Miracle-dz/UniFAR-FTUs huggingface dataset:
Chatbots Generativos como Ferramentas de Apoio ao Ensino em Cursos na Área de Ferrovias

Chatbots são sistemas de conversação capazes de simular interações utilizando linguagem natural. Essa tecnologia permite a interação com os usuários de forma rápida, e seu uso como ferramenta de apoio ao ensino oferece aos estudantes uma nova forma de acesso ao conteúdo. Este trabalho apresenta uma base de dados de domínio específico, bem como sua utilização na construção de um chatbot generativo para auxiliar alunos na área de Ferrovias. Para isso, foram utilizadas as redes neurais BiLSTM e GRU, ambas em uma arquitetura do tipo codificador-decodificador com mecanismo de atenção. Os experimentos realizados demonstraram que a arquitetura usando GRUs obteve melhor desempenho com base nas medidas de avaliação do BLEU e ROUGE-L.
SHOCK WAVES FROM A WATER-CONFINED LASER-GENERATED PLASMA

Generation of a high amplitude shock wave by laser plasma in a water confinement regime has been investigated for an incident 25–30 ns/40 J/λ=1.064 μm pulsed laser beam. Experimental measurements of temporal and spatial profiles of induced shock waves for this regime of laser shock processing of materials were performed using a velocimetry interferometer system for any reflector system. Above a 10 GW/cm2 laser intensity threshold, a saturation of the peak pressure is shown to occur while the pressure pulse duration is reduced by parasitic plasma occurring in the confining water. The observation of the interaction zone with a fast camera system shows that this breakdown plasma, which mainly occurs at the very surface of the water rather than within the water volume, limits the efficiency of the process. This plasma absorbs the incident laser energy, and the power density reaching the target gradually decreases with increasing power densities while the shock-wave duration is correspondingly reduced. Both pr.
Emotion Recognition from Large-Scale Video Clips with Cross-Attention and Hybrid Feature Weighting Neural Networks

The emotion of humans is an important indicator or reflection of their mental states, e.g., satisfaction or stress, and recognizing or detecting emotion from different media is essential to perform sequence analysis or for certain applications, e.g., mental health assessments, job stress level estimation, and tourist satisfaction assessments. Emotion recognition based on computer vision techniques, as an important method of detecting emotion from visual media (e.g., images or videos) of human behaviors with the use of plentiful emotional cues, has been extensively investigated because of its significant applications. However, most existing models neglect inter-feature interaction and use simple concatenation for feature fusion, failing to capture the crucial complementary gains between face and context information in video clips, which is significant in addressing the problems of emotion confusion and emotion misunderstanding. Accordingly, in this paper, to fully exploit the complementary information between face and context features, we present a novel cross-attention and hybrid feature weighting network to achieve accurate emotion recognition from large-scale video clips, and the proposed model consists of a dual-branch encoding (DBE) network, a hierarchical-attention encoding (HAE) network, and a deep fusion (DF) block. Specifically, the face and context encoding blocks in the DBE network generate the respective shallow features. After this, the HAE network uses the cross-attention (CA) block to investigate and capture the complementarity between facial expression features and their contexts via a cross-channel attention operation. The element recalibration (ER) block is introduced to revise the feature map of each channel by embedding global information. Moreover, the adaptive-attention (AA) block in the HAE network is developed to infer the optimal feature fusion weights and obtain the adaptive emotion features via a hybrid feature weighting operation. Finally, the DF block integrates these adaptive emotion features to predict an individual emotional state. Extensive experimental results of the CAER-S dataset demonstrate the effectiveness of our method, exhibiting its potential in the analysis of tourist reviews with video clips, estimation of job stress levels with visual emotional evidence, or assessments of mental healthiness with visual media.
A gene signature driven by abnormally methylated DEGs was developed for TP53 wild-type ovarian cancer samples by integrative omics analysis of DNA methylation and gene expression data

