Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-Dimensional Oral Mucosal Equivalents as Models for Transmucosal Drug Permeation Studies

Oral transmucosal administration, where drugs are absorbed directly through the non-keratinized, lining mucosa of the mouth, represents a solution to drug delivery with several advantages. Oral mucosal equivalents (OME) developed as 3D in vitro models are of great interest since they express the correct cell differentiation and tissue architecture, simulating the in vivo conditions better than monolayer cultures or animal tissues. The aim of this work was to develop OME to be used as a membrane for drug permeation studies. We developed both full-thickness (i.e., connective plus epithelial tissue) and split-thickness (i.e., only epithelial tissue) OME using non-tumor-derived human keratinocytes OKF6 TERT-2 obtained from the floor of the mouth. All the OME developed here presented similar transepithelial electrical resistance (TEER) values, comparable to the commercial EpiOral™. Using eletriptan hydrobromide as a model drug, we found that the full-thickness OME had similar drug flux to EpiOral™ (28.8 vs. 29.6 µg/cm2/h), suggesting that the model had the same permeation barrier properties. Furthermore, full-thickness OME showed an increase in ceramide content together with a decrease in phospholipids in comparison to the monolayer culture, indicating that lipid differentiation occurred due to the tissue-engineering protocols. The split-thickness mucosal model resulted in 4–5 cell layers with basal cells still undergoing mitosis. The optimum period at the air–liquid interface for this model was twenty-one days; after longer times, signs of apoptosis appeared. Following the 3R principles, we found that the addition of Ca2+, retinoic acid, linoleic acid, epidermal growth factor and bovine pituitary extract was important but not sufficient to fully replace the fetal bovine serum. Finally, the OME models presented here offer a longer shelf-life than the pre-existing models, which paves the way for the further investigation of broader pharmaceutical applications (i.e., long-term drug exposure, effect on the keratinocytes’ differentiation and inflammatory conditions, etc.).
I am ashamed of my brand-self! Consumer-brand identification as a moderator of emotional reactions following symbol-laden brand failures

Purpose The study of shame has a long tradition in intra- and inter-personal psychology. This paper aims to investigate whether consumers can experience brand shame after self-relevant consumption incidents. Specifically, this research proposes that consumers follow a complex shame-inducing process in the aftermath of unpleasant experiences involving their favorite brand. The moderating role of relational tie strength between consumers and their favorite brand existing prior to symbolic failures is examined. Design/methodology/approach A scenario-based, online survey (n = 660) among consumers who have recently experienced a self-relevant failure with their favorite brand was conducted. Confirmatory factor analysis ensured the reliability and validity of the measurement model. For testing the conceptual model, data was analyzed by means of a moderated mediation analysis. The proposed model was tested against, among others, common method bias and alternative models. The findings were cross-validated with a scenario-based online experiment (n = 1,616). Findings Results show that brand shame is a key mediator between customer dissatisfaction and brand anger when self-relevant, symbolic failures happen. Moreover, strong consumer-brand identification triggers brand-detrimental effects. It is shown to influence the connection between consumers’ inward- (i.e. brand shame) and resulting outward-directed (i.e. brand anger) negative emotions on brands, which lead to consumer vengeance. Originality/value To the best of the authors’ knowledge, this research is the first to introduce the concept of situational brand shame to the literature on favorite brands. Furthermore, it shows that consumer-brand identification moderates the direct and indirect (via brand shame) unfavorable effects of failure-induced dissatisfaction on brand anger. This research adds insights to the investigation of the “love-becomes-hate” effect arising after self-relevant failures involving consumers’ most preferred brand.
The Determinants of Online Seller Loyalty in Express Delivery Service Providers

The service quality of delivery service companies is a significant factor in creating online seller loyalty; especially consumers demand that online sellers offer trusted and reliable delivery services. Customer experience is essential information for delivery service providers and online sellers. This study examines the determinants of seller loyalty based on perceived service quality when dealing with delivery service providers. In the survey, as many as 280 sellers were involved voluntarily, and they were randomly selected. The questionnaires were distributed online through a chat service provided by the e-commerce platform. Then, the survey results were analyzed using the PLS-SEM technique to answer the research hypothesis. As a result, all dimensions of service quality, including reliability, visual identification, relational capital, responsiveness, and technical quality, positively and significantly affect online seller loyalty. Technical quality is the main factor in forming seller loyalty to delivery service providers.
Behavioural Models of Risk-Taking in Human–Robot Tactile Interactions

