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The complexation of plasmonic nanoparticles (NPs) and thermoresponsive microgels is widely exploited for applications. Yet, a microscopic description of the mechanisms governing spatial organization of the NPs is still lacking. Combining small angle X-ray scattering, state-of-the-art numerical simulations and a simple toy model, we uncover how the volume phase transition of microgels drives NP-NP interactions, inducing NP progressive rearrangement with temperature. These results are directly compared to the extinction spectra of microgel-NPs complexes, allowing us to establish for the first time a microscopic link between plasmon coupling and NP local structure.

In health, most large language model (LLM) research has focused on clinical tasks. However, mobile and wearable devices, which are rarely integrated into such tasks, provide rich, longitudinal data for personal health monitoring. Here we present Personal Health Large Language Model (PH-LLM), fine-tuned from Gemini for understanding and reasoning over numerical time-series personal health data. We created and curated three datasets that test 1) production of personalized insights and recommendations from sleep patterns, physical activity, and physiological responses, 2) expert domain knowledge, and 3) prediction of self-reported sleep outcomes. For the first task we designed 857 case studies in collaboration with domain experts to assess real-world scenarios in sleep and fitness. Through comprehensive evaluation of domain-specific rubrics, we observed that Gemini Ultra 1.0 and PH-LLM are not statistically different from expert performance in fitness and, while experts remain superior for sleep, fine-tuning PH-LLM provided significant improvements in using relevant domain knowledge and personalizing information for sleep insights. We evaluated PH-LLM domain knowledge using multiple choice sleep medicine and fitness examinations. PH-LLM achieved 79% on sleep and 88% on fitness, exceeding average scores from a sample of human experts. Finally, we trained PH-LLM to predict self-reported sleep quality outcomes from textual and multimodal encoding representations of wearable data, and demonstrate that multimodal encoding is required to match performance of specialized discriminative models. Although further development and evaluation are necessary in the safety-critical personal health domain, these results demonstrate both the broad knowledge and capabilities of Gemini models and the benefit of contextualizing physiological data for personal health applications as done with PH-LLM., Comment: 72 pages

Despite the proliferation of wearable health trackers and the importance of sleep and exercise to health, deriving actionable personalized insights from wearable data remains a challenge because doing so requires non-trivial open-ended analysis of these data. The recent rise of large language model (LLM) agents, which can use tools to reason about and interact with the world, presents a promising opportunity to enable such personalized analysis at scale. Yet, the application of LLM agents in analyzing personal health is still largely untapped. In this paper, we introduce the Personal Health Insights Agent (PHIA), an agent system that leverages state-of-the-art code generation and information retrieval tools to analyze and interpret behavioral health data from wearables. We curate two benchmark question-answering datasets of over 4000 health insights questions. Based on 650 hours of human and expert evaluation we find that PHIA can accurately address over 84% of factual numerical questions and more than 83% of crowd-sourced open-ended questions. This work has implications for advancing behavioral health across the population, potentially enabling individuals to interpret their own wearable data, and paving the way for a new era of accessible, personalized wellness regimens that are informed by data-driven insights., Comment: 38 pages

We use a combination of Planck cosmic microwave background (CMB) anisotropy data and non-CMB data that include Pantheon+ type Ia supernovae, Hubble parameter [$H(z)$], growth factor ($f\sigma_8$) measurements, and a collection of baryon acoustic oscillation (BAO) data, but not recent DESI 2024 BAO measurements, to confirm the DESI 2024 (DESI+CMB+PantheonPlus) data compilation support for dynamical dark energy with an evolving equation of state parameter $w(z) = w_0 + w_a z/(1+z)$. From our joint compilation of CMB and non-CMB data, in a spatially-flat cosmological model, we obtain $w_0 = -0.850 \pm 0.059$ and $w_a = -0.59^{+0.26}_{-0.22}$ and find that this dynamical dark energy is favored over a cosmological constant by $\sim 2\sigma$. Our data constraints on the flat $w_0w_a$CDM model are slightly more restrictive than the DESI 2024 constraints, with the DESI 2024 and our values of $w_0$ and $w_a$ differing by $-0.27\sigma$ and $0.44\sigma$, respectively. Our data compilation slightly more strongly favors the flat $w_0w_a$CDM model over the flat $\Lambda$CDM model than does the DESI 2024 data compilation., Comment: 9 pages, 3 figures

