Miracle-dz/UniFAR-FTUs huggingface dataset:
Reliability and Validity of the electronic Health Literacy Scale Among People at High Risk of Stroke in China: A Cross-sectional Study

Background: It is of great significance for brain and heart health managers to assess the electronic health literacy of people at high risk of stroke for improving the current situation of stroke in China. Although various measuring instruments have been developed, there is still a lack of suitable tools to match the development of network.Aim: To examine the reliability and validity of the electronic Health Literacy Scale（e-HLS）among people at high risk of stroke in China, so as to provide appropriate measurement tools for brain heart health managers.Methods: A demographic questionnaire, the electronic Health Literacy Scale（e-HLS）and the eHealth Literacy Scale (eHEALS) were administered to a sample of 648 people at high risk of stroke recruited from November to December 2020 in a tertiary hospital.Results: The Cronbach’α coefficient on the e-HLS-CHI was 0.91. Three factors were extracted by Exploratory Factor Analysis (EFA), accounting for 90.84% of the total variance. Confirmatory Factory Analysis (CFA) revealed that three factors of e-HLS-CHI fit well ( NFI = 0.979, RFI = 0.955, IFI = 0.987, TLI = 0.972, CFI=0.987, RMSEA = 0.070, CMIN/DF= 2.586). Good simultaneous validity was suggested by the positive correlation of 0.94 between the e-HLS-CHI and eHEALS. And when using eHEALS as the standard, the area under the ROC curve of e-HLS-CHI was 0.896 (95% CI: 0.831-0.960, P = 0.000). After calculation, the sensitivity and specificity were 97.8% and 70.4% respectively, indicating that it has nice predictive validity.Conclusions: The e-HLS can be used to evaluate electronic health literacy of people at high risk of stroke in China. This may be helpful for brain and heart health managers to assess the current situation of electronic health literacy in people at high risk of stroke and provide reference for future health promotion programs.
More than meat: the role of pigs in Timorese culture and the household economy

ABSTRACT In the Asia Pacific region, pigs play an important cultural and economic role in society. However, the cultural significance and use of livestock is often overlooked in agricultural research and development. This paper describes the contribution of pigs to the social and cultural life of rural households in Timor Leste from the perspectives of women, men and children. Three lowland patrilineal and two highland matrilineal communities in Bobonaro district were selected for qualitative research. Eight focus groups were held in five sub-villages in two locations with 20 women, 20 men and 21 children involved in pig raising and 16 people not raising pigs at the time. Ten key informants were individually interviewed. Our research found geographic, gender and age differences in ceremonial roles and pig husbandry including types and numbers of pigs used, ritual activities and pig housing or feeding. Pig raising is mostly done by women and children, with men organizing cultural events and making decisions on using pigs for rituals. Challenges and opportunities to improve pig husbandry in contexts, such as Timor Leste with limited resources, absence of formal meat markets, exotic disease outbreaks and strong cultural obligations, are discussed.
Thermal Runaway Prognosis of Battery Systems Using the Modified Multiscale Entropy in Real-World Electric Vehicles

The battery system is vital for the safety and durability of a real-world electric vehicle (EV), and the prognosis of battery thermal runaway trigged by various abuse conditions is critical for preventing security incidents, such as spontaneous combustion or explosion. This article presents a real-scenario-based thermal runaway prognosis on a Li(NiCoMn)O2 ternary battery in an actual electric bus, deriving historical operation data from the National Monitoring and Management Platform for Electric Vehicles of China. The multiscale detailed features of the fault signals before the accident are extracted using the modified multiscale entropy algorithm, avoiding entropy fluctuations and information redundancy due to the sliding averaging process, as well as overcoming the shortcomings of the single-scale coarse-graining and dimensional disaster. The results show that abnormal cells can be detected as early as one week before the accident. Furthermore, a real-time multilevel prognosis strategy based on the determined anomaly coefficients is proposed, which can highlight the faulty cells and improve the prognosis sensitivity by filtering out the redundant scales. The verification results on the accident vehicle show that the early abnormal cells can be effectively diagnosed, preventing the occurrence of thermal runaway and safeguarding the safety of drivers and passengers in real-world vehicular operation.
Comparing UAS LiDAR and Structure-from-Motion Photogrammetry for Peatland Mapping and Virtual Reality (VR) Visualization

