning techniques to determine which ones permit to obtain the best traffic classification results based on classification performance measurements and execution times, which is decisive for further real-time deployments. The CICIDS2017 dataset was selected in this work since it contains bidirectional traffic flows (derived from traffic captures) that include benign traffic and different types of up-to-date attacks. Each traffic flow is characterized by a set of connection-related attributes that can be used to model the traffic and distinguish between attacks and normal flows. The CICIDS2017 also contains the raw network traffic captures collected during the dataset creation in a packet-based format, thus permitting to extract the traffic flows from them. Various classification techniques have been evaluated using the Weka software: naive Bayes, logistic, multilayer perceptron, sequential minimal optimization, k-nearest neighbors, adaptive boosting, OneR, J48, PART, and random forest. As a general result, methods based on decision trees (PART, J48, and random forest) have turned out to be the most efficient with F1 values above 0.999 (average obtained in the complete dataset). Moreover, multiclass classification (distinguishing between different types of attack) and binary classification (distinguishing only between normal traffic and attack) have been compared, and the effect of reducing the number of attributes using the correlation-based feature selection (CFS) technique has been evaluated. By reducing the complexity in binary classification, better results can be obtained, and by selecting a reduced set of the most relevant attributes, less time is required (above 30% of decrease in the time required to test the model) at the cost of a small performance loss. The tree-based techniques with CFS attribute selection (six attributes selected) reached F1 values above 0.990 in the complete dataset. Finally, a conventional tool like Zeek has been used to process the raw traffic captures to identify the traffic flows and to obtain a reduced set of attributes from these flows. The classification results obtained using tree-based techniques (with 14 Zeek-based attributes) were also very high, with F1 above 0.997 (average obtained in the complete dataset) and low execution times (allowing several hundred thousand flows/s to be processed). These classification results obtained on the CICIDS2017 dataset allow us to affirm that the tree-based machine learning techniques may be appropriate in the flow-based intrusion detection problem and that algorithms, such as PART or J48, may offer a faster alternative solution to the RF technique.

El objetivo de este trabajo es dar a conocer el origen y la trayectoria del equipo docente estructurado IKAsasun (edIKDit-18-2, equipo docente para la Enseñanza Multidisciplinar Biosanitaria). El equipo está compuesto por personal docente investigador adscrito a los Campus de Leioa (Facultad de Medicina y Enfermería) y de Vitoria-Gasteiz (Facultad de Farmacia) de la Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU). La docencia que se imparte comprende a distintas disciplinas de Grados mayoritariamente relacionados con las Ciencias de la Salud (Enfermería, Farmacia, Fisioterapia, Medicina, Nutrición Humana y Dietética, Odontología, Ciencia y Tecnología de los Alimentos y Ciencias Ambientales). Desde el inicio de la andadura como equipo docente en el año 2015, el grupo ha participado en dos Proyectos de Innovación Educativa (PIE) subvencionados por la UPV/EHU (PIE nº 40 y nº 96). Estos proyectos están dirigidos a ayudar al alumnado a desarrollar, de una manera eficaz, las competencias específicas y transversales propias de cada Grado y a conseguir que se involucren en su propio aprendizaje. En ambos proyectos se ha incidido en la importancia de trabajar en equipos multidisciplinares, apostando por la utilización de metodologías de aprendizaje activo. En el primero de ellos (PIE nº 40; 2015-2017), se planteó la participación activa del alumnado en la resolución de casos clínicos complejos que requieren un abordaje multidisciplinar para garantizar la asistencia integral al paciente. En el segundo proyecto (PIE nº 96; 2018-2020), se llevó a cabo un abordaje multidisciplinar de temas relacionados con la salud biomédica y científica desde distintos Grados, y posterior divulgación enfocada a diferentes sectores de la sociedad. Los resultados obtenidos tras el desarrollo de ambos proyectos han sido muy alentadores, y alumnado, profesorado y personas externas colaboradoras han valorado muy positivamente la experiencia, que ha quedado reflejada en varias publicaciones y comunicaciones a congresos.