We study 6 LCDM models, with 4 allowing for non-flat geometry and 3 allowing for a non-unity lensing consistency parameter $A_L$. We also study 6 XCDM models with a dynamical dark energy density X-fluid with equation of state $w$. For the non-flat models we use two different primordial power spectra, Planck $P(q)$ and new $P(q)$. These models are tested against: Planck 2018 CMB power spectra (P18) and lensing potential power spectrum (lensing), and an updated compilation of BAO, SNIa, $H(z)$, and $f\sigma_8$ data [non-CMB data]. P18 data favor closed geometry for the LCDM and XCDM models and $w<-1$ (phantom-like dark energy) for the XCDM models while non-CMB data favor open geometry for the LCDM models and closed geometry and $w>-1$ (quintessence-like dark energy) for the XCDM models. When P18 and non-CMB data are jointly analyzed there is weak evidence for open geometry and moderate evidence for quintessence-like dark energy. Regardless of data used, $A_L>1$ is always favored. The XCDM model constraints obtained from CMB data and from non-CMB data are incompatible, ruling out the 3 $A_L = 1$ XCDM models at $> 3\sigma$. In the 9 models not ruled out, for the P18+lensing+non-CMB data set we find little deviation from flat geometry and moderate deviation from $w=-1$. In all 6 non-flat models (not ruled out), open geometry is mildly favored, and in all 3 XCDM+$A_L$ models (not ruled out) quintessence-like dark energy is moderately favored (by at most $1.6 \sigma$). In the $A_L = 1$ non-flat LCDM cases, we find for P18+lensing+non-CMB data $\Omega_k = 0.0009 \pm 0.0017$ [$0.0008 \pm 0.0017$] for the Planck [new] $P(q)$ model, favoring open geometry. The flat LCDM model remains the simplest (largely) observationally-consistent cosmological model. Our cosmological parameter constraints obtained for the flat LCDM model (and other models) are the most restrictive results to date (Abridged)., Comment: 71 pages, 33 figures. Accepted for publication in Physical Review D

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Fuzzychain, our proposed solution, introduces the use of fuzzy sets to define stake semantics, promoting decentralised and distributed processing control. This system selects validators based on their degree of membership to the stake fuzzy sets rather than just the size of their stakes. As a pioneer proposal in applying fuzzy sets to blockchain, Fuzzychain aims to rectify PoS's limitations. Our results indicate that Fuzzychain not only matches PoS in functionality but also ensures a fairer distribution of stakes among validators, leading to more inclusive validator selection and a better-distributed network., Comment: 16 pages, 13 figures, 2 tables, this article was submitted to a JCR journal

Technology enables a more sustainable and universally accessible educational model. However, technology has brought a paradox into students' lives: it helps them engage in learning activities, but it is also a source of distraction. During the academic year 2021-2022, the authors conducted a study focusing on classroom distractions. One of the objectives was to identify the main digital distractions from the point of view of students. The study was carried out at an engineering school, where technology is fully integrated in the classroom and in the academic routines of teachers and students. Discussions and surveys, complemented by a statistical study based on bivariate correlations, were used with participating students (n = 105). Students considered digital distractions to have a significant impact on their performance in lab sessions. This performance was mainly self-assessed as improvable. Contrary to other contemporary research, the results were not influenced by the year of study of the subject, as the issue is important regardless of the students' backgrounds. Professors should implement strategies to raise students' awareness of the significant negative effects of digital distractions on their performance, as well as to develop students' self-control skills. This is of vital importance for the use of technology to be sustainable in the long-term., Comment: 22 pages

In this work we present a unified model for the cosmological dark sector. The theory is based on a simple minimally coupled scalar field whose action only contains a canonical kinetic term and is invariant under transverse diffeomorphisms (TDiff). The model has the same number of free parameters as $\Lambda$CDM. We confront the predictions of the model at the background level with data from Planck 2018 CMB distance priors, Pantheon+ and SH0ES SNIa distance moduli, BAO data points from 6dFGS, BOSS, eBOSS and DES and measurements of the Hubble parameter from cosmic chronometers. The model provides excellent results in the joint fit analysis, showing very strong evidence compared to $\Lambda$CDM in the deviance information criterion (DIC). We also show that the Hubble tension between Planck 2018 and SH0ES measurements can be alleviated in the unified TDiff model although further analysis is still needed., Comment: 17 pages, 8 figures. Final version published in PRD

We address the issue of structure changes of a canola protein gel (as a solid food model) during gastrointestinal digestion. We present a method for synchrotron Small-Angle X-ray Scattering analysis of the digestion of a gel in a capillary. Scanning the capillary allows tracking the digestion under diffusion of enzymatic juices. The fitting parameters characterizing the sizes, scattering intensities and structures allow to distinguish the compact, unfolded or aggregated states of proteins. The evolutions of these parameters enable to detail the complex changes of proteins during gel digestion, involving back-and-forth evolutions with proteins unfolding (1 st and 3 rd steps), re-compaction (2 nd step) due to gastrointestinal pH and enzyme actions, before final protein scissions (4 th step) resulting in small peptides. This complexity is related to the wide ranges of successive pH and enzyme activity acting on large and charged protein assemblies. Digestion is therefore impacted by the conditions of food preparation.