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot. We used data collected with physiological sensors during the playing of a risk-taking game (the Balloon Analogue Risk Task, or BART). The results of a mixed-effects model were used as a baseline to predict risk-taking propensity from physiological measures, and these results were further improved through the use of two machine learning techniques—support vector regression (SVR) and multi-input convolutional multihead attention (MCMA)—to achieve low-latency risk-taking behaviour prediction during human–robot tactile interaction. The performance of the models was evaluated based on mean absolute error (MAE), root mean squared error (RMSE), and R squared score (R2), which obtained the optimal result with MCMA yielding an MAE of 3.17, an RMSE of 4.38, and an R2 of 0.93 compared with the baseline of 10.97 MAE, 14.73 RMSE, and 0.30 R2. The results of this study offer new insights into the interplay between physiological data and the intensity of risk-taking behaviour in predicting human risk-taking behaviour during human–robot tactile interactions. This work illustrates that physiological activation and the intensity of tactile interaction play a prominent role in risk processing during human–robot tactile interaction and demonstrates that it is feasible to use human physiological data and behavioural data to predict risk-taking behaviour in human–robot tactile interaction.
Comparisons of electron acceleration efficiency among different structures during magnetic reconnection: a Cluster multicase study

Abstract. Magnetic reconnection has long been believed to be an efficient engine for energetic electrons production. Four different structures have been proposed for electrons being energized: flux pileup region, density cavity located around the separatrix, magnetic island and thin current sheet. In this paper, we compare the electron acceleration efficiency among these structures based on 12 magnetotail reconnection events observed by the Cluster spacecraft in 2001–2006. We used the flux ratio between the energetic electrons (> 50 keV) and lower energy electrons (
Biometrically Measured Affect for Screen-Based Drone Pilot Skill Acquisition

Drones have been used increasingly to aid in Industry 4.0 activities including inspection of the nation ’ s infrastructure. We investigate several potential underlying affective behaviors related to drone pilot skill acquisition, with the eventual goal of developing methods to enhance human performance. We employ Electroencephalography (EEG) and Eye tracking instrumentation to measure human affect in a series of simulated drone piloting experiments to examine performance using behavioral variables, controller input variables, as well as measures of individual cognitive ability. Current results show that task difficulty impacts the performance/learning process and varies by the nature of the task. The behavioral and biometric measures associated with performance/learning varied significantly among activities. We conclude that drone specifications and training requirements can and should be calibrated to the drone mission. In addition to developing specifications and training requirements, psychological and behavioral measures can also serve as theoretical foundations for modeling complex tasks.
A review on waste wood reinforced polymer composites and their processing for construction materials

ABSTRACT The necessity to utilise environmentally friendly resources has emerged due to environmental management. With the demand for the production of plastics and wood materials, pollution has increased. Consequently, the attraction in natural fibre-reinforced polymer composites (NFPCs) is rapidly emerging in the construction industry, mainly to replace synthetic fibre composites. The intensified interest is associated with manufacturing ‘green’ and lightweight panels. This review provides insight into the prospects and challenges related to the processing of wood waste-reinforced polymer composites. Using natural fibres, especially waste as raw material, is desirable for developing value-added products to mitigate environmental pollution. The current materials used for the wood-based composites are reviewed as disadvantages associated with wood plastic composites, such as low interfacial bonding. Efforts such as chemical treatment are outlined to wood fibres to manufacture an environmentally friendly, cost-effective, lightweight, and biodegradable composite with enhanced structural properties was provided. Various waste plastics, plant dust, and coupling agents-based composites investigated for applications, and emerging aspects of wood plastic composite for construction materials applications are outlined.
Supervised exercise training improves cardiorespiratory fitness and reduces perioperative risk in peripheral artery disease patients with intermittent claudication

Introduction This study examined to what extent supervised aerobic and resistance exercise combined with continued unsupervised exercise training improves cardiorespiratory fitness and corresponding perioperative risk in peripheral artery disease (PAD) patients with intermittent claudication. Methods A total of 106 patients (77% male) were enrolled into the study, alongside 155 healthy non-PAD control participants. Patients completed supervised exercise therapy (aerobic and resistance exercises of the upper and lower limbs) twice a week for 10 weeks. Thereafter, 52 patients completed 12 weeks of an unsupervised tailored home-based exercise. Pain-free walking distance (PWD), maximum walking distance (MWD), peak oxygen uptake (V˙O2PEAK) and perioperative risk were assessed before and after both exercise interventions. Results Patients were highly unconditioned relative to healthy controls (V˙O2PEAK=11.9 vs 24.2ml/kg/min, p=<0.001) with 91% classified as high perioperative risk (peak oxygen uptake <15ml/kg/min). Supervised exercise increased PWD (+44±81m, p=<0.001), MWD (+44±71m, p=<0.001) and V˙O2PEAK (+1.01±1.63ml/kg/min, p=<0.001) and lowered perioperative risk (91% to 85%, p=<0.001). When compared with supervised exercise, the improvements in PWD were maintained following unsupervised exercise (+11±91m vs supervised exercise, p=0.572); however, MWD and V˙O2PEAK decreased (−15±48m, p=0.030 and −0.34±1.11ml/kg/min, p=0.030, respectively) and perioperative risk increased (+3%, p=<0.001) although still below baseline (p=<0.001). Conclusions Supervised aerobic and resistance exercise training and, to a lesser extent, unsupervised tailored exercise improves walking capacity and cardiorespiratory fitness and reduces perioperative risk in PAD patients with intermittent claudication.
C‐programming self‐assessment exercises versus final exams: 12 years of experience

