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We report on frequency tripling of high energy, high repetition rate ns pulses from a cryogenically gas cooled multi-slab Yb:YAG laser system Bivoj/DiPOLE, using a type-I phase matched LBO crystal for second harmonic generation and type-II phase matched LBO crystal for sum frequency generation. We achieved conversion to 343 nm with stable energy of 50 J at repetition rate of 10 Hz and conversion efficiency of 53%.

In this paper, we propose a computationally simple estimator of the asymptotic covariance matrix of the Principal Components (PC) factors valid in the presence of cross-correlated idiosyncratic components. The proposed estimator of the asymptotic Mean Square Error (MSE) of PC factors is based on adaptive thresholding the sample covariances of the id iosyncratic residuals with the threshold based on their individual variances. We compare the nite sample performance of condence regions for the PC factors obtained using the proposed asymptotic MSE with those of available extant asymptotic and bootstrap regions and show that the former beats all alternative procedures for a wide variety of idiosyncratic cross-correlation structures.

The electromagnetic properties and microwave absorption capabilities are studied under real radar conditions for layer-type composite samples consisting of a polymeric matrix and a magnetic powder filler, either metallic or ceramic. The effect of the filler type and the filling factor is investigated. The obtained results show remarkable broadband microwave absorption, which make these materials suitable for stealth technology. The experimentally measured absorptions reach -40 dB, while model-based predictions suggest that these systems could overpass the barrier of -50 dB. The experimental results are supported by models, both for single and bilayer systems.

This study aims to evaluate the performance of Cox regression with lasso penalty and adaptive lasso penalty in high-dimensional settings. Variable selection methods are necessary in this context to reduce dimensionality and make the problem feasible. Several weight calculation procedures for adaptive lasso are proposed to determine if they offer an improvement over lasso, as adaptive lasso addresses its inherent bias. These proposed weights are based on principal component analysis, ridge regression, univariate Cox regressions and random survival forest (RSF). The proposals are evaluated in simulated datasets. A real application of these methodologies in the context of genomic data is also carried out. The study consists of determining the variables, clinical and genetic, that influence the survival of patients with triple-negative breast cancer (TNBC), which is a type breast cancer with low survival rates due to its aggressive nature., Comment: 39 pages, 2 figures, 8 tables

Magnetic metal-organic frameworks (MMOFs), where magnetic metal nodes are connected into a crystal structure by organic linkers, have a potential to host exotic magnetic states. We present a study of bulk magnetic properties of four metal-organic frameworks with boron imidazolate linkers, Zn-BIF, Cu-BIF, Co-BIF, and Ni-BIF, displaying a variety of lattice structures and nontrivial magnetic behaviors. While non-magnetic Zn-BIF provides an offset of magnetic response, magnetic susceptibility measurements of the other three magnetic materials demonstrate the presence of weak magnetic interactions in these MOFs, which differ between materials by sign and size. Cu-MOF, where magnetic nodes are connected into octahedral cages, shows simple paramagnetic behavior. Triangular lattice Co-MOF shows antiferromagnetic interactions on the order of 1 K, and a spin-crossover-like effect in magnetic susceptibility due to thermal depopulation of excited crystal electric field levels. Magnetic properties of Ni-BIF suggest sizable ferromagnetic interactions. Using DC/AC susceptibility and variable-field DC magnetization, we detect behavior that resembles that of superparamagnetic single-magnetic-domain nanoparticles, but with particle sizes on the scale of the unit cell, and discuss possible microscopic origins of this behavior. This work demonstrates the variety of magnetic properties that are possible with a single organic ligand, and establishes the low energy scale of magnetic interactions through superexchange in boron imidazolate frameworks.