Myoelectric control is an area of electromyography of increasing interest nowadays, particularly in applications such as Hand Gesture Recognition (HGR) for bionic prostheses. Today's focus is on pattern recognition using Machine Learning and, more recently, Deep Learning methods. Despite achieving good results on sparse sEMG signals, the latter models typically require large datasets and training times. Furthermore, due to the nature of stochastic sEMG signals, traditional models fail to generalize samples for atypical or noisy values. In this paper, we propose the design of a Vision Transformer (ViT) based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform Hand Gesture Recognition from surface electromyography (sEMG) signals. The proposed EMGTFNet architecture can accurately classify a variety of hand gestures without any need for data augmentation techniques, transfer learning or a significant increase in the number of parameters in the network. The accuracy of the proposed model is tested using the publicly available NinaPro database consisting of 49 different hand gestures. Experiments yield an average test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793 trainable parameters. Our results outperform the ViT without FNB, thus demonstrating that including FNB improves its performance. Our proposal framework EMGTFNet reported the significant potential for its practical application for prosthetic control.

Most of today's wearable technology provides seamless cardiac activity monitoring. Specifically, the vast majority employ Photoplethysmography (PPG) sensors to acquire blood volume pulse information, which is further analysed to extract useful and physiologically related features. Nevertheless, PPG-based signal reliability presents different challenges that strongly affect such data processing. This is mainly related to the fact of PPG morphological wave distortion due to motion artefacts, which can lead to erroneous interpretation of the extracted cardiac-related features. On this basis, in this paper, we propose a novel personalised and adjustable Interval Type-2 Fuzzy Logic System (IT2FLS) for assessing the quality of PPG signals. The proposed system employs a personalised approach to adapt the IT2FLS parameters to the unique characteristics of each individual's PPG signals.Additionally, the system provides adjustable levels of personalisation, allowing healthcare providers to adjust the system to meet specific requirements for different applications. The proposed system obtained up to 93.72\% for average accuracy during validation. The presented system has the potential to enable ultra-low complexity and real-time PPG quality assessment, improving the accuracy and reliability of PPG-based health monitoring systems at the edge.

Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because such artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this paper, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that our proposed context-aware features can achieve up to $6\%$ and $26\%$ more accurate results than other context- and non-context-aware solutions, respectively, depending on the specific task. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than others.

The structure-properties relationship of rhamnolipids, RLs, well known microbial bioamphiphiles (biosurfactants), is exlored in detail by coupling cryogenic transmission electron microscopy (cryo-TEM) and both ex situ and in situ small angle X-ray scattering (SAXS). The self-assembly of three RLs with reasoned variation of their molecular structure (RhaC10, RhaC10C10 and RhaRhaC10C10) and a rhamnose-free C10C10 fatty acid is studied in water as a function of pH. It is found that RhaC10 and RhaRhaC10C10 form micelles in a broad pH range and RhaC10C10 undergoes a micelle-to-vesicle transition from basic to acid pH occurring at pH 6.5. Modelling coupled to fitting SAXS data allows a good estimation of the hydrophobic core radius (or length), the hydrophilic shell thickness, the aggregation number and the surface area per RL. The essentially micellar morphology found for RhaC10 and

Stability of power electronic converters connected to power grids is commonly assessed by using the impedance criterion. On the contrary, stability of power grids is typically analysed by using network state-space representation and system eigenvalues. It has been already reported in the literature that the impedance criterion may lead to erroneous results if the dynamics of the grid frequency is not taken into consideration. Meanwhile, the eigenvalue analysis is still considered as a reliable method for finding instabilities. However, the equivalence between these two methods (impedance criterion with frequency dynamics included and eigenvalue analysis) has not been thoroughly studied before. In this paper, the impedance method with frequency dynamics included is compared with the conventional eigenvalue analysis for a system fully based on electronic interfaces. Analytical expressions that link both methods are provided, showing that they are identical under certain conditions. Then, both methods are applied to a system based on a grid-forming and a grid-following converter. The results obtained from real-time simulations performed in OPAL-RT are used to validate the theoretical developments and main contributions of this work.

