Miracle-dz/UniFAR-FTUs huggingface dataset:
Relationship between the Dynamic Parameters of a Structure’s Vibration Process and the Loading Model Moving on a Bridge

A sufficiently strict conduction of supervision during bridge operation is a crucial matter for many countries, including the underdeveloped country of Viet Nam. In recent times, the budgets in developed countries used for funding the implementation of quality-assessment procedures are quite high compared to the lower budgets in underdeveloped countries. The plan proposed in this work addresses the current lack of information available in the process of structure-quality evaluation. The vibration signals will be acquired from the random circulation status to determine the structure’s behavior so as to utilize the signal information during the bridge span’s operation. The study’s main goal is to find various parameters that can be used to evaluate the actual bridge performance. These parameters must meet certain criteria, such as high sensitivity, low measurement cost, and efficiency in the measurement process, but must not affect the itinerary of vehicles moving on the bridge. The actual structural vibration signals used in this work currently serve as a best trend model for evaluating the operation of the bridge span structure. This study will focus on determining the relationship among deflection, acceleration, and vehicle load so as to evaluate the structure’s working process. This study has also fabricated an experimental model to evaluate and test the sensitivity of the parameters utilized in this study in order to verify the results obtained. The results obtained in this research will be applied for the quality-control process in several bridge models with span structures built with the composite steel concrete cross section of the beam. Many developing countries, including Viet Nam, will receive benefit in the future from the useful advantages presented in this study.
Nominal Number Agreement in Portuguese Used in Oiapoque–AP

Received on: 08/14/2019 Accepted on: 06/05/2020 Abstract: This study presents a profile of research on the nominal number agreement in the context of linguistic contact that culminated in a thesis developed from oral data from speakers living in the city of Oiapoque (Amapá); three groups of users of Brazilian Portuguese were considered, Oiapoqueans who speak Portuguese as their mother tongue (L1); French speakers of Portuguese second language (L2) and indigenous speakers of Portuguese L2. However, in this text only the uses related to the first group will be approached. The linguistic phenomenon used was the process of nominal number agreement. The sample is composed of oral data from 18 Oiapoqueans, collected from spontaneous narratives and personal story. The theoretical-methodological assumptions follow the parameters of the variationist theory defended by Labov (1972). The results reveal a context of change in progress, triggered by the incorporation of more urban features into the city, which is reflected, above all, in the younger speakers who tend to make greater use of plural marks, in the constituent elements of the noun phrase, in the variety of the Brazilian Portuguese, used in Oiapoque.
Impact of Thrombus Aspiration one-year Cardiac Mortality During Primary PCI in STEMI Patients with total Occlusion; from Korea Acute Myocardial Infarction Registry National Institute of Health

Background: The clinical impact of intracoronary thrombus aspiration (TA) during primary percutaneous coronary intervention (PCI) in patients with ST-segment elevation myocardial infarction (STEMI) is unclear. We investigated to evaluate whether TA during primary PCI reduces one-year mortality. Methods: From the Korea Acute Myocardial Infarction Registry-National Institute of Health, 3749 patients with STEMI who were undergoing primary PCI within 12 hours (60.8±12.9 years, 18.7% women) and shown pre-procedural Thrombolysis In Myocardial Infarction (TIMI) flow 0, 1 in coronary angiography were enrolled between November 2011 and November 2015 during one-year follow up. The patients were divided into two groups according to TA during primary PCI: PCI with TA (n=1630) and PCI alone (n=2119). The primary end-point was major adverse cardiac events (MACE), defined as the composite of cardiovascular death (CVD), stroke or recurrent MI at one-year. The secondary efficacy end-point were all-cause of mortality and CVD at one-year. Results: PCI with TA did not reduce the risk of MACE (OR: 0.598, 95% CI: 0.737-1.160, p=0.499), all-cause mortality (OR: 0.898, 95% CI: 0.705-1.144, p=0.383) and CVD (OR: 0.893, 95% CI: 0.683-1.168, p=0.408) in all patients during one-year follow up. The trend did not change after propensity score matching. In subgroup analysis, there was no efficacy of clinical outcome during one-year follow up. Conclusions: Primary PCI with TA did not reduce MACE and all-cause mortality among patients with STEMI and pre-procedural TIMI flow 0,1 during one-year follow up.
Examining the Role of Transparent Organizational Communication for 
Employees’ Job Engagement and Disengagement during the COVID-19 
Pandemic in Austria