In this paper, we study the relation of the M2-brane with fluxes and monodromy in $SL(2,\mathbb{Z})$, which has a quantum discrete supersymmetric spectrum with finite multiplicity, and type IIB gauged supergravities in nine dimensions. $SL(2,\mathbb{Z})$ is the group of isotopy classes of the area preserving diffeomorphisms. The global description of these M2-branes we are considering is formulated on twisted torus bundles, and they are classified in terms of $H^2(\Sigma,\mathcal{Z}_{\mathcal{\rho}})$, or equivalently, by their coinvariants for a given monodromy subgroup. We find the 'gauge' symmetries between equivalent M2-branes, on torus bundles with monodromy, that leads to $\mathbb{R}$, $SO(2)$ or $SO(1,1)$, the symmetry groups of type IIB gauged supergravities in 9d. We obtain an explicit relation between the equivalent classes of M2-brane bundles and the mass parameters that classify the gaugings of type IIB supergravities in 9d. We also find that the symmetries, between inequivalent M2-branes on twisted torus bundles for a given monodromy, are related with $\mathbb{Z}$, $\mathcal{Z}_3$, $\mathcal{Z}_5$, $\mathcal{Z}_9$ or $\mathcal{Z}_{2n-7}$ for $n\geq 5$, the U-duality symmetry group, a subgroup of $SL(2,\mathbb{Z})$. In distinction, in the case without monodromy, related to type II maximal supergravity at low energies, its U-duality group corresponds to the full $SL(2,\mathbb{Z})$., Comment: 32 pages, 1 figure

Mechanisms for data recovery and packet reliability are essential components of the upcoming 6th generation (6G) communication system. In this paper, we evaluate the interaction between a fast hybrid automatic repeat request (HARQ) scheme, present in the physical and medium access control layers, and a higher layer automatic repeat request (ARQ) scheme which may be present in the radio link control layer. Through extensive system-level simulations, we show that despite its higher complexity, a fast HARQ scheme yields > 66 % downlink average user throughput gains over simpler solutions without energy combining gains and orders of magnitude larger gains for users in challenging radio conditions. We present results for the design trade-off between HARQ and higher-layer data recovery mechanisms in the presence of realistic control and data channel errors, network delays, and transport protocols. We derive that, with a suitable design of 6G control and data channels reaching residual errors at the medium access control layer of 5 E-5 or better, a higher layer data recovery mechanism can be disabled. We then derive design targets for 6G control channel design, as well as promising enhancements to 6G higher layer data recovery to extend support for latency-intolerant services., Comment: Accepted for publication in EUCNC 2024

We present a theoretical model of electromagnetic pulse emission and nozzle damage from high-power laser interactions with gas jets. As hot electrons are accelerated beyond the laser channel, they produce a positively-charged plasma that expands into the ambient gas until it reaches the gas jet nozzle. A strong plasma potential leads to electrical breakdown that ionises the gas even faster than the expanding plasma ions. When contact is made between the plasma and the conducting nozzle surface, a discharge current propagates to ground and an electromagnetic pulse is radiated via an antenna emission mechanism. Damage to gas jet nozzles is caused by heating from plasma ions rather than Ohmic heating via a discharge current. Our model agrees with experimental data from experiments on proton acceleration in near-critical plasmas at the Vulcan and VEGA-3 lasers., Comment: 32 pages (total), 8 figures, 1 table

For robots to interact socially, they must interpret human intentions and anticipate their potential outcomes accurately. This is particularly important for social robots designed for human care, which may face potentially dangerous situations for people, such as unseen obstacles in their way, that should be avoided. This paper explores the Artificial Theory of Mind (ATM) approach to inferring and interpreting human intentions. We propose an algorithm that detects risky situations for humans, selecting a robot action that removes the danger in real time. We use the simulation-based approach to ATM and adopt the 'like-me' policy to assign intentions and actions to people. Using this strategy, the robot can detect and act with a high rate of success under time-constrained situations. The algorithm has been implemented as part of an existing robotics cognitive architecture and tested in simulation scenarios. Three experiments have been conducted to test the implementation's robustness, precision and real-time response, including a simulated scenario, a human-in-the-loop hybrid configuration and a real-world scenario., Comment: Associated mpeg file see https://youtu.be/87UEB8P97KY

Our ability to calculate rates of biochemical processes using molecular dynamics simulations is severely limited by the fact that the time scales for reactions, or changes in conformational state, scale exponentially with the relevant free-energy barriers. In this work, we improve upon a recently proposed rate estimator that allows us to predict transition times with molecular dynamics simulations biased to rapidly explore one or several collective variables. This approach relies on the idea that not all bias goes into promoting transitions, and along with the rate, it estimates a concomitant scale factor for the bias termed the collective variable biasing efficiency $\gamma$. First, we demonstrate mathematically that our new formulation allows us to derive the commonly used Infrequent Metadynamics (iMetaD) estimator when using a perfect collective variable, $\gamma=1$. After testing it on a model potential, we then study the unfolding behavior of a previously well characterized coarse-grained protein, which is sufficiently complex that we can choose many different collective variables to bias, but which is sufficiently simple that we are able to compute the unbiased rate dire ctly. For this system, we demonstrate that our new Exponential Average Time-Dependent Rate (EATR) estimator converges to the true rate more rapidly as a function of bias deposition time than does the previous iMetaD approach, even for bias deposition times that are short. We also show that the $\gamma$ parameter can serve as a good metric for assessing the quality of the biasing coordinate. Finally, we demonstrate that the approach works when combining multiple less-than-optimal bias coordinates., Comment: 14 pages, 5 figures; 4 pages of supporting information with 2 tables and 6 additional figures