A substantial body of phenomenological and theoretical work over the last few years strengthens the possibility that the vacuum energy density (VED) of the universe is dynamical, and in particular that it adopts the `running vacuum model' (RVM) form, in which the VED evolves mildly as $\delta \rho_{\rm vac}(H)\sim \nu_{\rm eff} m_{\rm Pl}^2{\cal O}\left(H^2\right)$, where $H$ is the Hubble rate and $\nu_{\rm eff}$ is a (small) free parameter. This dynamical scenario is grounded on recent studies of quantum field theory (QFT) in curved spacetime and also on string theory. It turns out that what we call the `cosmological constant', $\Lambda$, is no longer a rigid parameter but the nearly sustained value of $8\pi G(H)\rho_{\rm vac}(H)$ around (any) given epoch $H(t)$, where $G(H)$ is the gravitational coupling, which can also be very mildly running (logarithmically). Of particular interest is the possibility suggested in past works that such a running may help to cure the cosmological tensions afflicting the $\Lambda$CDM. In the current study, we reanalyze it in full and we find it becomes further buttressed. Using the modern cosmological data, namely a compilation of the latest $SNIa+BAO+$H(z)$+LSS+CMB$ observations, we probe to which extent the RVM provides a quality fit better than the concordance $\Lambda$CDM model, paying particular emphasis on its impact on the $\sigma_8$ and $H_0$ tensions. We utilize the Einstein-Boltzmann system solver $CLASS$ and the Monte Carlo sampler $MontePython$ for the statistical analysis, as well as the statistical $DIC$ criterion to compare the running vacuum against the rigid vacuum ($\nu_{\rm eff} = 0$). We show that with a tiny amount of vacuum dynamics ($|\nu_{\rm eff}|\ll 1$) the global fit can improve significantly with respect to the $\Lambda$CDM and the mentioned tensions may subside to inconspicuous levels., Comment: 47 pages, 11 Tables and 4 Figures. Expanded discussion and references added. Version accepted for publication in Universe

In recent years, transmission system operators have started requesting converter-interfaced generators (CIGs) to participate in grid services such as power oscillation damping (POD). As power systems are prone to topology changes because of connection and disconnection of generators and electrical lines, one of the most important requirements in the POD controller design is to account for these changes and to deal with them by using either adaptive or robust approaches. The robust approach is usually preferred by system operators because of the fixed structure of the controller. In this paper, a procedure to design POD controllers for CIG-based power plants that takes into consideration all possible network configurations is presented. This procedure is based on frequency-response techniques, so it is suitable for the commissioning in newly installed power plants, even in those cases when a detailed small-signal model of the system is not available. This procedure can be used to damp critical system modes by using active power, reactive power, or both power components simultaneously. The proposed procedure is applied to the design of the POD controller for a CIG-based power plant connected to the IEEE 39 Bus system. Simulations performed in Matlab and SimPowerSystems are used to validate the proposed design procedure.

Low-frequency oscillations are an inherent phenomena in transmission networks and renewable energy plants should be configured to damp them. Commonly, a centralised controller is used in PV plants to coordinate PV generators via communication channels. However, the communication systems of PV plants introduce delays of a stochastic nature that degrade the performance of centralised control algorithms. Therefore, controllers for oscillation damping may not operate correctly unless the communication channel characteristics are not considered and compensated. In this paper, a centralised controller is proposed for the oscillation damping that uses a PV plant with all the realistic effects of communication channels taken into consideration. The communication channels are modelled based on measurements taken in a laboratory environment. The controller is designed to damp several modes of oscillation by using the open-loop phase shift compensation. Theoretical developments were validated in a laboratory using four converters acting as two PV inverters, a battery and a STATCOM. A real-time processing platform was used to implement the centralised controller and to deploy the communication infrastructure. Experimental results show the communication channels impose severe restrictions on the performance of centralised POD controllers, highlighting the importance of their accurate modelling and consideration during the controller design stage.

The plastic deformation of Polybutylene terephthalate (PBT) is investigated at three temperatures (120$^\circ$C, 150$^\circ$C, 180$^\circ$C) representative of the span between glass transition, $T_g \approx 55^\circ$C and melting $T_m \approx 225^\circ$C temperatures. The mechanisms of tensile deformation are analyzed as a function of the true strain using a combination of temperature-controlled tensile experiments coupled with 2D digital image correlation and synchrotron sourced wide angle (WAXS) and small angle (SAXS) X-ray scattering the amorphous and crystalline regions causing the transition between polymorphs, respectively from the isotropic amorphous phase to a smectic phase and from the $\alpha$ crystalline phase to the $\beta$ phase.

