Miracle-dz/UniFAR-FTUs huggingface dataset:
On the brain struggles to recognize basic facial emotions with face masks: an fMRI study

Introduction The COVID-19 pandemic impacted public health and our lifestyles, leading to new social adaptations such as quarantine, social distancing, and facial masks. Face masks, covering extended facial zones, hamper our ability to extract relevant socio-emotional information from others’ faces. In this fMRI study, we investigated how face masks interfere with facial emotion recognition, focusing on brain responses and connectivity patterns as a function of the presence of a face mask. Methods A total of 25 healthy participants (13F; mean age: 32.64 ± 7.24y; mean education: 18.28 ± 1.31y) were included. Participants underwent task-related fMRI during the presentation of images of faces expressing basic emotions (joy or fear versus neutral expression). Half of the faces were covered by a face mask. Subjects had to recognize the facial emotion (masked or unmasked). FMRI whole-brain and regions-of-interest analyses were performed, as well as psychophysiological interaction analysis (PPI). Results Subjects recognized better and faster emotions on unmasked faces. FMRI analyses showed that masked faces induced a stronger activation of a right occipito-temporal cluster, including the fusiform gyrus and the occipital face area bilaterally. The same activation pattern was found for the neutral masked > neutral unmasked contrast. PPI analyses of the masked > unmasked contrast showed, in the right occipital face area, a stronger correlation with the left superior frontal gyrus, left precentral gyrus, left superior parietal lobe, and the right supramarginal gyrus. Discussion Our study showed how our brain differentially struggles to recognize face-masked basic emotions, implementing more neural resources to correctly categorize those incomplete facial expressions.
Stress Concentration Factors for Non-Load-Carrying Welded Cruciform Joints Subjected to Tension, Bending, and Shear

This paper deals with the problem of stress concentration at the weld toe of non-load-carrying-type plate cruciform joints under tension, bending, and shear. Theoretical stress concentration factors were derived using the finite element method. Five of the most important geometrical parameters: the thickness of the main plate and the attachments, the weld throat thickness, the weld toe radius, and the weld face inclination angle were treated as independent variables. For each loading mode—tension, bending, and shear—parametric expression of high accuracy was obtained, covering the range used in real structures for cruciform connections. The maximum percentage error was lower than 2.5% as compared to numerical values. The presented solutions proved to be valid for the toe radius ρ tending to zero.
Demand Prediction and Influencing Factors Analysis of Shared Bicycles near Public Transportation Stations based on GCN

Traffic prediction is a pivotal technology in the development of intelligent transportation systems. Real-time and accurate traffic prediction holds significance in route planning, resource allocation, and improving travel efficiency. To enhance the convenience of shared bicycle travel near public transportation hubs and optimize shared bicycle deployment strategies, this paper proposes the utilization of a graph convolutional neural network prediction model. Initially, shared bicycle travel data is scrutinized for spatiotemporal characteristics. Subsequently, an adjacency matrix is constructed based on the spatial attributes of the data to establish the graph convolutional network. Lastly, global spatial autocorrelation analysis and a coupled coordination model are integrated for validation and augmentation. This model is employed to predict shared bicycle travel patterns near public transportation hubs in the Xiamen Island area. Experimental results demonstrate a high level of precision, underscoring the model's effectiveness in providing valuable guidance for shared bicycle travel. The effectiveness of this method can be further verified in subsequent empirical studies.
Research on 2D SDE Method of Fluid Field Effect above Airfoil at Transonic Speed

In this research, a Python-centric approach is proposed for simulating fluid dynamics over transonic airfoils. The study employs the finite difference method to solve the small disturbance equation (SDE), incorporating Newman boundary conditions. Iterative computations are adeptly managed using the successive over-relaxation (SOR) method. These methodologies, as validated through rigorous code testing, enable precise calculations of velocity distributions and vivid visualizations of fluid velocity streamlines. For this investigation, the airfoil is conceptualized as a minor obstruction and is abstracted into a trigonometric function. A pivotal aspect of this study is the exploration of the interplay between the Mach number and the 'shark angle'—a critical determinant in aerodynamic performance. Simulation outcomes reveal an inverse relationship between the Mach number and the airfoil's shark angle; a surge in the Mach number corresponds to a reduction in the shark angle. These findings, encapsulated in flow field diagrams, offer invaluable insights for the strategic design and optimization of airfoils, aiming to maximize lift while concurrently curtailing drag, thereby enhancing overall aerodynamic efficiency.
Monaural Speech Dereverberation Using Deformable Convolutional Networks