The aim of this work is to introduce a thermo-electromagnetic model for calculating the temperature and the power dissipated in cylindrical pieces whose geometry var\'ies with time and undergoes large deformations; the motion will be a known data. The work will be a first step towards building a complete thermoelectromagnetic-mechanical model suitable for simulating electrically assisted forming processes, which is the main motivation of the work. The electromagnetic model will be obtained from the time-harmonic eddy current problem with an inplane current; the source will be given in terms of currents or voltages defined at sorne parts of the boundary. Finite element methods based on a Lagrangian weak formulation will be used for the numerical solution. This approach will avoid the need to compute and remesh the thermo-electromagnetic domain along the time. The numerical tools will be implemented in FEniCS and validated by using a suitable test also solved in Eulerian coordinates.

Ferromagnetic/Antiferromagnetic (FM/AFM) bilayers dynamics have been a recent topic of interest due to the interaction occurring at the interface, where the magnetic moments of the AFM can be imprinted into the FM, and the exchange bias field can affect these dynamics. Here, we investigate Permalloy (Py) and NiO (Py/NiO) hybrids and for comparison single Py films in the broad Py thickness range varied from few nm to 200 nm by using static (Kerr effect) and dynamic (spin waves) measurements along with micromagnetic simulations. We observe hybrid modes between uniform (ferromagnetic resonance FMR, n=0) and perpendicular standing spin waves (PSSWs, n=1, 2) and a clear enhancement of the PSSWs modes frequencies upon interfacing Py with NiO both from experiments and simulations. This enhancement becomes less pronounced as the thickness of the film increases, demonstrating its interfacial origin rooted in the exchange coupling between the FM and AFM layers. Besides, through micromagnetic simulations, we investigate and correlate changes in spatial profiles of the PSSWs with the interfacial exchange coupling. As the thickness is increased, we see that the n=1 and n=2 modes begin to couple with the fundamental FMR mode, resulting in asymmetric (with respect the Py layer center) modes. Our results suggest that PSSWs detection in a ferromagnet offers a means of probing the interfacial exchange coupling with the adjacent AFM layer. Furthermore, the controlled spatial symmetry breaking by the AFM layer enables engineering of PSSWs with different spatial profiles in the FM., Comment: 15 pages, 18 figures

During nursing studies, it is crucial to develop emotional skills for both academic success and quality patient care. Utilizing technologies like thermography can be instrumental in nursing education to assess and enhance these skills. The study aims to evaluate the effectiveness of thermography in monitoring and improving the emotional skills of nursing students through a case study approach. The case study involved exposing a student to various emotional stimuli, including videos and music, and measuring facial temperature changes. These changes were recorded using a FLIR E6 camera across three phases: acclimatization, stimulus, and response. Environmental factors such as temperature and humidity were also recorded. Distinct thermal responses were observed for different emotions. For instance, during the acclimatization phase with video stimuli, forehead temperatures varied between positive emotions (joy: 34.5\textdegree C to 34.5\textdegree C) and negative emotions (anger: 36.1\textdegree C to 35.1\textdegree C). However, there was a uniform change in temperature during both stimulus (joy: 34.7\textdegree C to 35.0\textdegree C, anger: 35.0\textdegree C to 35.0\textdegree C) and response phases (joy: 35.0\textdegree C to 35.0\textdegree C, anger: 34.8\textdegree C to 35.0\textdegree C). Music stimuli also induced varying thermal patterns (joy: 34.2\textdegree C to 33.9\textdegree C to 33.4\textdegree C, anger: 33.8\textdegree C to 33.4\textdegree C to 33.8\textdegree C).Thermography revealed consistent thermal patterns in response to emotional stimuli, with the exception of the nose area, suggesting its suitability as a non-invasive, quantifiable, and accessible method for emotional skill training in nursing education.