Extensions of the gravitational framework of Brans-Dicke (BD) are studied by considering two different scenarios: i) `BD-$\Lambda$CDM', in which a rigid cosmological constant, $\Lambda$, is included, thus constituting a BD version of the vanilla concordance $\Lambda$CDM model (the current standard model of cosmology with flat three-dimensional geometry), and ii) `BD-RVM', a generalization of i) in which the vacuum energy density (VED), $\rho_{\textrm{vac}}$, is a running quantity evolving with the square of the Hubble rate: $\delta\rho_{\textrm{vac}}(H)\propto \nu\, m^2_{\textrm{Pl}} (H^2-H_0^2)$ (with $|\nu|\ll 1$). This dynamical scenario is motivated by recent studies of quantum field theory (QFT) in curved spacetime, which lead to the running vacuum model (RVM). We solve the background as well as the perturbation equations for each cosmological model and test their performance against the modern wealth of cosmological data, namely a compilation of the latest SNIa+$H(z)$+BAO+LSS+CMB observations. We utilize the AIC and DIC statistical information criteria in order to determine if they can fit better the observations than the concordance model. The two BD extensions are tested by considering three different datasets. According to the AIC and DIC criteria, both BD extensions i) and ii) are competitive, but the second one (the BD-RVM scenario) is particularly favored when it is compared with the vanilla model. This fact may indicate that the current observations favor a mild dynamical evolution of the Newtonian coupling $G_N$ as well as of the VED. This is in agreement with recent studies suggesting that the combination of these two features can be favorable for a possible resolution of the $\sigma_8$ and $H_0$ tensions. In this work, we show that the Brans-Dicke theory with running vacuum has the potential to alleviate the two tensions at the same time., Comment: Accepted for publication in Phys.Dark Univ., extended discussions, references added, additional figures and tables, 33 pages, 14 figures

In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying more advanced statistical analyses to infant fNIRS data and compared the most commonly used baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches, to show how the conclusions one would draw based on these different analysis approaches converge or differ. The different analysis methods were tested using a face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured in thirty 4-to-6-month-old infants. By including more standard approaches together with recent machine learning techniques, we aim to inform the fNIRS community on alternative ways to analyse infant fNIRS datasets.

We study the performance of three pairs of tilted $\Lambda$CDM cosmological models, two pairs allowing for non-flat spatial hypersurfaces with CMB temperature and polarization power spectrum data (P18) from Planck, P18 lensing (lensing), and non-CMB data (non-CMB). For the six models, we measure cosmological parameters and study whether or not pairs of the data sets are mutually consistent in these models. Half of these models allow the lensing consistency parameter $A_L$ to be an additional free parameter, while the other three have $A_L = 1$. The tilted spatially-flat models assume the usual primordial spatial inhomogeneity power spectrum. The tilted non-flat models assume either the primordial power spectrum used in the Planck group analyses [Planck $P(q)$] or a recently computed power spectrum [new $P(q)$]. In the tilted non-flat models with $A_L=1$ we find differences between P18 data and non-CMB data cosmological parameter constraints, which are large enough to rule out the Planck $P(q)$ model at 3$\sigma$ but not the new $P(q)$ model. While both P18 data and non-CMB data separately favor a closed geometry when P18+non-CMB data are jointly analyzed the evidence in favor of non-flat hypersurfaces subsides. Differences between P18 data and non-CMB data cosmological constraints subside when $A_L$ is allowed to vary. From the most restrictive P18+lensing+non-CMB data combination we get almost model-independent constraints and find that the $A_L>1$ option is preferred over the $\Omega_k<0$ one, with the $A_L$ parameter, for all models, being larger than unity by $\sim 2.5\sigma$. According to the deviance information criterion, in the P18+lensing+non-CMB analysis, the varying $A_L$ option is on the verge of being strongly favored over the $A_L=1$ one, which could indicate a problem for the standard tilted flat $\Lambda$CDM model (Abridged abstract)., Comment: 83 pages, 39 figures. PRD accepted version, minor changes only

This paper proposes a recursive, virtual admittance based, secondary controller for DG units that improves the voltage profile in distribution networks. First, the adaptation of the virtual admittance concept for the goal of voltage regulation is explained. Then, a recursive secondary controller is developed to periodically update the virtual admittance gains. The controller is formulated as an optimization problem with current and stability limitations as constraints. Measurements across the grid, transmitted through low-bandwidth communications, are used to simplify the calculations, resulting in a recursive algorithm. Weight vectors are included in the objective function to allow participation flexibility of each converter. Results show that the primary virtual admittance controller is able to mitigate over- and under-voltages in steady state and under transient conditions. Subsequently, the secondary controller is shown to further improve the voltage profiles across the grid. Experimental results obtained from a laboratory environment, comprising three DG units and a grid emulator, validate the functionality of the complete control structure., Comment: in IEEE Transactions on Smart Grid