Reverberation and background noise can degrade speech quality and intelligibility when captured by a distant microphone. In recent years, researchers have developed several deep learning (DL)-based single-channel speech dereverberation systems that aim to minimize distortions introduced into speech captured in naturalistic environments. A majority of these DL-based systems enhance an unseen distorted speech signal by applying a predetermined set of weights to regions of the speech spectrogram, regardless of the degree of distortion within the respective regions. Such a system might not be an ideal solution for dereverberation task. To address this, we present a DL-based end-to-end single-channel speech dereverberation system that uses deformable convolution networks (DCN) that dynamically adjusts its receptive field based on the degree of distortions within an unseen speech signal. The proposed system includes the following components to simultaneously enhance the magnitude and phase responses of speech, which leads to improved perceptual quality: (i) a complex spectrum enhancement module that uses multi-frame filtering technique to implicitly correct the phase response, (ii) a magnitude enhancement module that suppresses dominant reflections and recovers the formant structure using deep filtering (DF) technique, and (iii) a speech activity detection (SAD) estimation module that predicts frame-wise speech activity to suppress residuals in non-speech regions. We assess the performance of the proposed system by employing objective speech quality metrics on both simulated and real speech recordings from the REVERB challenge corpus. The experimental results demonstrate the benefits of using DCNs and multi-frame filtering for speech dereverberation task. We compare the performance of our proposed system against other signal processing (SP) and DL-based systems and observe that it consistently outperforms other approaches across all speech quality metrics.
Anticipation and its degrees of critical-reflective radicality: opening up the affordances of engaging with futures to problematize STI

Anticipation is increasingly recognized as a valuable dimension for promoting more responsible STI practices. Various normative frameworks acknowledge anticipation as a means to enable critique and/or re ﬂ ection. However, the degrees of critique and re ﬂ ection that anticipation can or should enable have remained under-researched. By exploring the critical-re ﬂ ective a ﬀ ordances that anticipation could o ﬀ er for problematizing STIs in the present, this article aims to advance the theoretical development of anticipation as a dimension for promoting more responsible STIs. The article suggests that the potential critical-re ﬂ ective radicality of anticipation is modulated by the critical-re ﬂ ective spaces of problematization and/or scrutiny a ﬀ orded by the normative frameworks in which anticipation is interpreted and for which it is enacted. Against this background, the article provides some tentative variables for assessing these critical-re ﬂ ective a ﬀ ordances and speci ﬁ es the roles that di ﬀ erent modes of anticipation might play in opening up distinct, interconnected aspects of STI to problematization and/or scrutiny.
Industrial Chain Correlation and Total Factor Productivity

As a matter of fact, the total factor productivity, as a crucial metric for measuring the speed and quality of output in enterprises, plays a vital role in optimizing resource allocation and fostering the synergistic development of industries especially in recent years. With this in mind, this paper studies and investigates how the degree of industrial chain correlation affects the total factor productivity. By using the methods of benchmark regression analysis, robustness test, heterogeneity analysis and quantile regression, the empirical findings indicate that the impact of industrial chain connectivity on enterprise total factor productivity follows a "U"-shaped distribution and remains robust. Furthermore, its influence varies among enterprises with different ownership structures as well as production factor intensities according to the analysis. At the same time, the panel quantile regression reveals that the effect of industrial chain connectivity on enterprise total factor productivity differs across firms with varying levels of total factor productivity.
Tensor Completion Algorithm and Its Applications to Wireless Edge Caching and Hyper- Spectral Imaging

This paper presents a lightweight tensor completion algorithm with applications in wireless edge caching and hyper-spectral imaging. In wireless edge caching, the dynamic and selective nature of content popularity inevitably leads to missing observations. We address this challenge by employing tensor-based models, which are notably practical for imputing missing data. We approach cooperative caching as a problem of third-order tensor completion and prediction, acknowledging the correlations across time, files, and base stations. Given the potentially large content libraries, we modify the latent norm-based Frank-Wolfe (FW) algorithm. This modification, which incorporates multi-rank updates instead of the traditional rank-1 updates, significantly reduces computational time complexity. This advancement facilitates the development of an efficient online caching algorithm. Through simulations using Movielens datasets and comparing them with the conventional FW algorithm, our proposed algorithm demonstrates a reduced computational overhead reduction and improved normalized cache hit rates within linear prediction models. Furthermore, since hyper-spectral images often have missing or corrupted data due to various noise sources, we conduct simulations on hyper-spectral images. From the Pavia University and Pavia City datasets with noisy data, our proposed tensor completion algorithm can effectively recover the original images from heavily interfered with communication noise. The effectiveness of our proposed tensor completion algorithm across various applications, including wireless edge caching and hyper-spectral imaging, accentuates its versatility and significant utility. Its ability to adeptly handle diverse challenges underscores its value as a comprehensive solution for solving intricate data recovery problems.
SuperRail–World-First HTS Cable to be Installed on a Railway Network in France