Background Integrated omics analysis based on transcriptome and DNA methylation data combined with machine learning methods is very promising for the diagnosis, prognosis, and classification of cancer. In this study, the DNA methylation and gene expression data of ovarian cancer (OC) were analyzed to identify abnormally methylated differentially expressed genes (DEGs), screen potential therapeutic agents for OC, and construct a risk model based on the abnormally methylated DEGs to predict patient prognosis. Methods The gene expression and DNA methylation data of primary OC samples with tumor protein 53 (TP53) wild-type and normal samples were obtained from The Cancer Genome Atlas (TCGA) database. DEGs with aberrant methylation were analyzed by screening the intersection between DEGs and differentially methylated genes (DMGs). We attempted to search for potential drugs targeting DEGs with aberrant methylation by employing a network medicine framework. A gene signature based on the DEGs with aberrant methylation was constructed by regularized least absolute shrinkage and selection operator (LASSO) regression analysis. Results A total of 440 aberrant methylated DEGs were screened. Based on their gene expression profiles and methylation data from different regions, the results of both discriminative pattern recognition analysis and principal component analysis (PCA) showed a significant separation between tumor tissue and healthy ovarian tissue. In total, 126 potential therapeutic drugs were identified for OC by network-based proximity analysis. Five genes were identified in 440 aberrant methylated DEGs, which formed an aberrant methylated DEGs-driven gene signature. This signature could significantly distinguish the different overall survivals (OS) of OC patients and showed better predictive performance in both the training and validation sets. Conclusions In this study, the DNA methylation and gene expression data of OC were analyzed to identify abnormally methylated DEGs and potential therapeutic drugs, and a gene signature based on five aberrant methylation DEGs was constructed, which could better predict the risk of death in patients.
Loss of Sour Taste Is the Striking Feature among Four Basic Taste Qualities in Tunisian COVID-19 Patients

Background: Taste disorders (TDs) have been reported to be very common in patients suffering from coronavirus disease 2019 (COVID-19), which is caused by the SARS-CoV-2 virus. In most of the hitherto conducted studies, a gustatory assessment was performed on the basis of surveys or self-reports by patients. The aim of our study was to undertake an objective assessment of four basic taste qualities by conducting tasting sessions that allowed detection thresholds in COVID-19 Tunisian patients and to study their associations with inflammation. Methods: This analytical cross-sectional study was conducted on 89 patients aged between 21 to 70 years who had been diagnosed with COVID-19. We used Burghart taste strips to assess taste perception of the four taste qualities, i.e., sour, bitter, sweet, and salty. Serum levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and C-reactive protein (CRP) were measured. Results: Taste disorders were reported by 40.4% of the patients, while objective assessments revealed that 63.8% of participants were suffering from hypogeusia and/or ageusia. Sour taste was the most altered (70.8%) gustatory quality. Patients with severe COVID-19 had significantly lower sour and bitter taste scores when compared to patients with minor/moderate forms. There was no significant association between serum inflammatory markers and taste disorders. However, the relationship between bitter and sweet taste qualities and IL-1β levels was significant (p = 0.018 and p = 0.041). Conclusions: Our results demonstrate the interest in the objective assessment of taste dysfunctions in COVID-19 patients.
Horizontal CO2 Compensation in the Yangtze River Delta Based on CO2 Footprints and CO2 Emissions Efficiency

Purpose: In this study, we attempted to reduce the negative economic externalities related to Carbon Dioxide (CO2) emissions in the Yangtze River Delta region (YRD) and designed a cross-municipality responsibility-sharing mechanism. Methods: We estimated the municipal CO2 footprints in the YRD from 2000 to 2019 based on nighttime light data and measured CO2 emissions efficiency using a super slack-based measurement (super-SBM) model. Based on this, we designed a scenario of horizontal CO2 compensation among the YRD’s municipalities from the perspectives of both CO2 footprints and CO2 trading (CO2 unit prices in trading were determined based on CO2 emissions efficiency). Results: The results showed the following: (1) The CO2 footprints evolution of the YRD municipalities could be divided into four categories, among which, eleven municipalities showed a decreasing trend. Thirteen municipalities stabilized their CO2 footprints. Thirteen municipalities exhibited strong growth in their CO2 footprints, whereas four municipalities maintained a low level of slow growth. (2) Spatially, CO2 emissions efficiency evolved from a broad distribution of low values to a mosaic distribution of multi-type zones. (3) After 2011, the ratio of CO2 footprint compensation amounts to local Gross Domestic Product (GDP) in most municipalities was less than 0.01%, with its center of gravity shifting cyclically. It was appropriate to start charging the CO2 footprint compensation amounts after 2011, with a dynamic adjustment of 3 years. (4) After 2007, the supply–demand relationship of CO2 trading continued to deteriorate, and it eased in 2016. However, its operational mechanism was still very fragile and highly dependent on a few pioneering municipalities. Innovations: In this study, we designed a horizontal CO2 compensation mechanism from the binary perspective of CO2 footprints and CO2 trading. In this mechanism, the former determines the CO2 footprint compensation amounts paid by each municipality based on whether the CO2 footprint exceeds its CO2 allowance. The latter determines the CO2 trading compensation amounts paid by the purchasing municipalities based on their CO2 emissions efficiency. This system balances equity and efficiency and provides new ideas for horizontal CO2 compensation.
Physical exercise and children’s self-concept of emotional and behavioural well-being: A randomised controlled trial