There is a great concern nowadays regarding alcohol consumption and drug abuse, especially in young people. Analyzing the social environment where these adolescents are immersed, as well as a series of measures determining the alcohol abuse risk or personal situation and perception using a number of questionnaires like AUDIT, FAS, KIDSCREEN, and others, it is possible to gain insight into the current situation of a given individual regarding his/her consumption behavior. But this analysis, in order to be achieved, requires the use of tools that can ease the process of questionnaire creation, data gathering, curation and representation, and later analysis and visualization to the user. This research presents the design and construction of a web-based platform able to facilitate each of the mentioned processes by integrating the different phases into an intuitive system with a graphical user interface that hides the complexity underlying each of the questionnaires and techniques used and presenting the results in a flexible and visual way, avoiding any manual handling of data during the process. Advantages of this approach are shown and compared to the previous situation where some of the tasks were accomplished by time consuming and error prone manipulations of data.

Objectives: This study analyzed adolescent physical activity, its link to overweight, and the social network structure in group sports participants, focusing on centrality measures. Setting: Conducted in 11 classrooms across 5 schools in Ponferrada, Spain. Participants: Included 235 adolescents (49.4% female), categorized as normal weight or overweight. Methods: The Physical Activity Questionnaire for Adolescents (PAQ-A) assessed physical activity levels. Social network analysis evaluated centrality in varying contact degrees. Results: 30.2% were overweight. Males scored higher in PAQ-A and were more likely to engage in group sports. No significant correlation was found between physical activity and weight in the total sample. However, overweight females reported higher exercise levels. Centrality analysis showed gender differences; women in group sports had lower centrality, whereas men had higher. Conclusions: The study highlights the importance of gender and social network centrality in designing future strategies, considering peer interaction intensity

The COVID-19 pandemic has meant that young university students have had to adapt their learning and have a reduced relational context. Adversity contexts build models of human behaviour based on relationships. However, there is a lack of studies that analyse the behaviour of university students based on their social structure in the context of a pandemic. This information could be useful in making decisions on how to plan collective responses to adversities. The Social Network Analysis (SNA) method has been chosen to address this structural perspective. The aim of our research is to describe the structural behaviour of students in university residences during the COVID-19 pandemic with a more in-depth analysis of student leaders. A descriptive cross-sectional study was carried out at one Spanish Public University, Le\'on, from 23th October 2020 to 20th November 2020. The participation was of 93 students, from four halls of residence. The data were collected from a database created specifically at the university to "track" contacts in the COVID-19 pandemic, SiVeUle. We applied the SNA for the analysis of the data. The leadership on the university residence was measured using centrality measures. The top leaders were analyzed using the Egonetwork and an assessment of the key players. Students with higher social reputations experience higher levels of pandemic contagion in relation to COVID-19 infection. The results were statistically significant between the centrality in the network and the results of the COVID-19 infection. The most leading students showed a high degree of Betweenness, and three students had the key player structure in the network. Networking behaviour of university students in halls of residence could be related to contagion in the COVID-19 pandemic.

The aims: (i) analyze connectivity between subgroups of university students, (ii) assess which bridges of relational contacts are essential for connecting or disconnecting subgroups and (iii) to explore the similarities between the attributes of the subgroup nodes in relation to the pandemic context. During the COVID-19 pandemic, young university students have experienced significant changes in their relationships, especially in the halls of residence. Previous research has shown the importance of relationship structure in contagion processes. However, there is a lack of studies in the university setting, where students live closely together. The case study methodology was applied to carry out a descriptive study. The participation consisted of 43 university students living in the same hall of residence. Social network analysis has been applied for data analysis. Factions and Girvan Newman algorithms have been applied to detect the existing cohesive subgroups. The UCINET tool was used for the calculation of the SNA measure. A visualization of the global network will be carried out using Gephi software. After applying the Girvan-Newman and Factions, in both cases it was found that the best division into subgroups was the one that divided the network into 4 subgroups. There is high degree of cohesion within the subgroups and a low cohesion between them. The relationship between subgroup membership and gender was significant. The degree of COVID-19 infection is related to the degree of clustering between the students. College students form subgroups in their residence. Social network analysis facilitates an understanding of structural behavior during the pandemic. The study provides evidence on the importance of gender, race and the building where they live in creating network structures that favor, or not, contagion during a pandemic.