This study evaluates the impacts of slot tagging and training data length on joint natural language understanding (NLU) models for medication management scenarios using chatbots in Spanish. In this study, we define the intents (purposes of the sentences) for medication management scenarios and two types of slot tags. For training the model, we generated four datasets, combining long/short sentences with long/short slots, while for testing, we collect the data from real interactions of users with a chatbot. For the comparative analysis, we chose six joint NLU models (SlotRefine, stack-propagation framework, SF-ID network, capsule-NLU, slot-gated modeling, and a joint SLU-LM model) from the literature. The results show that the best performance (with a sentence-level semantic accuracy of 68.6%, an F1-score of 76.4% for slot filling, and an accuracy of 79.3% for intent detection) is achieved using short sentences and short slots. Our results suggest that joint NLU models trained with short slots yield better results than those trained with long slots for the slot filling task. The results also indicate that short slots could be a better choice for the dialog system because of their simplicity. Importantly, the work demonstrates that the performance of the joint NLU models can be improved by selecting the correct slot configuration according to the usage scenario.

Establishing the interactome of the cancer-associated stress protein Nuclear Protein 1 (NUPR1), we found that it binds to several hundreds of proteins, including proteins involved in nuclear translocation, DNA repair, and key factors of the SUMO pathway. We demonstrated that the NUPR1 inhibitor ZZW-115, an organic synthetic molecule, competes with importins for the binding to the NLS region of NUPR1, thereby inhibiting its nuclear translocation. We hypothesized, and then proved, that inhibition of NUPR1 by ZZW-115 sensitizes cancer cells to DNA damage induced by several genotoxic agents. Strikingly, we found that treatment with ZZW-115 reduced SUMOylation of several proteins involved in DNA damage response (DDR). We further report that the presence of recombinant NUPR1 improved the SUMOylation in a cell-free system, indicating that NUPR1 directly stimulates the SUMOylation machinery. We propose that ZZW-115 sensitizes cancer cells to genotoxic agents by inhibiting the nuclear translocation of NUPR1 and thereby decreasing the SUMOylation-dependent functions of key proteins involved in the DDR.

Psoriasis is a chronic skin disease that affects 125 million people worldwide and, particularly, 2% of the Spanish population, characterized by the appearance of skin lesions due to a growth of the epidermis that is seven times larger than usual. Its diagnosis and monitoring are based on the use of methodologies for measuring the severity and extent of these spots, and this includes a large subjective component. For this reason, this paper presents an automatic method for characterizing psoriasis images that is divided into four parts: image preparation or pre-processing, feature extraction, classification of the lesions, and the obtaining of parameters. The methodology proposed in this work covers different digital-image processing techniques, namely, marker-based image delimitation, hair removal, nipple detection, lesion contour detection, areal-measurement-based lesion classification, as well as lesion characterization by means of red and white intensity. The results obtained were also endorsed by a professional dermatologist. This methodology provides professionals with a common software tool for monitoring the different existing typologies, which proved satisfactory in the cases analyzed for a set of 20 images corresponding to different types of lesions.

The COVID-19 pandemic outbreak prompts an urgent need for efficient therapeutics, and repurposing of known drugs has been extensively used in an attempt to get to anti-SARS-CoV-2 agents in the shortest possible time. The glycoside rutin shows manifold pharmacological activities and, despite its use being limited by its poor solubility in water, it is the active principle of many pharmaceutical preparations. We herein report our in silico and experimental investigations of rutin as a SARS-CoV-2 Mpro inhibitor and of its water solubility improvement obtained by mixing it with l-arginine. Tests of the rutin/l-arginine mixture in a cellular model of SARS-CoV-2 infection highlighted that the mixture still suffers from unfavorable pharmacokinetic properties, but nonetheless, the results of this study suggest that rutin might be a good starting point for hit optimization.