Physical exercise programmes are considered to hold psychosocial benefits for children and young people. In this relatively unpopulated research area, investigation of exercise variables (type, duration, frequency) and their impact upon a range of psychosocial variables is needed. This study reports an experimental evaluation of the effects of a 10-minute daily, six-week, aerobic exercise programme (‘Wake up, Shake up’) on the self-perception of emotional and behavioural adjustment of primary school children. Participants (N=71; age 8 to 11 years) with scores below the average ‘school connectedness’ score for their school were randomly assigned to an experimental (aerobic exercise), comparison (‘Circle Time’) or control condition. The intervention reduced pupils’ perceptions of their emotional and behavioural difficulties. Aerobic exercise programmes are recommended as an intervention to enhance aspects of emotional and behavioural well-being for children in this age group. The need for further research on the effects of exercise programmes for this age group, and for developing reliable assessment measures, is highlighted.
Disability and social policy: Global evidence and perspectives

Introduction The term ‘disability’ is complex and elusive as individuals and cultures often have different understandings of disability (Goodley, 2016). As noted by Oliver (1986), if disability is considered within ‘a personal tragedy theory of disability’, it is then outside the realm of social policy. Similarly, if it is understood as a medical notion, its relevance is limited to health care policy. However, modern conceptualizations of disability frame disability as an interactional or relational notion, one that results from an individual with a health condition interacting with structural factors and resources (Goodley, 2016; Mitra, 2018). Structural factors, be they barriers in the physical environment, negative attitudes, discrimination or resources, may and should be addressed by social policies. The United Nations (UN) has adopted a human rights perspective on disability. The UN Convention on the Rights of Persons with Disabilities (CRPD) is a global human rights treaty adopted in 2006. The CRPD aims ‘to promote, protect, and ensure the full and equal enjoyment of all human rights and fundamental freedoms by all persons with disabilities, and to promote respect for their inherent dignity’. It has been ratified by 185 countries as of November 2022. There are two broad components to the implementation of the CRPD. The first consists in adopting laws and policies in line with the provisions of the CRPD and the second includes non-legal strategies toward advocacy and social change. Both aim to lead to the full participation of persons with disabilities in society by mainstreaming disability in development strategies (United Nations, 2019). Disability has received little attention in the field of global social policy. While there has been research on social protection policies targeting individuals with disability and their families, limited work has considered disability in other areas of global social policy such as education, health, healthcare, and employment. Guided by the human rights
Conflict, as the Achilles heel of perceptual control, offers a unifying approach to the formulation of psychological problems

The formulation of psychological problems is an integral part of psychological treatment. Since perceptual control theory provides one explanation of how problem-free living occurs it also suggests the form that problems might take when satisfactory day-to-day living is disrupted. In this article I briefly outline perceptual control theory and explain how psychological difficulties are conceptualised with this theory. Next I offer some examples from clinical practice which illustrate the problem formulation of perceptual control theory. Finally I explore the implications of this formulation approach for people who assist others in the amelioration of psychological troubles.
Influence of buffer layer texture on magnetic and electrical properties of IrMn spin valve magnetic tunnel junctions

Spin valve magnetic tunnel junctions (SV-MTJs) with the structure Si(100)∕SiOx∕buffer(A,B)∕IrMn∕CoFe∕AlOx∕NiFe∕Ta have been deposited on two buffers: Cu (group A) and Ta∕Cu (group B). The A junctions were characterized by a low degree of texture and a small amplitude of roughness, and B junctions by a high degree of texture and a high amplitude of roughness. The strongly textured buffer Ta∕Cu (group B) grew in a columnarlike fashion and induced interfacial roughness. The texture and the roughness modified strongly the interlayer and the exchange bias coupling fields in the SV-MTJs. A substantial influence of the roughness, due to barrier thickness fluctuation, on the resistance area product (R×A) of junctions was also observed. The influence on the temperature dependence of conductance and tunnel magnetoresistance (TMR) was, however, small and only observable at low temperature for the two groups of junctions. A significantly larger increase of the conductance and decrease of the TMR with increasing bias .
The Asymmetric Boundary layer Flow Under a Translating Hurricane

Abstract An investigation is made of the role of the translation of a hurricane in determining the distribution of boundary layer winds and in the organization of convection. A slab boundary layer model of constant depth is used to analyze the steady flow under a specified translating symmetric vortex in gradient balance. A truncated spectral formulation is used, including asymmetries through wavenumber 2. The role of linear and nonlinear asymmetric effects in the determination of the boundary layer response is diagnosed. These effects am relevant to relatively slowly and rapidly translating hurricanes, respectively. The analysis is compared to observations of Hurricanes Frederic of 1979 and Allen of 1980, as well as to other observational and theoretical cures. Allen's translation speed was approximately twice that of Frederic. It is found that the simple boundary layer formulation simulates the qualitative features of the wind field observed in Frederic. The distribution of convection in Frederic and Al.
An Inductorless Optical Receiver Front-End Employing a High Gain-BW Product Differential Transimpedance Amplifier in 16-nm FinFET Process