The Roman domination in graphs is well-studied in graph theory. The topic is related to a defensive strategy problem in which the Roman legions are settled in some secure cities of the Roman Empire. The deployment of the legions around the Empire is designed in such a way that a sudden attack to any undefended city could be quelled by a legion from a strong neighbour. There is an additional condition: no legion can move if doing so leaves its base city defenceless. In this manuscript we start the study of a variant of Roman domination in graphs: the triple Roman domination. We consider that any city of the Roman Empire must be able to be defended by at least three legions. These legions should be either in the attacked city or in one of its neighbours. We determine various bounds on the triple Roman domination number for general graphs, and we give exact values for some graph families. Moreover, complexity results are also obtained.

In this paper, by applying the multiplier method we obtain a complete classification of low-order local conservation laws for a generalized seventh-order KdV equation depending on seven arbitrary nonzero parameters. We apply the Lie method in order to classify all point symmetries admitted by the equation in terms of the arbitrary parameters. We find that there are no special cases of the parameters for which the equation admits extra symmetries, other than those that can be found by inspection (scaling symmetry and space and time translation symmetries). We consider the reduced ordinary differential equations and we determined all integrating factors of the reduced equation from the combined $x$- and $t$- translation symmetries. Finally, we observe that all integrating factors arise by reduction of the low-order multipliers of the generalized seventh-order KdV equation., Comment: 12 pages

Objective: To analyze the current scientific knowledge and research lines focused on environmentally sustainable health systems, including the role of nurses. Background: There seem to be differences between creating interventions focused on environmentally sustainable health systems, including nurses, and the scarcity of research on this topic, framed on the Sustainable Development Goals. Methods: A bibliometric analysis was carried out, via three databases (Web of Science, Scopus, and Pubmed), and the guideline recommendations were followed to select bibliometric data. Results: The search resulted in 159 publications, significantly increasing the trends from 2017 to 2021 (p=0.028). The most relevant countries in this area were the United States of America, the United Kingdom, and Sweden. Also, the top articles were from relevant journals, indexed in Journal Citation Report, and the first and the second quartile linked to the nursing field and citations (p<0.001). Conclusion: Education is key to achieving environmentally sustainable health systems via institutions and policies. Implications for nursing management: There is a lack of experimental data and policies on achieving or maintaining environmentally sustainable health care systems, indicating that nurses have an important role and should be consulted and included in decision-making policies regarding sustainability in the healthcare systems., Comment: 9 pages,4 figures,2 tables

Aim: To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. Methods: 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. Results: After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. Conclusion: Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF.

Background: Technology has provided a new way of life for adolescents. Strategies aimed at improving health behaviors through digital platforms can offer promising results. However, since peers can modify behaviors related to food and exercise, studying digital interventions based on peer influence to improve weight status of adolescents is important. Objective: To assess an eHealth app's effectiveness in adolescent population improvements in age- and sex-adjusted BMI percentiles. The study also examined social relationships of adolescents pre- and postintervention, identifying group leaders to study their profiles, eating, physical activity habits, and app usage. Methods: BMI percentiles were calculated following World Health Organization guidelines. Participants' diets and physical activity levels were assessed using the KIDMED questionnaire and PAQ-A. Social network variables were analyzed using SNA methodology, with reciprocal friendships used to compute the "degree" measure for centrality. Results: The sample included 210 individuals in the intervention group and 91 in the control group, with a 60.1% participation rate. Adolescents in the intervention group modified their BMI toward the 50th percentile, improving diet and increasing social network postintervention. Group leaders were also leaders in physical activity and app usage.

Perovskite solar cells (PSCs) continue to be the front runner technology among emerging photovoltaic devices in terms of power conversion efficiency and application versatility. However, not only the stability but also the understanding of their ionic-electronic transport mechanisms continues to be challenging. In this work, the case study of NiOx-based inverted PSCs and the effect of different interface passivating treatments on the device performance are approached. Our experiments include impedance spectroscopy (IS) measurements in short-circuit under different illumination intensities and operational stability tests under constant illumination intensity. It is found that certain surface treatments lead to more stable performance. However, protic anion donors can induce, both, an initial performance decrease and a subsequent reactivity during light exposure which apparently improves the cells performance. Our drift-diffusion simulations suggest that the modification of the interface with the hole transport material may have decrease the conductivity, as well as the ion and electron mobilities at the perovskite and the NiOx, respectively. Importantly, capacitance and resistance are shown to peak maximum and minimum values, respectively, around specific ranges of mobile ion concentration. Our results introduce a general route for characterization of degradation paths in PSCs via IS in short-circuit.