Inhibiting the main protease 3CLpro is the most common strategy in the search for antiviral drugs to fight the infection from SARS-CoV-2. We report that the natural compound eugenol is able to hamper in vitro the enzymatic activity of 3CLpro, the SARS-CoV-2 main protease, with an inhibition constant in the sub-micromolar range (Ki = 0.81 μM). Two phenylpropene analogs were also tested: the same effect was observed for estragole with a lower potency (Ki = 4.1 μM), whereas anethole was less active. The binding efficiency index of these compounds is remarkably favorable due also to their small molecular mass (MW < 165 Da). We envision that nanomolar inhibition of 3CLpro is widely accessible within the chemical space of simple natural compounds.

The widespread adoption of smartphones and the new-generation wireless networks have changed the way that people interact among themselves and with their environment. The use of messaging platforms, such as WhatsApp, has become deeply ingrained in peoples’ lives, and many digital services have started to be delivered using these communication channels. In this work, we propose a new OAuth grant type to be used when the interaction between the resource owner and the client takes place through a messaging platform. This new grant type firstly allows the authorization server to be sure that no Man-in-the-Middle risk exists between the resource owner and the client before issuing an access token. Secondly, it allows the authorization server to interact with the resource owner through the same user-agent already being used to interact with the client, i.e., the messaging platform, which is expected to improve the overall user experience of the authorization process. To verify this assumption, we conducted a usability study in which subjects were required to perform the full authorization process using both the standard authorization code grant type (through a web-browser) and the new grant type defined in this work. They have also been required to fill in a small questionnaire including some demographic information and their impressions about both authorization flows. The results suggest that the proposed grant type eases the authorization process in most cases.

The development of new antiviral drugs against SARS-CoV-2 is a valuable long-term strategy to protect the global population from the COVID-19 pandemic complementary to the vaccination. Considering this, the viral main protease (Mpro) is among the most promising molecular targets in light of its importance during the viral replication cycle. The natural flavonoid quercetin 1 has been recently reported to be a potent Mpro inhibitor in vitro, and we explored the effect produced by the introduction of organoselenium functionalities in this scaffold. In particular, we report here a new synthetic method to prepare previously inaccessible C-8 seleno-quercetin derivatives. By screening a small library of flavonols and flavone derivatives, we observed that some compounds inhibit the protease activity in vitro. For the first time, we demonstrate that quercetin (1) and 8-(p-tolylselenyl)quercetin (2d) block SARS-CoV-2 replication in infected cells at non-toxic concentrations, with an IC50 of 192 μM and 8 μM, respectively. Based on docking experiments driven by experimental evidence, we propose a non-covalent mechanism for Mpro inhibition in which a hydrogen bond between the selenium atom and Gln189 residue in the catalytic pocket could explain the higher Mpro activity of 2d and, as a result, its better antiviral profile.

The pandemic, due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has stimulated the search for antivirals to tackle COVID-19 infection. Molecules with known pharmacokinetics and already approved for human use have been demonstrated or predicted to be suitable to be used either directly or as a base for a scaffold-based drug design. Among these substances, quercetin is known to be a potent in vitro inhibitor of 3CLpro, the SARS-CoV-2 main protease. However, its low in vivo bioavailability calls for modifications to its molecular structure. In this work, this issue is addressed by using rutin, a natural flavonoid that is the most common glycosylated conjugate of quercetin, as a model. Combining experimental (spectroscopy and calorimetry) and simulation techniques (docking and molecular dynamics simulations), we demonstrate that the sugar adduct does not hamper rutin binding to 3CLpro, and the conjugated compound preserves a high potency (inhibition constant in the low micromolar range, Ki = 11 μM). Although showing a disruption of the pseudo-symmetry in the chemical structure, a larger steric volume and molecular weight, and a higher solubility compared to quercetin, rutin is able to associate in the active site of 3CLpro, interacting with the catalytic dyad (His41/Cys145). The overall results have implications in the drug-design of quercetin analogs, and possibly other antivirals, to target the catalytic site of the SARS-CoV-2 3CLpro.