The SuperRail project consists in the development, manufacturing, installation and long-term operation of a high temperature superconducting (HTS) dc cable system for railway applications. The objectives of the project are to reinforce the power supply of the Montparnasse railway station in Paris, to increase the traffic and to participate to the reduction of CO2 emission. The saturated underground in the center of Paris does not allow to create new right of ways and only the HTS cable technology allows to carry the required power to railway tracks using the few available 100 mm conduits left. The HTS cable system must answer to a load chart with a high level of requirements corresponding to the connection of a substation to a set of railways tracks in very constraints areas. In the case of SuperRail, two 60-meter long 1.5 kV–3.5 kA HTS dc cables made of second-generation (2G) conductors will be installed in parallel. They need to sustain a 67 kA–200 ms short-circuit current. The cryogenic system is specifically designed for this project. It is based on a Reverse Turbo-Brayton cycle producing cooling power of about 1700 W at 67 K. This system intends to ensure low maintenance during life cycle, high reliability and would be scalable for future application (not limited to railways). In parallel, prospective studies are being carried out to simulate superconducting cables in railway networks, and to find original solutions to replace the current leads. SuperRail is a landmark project, as it is the first time that a dc HTS cable system is installed on a commercially operated railway network. The technologies developed have the potential to be reproduced in similar cases existing in large cities.
Artificial Intelligence Gamers Based on Deep Reinforcement Learning

This study investigates the design and implementation of Artificial Intelligence (AI) game players based on deep reinforcement learning, offering a novel approach to autonomous decision-making and strategy acquisition in intelligent games. Initially, the fundamental principles and algorithms of deep reinforcement learning are introduced, along with the fusion of deep learning and reinforcement learning. Subsequently, existing research is reviewed, and the pros and cons of current methodologies are examined, highlighting the underlying issues and challenges. The utilization of AI players in mainstream games is then introduced, and the influence of AI players on contemporary games is analyzed. Through this analysis of AI players in mainstream games, the strengths and weaknesses of current AI players are identified, and recommendations for optimizing them are provided. This study holds significant implications for guiding the design and development of intelligent game players, while also enriching the application of deep reinforcement learning within the gaming domain.
Handbike for Daily Use, Sport, and Rehabilitation Purposes: A Literature Review of Actuation and Technical Characteristics

With respect to alternative devices like traditional wheelchairs, handbikes can offer advantages from biomechanical and physiological perspectives, to several kinds of users. Assuring high mechanical efficiency and homogeneous force distributions along cycles, and being suitable for indoor and outdoor activities, these systems are used for rehabilitation, sports, and daily applications. From a technical perspective, their main characteristics can vary with the device final purpose and operational context. This review aims to provide an overall outline of handbikes in the literature from a general and comprehensive point of view, up until 2022. The analysis is performed (i) with a systematic approach, without a priori limitations on document type and content focus, and (ii) to identify the areas of interest for the scientific development of these systems. A systematic evaluation method for the identification and analysis of the documents was designed and implemented and the selection criteria, as well as the rationale for the procedure, are described. A specific taxonomy was defined and applied for the subsequent analysis, and each category is specifically evaluated and described, detailing the main outcomes of the literature analysis and relative discussion. Particular attention is paid to actuation strategies and propulsion efficiency. Finally, the main results of the work and future developments for handbikes are briefly synthesized.
Power Loss Modelling and Performance Comparison of Three-Level GaN-Based Inverters Used for Electric Traction

The main aim of this work is to present a step-by-step procedure to model and analyze the power loss distribution of three-level Gallium Nitride (GaN) inverters. It has been applied to three distinct three-phase three-level voltage source inverters utilized in electric traction drives: Active Neutral Point Clamped, Neutral Point Clamped and T-Type Neutral Point Clamped. The proposed analytical power loss modelling, combined with an equivalent representation of the electrical machine proved to be a viable solution to achieve a time-saving and low computational burden simulation platform, leading to satisfying accurate results. This has been confirmed by comparing the results carried out from the simulations of a 110 kW permanent magnet synchronous motor drive and those determined by considering a simplified circuital representation based on the proposed analytical power loss modelling.
Comparative Metagenomic Analysis of Bacteriophages and Prophages in Gnotobiotic Mouse Models