Explainable Artificial Intelligence (XAI) is a paradigm that delivers transparent models and decisions, which are easy to understand, analyze, and augment by a non-technical audience. Fuzzy Logic Systems (FLS) based XAI can provide an explainable framework, while also modeling uncertainties present in real-world environments, which renders it suitable for applications where explainability is a requirement. However, most real-life processes are not characterized by high levels of uncertainties alone; they are inherently time-dependent as well, i.e., the processes change with time. In this work, we present novel Temporal Type-2 FLS Based Approach for time-dependent XAI (TXAI) systems, which can account for the likelihood of a measurement's occurrence in the time domain using (the measurement's) frequency of occurrence. In Temporal Type-2 Fuzzy Sets (TT2FSs), a four-dimensional (4D) time-dependent membership function is developed where relations are used to construct the inter-relations between the elements of the universe of discourse and its frequency of occurrence. The TXAI system manifested better classification prowess, with 10-fold test datasets, with a mean recall of 95.40\% than a standard XAI system (based on non-temporal general type-2 (GT2) fuzzy sets) that had a mean recall of 87.04\%. TXAI also performed significantly better than most non-explainable AI systems between 3.95\%, to 19.04\% improvement gain in mean recall. In addition, TXAI can also outline the most likely time-dependent trajectories using the frequency of occurrence values embedded in the TXAI model; viz. given a rule at a determined time interval, what will be the next most likely rule at a subsequent time interval. In this regard, the proposed TXAI system can have profound implications for delineating the evolution of real-life time-dependent processes, such as behavioural or biological processes.

Lipid nanotube-vesicle networks are important channels for intercellular communication and transport of matter. Experimentally observed in neighboring mammalian cells, but also reproduced in model membrane systems, a broad consensus exists on their formation and stability. Lipid membranes must be composed of at least two molecular components, each stabilizing low (generally a phospholipid) and high curvatures. Strong anisotropy or enhanced conical shape of the second amphiphile is crucial for the formation of nanotunnels. Anisotropic driving forces generally favor nanotube protrusions from vesicles. In the present work, we report the unique case of topologically-connected nanotubes-vesicles obtained in the absence of directional forces, in single-molecule membranes, composed of an anisotropic bolaform glucolipid, above its melting temperature, Tm. Cryo-TEM and fluorescence confocal microscopy show the interconnection between vesicles and nanotubes in a single-phase region, between 60{\textdegree} and 90{\textdegree}C under diluted conditions. Solid-state NMR demonstrates that the glucolipid can assume two distinct configurations, head-head and head-tail. These arrangements, seemingly of comparable energy above the Tm, could explain the existence and stability of the topologically-connected vesicles and nanotubes, which are generally not observed for classical single-molecule phospholipid-based membranes above their Tm.

This paper proposes a taxonomy of power converter control schemes based on the recently proposed concept of complex frequency. This quantity captures local frequency variations due to the change of both the phase angle and amplitude of bus voltages and current injections. The paper derives the analytical expressions of the link between complex power variations and complex frequency of each converter controller as well as the identification of critical control parameters. The main contribution of this work is to provide a general framework that allows classifying converters synchronization mechanisms and controllers. This framework also allows comparing converters with synchronous machines. To validate the theoretical results, extensive simulations are performed using a modified version of the WSCC 9-bus system. Examples of how the theoretical formulations of the paper can be used to improve power converter control in power system applications are showcased., Comment: 10 pages, 22 figures

Human beings have an inherent capability to use linguistic information (LI) seamlessly even though it is vague and imprecise. Computing with Words (CWW) was proposed to impart computing systems with this capability of human beings. The interest in the field of CWW is evident from a number of publications on various CWW methodologies. These methodologies use different ways to model the semantics of the LI. However, to the best of our knowledge, the literature on these methodologies is mostly scattered and does not give an interested researcher a comprehensive but gentle guide about the notion and utility of these methodologies. Hence, to introduce the foundations and state-of-the-art CWW methodologies, we provide a concise but a wide-ranging coverage of them in a simple and easy to understand manner. We feel that the simplicity with which we give a high-quality review and introduction to the CWW methodologies is very useful for investigators, especially those embarking on the use of CWW for the first time. We also provide future research directions to build upon for the interested and motivated researchers.