Extracting consistent statistics between relevant free-energy minima of a molecular system is essential for physics, chemistry and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we develop an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one Normalizing Flow (NF) and one MLP per state, achieving rapid convergence towards the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free-energy differences or optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster, belonging to a set of systems with diverse applications in the fields of medicine and catalysis.

Technical debt (TD) is a metaphor that is used to communicate the consequences of poor software development practices to non-technical stakeholders. In recent years, it has gained significant attention in agile software development (ASD). The purpose of this study is to analyze and synthesize the state of the art of TD, and its causes, consequences, and management strategies in the context of ASD. Using a systematic literature review (SLR), 38 primary studies, out of 346 studies, were identified and analyzed. We found five research areas of interest related to the literature of TD in ASD. Among those areas, managing TD in ASD received the highest attention, followed by architecture in ASD and its relationship with TD. In addition, eight categories regarding the causes and five categories regarding the consequences of incurring TD in ASD were identified. Focus on quick delivery and architectural and design issues were the most popular causes of incurring TD in ASD. Reduced productivity, system degradation and increased maintenance cost were identified as significant consequences of incurring TD in ASD. Additionally, we found 12 strategies for managing TD in the context of ASD, out of which refactoring and enhancing the visibility of TD were the most significant. The results of this study provide a structured synthesis of TD and its management in the context of ASD as well as potential research areas for further investigation.

Every non-solvable and non-semisimple quadratic Lie algebra can be obtained as a double extension of a solvable quadratic Lie algebra. Thanks to a partial classification of nilpotent Lie algebras and this result, we can design different techniques to obtain any quadratic Lie algebra whose (solvable) radical ideal is nilpotent. To achieve this, we propose two alternative methods, both involving the use of quotients. In addition to their mathematical description, both approaches introduced in this paper have been computationally implemented and are publicly available to use for generating these algebras., Comment: 20 pages

A Ni/CeO2/ZrO2 catalyst with improved redox properties has been washcoated onto a honeycomb cordierite monolith in the form of a nonconventional alumina-catalyst layer, just a few nanometer thick. In spite of the very low active phase loading, the monolith depicts outstanding performance in dry reforming of methane, both in terms of activity, with values reaching the thermodynamic limit already at 750 C, even under extreme weight Hourly Space Velocities (WHSV 115-346 L.g-1.h-1), as well as in terms of stability during prolonged time on stream (TOS 24-48 h)., Comment: 7 pages, 3 figures, 19 references. Gold Open Access

Supported Ni catalysts (4,5% wt%) using a Ce-Zr oxide (18/82 molar ratio and a ceria rich surface) depicting advanced redox properties, were deposited by washcoating over cordierite honeycombs (230 and 400 cpsi). FIB-STEM unveiled nanostructure details otherwise undistinguishable by conventional techniques. The catalytic performance was evaluated in the dry reforming of methane al 700-900 degree C, using a CH4:CO2 1:1 feddstock, and exploring high Weight Hourly Space Velocity (115-346 L.g-1.h-1). The structured catalyts exhibited better performance than the corresponding powers, reaching values close to rhermodynamics limits for booth reactants conversion and H2/CO ratio, from 750 dgree C, and no deactivation was observed in prolongued experiments (24-48 h). This was related to both the high catalytic efficiency after being deposited with low loading on the cordierite and the intrinsic advantages of the monolithic reactor, like preventing from the kinetic control that operates in powdered samples under high WHSV or limiting the deactivation., Comment: 10 pages, 8 figures, 3 tables, 61 references

In our contribution we describe some on-going improvements concerning the Automated Reasoning Tools developed in GeoGebra Discovery, providing different examples of the performance of these new features. We describe the new ShowProof command, that outputs both the sequence of the different steps performed by GeoGebra Discovery to confirm a certain statement, as well as a number intending to grade the difficulty or interest of the assertion. The proposal of this assessment measure, involving the comparison of the expression of the thesis (or conclusion) as a combination of the hypotheses, will be developed., Comment: In Proceedings ADG 2023, arXiv:2401.10725