In this paper, a fully-differential transimpedance amplifier (TIA) providing a high gain-BW product (GBP) is introduced. In the proposed architecture, a cascode cross-coupled structure is employed to double the effective transconductance of the cascode devices, improving the BW of the TIA. Moreover, a differential architecture is implemented using an RC high-pass filter along with a buffer stage requiring smaller capacitance and resistance. Furthermore, a single-ended negative capacitance generation (NCG) circuit is employed at the input of the TIA to partially compensate for the input parasitic capacitances. A TIA including the proposed techniques, designed and laid out in a 16-nm FinFET process, demonstrates 57% and 79% better figure-of-merit compared to cascode and conventional TIAs designed along with the proposed TIA for a fair comparison, respectively. Post-layout simulations in companion with statistical analysis are employed to verify the effectiveness of the proposed architecture. From simulation results, the optical receiver achieves a peak transimpedance gain of 58.5 dB $\Omega $ , a BW of 14.8 GHz, an input-referred noise of 33.6 pA/ $\surd $ Hz, and an eye-opening of 30 mV at a data-rate of 56 Gbps PAM4 and at a bit-error-rate (BER) of 1E-6. The whole circuit consumes 49 mW and occupies an active area of 0.0076 mm 2.
Development and Evaluation of Cellulose Derivative and Pectin Based Swellable pH Responsive Hydrogel Network for Controlled Delivery of Cytarabine

In the present study, pH-sensitive, biodegradable, and biocompatible Na-CMC/pectin poly(methacrylic acid) hydrogels were synthesized using an aqueous free radical polymerization technique and encapsulated by cytarabine (anti-cancer drug). The aim of the project was to sustain the plasma profile of cytarabine through oral administration. Sodium carboxymethyl cellulose (Na-CMC) and pectin were cross-linked chemically with methacrylic acid (MAA) as a monomer, using methylene bisacrylamide (MBA) as cross-linker and ammonium per sulfate (APS) as an initiator. Prepared hydrogel formulations were characterized for their texture, morphology, cytarabine loading efficiency, compositional and structural properties, thermal nature, stability, swelling response, drug release profile (pH 1.2 and pH 7.4), and in-vivo pharmacokinetic evaluation. Cytarabine-loaded hydrogels were also evaluated for their safety profile by carrying out toxicity studies in rabbits. Results demonstrated efficient encapsulation of cytarabine into the prepared network with loading ranging from 48.5–82.3%. The highest swelling ratio of 39.38 and maximum drug release of 83.29–85.27% were observed at pH 7.4, highlighting the pH responsiveness of the grafted system. Furthermore, cytarabine maximum release was noticed over 24 h, ensuring a sustained release response for all formulations. Histopathological studies and hemolytic profiles confirmed that the prepared hydrogel system was safe, biocompatible, and non-irritant, showing no symptoms of any toxicities and degeneration in organs. Moreover, pharmacokinetic estimation of the cytarabine-loaded hydrogel showed a remarkable increase in the plasma half-life from 4.44 h to 9.24 h and AUC from 22.06 μg/mL.h to 56.94 μg/mL.h. This study revealed that the prepared hydrogel carrier system has excellent abilities in delivering the therapeutic moieties in a controlled manner.
Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System

Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.
InFocus: Amplifying Critical Feature Influence on Non-Intrusive Load Monitoring Through Self-Attention Mechanisms

Non-intrusive load monitoring (NILM) enables extracting individual appliances’ power consumption data from an aggregated power signal in a cost-effective way. The extracted appliance-level power data can greatly facilitate tasks such as malfunction diagnosis and load forecasting, which are of significant importance for efficient energy use. Various neural networks including convolutional neural network (CNN), recurrent neural network (RNN), and transformer (self-attention-based neural network) are employed in the design of NILM solutions since 2015. In particular, CNN is revealed to extract certain critical features such as power level and typical usage duration, thus, achieving superior performance. However, the global features especially the dependency correlations between different positions in a sequence cannot be properly acquired. Accordingly, we devise a novel model incorporating an added attention layer to overcome this limitation. The added self-attention mechanism can automatically assign attention scores/weights to different features outputted by convolutional layers, which amplifies the positive influence of critical knowledge while realizing global reference. Moreover, this model can explicitly extract the appliance’s multi-state information, which endows the model with more interpretability. We further improve our model by substituting the added self-attention mechanism with a lightweight one, which decreases the number of model parameters while maintaining the decomposing accuracy. Experimental results over two real-world datasets, REDD and UK-DALE, demonstrate that our models outperform the state-of-the-art, achieving 6.5%-52% improvement on average for three standard evaluation metrics. Moreover, we offer an extracted NILM solution system architecture incorporating neural networks, aiming to establish a framework to support future research.