The new curricula derived from the Bologna process encourage continuous evaluations during the teaching period. This situation causes already depleted teachers to have an additional workload during the course, so the emergence of tools that automatically evaluate this work is necessary. In computer science, the ideal evaluation technique would be to use automatic code evaluators (such as C, Java, C++, etc.). The main objective of this work is to analyze whether the use of C‐coding self‐assessment exercises correlates with an improvement in exam performance. For this purpose, the implementation of self‐assessment exercises on the AulaWeb platform during the last 12 academic years (2010/2011–2021/2022) in a Programming Fundamentals course (Fundamentos de Programación) in an engineering faculty at the Universidad Politécnica de Madrid (UPM) was collected. The main tasks carried out in this research have been: (i) recovery and analysis of the information collected from the AulaWeb platform and (ii) the study of the influence of the self‐assessment exercises on the final grades obtained by the students of the course through correlations. The most important findings are: (a) self‐assessment exercises generate experience and confidence when facing problems (this situation translates into a higher probability of passing the course) and (b) self‐assessment exercises influence the final exams taken at the end of the semester, mainly due to the short time between the end of classes and the exam. Self‐assessment exercises provide valuable information for teachers to monitor the progress of large groups of students during the semester and for students to pass exams.
Low-Latency Low-Energy Adaptive Clustering Hierarchy Protocols for Underwater Acoustic Networks

Underwater acoustic cluster networks (UACNs) are commonly used due to their adaptability in dynamic underwater environments. While the low-energy adaptive clustering hierarchy (LEACH) protocol was initially designed for radio frequency (RF) cluster networks, it has also been applied to UACNs. However, the LEACH protocol uses lengthy overheads per packet due to its use of global or wide-scale IDs, leading to increased communication latency and energy usage per packet. To address this issue, we propose the low-latency low-energy adaptive clustering hierarchy (<inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH) protocol. The <inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH protocol is a comprehensive framework that integrates ID assignment, time slot reservation, packet routing, and self-network organization. The protocol uses shorter overheads by assigning local IDs instead of global or wide-scale IDs. It assigns unique IDs to nodes within a cluster and reassigns the same IDs to nodes in other clusters, i.e., spatial ID reuse. The protocol also allocates IDs and time slots on demand to maximize network resources. To further enhance the protocol, we introduce the <inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH-Version 2 (<inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH-V2) protocol, which modulates the preamble bits to embed the IDs in the overhead rather than inserting extra bits. We also provide the computational complexity of the <inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH-V2 protocol. Compared to the DIVE protocol, the <inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH protocol reduces the ID length and average energy per packet by 50% and 13%, respectively. Furthermore, the <inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH-V2 protocol reduces the average energy per packet by 27% and increases the network throughput by 16% compared to the <inline-formula> <tex-math notation="LaTeX">$\text{L}^{3}$ </tex-math></inline-formula>EACH protocol, making it an efficient and scalable solution, especially, for dense UACNs.
Semantic Bimodal Presentation Differentially Slows Working Memory Retrieval

Although evidence has shown that working memory (WM) can be differentially affected by the multisensory congruency of different visual and auditory stimuli, it remains unclear whether different multisensory congruency about concrete and abstract words could impact further WM retrieval. By manipulating the attention focus toward different matching conditions of visual and auditory word characteristics in a 2-back paradigm, the present study revealed that for the characteristically incongruent condition under the auditory retrieval condition, the response to abstract words was faster than that to concrete words, indicating that auditory abstract words are not affected by visual representation, while auditory concrete words are. Alternatively, for concrete words under the visual retrieval condition, WM retrieval was faster in the characteristically incongruent condition than in the characteristically congruent condition, indicating that visual representation formed by auditory concrete words may interfere with WM retrieval of visual concrete words. The present findings demonstrated that concrete words in multisensory conditions may be too aggressively encoded with other visual representations, which would inadvertently slow WM retrieval. However, abstract words seem to suppress interference better, showing better WM performance than concrete words in the multisensory condition.
Genes Involved in miRNA Biogenesis Are Not Downregulated in SARS-CoV-2 Infection