Lactic acid is one of the key biobased chemical building blocks, given its readily availability from sugars through fermentation and facile conversion into a range of important chemical intermediates and polymers. Herein, well-defined rubbery polymers derived from butyl lactate solvent were successfully prepared by reversible addition–fragmentation chain transfer (RAFT) polymerization of the corresponding monomeric acrylic derivative. Good control over molecular weight and molecular weight distribution was achieved in bulk using either monofunctional or bifunctional trithiocarbonate-type chain transfer agents. Subsequently, poly(butyl lactate acrylate), with a relative low Tg (−20 °C), good thermal stability (5% wt. loss at 340 °C) and low toxicity was evaluated as a sustainable middle block in all-acrylic ABA copolymers using isosorbide and vanillin-derived glassy polyacrylates as representative end blocks. Thermal, morphological and mechanical properties of copolymers containing hard segment contents of

The trend of research towards more sustainable materials is pushing the application of biopolymers in a variety of unexplored fields. In this regard, hydrogels are attracting significant attention as electrolytes for flexible electrochemical devices thanks to their combination of ionic conductivity and mechanical properties. In this context, we present the use of cellulose-based hydrogels as aqueous electrolytes for electrochemical devices. These materials were obtained by crosslinking of hydroxyethyl cellulose (HEC) with divinyl sulfone (DVS) in the presence of carboxymethyl cellulose (CMC), creating a semi-IPN structure. The reaction was confirmed by NMR and FTIR. The small-amplitude oscillatory shear (SAOS) technique revealed that the rheological properties could be conveniently varied by simply changing the gel composition. Additionally, the hydrogels presented high ionic conductivity in the range of mS cm−1. The ease of synthesis and processing of the hydrogels allowed the assembly of an all-in-one electrochromic device (ECD) with high transmittance variation, improved switching time and good color efficiency. On the other hand, the swelling ability of the hydrogels permits the tuning of the electrolyte to improve the performance of a printed Zinc/MnO2 primary battery. The results prove the potential of cellulose-based hydrogels as electrolytes for more sustainable electrochemical devices.

Numerous carrier proteins intervene in protein transport from the cytoplasm to the nucleus in eukaryotic cells. One of those is importin α, with several human isoforms; among them, importin α3 (Impα3) features a particularly high flexibility. The protein NUPR1L is an intrinsically disordered protein (IDP), evolved as a paralogue of nuclear protein 1 (NUPR1), which is involved in chromatin remodeling and DNA repair. It is predicted that NUPR1L has a nuclear localization sequence (NLS) from residues Arg51 to Gln74, in order to allow for nuclear translocation. We studied in this work the ability of intact NUPR1L to bind Impα3 and its depleted species, ∆Impα3, without the importin binding domain (IBB), using fluorescence, isothermal titration calorimetry (ITC), circular dichroism (CD), nuclear magnetic resonance (NMR), and molecular docking techniques. Furthermore, the binding of the peptide matching the isolated NLS region of NUPR1L (NLS-NUPR1L) was also studied using the same methods. Our results show that NUPR1L was bound to Imp α3 with a low micromolar affinity (~5 μM). Furthermore, a similar affinity value was observed for the binding of NLS-NUPR1L. These findings indicate that the NLS region, which was unfolded in isolation in solution, was essentially responsible for the binding of NUPR1L to both importin species. This result was also confirmed by our in silico modeling. The binding reaction of NLS-NUPR1L to ∆Impα3 showed a larger affinity (i.e., lower dissociation constant) compared with that of Impα3, confirming that the IBB could act as an auto-inhibition region of Impα3. Taken together, our findings pinpoint the theoretical predictions of the NLS region in NUPR1L and, more importantly, suggest that this IDP relies on an importin for its nuclear translocation.