The mapping of peatland microtopography (e.g., hummocks and hollows) is key for understanding and modeling complex hydrological and biochemical processes. Here we compare unmanned aerial system (UAS) derived structure-from-motion (SfM) photogrammetry and LiDAR point clouds and digital surface models of an ombrotrophic bog, and we assess the utility of these technologies in terms of payload, efficiency, and end product quality (e.g., point density, microform representation, etc.). In addition, given their generally poor accessibility and fragility, peatlands provide an ideal model to test the usability of virtual reality (VR) and augmented reality (AR) visualizations. As an integrated system, the LiDAR implementation was found to be more straightforward, with fewer points of potential failure (e.g., hardware interactions). It was also more efficient for data collection (10 vs. 18 min for 1.17 ha) and produced considerably smaller file sizes (e.g., 51 MB vs. 1 GB). However, SfM provided higher spatial detail of the microforms due to its greater point density (570.4 vs. 19.4 pts/m2). Our VR/AR assessment revealed that the most immersive user experience was achieved from the Oculus Quest 2 compared to Google Cardboard VR viewers or mobile AR, showcasing the potential of VR for natural sciences in different environments. We expect VR implementations in environmental sciences to become more popular, as evaluations such as the one shown in our study are carried out for different ecosystems.
Rejection rates of bootstrapped and exact heteroskedasticity tests in response to skedastic function and normal or skewed disturbances

Abstract This study focuses on examining the power of bootstrapped and exact tests to detect variance heterogeneity in linear models via Monte Carlo. Three independent MC schemes were set up corresponding to distinct forms of heteroskedasticity, degree of heteroskedasticity and with or without normality. The tests produced unequal power in the simulated combinations; some performed poorly, when exposed to the unusual type of heteroskedasticity from the point of the test. The Bamset LR test had adequate power for most skedastic functions and should be extensively used. Park’s test had inadequate power in most scenarios. The power of White’s test varied strongly depending on the skedastic function. The bootstrapped tests had generally lower power and were often oversized, although the only resampled Harrison and McCabe test had outstanding power in several MC schemes. Reduced power and increased Type I Error Rates were noticed in most exact or resampled tests with skewed disturbances.
Probing the impact of transformational leadership on job embeddedness: the moderating role of job characteristics

Purpose This paper aims to evaluate how transformational leadership can increase job embeddedness in their employees that persuade them to stay in their organization and how this relationship is contingent upon the job characteristics. Design/methodology/approach Sample of 328 useable responses was available for analysis. Questionnaires were distributed to the employees who are working in different Pakistani organizations. Regression analysis was used to test for hypotheses. Findings The findings support that there is a significant impact of transformational leadership for shaping job embeddedness, and the results endorsed the role of job characteristics as a moderator in describing the relationship of transformational leadership and job embeddedness. Transformational leaders would motivate employees to work together in productive manners in challenging work settings. Originality/value This paper makes three key contributions to the literature on job design. First, this inquiry shows that a strong link does exist between transformational leadership in creating organizational job embeddedness. Second, it highlights how job characteristics of highly challenging work settings may shape employees’ job embeddedness. Third, this paper offers a novel perspective in leadership research by incorporating high challenging work setting (i.e. job characteristics) as moderator. Managers may get new insight by opting for transformational leaders' attributes and concentrating on high challenging work settings for creating embeddedness in employees to prolong their stay with the job and firm.
Analysis of the Effectiveness of Promotion Strategies of Social Platforms for the Elderly with Different Levels of Digital Literacy

This paper aimed to examine the effectiveness of social platform promotion strategies for the elderly with different digital literacy. Despite extensive research on the development of youth-oriented social platforms, research on the development of social platforms specifically targeting older adults with varying levels of digital literacy is lacking. The elderly population is divided into passive information receivers (PIRs) and active information seekers (AISs) according to their information seeking expertise, and an empirical study was conducted to assess the behavioral characteristics of PIRs and AISs. Grounded in innovation diffusion research and our empirical results, an agent-based model was developed, and the impact of the proportion of PIRs on the macro result of the social platform adoption (i.e., market penetration) and the impact of promotional strategies on market penetration under different proportions of PIRs were analyzed. The results demonstrate a direct negative effect of the proportion of PIRs on market penetration and a moderating effect on the effectiveness of various promotional strategies.
RhoA G17E/Vav1 Signaling Induces Cancer Invasion via Matrix Metalloproteinase-9 in Gastric Cancer