Gnotobiotic murine models are important to understand microbiota–host interactions. Despite the role of bacteriophages as drivers for microbiome structure and function, there is no information about the structure and function of the gut virome in gnotobiotic models and the link between bacterial and bacteriophage/prophage diversity. We studied the virome of gnotobiotic murine Oligo-MM12 (12 bacterial species) and reduced Altered Schaedler Flora (ASF, three bacterial species). As reference, the virome of Specific Pathogen-Free (SPF) mice was investigated. A metagenomic approach was used to assess prophages and bacteriophages in the guts of 6-week-old female mice. We identified a positive correlation between bacteria diversity, and bacteriophages and prophages. Caudoviricetes (82.4%) were the most prominent class of phages in all samples with differing relative abundance. However, the host specificity of bacteriophages belonging to class Caudoviricetes differed depending on model bacterial diversity. We further studied the role of bacteriophages in horizontal gene transfer and microbial adaptation to the host’s environment. Analysis of mobile genetic elements showed the contribution of bacteriophages to the adaptation of bacterial amino acid metabolism. Overall, our results implicate virome “dark matter” and interactions with the host system as factors for microbial community structure and function which determine host health. Taking the importance of the virome in the microbiome diversity and horizontal gene transfer, reductions in the virome might be an important factor driving losses of microbial biodiversity and the subsequent dysbiosis of the gut microbiome.
Effect of burning and cultivation on soil properties and microbial population of four different land use systems in Abakaliki.

Measurement of the effect of burning and cultivation on soil prosperities and microbial population of four different land use systems – forest land, bush fallow systems, continuous cropping land and alley cropping system in Abakaliki was carried out in a complete randomized design (CRD). Burning improved soil pH from strongly acid to moderately acid in the bush fallow system and slightly acid (pH 6.1) in the alley cropping system but reduced organic C by 36% in the forest land, 44% in both the bush fallow and continuous copping land 34% in the alley cropping system. Also total soil N declined in all the land use systems. Available P, exchangeable Ca, Mg and K increased after burning. Cultivation inversely depleted nutrient level with the lowest soil nutrient content occurring in the continuous cropping land. Burning did not have lasting effect on the microbial populations. At 5 days post-burn, the population of micro organisms were very low compared to the pre-burn populations. But at 25 days post-burn there was a surge in microbial population. In the bush fallow and alley cropping systems, both bacteria and the cellulolytic groups increased in numbers but in the strongly acid forest and continuous cropping land the population of fungi dominated. Continuous cultivation led to a decrease in the population of bacteria, actinomycetes and algae. The general decline in the population of microorganisms under cultivation was associated with the over all fertility decline of cultivated soil.
Impact of Digital Financial Inclusion on Small and Medium-Sized Enterprises

With the rapid development of digital technology, Digital Inclusive Finance (DIF) has emerged and gradually become an important part of financial services. DIF expands service coverage and financing channels through digital technology, bringing a new kind of development opportunity for Small and Medium-Sized Enterprises (SMEs). This paper analyses the enterprises broken down into different regional and different property rights systems in order to study the degree of impact of DIF on SMEs from the perspectives of financing constraints and transformation and upgrading. It then draws the conclusion that DIF has a facilitating effect on SMEs. It can alleviate the financing constraints and reduce the financing cost of SMEs through DIF, which can help to reduce the financing cost of SMEs and improve their development. financing constraints and reduce financing costs, thus promoting innovation and upgrading of SMEs. The effects of digital financial inclusion on SMEs vary depending on location and ownership structures. From a regional perspective, digital financial inclusion has a greater impact on SMEs in the central and western regions than it does on SMEs in the eastern regions. From the perspective of property rights, non-state-owned SMEs have a greater impact on digital financial inclusion than state-owned businesses.
Islamic Religious Education in Student Character Development

This article aims to analyze students' character development in learning Islamic education and character, describing supporting and inhibiting factors as well as solutions. This type of research is descriptive qualitative research using phenomenological and pedagogical approaches. The data collection techniques used were observation, interviews and documentation at the research location at UPT SD Negeri 1 Tamalatea. Data validity using triangulation. Data analysis techniques consist of data reduction, data presentation, and conclusion. The research results show that the form of character building for students in learning Islamic education and character is that the teacher provides a good example, instils Islamic religious values and character in students, and gives assignments as an evaluation. Supporting factors include adequate facilities and infrastructure and cooperation with parents while inhibiting factors include environmental factors and the influence of technology. The solution to this problem is for the school to provide advice, apply applicable regulations, and provide sanctions. This research provides knowledge about the correlation between subjects and educational goals in character development. Using this model, educational purposes can be achieved according to expectations, especially in student character or manners.