Surgical ablation (SA) is the most effective procedure to terminate atrial fibrillation (AF) in patients requiring concomitant open heart surgery. However, considering the great stress provoked in the patients heart, along with the benefits of anticipating antiarrhythmic therapeutical decisions, preoperative prediction of long term failure of the procedure is an interesting clinical challenge. Hence, the present work introduces a novel algorithm to anticipate SA outcome after one year of follow up by just analyzing the surface ECG. The method firstly extracts fibrillatory waves reflected on standard lead V1 using an adaptive QRST cancellation approach. The resulting signal is then segmented into 1 s length intervals and wavelet energy is computed for all of them. Finally, the coefficient of variation of the time series obtained for the 7th scale is computed. Analyzing 20 second length preoperative ECG excerpts from 53 persistent AF patients undergoing concomitant openheart surgery, only the proposed method reported statistically significant differences between the patients who relapsed to AF and those who maintained sinus rhythm during the follow up. The algorithm also provided values of sensitivity, specificity, and accuracy between 10 and 20% better than the well established dominant atrial frequency and fibrillatory waves amplitude, thus suggesting to be a promising predictor of AF recurrence after SA.

Although catheter ablation (CA) is still the first-line treatment for persistent atrial fibrillation (AF) patients, its limited long-term success rate has motivated clinical interest in preoperative prediction on the procedures outcome to provide optimized patient selection, limit repeated procedures, hospitalization rates, and treatment costs. To this respect, dominant frequency (DF) and amplitude of fibrillatory waves (f-waves) reflected on the ECG have provided promising results. Hence this work explores the ability of a novel set of frequency and amplitud f-waves features, such as spectral entropy (SE), spectral flatness measure (SFM), and amplitud spectrum area (AMSA), along with DF and normalized f-wave amplitude (NFWA), to improve CA outcome prediction. Despite all single indices reported statistically significant differences between patients who relapsed to AF and those who maintained sinus rhythm after a follow up of 9 months for 204 6 s-length ECG intervals extracted from 51 persistent AF patients, they obtained a limited discriminant ability ranging between 55 and 62%, which was overcome by 15 - 23% when NFWA, SE and AMSA were combined. Consequently, this combination of frequency and amplitude features of the fwaves seems to provide new insights about the atrial substrate remodeling, which could be helpful in improving preoperative CA outcome prediction.

Learning disorders are neurological conditions that affect the brain's ability to interconnect communication areas. Dyslexic students experience problems with reading, memorizing, and exposing concepts; however the magnitude of these can be mitigated through both therapies and the creation of compensatory mechanisms. Several efforts have been made to mitigate these issues, leading to the creation of digital resources for students with specific learning disorders attending primary and secondary education levels. Conversely, a standard approach is still missed in higher education. The VRAIlexia project has been created to tackle this issue by proposing two different tools: a mobile application integrating virtual reality (VR) to collect data quickly and easily, and an artificial intelligencebased software (AI) to analyze the collected data for customizing the supporting methodology for each student. The first one has been created and is being distributed among dyslexic students in Higher Education Institutions, for the conduction of specific psychological and psychometric tests. The second tool applies specific artificial intelligence algorithms to the data gathered via the application and other surveys. These AI techniques have allowed us to identify the most relevant difficulties faced by the students' cohort. Our different models have obtained around 90\% mean accuracy for predicting the support tools and learning strategies., Comment: 7 pages, 5 figures, 3 tables, MetroXRAINE 2022 Conference, VRAILEXIA european project

Dyslexia is a neurodevelopmental disorder that is estimated to affect about 5-10% of the population. In particular, phonological dyslexia causes problems in connecting the sounds of words with their written forms. This results in difficulties such as slow reading speed, inaccurate reading, and difficulty decoding unfamiliar words. Moreover, dyslexia can also be a challenging and frustrating experience for students as they may feel misunderstood or stigmatized by their peers or educators. For these reasons, the use of compensatory tools and strategies is of crucial importance for dyslexic students to have the same opportunities as non-dyslexic ones. However, generally, people underestimate the problem and are not aware of the importance of support methodologies. In the light of this, the main purpose of this paper is to propose a virtual reality (VR) serious game through which teachers, students and, in general, non-dyslexic people could understand which are some of the issues of student with dyslexia and the fundamental utility of offering support to them. In the game, players must create a potion by following a recipe written in an alphabet that is specifically designed to replicate the reading difficulties experienced by individuals with dyslexia. The task must be solved first without any help and then by receiving supporting tools and strategies with the idea that the player can put himself in the place of the dyslexic person and understand the real need for support methodologies., Comment: 5 pages, 5 figures, MetroXRAINE 2023