Background: RAS homolog family member A (RhoA), a member of the Rho family of small GTPases, and Vav1, a guanine nucleotide exchange factor for Rho family GTPases, have been reported to activate pathways related to the actin cytoskeleton and regulation of cell shape, attachment, and motility. The interaction between these molecules in lymphoma is involved in malignant signaling, but its function in epithelial malignancy is unknown. Here, we investigated the malignant signal of mutant RhoA in gastric cancer and demonstrated the potential of RhoA G17E/Vav1 as a therapeutic target for diffuse gastric cancer. Methods: The RhoA mutants R5W, G17E, and Y42C were retrovirally transduced into the gastric cancer cell line MKN74. The stably transduced cells were used for morphology, proliferation, and migration/invasion assays in vitro. MKN74 cells stably transduced with ectopic wild-type RhoA and mutant RhoA (G17E) were used in a peritoneal xenograft assay. Results: The RhoA mutations G17E and Y42C induced morphological changes in MKN74. G17E induced Vav1 expression at the mRNA and protein levels and promoted the migration and invasion of MKN74. An RNA interference assay of Vav1 revealed that RhoA G17E enhanced cancer cell invasion via Vav1. Furthermore, immunoprecipitation revealed that Vav1 and RhoA G17E specifically bind and function together through matrix metalloproteinase −9. In a peritoneal xenograft model of nude mice, RhoA G17E promoted peritoneal dissemination, whereas Vav1 knockdown suppressed it. Conclusion: Overall, our findings indicate that RhoA G17E is associated with Vav1 and promoted cancer invasion via matrix metalloproteinase −9 in gastric cancer cells. Thus, RhoA G17E/Vav1 signaling in diffuse gastric cancer may be a useful therapeutic target.
Inertial Parameter Identification in Robotics: A Survey

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.
Flow Stress of 6061 Aluminum Alloy at Typical Temperatures during Friction Stir Welding Based on Hot Compression Tests

The purpose of this paper is to report quantitative data and models for the flow stress for the computer simulation of friction stir welding (FSW). In this paper, the flow stresses of the commercial 6061 aluminum alloy at the typical temperatures in FSW are investigated quantitatively by using hot compression tests. The typical temperatures during FSW are determined by reviewing the literature data. The measured data of flow stress, strain rate and temperature during hot compression tests are fitted to a Sellars–Tegart equation. An artificial neural network is trained to implement an accurate model for predicting the flow stress as a function of temperature and strain rate. Two models, i.e., the Sellars–Tegart equation and artificial neural network, for predicting the flow stress are compared. It is found that the root-mean-squared error (RMSE) between the measured and the predicted values are found to be 3.43 MPa for the model based on the Sellars–Tegart equation and 1.68 MPa for the model based on an artificial neural network. It is indicated that the artificial neural network has better flexibility than the Sellars–Tegart equation in predicting the flow stress at typical temperatures during FSW.
Using Scratch to Improve Learning Programming in College Students: A Positive Experience from a Non-WEIRD Country

Teaching computer programming is a real challenge in the State University of Milagro (UNEMI), located in one of the least-developed zones in Ecuador, a non-WEIRD country (WEIRD stands for Western, Educated, Industrialized, Rich and Democratic). Despite the application of various learning strategies, the historical pass rate does not exceed 43%. To solve this problem, we have relied on visual programming languages, specifically Scratch. Scratch is an open source software to learn programming that has a strong assumption of the benefits of community work. A quasi-experiment conducted with 74 undergraduate students during the first semester of CS showed that: (1) Both groups (control and experimental) are homogeneous in terms of their demographic characteristics, previous academic performance and motivation (expectations) concerning the course; (2) Scratch is strongly accepted by students in the experimental group and concerning the learning process, both groups showed similar levels of satisfaction; (3) the experimental group showed a pass rate four times higher than the control group; (4) in general, student success is associated with having learned programming with Scratch. While limited, our results are an important step in our road to improve the learning of programming in a low social status area of Ecuador.
Nitrogen and phosphorous-doped porous carbon xerogels as metal-free catalysts for environmental catalytic peroxide oxidation of 4-nitrophenol

This work reports an environmental benign porous carbon catalyst for the catalytic peroxide oxidation of 4-nitrophenol (4-NP) pollutant. Nitrogen and phosphorous-doped carbon xerogels as metal-free catalysts, which are namely as CX30, CX50 and CX75 with corresponding to initial concentrations of H3PO4 acid (30, 50 and 75 v/v %) were synthesized. Nitrogen gas adsorption measurements of the prepared carbon catalysts were performed at −196 °C, while Fourier transform infrared spectroscopy (FTIR) and high resolution scanning electron microscopy–energy dispersive X-ray spectroscopy (SEM–EDS) analyses were examined for the sample of CX75 which appeared high catalytic performance. The effect of various factors such as temperature, catalyst loading, H2O2 dose and initial concentration of 4-NP on the catalytic oxidation performance of the metal-free catalysts was studied at pH = 4. Results revealed that the phosphoric acid activation of nitrogen-doped resorcinol-formaldehyde xerogel developed significant influence on the porosity and surface chemistry characteristics of the hereby catalysts. Increasing the concentration of H3PO4 enhances the microporosity at expense of mesoporosity yielding micro-mesoporous carbons with surface area as high as 442 m2/g. EDS and FTIR analyses confirmed the presence of phosphorous- and nitrogen-containing functional groups with high content of carbon and oxygen groups. During catalytic wet peroxide oxidation experiments, the CX75 catalyst exhibits complete decomposition 4-NP (100%) within 60 min. Moreover, reusability experiments with this catalyst were run through three cycles and revealed high stability where up to 52.6% conversion of 4-NP in the third recycle was occurred at 120 min. © 2016 Curtin University of Technology and John Wiley & Sons, Ltd.
Where we've been and where we're going: the importance of source communities in predicting establishment success from phylogenetic relationships