This study provides an understanding of how employees’ perception of organizational transparency during the long-lasting situation of the COVID-19 pandemic engendered their job engagement as well as job disengagement. Data were collected by means of an online survey among 410 employees in Austria during March 2021. Results show that employees’ perception of their organization’s approach to transparency directly influenced their job engagement and disengagement. Importantly, the relationship between transparency and job engagement was also mediated through organizational trust, and job-specific state anxiety mediated the relationship between transparency and job disengagement. The results imply the importance of transparency during times of great uncertainty and emphasize the necessity to closely consider employees’ emotional states and worries during a crisis.
Exploring Green Purchasing Behaviour among College Students in a Developing Economy

This study explores the relationships between environmental attitude, green product knowledge, attitude towards purchasing green products, green product purchasing intention, and green purchasing behaviour. Using a cross-sectional survey approach, a random sample of 284 undergraduate students in a Zimbabwean polytechnic completed a self-administered questionnaire. Structural equation modelling (maximum likelihood estimation) was used to analyse the data. The findings indicate that there are positive associations between the following pairs of variables: environmental attitude and attitude towards green purchasing; green product knowledge and green purchasing intention; green product knowledge and green purchasing behaviour; attitude towards green purchasing and green purchasing intention; green purchasing intention and green purchasing behaviour; as well as a non-significant relationship between green product knowledge and attitude towards green. The findings reported that the Theory of Reasoned Action fully supported the students’ intention to buy green products, which then affects their green purchase behaviour. The inclusion of additional constructs to the proposed model was partially supported. The study results highlight the importance of considering product knowledge and other attitudinal factors—specifically environmental attitude and attitude towards green purchasing—when marketing environmentally-friendly products to college-level students.
Effective Communication during a Global Health Crisis: A Content Analysis of Presidential Addresses on the COVID-19 Pandemic in Ghana

The COVID-19 pandemic is causing incessant disruption to the social and economic lives of societies. Public health and crisis communicators have recommended some best practices in crisis and emergency risk messaging for effective health messages during pandemics. Thus, this study utilized seven crisis and emergency risk messaging best practices to qualitatively analyze 14 speeches delivered by the Ghanaian President on the COVID-19 pandemic in the country to demonstrate how the speeches conveyed information about the pandemic to the public. The study found that all seven best practices (i.e., explain what is known, explain what is not known, explain how or why the event happened, promote action steps, express empathy, express accountability, and express commitment) were demonstrated in all the 14 speeches. Implications for practice are discussed.
Fuglede's conjecture holds in $\mathbb{Z}_{p}\times\mathbb{Z}_{p^{n}}$

Fuglede's conjecture states that for a subset $\Omega$ of a locally compact abelian group $G$ with positive and finite Haar measure, there exists a subset of the dual group of $G$ which is an orthogonal basis of $L^{2}(\Omega)$ if and only if it tiles the group by translation. In this paper, we prove a divisibility property for a set in $\mathbb{Z}_{p}\times\mathbb{Z}_{p^{n}}$. Then using the divisibility property and equi-distributed property, we prove that Fuglede's conjecture holds in the group $\mathbb{Z}_{p}\times\mathbb{Z}_{p^{n}}$.
Revelations of science: discussing images of prenatal development by Ernst Haeckel and Lennart Nilsson

The paper discusses images of prenatal development created by Ernst Haeckel and Lennart Nilsson. Despite the obvious differences between a 19th-century biologist and philosopher of nature and a 20th-century photographer, substantial similarities exist in the way their respective narrations situate embryos and fetuses within the cultural realm. The paper traces the processes of creating the representations of stages of embryogenesis and the controversies surrounding them, analyzes the discursive frame within which the images are produced and function, and discusses their media specificity. It also examines the metaphysical ambitions surrounding the process of producing embryoand fetal identities and the relation of these identities to the important cultural characteristics of their historical epochs.
Screening of bacterial strains for pectate lyase production and detection of optimal growth conditions for enhanced enzyme activity

In the present study, the pectatelyase production by fifty two bacterial strains isolated from ramie grown soils were studied and the strain RDSM01 showed maximum pectate lyase activity. According to sequence homology of Genbank, the strain RDSM01 was identified as Bacillus subtilis (Genbank Accession No. KX035109). Maximum pectate lyase activity of the strain was observed when 1.5% (v/v) inoculum was added to the growth medium and was incubated for 48 hours at 34-370C and at pH 7.0. The relative activity of the strain was 19% higher when apple pectin was used as carbon source compared to citrus pectin. Maximum enzyme production (149.1 â€“ 153.4 IU/ml) was recorded when ammonium chloride or ammonium sulphate at 0.4% concentration was used as nitrogen source. Thus, B. subtilis strain RDSM01 possessing high pectate lyase activity may be effectively utilized for removal of gum from ramie fibre, which is primarily made of pectin and hemicellulose.
Effective removal of cesium by pristine graphene oxide: performance, characterizations and mechanisms