Several carrier proteins are involved in protein transport from the cytoplasm to the nucleus in eukaryotic cells. One of those is importin α, of which there are several human isoforms; among them, importin α3 (Impα3) has a high flexibility. The protein NUPR1, a nuclear protein involved in the cell-stress response and cell cycle regulation, is an intrinsically disordered protein (IDP) that has a nuclear localization sequence (NLS) to allow for nuclear translocation. NUPR1 does localize through the whole cell. In this work, we studied the affinity of the isolated wild-type NLS region (residues 54–74) of NUPR1 towards Impα3 and several mutants of the NLS region by using several biophysical techniques and molecular docking approaches. The NLS region of NUPR1 interacted with Impα3, opening the way to model the nuclear translocation of disordered proteins. All the isolated NLS peptides were disordered. They bound to Impα3 with low micromolar affinity (1.7–27 μM). Binding was hampered by removal of either Lys65 or Lys69 residues, indicating that positive charges were important; furthermore, binding decreased when Thr68 was phosphorylated. The peptide phosphorylated at Thr68, as well as four phospho-mimetic peptides (all containing the Thr68Glu mutation), showed the presence of a sequential NN(i,i + 1) nuclear Overhauser effect (NOE) in the 2D-1H-NMR (two-dimensional–proton NMR) spectra, indicating the presence of turn-like conformations. Thus, the phosphorylation of Thr68 modulates the binding of NUPR1 to Impα3 by a conformational, entropy-driven switch from a random-coil conformation to a turn-like structure.

Over 750 million cases of COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), have been reported since the onset of the global outbreak. The need for effective treatments has spurred intensive research for therapeutic agents based on pharmaceutical repositioning or natural products. In light of prior studies asserting the bioactivity of natural compounds of the autochthonous Peruvian flora, the present study focuses on the identification SARS-CoV-2 Mpro main protease dimer inhibitors. To this end, a target-based virtual screening was performed over a representative set of Peruvian flora-derived natural compounds. The best poses obtained from the ensemble molecular docking process were selected. These structures were subjected to extensive molecular dynamics steps for the computation of binding free energies along the trajectory and evaluation of the stability of the complexes. The compounds exhibiting the best free energy behaviors were selected for in vitro testing, confirming the inhibitory activity of Hyperoside against Mpro, with a Ki value lower than 20 µM, presumably through allosteric modulation. [ABSTRACT FROM AUTHOR]

La detección de defectos en tubos sometidos a presión interna es un aspecto poco estudiado. Unode los problemas a resolver es el modelado de los defectos, en particular los del tipo grieta cerrada.La dinámica de un tubo con un fluido interno a presión implica dificultades adicionales al problema.Se presentan los principios y las ecuaciones necesarias para poder modelar por el método de loselementos finitos la presencia de una grieta transversal parcial en un tubo de paredes delgadas envarios escenarios: vacío, con un fluido interno y con un fluido a presión. El análisis modal esempleado para determinar los cambios en los parámetros del modelo y valorar la influencia de lagrieta en los mismos. Se parte del experimento de S.M. Murigendrappa y S.K. Maiti, el efecto de lagrieta está representado por una reducción de la rigidez a la flexión del tubo en la zona de extensiónd, cuya extensión es determinada a partir del concepto de energía potencial de la deformación,planteado por A P. Bovsunovsky y V. V. Matveev.Palabras claves: grieta cerrada, análisis modal, tubos, elementos finitos._____________________________________________________________________________AbstractFail detection in pipes under internal pressure is a not enough studied matter. A main problem is faultmodeling, particularly the closed crack type. The dynamic of a pipe filled with a fluid under pressureimplies additional challenges. The equations and principles needed to obtain a finite elements modelof a partial transverse crack in a thin walled pipe are shown in different sceneries: empty, filled withfluid and filled with fluid under certain pressure. A modal analysis is performed to obtain the changesin modal parameters and crack influence. The result shows a good agreement with those obtained byS.M. Murigendrappa and S.K. Maiti. The crack is modeled trough a reduction of bending stiffness in areduced zone. Its extension is calculated using the principle of equal strain energy between the crackzone and the model, exposed by A P. Bovsunovsky and V. V. Matveev.Key words: crack detection, modal analysis, pipes.