miRNAs, small non-coding RNAs that regulate gene expression, are involved in various pathological processes, including viral infections. Virus infections may interfere with the miRNA pathway through the inhibition of genes involved in miRNA biogenesis. A reduction in the number and the levels of miRNAs expressed in nasopharyngeal swabs of patients with severe COVID-19 was lately observed by us, pointing towards the potential of miRNAs as possible diagnostic or prognostic biomarkers for predicting outcomes among patients with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. The objective of the present study was to investigate whether SARS-CoV-2 infection influences the expression levels of messenger RNAs (mRNAs) of key genes involved in miRNA biogenesis. mRNA levels of AGO2, DICER1, DGCR8, DROSHA, and Exportin-5 (XPO5) were measured by quantitative reverse-transcription polymerase chain reaction (RT-qPCR) in nasopharyngeal swab specimens from patients with COVID-19 and controls, as well as in cells infected with SARS-CoV-2 in vitro. Our data showed that the mRNA expression levels of AGO2, DICER1, DGCR8, DROSHA, and XPO5 were not significantly different in patients with severe COVID-19 when compared to patients with non-severe COVID-19 and controls. Similarly, the mRNA expression of these genes was not affected by SARS-CoV-2 infection in NHBE and Calu-3 cells. However, in Vero E6 cells, AGO2, DICER1, DGCR8, and XPO5 mRNA levels were slightly upregulated 24 h after infection with SARS-CoV-2. In conclusion, we did not find evidence for downregulation of mRNA levels of miRNA biogenesis genes during SARS-CoV-2 infection, neither ex vivo nor in vitro.
Dimension of Marginals of Kronecker Product Models; Geometry of hidden-visible products of exponential families

A Kronecker product model is the set of visible marginal probability distributions of an exponential family whose sufficient statistics matrix factorizes as a Kronecker product of two matrices, one for the visible variables and one for the hidden variables. We estimate the dimension of these models by the maximum rank of the Jacobian in the limit of large parameters. The limit is described by the tropical morphism; a piecewise linear map with pieces corresponding to slicings of the visible matrix by the normal fan of the hidden matrix. We obtain combinatorial conditions under which the model has the expected dimension, equal to the minimum of the number of natural parameters and the dimension of the ambient probability simplex. Additionally, we prove that the binary restricted Boltzmann machine always has the expected dimension.
A New Algorithm for X-corner Detection

X-corner patterns are most widely used in camera calibration. In order to do online calibration for blurred image,we propose a new algorithm for X-corner detection based on the special character of X-corners: the pattern is symmetrical and the gray contrast greaty with each other In the algorithm the operator SV is designed to recognize X-corners which consists of symmetry operator S and variance operator V.The method is smart,easy to execute and robust to rotation,intensity change.It is also robust to image noise and edge blur where the intervention of man is not required.Experimental results show that the proposed algorithm can achieve excellent effects in X-corners detecting.in on line camera calibration.
Multiscanning-Based RNN–Transformer for Hyperspectral Image Classification

The goal of hyperspectral image (HSI) classification is to assign land-cover labels to each HSI pixel in a patchwise manner. Recently, sequential models, such as recurrent neural networks (RNNs), have been developed as HSI classifiers, which need to scan the HSI patch into a pixel sequence with the scanning order first. However, RNNs have a biased ordering that cannot effectively allocate attention to each pixel in the sequence, and previous methods that use multiple scanning orders to average the features of RNNs are limited by the validity of these orders. To solve this issue, it is naturally inspired by Transformer and its self-attention to discriminatively distribute proper attention for each pixel of the pixel sequence and each scanning order. Hence, in this study, we further develop the sequential HSI classifiers by a specially designed RNN–Transformer (RT) model to feature the multiple sequential characters of the HSI pixels in the HSI patch. Specifically, we introduce a multiscanning-controlled positional embedding strategy for the RT model to complement multiple feature fusion. Furthermore, the RT encoder is proposed for integrating ordering bias and attention reallocation for feature generation at the sequence level. In addition, the spectral–spatial-based soft masked self-attention (SMSA) is proposed for suitable feature enhancement. Finally, an additional fusion Transformer (FT) is deployed for scanning order-level attention allocation. As a result, the whole network can achieve competitive classification performance on four accessible datasets than other state-of-the-art methods. Our study further extends the research on sequential HSI classifiers.