Radioactive Cs is a major by-product of nuclear power plants, with high radioactivity and long half-life. It is highly soluble in water and is difficult to remove. In this study, pristine graphene oxide (GO) synthesized via a Hummer's method has been demonstrated as a very efficient Cs sorbent with the maximum adsorption capacity of GO found to be 180, 465, 528 mg Cs/g sorbent at pH of 3, 7, and 12, respectively. The results from Fourier-transform infrared (FTIR) spectroscopy of GO before and after Cs sorption at various pH values reveal the mechanism of Cs sorption by GO. Several functional groups which are carboxyls, phenols, and hole defects containing multi-ether groups, are shown to play an important role in Cs capture. GO's affinity for other major cations found in seawater, namely, Na, K, and Mg was also evaluated, and the effect of these cations in competing with Cs for adsorption on GO was also studied. This reveals GO's exceptional ability in capturing Cs even in the presence of high concentrations of competitive cations and its high potential for use in Cs decontamination, as well as other heavy metal removal applications.
Short Selling Threats and Corporate Default Risk: Evidence from the Chinese Stock Market

Employing the staggered short-sale deregulation on the Chinese stock market as quasi-exogenous shocks, we find that short selling threats is associated with higher corporate default risk, especially for firms that are more financially constrained, with higher growth rates, and higher information asymmetries. In addition, firms with higher ex-ante default risk experience an increase in the cost of debt and a reduction in new debt financing following the regulatory changes. The increase in default risk does not appear in U.S. listed firms in the context of the Regulation SHO’s pilot program. Overall, our findings indicate that short selling can adversely affect firm prospects through increasing the likelihood of default, especially in an environment where investors are more alien to short selling, short sellers are more able to be manipulative due to weak investor protection, and firms are lack of effective tools to avoid downside risk.
Hybrid Transceiver Design for Tera-Hertz MIMO Systems Relying on Bayesian Learning Aided Sparse Channel Estimation

Hybrid transceiver design in multiple-input multiple-output (MIMO) Tera-Hertz (THz) systems relying on sparse channel state information (CSI) estimation techniques is conceived. To begin with, a practical MIMO channel model is developed for the THz band that incorporates its molecular absorption and reflection losses, as well as its non-line-of-sight (NLoS) rays associated with its diffused components. Subsequently, a novel CSI estimation model is derived by exploiting the angular-sparsity of the THz MIMO channel. This is followed by designing a sophisticated Bayesian learning (BL)-based approach for efficient estimation of the sparse THz MIMO channel. The Bayesian Cramer-Rao Lower Bound (BCRLB) is also determined for benchmarking the performance of the CSI estimation techniques developed. Finally, an optimal hybrid transmit precoder and receiver combiner pair is designed, which directly relies on the beamspace domain CSI estimates and only requires limited feedback. Finally, simulation results are provided for quantifying the improved mean square error (MSE), spectral-efficiency (SE) and bit-error rate (BER) performance for transmission on practical THz MIMO channel obtained from the HIgh resolution TRANsmission (HITRAN)-database.
BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese

We present BARTpho with two versions, BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the"large"architecture and the pre-training scheme of the sequence-to-sequence denoising autoencoder BART, thus it is especially suitable for generative NLP tasks. We conduct experiments to compare our BARTpho with its competitor mBART on a downstream task of Vietnamese text summarization and show that: in both automatic and human evaluations, BARTpho outperforms the strong baseline mBART and improves the state-of-the-art. We further evaluate and compare BARTpho and mBART on the Vietnamese capitalization and punctuation restoration tasks and also find that BARTpho is more effective than mBART on these two tasks. We publicly release BARTpho to facilitate future research and applications of generative Vietnamese NLP tasks. Our BARTpho models are available at https://github.com/VinAIResearch/BARTpho
Wellbeing Supportive Design – Research-Based Guidelines for Supporting Psychological Wellbeing in User Experience

Abstract While human beings have a right to digital experiences that support, rather than diminish, their psychological wellbeing, technology designers lack research-based practices for ensuring psychological needs are met. To help address this gap, we draw on findings from over 30 years of research in psychology (specifically, self-determination theory) that has identified contextual factors shown to support psychological wellbeing. We translate these findings into a list of 15 heuristics and 30 design strategies to provide technology makers with theoretically grounded, research-based, and actionable ways to support wellbeing in user experience.
Splitfed learning without client-side synchronization: Analyzing client-side split network portion size to overall performance