Heart rate variability (HRV) analysis is a noninvasive tool widely used to assess autonomic nervous system state. The market for wearable devices that measure the heart rate has grown exponentially, as well as their potential use for healthcare and wellbeing applications. Still, there is a lack of validation of these devices. In particular, this work aims to validate the Apple Watch in terms of HRV derived from the RR interval series provided by the device, both in temporal (HRM (mean heart rate), SDNN, RMSSD and pNN50) and frequency (low and high frequency powers, LF and HF) domain. For this purpose, a database of 20 healthy volunteers subjected to relax and a mild cognitive stress was used. First, RR interval series provided by Apple Watch were validated using as reference the RR interval series provided by a Polar H7 using Bland-Altman plots and reliability and agreement coefficients. Then, HRV parameters derived from both RR interval series were compared and their ability to identify autonomic nervous system (ANS) response to mild cognitive stress was studied. Apple Watch measurements presented very good reliability and agreement (>0.9). RR interval series provided by Apple Watch contain gaps due to missing RR interval values (on average, 5 gaps per recording, lasting 6.5 s per gap). Temporal HRV indices were not significantly affected by the gaps. However, they produced a significant decrease in the LF and HF power. Despite these differences, HRV indices derived from the Apple Watch RR interval series were able to reflect changes induced by a mild mental stress, showing a significant decrease of HF power as well as RMSSD in stress with respect to relax, suggesting the potential use of HRV measurements derived from Apple Watch for stress monitoring.

The photoplethysmogram (PPG) is a biomedical signal that can be used to estimate volumetric blood flow changes in the peripheral circulation. During the past few years, several works have been published in order to assess the potential for PPGs to be used in biometric authentication systems, but results are inconclusive. In this paper we perform an analysis of the feasibility of using the PPG as a realistic biometric alternative in the long term. Several feature extractors (based on the time domain and the Karhunen–Loève transform) and matching metrics (Manhattan and Euclidean distances) have been tested using four different PPG databases (PRRB, MIMIC-II, Berry, and Nonin). We show that the false match rate (FMR) and false non-match rate (FNMR) values remain constant in different time instances for a selected threshold, which is essential for using the PPG for biometric authentication purposes. On the other hand, obtained equal error rate (EER) values for signals recorded during the same session range from 1.0% for high-quality signals recorded in controlled conditions to 8% for those recorded in conditions closer to real-world scenarios. Moreover, in certain scenarios, EER values rise up to 23.2% for signals recorded over different days, signaling that performance degradation could take place with time.

Electrochromic devices (ECDs) have aroused great interest because of their potential applicability in displays and smart systems, including windows, rearview mirrors, and helmet visors. In the last decades, different device structures and materials have been proposed to meet the requirements of commercial applications to boost market entry. To this end, employing simple device architectures and achieving a competitive electrolyte are crucial to accomplish easily implementable, high-performance ECDs. The present review outlines devices comprising gel electrolytes as a single electroactive layer (“all-in-one”) ECD architecture, highlighting some advantages and opportunities they offer over other electrochromic systems. In this context, gel electrolytes not only overcome the drawbacks of liquid and solid electrolytes, such as liquid’s low chemical stability and risk of leaking and soil’s slow switching and lack of transparency, but also exhibit further strengths. These include easier processability, suitability for flexible substrates, and improved stabilization of the chemical species involved in redox processes, leading to better cyclability and opening wide possibilities to extend the electrochromic color palette, as discussed herein. Finally, conclusions and outlook are provided.