1 –––––––––––––––––––––––––––––––––––––––– © 2021 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Subject Editor: Manuel J. Steinbauer Editor-in-Chief: Miguel Araújo Accepted 18 April 2021 44: 1–14, 2021 doi: 10.1111/ecog.05406 44 1–14
Solid-Liquid Phase Equilibria of Pu(VI) and U(VI) in Aqueous Carbonate Systems. Determination of Stability Constants

The carbonate complexation constants of hexavalent plutonyl and uranyl ions are determined by solubility study of PuOJCOJCs) and UO^COJCs) under 100% and 1% COj atmosphere for Pu(Vl) and 100% CO2 atmosphere for U(VI). pH is varied in the ränge from 3.5 to 8 in 0.1 M NaC104 at room temperature (22±2°C). Based on the solubility products from the previous experiment, the following carbonate complexation constants are evaluated: log/S„(U02C03) = 8.93 ±0.05 log)? „(PuO,CO,) = 8.7+0.3 \ogß,,(\iO^(CO,)l-) = 15.3 ±0.2 log^„(Pu02(C03)i-) = 14.1 ±0.5 log ^,3(U02(C03)r) = 21.0±0.3 l0g/? ,3(Pu0,(C03)r) = 17.8±0.2. The ion-interaction approach (Pitzer equations) is parameterized for the U(VI) system on the basis of the literature data. The present solubility data are found to be in dose agreement with calculated results based on the parameters. The same parameters appear also applicable to the Pu(VI) system for a good approximation.
An FPGA-Based Residual Recurrent Neural Network for Real-Time Video Super-Resolution

In this paper, we propose a hardware-efficient residual recurrent neural network for real-time video super-resolution (VSR) based on field programmable gate array (FPGA). Although recent learning-based VSR methods have achieved remarkable performance, the large computational complexity prohibits the deployment of the sophisticated VSR models on FPGA for real-time applications. Limited by the hardware resources, state-of-the-art FPGA-based VSR methods perform single-image super-resolution over the video sequence and suffer from temporal inconsistency. In order to exploit the inter-frame temporal correlation for real-time VSR on low-complexity hardware, we introduce a hardware-efficient recurrent neural network ERVSR. Specially, the proposed ERVSR leverages the input frame and the temporal information entailed in the hidden state to reconstruct the high-resolution counterpart. To reduce the network parameters, the low-resolution input branch and the hidden state branch are convolved individually and a channel modulation coefficient is proposed to explicitly guide the network to allocate the amount of output feature channels to each branch. Additionally, in order to reduce the memory consumption, we perform a dedicated lightweight compression of the hidden state by introducing a statistical normalization scheme followed by a fixed-point quantization. Besides, we adopt group convolution and depthwise separable convolution to further compact the network. We evaluated the proposed ERVSR on multiple public datasets from different aspects. Experimental results demonstrate that ERVSR performs better than the existing state-of-the-art FPGA-based VSR methods in both image quality and data throughput.
The Role of Digital Maturity Assessment in Technology Interventions with Industrial Internet Playground

The use of digital technologies such as Internet of Things and advanced data analytics are central in digitally transforming manufacturing companies towards Industry 4.0. Success cases are frequently reported, and there is clear evidence of technology interventions conducted by industry. However, measuring the impact and effect of such interventions on digital maturity and on the organizational adoption can be challenging. Therefore, the research aim of this paper is to explore how the combination of the different methods of Industrial Internet Playground (IIP) pilots, Shadow Infrastructure (SI) and digital maturity assessment can assist in conducting and documenting the technical, as well as organisational, impact of digital interventions. Through an elaborate literature review of existing digital maturity assessment tools and key dimensions in digital transformation, we have developed a digital maturity assessment tool (DMAT), which is presented and applied in the paper to identify digital development areas and to evaluate and document the effects of digital interventions. Thus, the paper contributes with new knowledge of how the IIP pilot and SI combined with digital maturity assessment can support effective, transparent and documented digital transformation throughout an organisation, as explored through theory and a practice case.