Federated Learning (FL), Split Learning (SL), and SplitFed Learning (SFL) are three recent developments in distributed machine learning that are gaining attention due to their ability to preserve the privacy of raw data. Thus, they are widely applicable in various domains where data is sensitive, such as large-scale medical image classification, internet-of-medical-things, and cross-organization phishing email detection. SFL is developed on the confluence point of FL and SL. It brings the best of FL and SL by providing parallel client-side machine learning model updates from the FL paradigm and a higher level of model privacy (while training) by splitting the model between the clients and server coming from SL. However, SFL has communication and computation overhead at the client-side due to the requirement of client-side model synchronization. For the resource-constrained client-side, removal of such requirements is required to gain efficiency in the learning. In this regard, this paper studies SFL without client-side model synchronization. The resulting architecture is known as Multi-head Split Learning. Our empirical studies considering the ResNet18 model on MNIST data under IID data distribution among distributed clients find that Multi-head Split Learning is feasible. Its performance is comparable to the SFL. Moreover, SFL provides only 1%-2% better accuracy than Multi-head Split Learning on the MNIST test set. To further strengthen our results, we study the Multi-head Split Learning with various client-side model portions and its impact on the overall performance. To this end, our results find a minimal impact on the overall performance of the model.
The Devil is in the Details: An Efficient Convolutional Neural Network for Transport Mode Detection

Transport mode detection is a classification problem aiming to design an algorithm that can infer the transport mode of a user given multimodal signals (GPS and/or inertial sensors). It has many applications, such as carbon footprint tracking, mobility behaviour analysis, or real-time door-to-door smart planning. Most current approaches rely on a classification step using Machine Learning techniques, and, like in many other classification problems, deep learning approaches usually achieve better results than traditional machine learning ones using handcrafted features. Deep models, however, have a notable downside: they are usually heavy, both in terms of memory space and processing cost. We show that a small, optimized model can perform as well as a current deep model. During our experiments on the GeoLife and SHL 2018 datasets, we obtain models with tens of thousands of parameters, that is, 10 to 1,000 times less parameters and operations than networks from the state of the art, which still reach a comparable performance. We also show, using the aforementioned datasets, that the current preprocessing used to deal with signals of different lengths is suboptimal, and we provide better replacements. Finally, we introduce a way to use signals with different lengths with the lighter Convolutional neural networks, without using the heavier Recurrent Neural Networks.
Edge Prior Augmented Networks for Motion Deblurring on Naturally Blurry Images

Motion deblurring has witnessed rapid development in recent years, and most of the recent methods address it by using deep learning techniques, with the help of different kinds of prior knowledge. Concerning that deblurring is essentially expected to improve the image sharpness, edge information can serve as an important prior. However, the edge has not yet been seriously taken into consideration in previous methods when designing deep models. To this end, we present a novel framework that incorporates edge prior knowledge into deep models, termed Edge Prior Augmented Networks (EPAN). EPAN has a content-based main branch and an edge-based auxiliary branch, which are constructed as a Content Deblurring Net (CDN) and an Edge Enhancement Net (EEN), respectively. EEN is designed to augment CDN in the deblurring process via an attentive fusion mechanism, where edge features are mapped as spatial masks to guide content features in a feature-based hierarchical manner. An edge-guided loss function is proposed to further regulate the optimization of EPAN by enforcing the focus on edge areas. Besides, we design a dual-camera-based image capturing setting to build a new dataset, Real Object Motion Blur (ROMB), with paired sharp and naturally blurry images of fast-moving cars, so as to better train motion deblurring models and benchmark the capability of motion deblurring algorithms in practice. Extensive experiments on the proposed ROMB and other existing datasets demonstrate that EPAN outperforms state-of-the-art approaches qualitatively and quantitatively.
Machine-learning hidden symmetries

We present an automated method for finding hidden symmetries, defined as symmetries that become manifest only in a new coordinate system that must be discovered. Its core idea is to quantify asymmetry as violation of certain partial differential equations, and to numerically minimize such violation over the space of all invertible transformations, parametrized as invertible neural networks. For example, our method rediscovers the famous Gullstrand-Painlevé metric that manifests hidden translational symmetry in the Schwarzschild metric of nonrotating black holes, as well as Hamiltonicity, modularity, and other simplifying traits not traditionally viewed as symmetries.
Multilayer SIS Model With an Infrastructure Network

In this article, we develop a layered networked spread model for a susceptible-infected-susceptible pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible model (SIWS). The “W” in SIWS represents any infrastructure network contamination, not necessarily restricted to a water distribution network. We identify sufficient conditions for the existence, uniqueness, and stability of various equilibria of the aforementioned model. Further, we study an observability problem, where, assuming that the measurements of the pathogen levels in the infrastructure network are available, we provide a necessary and sufficient condition for estimation of the sickness levels of the nodes in the human contact network. Our results are illustrated through an in-depth set of simulations.