Zeros of rational transfer function matrices $R(\lambda)$ are the eigenvalues of associated polynomial system matrices $P(\lambda)$, under minimality conditions. In this paper we define a structured condition number for a simple eigenvalue $\lambda_0$ of a (locally) minimal polynomial system matrix $P(\lambda)$, which in turn is a simple zero $\lambda_0$ of its transfer function matrix $R(\lambda)$. Since any rational matrix can be written as the transfer function of a polynomial system matrix, our analysis yield a structured perturbation theory for simple zeros of rational matrices $R(\lambda)$. To capture all the zeros of $R(\lambda)$, regardless of whether they are poles or not, we consider the notion of root vectors. As corollaries of the main results, we pay particular attention to the special case of $\lambda_0$ being not a pole of $R(\lambda)$ since in this case the results get simpler and can be useful in practice. We also compare our structured condition number with Tisseur's unstructured condition number for eigenvalues of matrix polynomials, and show that the latter can be unboundedly larger. Finally, we corroborate our analysis by numerical experiments., Comment: 20 pages, 6 figures

This paper presents a vehicle lateral controller based on spiking neural networks capable of replicating the behavior of a model-based controller but with the additional ability to perform online adaptation. By making use of neural plasticity and thanks to reward modulation learning, neural connections are modified to adjust the response according to the committed error. Therefore, the error performs a similar role to dopamine in a biological system, modulating the learning process based on spiking time dependency. The connections are initially set to replicate behavior of the model-based controller. Online adaptation allows tuning connection parameters to improve controller performance. A path controller with a preview is used as a baseline controller to evaluate the performance of the proposed approach. Key performance indicators are obtained from simulation with a step response and a handling track with 20 curves of different radii., Comment: submitted to AVEC 2022

To explore dense packings of soft colloids, scattering experiments are ideal to access the structure factor. However, for soft microgels determination of the structure factor is difficult because of the low contrast of the polymer network and potential microgel interpenetration and deformation that change the form factor contribution. Here, we employ small-angle X-ray scattering (SAXS) to study soft, thermoresponsive microgels with poly-N-isopropylacrylamide (PNIPAM) shells and gold nanoparticle cores. The scattering of the gold cores dominates the scattering patterns and allows precise determination of the microgel volume fraction over a broad range of concentrations. At high volume fractions we find distinct patterns with sharp Bragg peaks allowing extraction of the structure factor and characterization of the phases combined with UV-Vis spectroscopy. The unique scattering contrast of our core-shell microgels combined with SAXS opens up new ways to investigate dense packings of soft microgels including in situ studies of phase transitions.

In recent years, neural networks and especially deep architectures have received substantial attention for EEG signal analysis in the field of brain-computer interfaces (BCIs). In this ongoing research area, the end-to-end models are more favoured than traditional approaches requiring signal transformation pre-classification. They can eliminate the need for prior information from experts and the extraction of handcrafted features. However, although several deep learning algorithms have been already proposed in the literature, achieving high accuracies for classifying motor movements or mental tasks, they often face a lack of interpretability and therefore are not quite favoured by the neuroscience community. The reasons behind this issue can be the high number of parameters and the sensitivity of deep neural networks to capture tiny yet unrelated discriminative features. We propose an end-to-end deep learning architecture called EEG-ITNet and a more comprehensible method to visualise the network learned patterns. Using inception modules and causal convolutions with dilation, our model can extract rich spectral, spatial, and temporal information from multi-channel EEG signals with less complexity (in terms of the number of trainable parameters) than other existing end-to-end architectures, such as EEG-Inception and EEG-TCNet. By an exhaustive evaluation on dataset 2a from BCI competition IV and OpenBMI motor imagery dataset, EEG-ITNet shows up to 5.9\% improvement in the classification accuracy in different scenarios with statistical significance compared to its competitors. We also comprehensively explain and support the validity of network illustration from a neuroscientific perspective. We have also made our code open at https://github.com/AbbasSalami/EEG-ITNet

The processing and classification of electroencephalographic signals (EEG) are increasingly performed using deep learning frameworks, such as convolutional neural networks (CNNs), to generate abstract features from brain data, automatically paving the way for remarkable classification prowess. However, EEG patterns exhibit high variability across time and uncertainty due to noise. It is a significant problem to be addressed in P300-based Brain Computer Interface (BCI) for smart home interaction. It operates in a non-optimal natural environment where added noise is often present. In this work, we propose a sequential unification of temporal convolutional networks (TCNs) modified to EEG signals, LSTM cells, with a fuzzy neural block (FNB), which we called EEG-TCFNet. Fuzzy components may enable a higher tolerance to noisy conditions. We applied three different architectures comparing the effect of using block FNB to classify a P300 wave to build a BCI for smart home interaction with healthy and post-stroke individuals. Our results reported a maximum classification accuracy of 98.6% and 74.3% using the proposed method of EEG-TCFNet in subject-dependent strategy and subject-independent strategy, respectively. Overall, FNB usage in all three CNN topologies outperformed those without FNB. In addition, we compared the addition of FNB to other state-of-the-art methods and obtained higher classification accuracies on account of the integration with FNB. The remarkable performance of the proposed model, EEG-TCFNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced P300-based BCIs for smart home interaction within natural settings.

In this paper we will list a few important goals that need to be addressed in the next decade, also taking into account the current discordances between the different cosmological probes, such as the disagreement in the value of the Hubble constant $H_0$, the $\sigma_8$--$S_8$ tension, and other less statistically significant anomalies. While these discordances can still be in part the result of systematic errors, their persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the necessity for new physics or generalisations beyond the standard model. In this paper, we focus on the $5.0\,\sigma$ tension between the {\it Planck} CMB estimate of the Hubble constant $H_0$ and the SH0ES collaboration measurements. After showing the $H_0$ evaluations made from different teams using different methods and geometric calibrations, we list a few interesting new physics models that could alleviate this tension and discuss how the next decade's experiments will be crucial. Moreover, we focus on the tension of the {\it Planck} CMB data with weak lensing measurements and redshift surveys, about the value of the matter energy density $\Omega_m$, and the amplitude or rate of the growth of structure ($\sigma_8,f\sigma_8$). We list a few interesting models proposed for alleviating this tension, and we discuss the importance of trying to fit a full array of data with a single model and not just one parameter at a time. Additionally, we present a wide range of other less discussed anomalies at a statistical significance level lower than the $H_0$--$S_8$ tensions which may also constitute hints towards new physics, and we discuss possible generic theoretical approaches that can collectively explain the non-standard nature of these signals.[Abridged], Comment: Contribution to Snowmass 2021. 224 pages, 27 figures. Accepted for publication in JHEAp

The last decades have seen significant advancements in non-invasive neuroimaging technologies that have been increasingly adopted to examine human brain development. However, these improvements have not necessarily been followed by more sophisticated data analysis measures that are able to explain the mechanisms underlying functional brain development. For example, the shift from univariate (single area in the brain) to multivariate (multiple areas in brain) analysis paradigms is of significance as it allows investigations into the interactions between different brain regions. However, despite the potential of multivariate analysis to shed light on the interactions between developing brain regions, artificial intelligence (AI) techniques applied render the analysis non-explainable. The purpose of this paper is to understand the extent to which current state-of-the-art AI techniques can inform functional brain development. In addition, a review of which AI techniques are more likely to explain their learning based on the processes of brain development as defined by developmental cognitive neuroscience (DCN) frameworks is also undertaken. This work also proposes that eXplainable AI (XAI) may provide viable methods to investigate functional brain development as hypothesised by DCN frameworks.

In this paper classification of mental task-root Brain-Computer Interfaces (BCI) is being investigated, as those are a dominant area of investigations in BCI and are of utmost interest as these systems can be augmented life of people having severe disabilities. The BCI model's performance is primarily dependent on the size of the feature vector, which is obtained through multiple channels. In the case of mental task classification, the availability of training samples to features are minimal. Very often, feature selection is used to increase the ratio for the mental task classification by getting rid of irrelevant and superfluous features. This paper proposes an approach to select relevant and non-redundant spectral features for the mental task classification. This can be done by using four very known multivariate feature selection methods viz, Bhattacharya's Distance, Ratio of Scatter Matrices, Linear Regression and Minimum Redundancy & Maximum Relevance. This work also deals with a comparative analysis of multivariate and univariate feature selection for mental task classification. After applying the above-stated method, the findings demonstrate substantial improvements in the performance of the learning model for mental task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and comparing different combinations of power spectral density and feature selection methods.

Depersonalisation/derealisation disorder (DPD) refers to frequent and persistent detachment from bodily self and disengagement from the outside world. As a dissociative disorder, DPD affects 1-2% of the population, but takes 7-12 years on average to be accurately diagnosed. In this systematic review, we comprehensively describe research targeting the neural correlates of core DPD symptoms, covering publications between 1992 and 2020 that have used electrophysiological techniques. The aim was to investigate the diagnostic potential of these relatively inexpensive and convenient neuroimaging tools. We review the EEG power spectrum, components of the event-related potential (ERP), as well as vestibular and heartbeat evoked potentials as likely electrophysiological biomarkers to study DPD symptoms. We argue that acute anxiety- or trauma-related impairments in the integration of interoceptive and exteroceptive signals play a key role in the formation of DPD symptoms, and that future research needs analysis methods that can take this integration into account. We suggest tools for prospective studies of electrophysiological DPD biomarkers, which are urgently needed to fully develop their diagnostic potential.