Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposing Decking Assemblies to Continuous Wind-Driven Firebrand Showers

A series of experiments were conducted to examine potential vulnerabilities of wood decks to continuous, wind-driven firebrand showers. Sections of wood decks (1.2 m by 1.2 m) were constructed and attached to a reentrant corner assembly. The deck/reentrant corner assembly was then exposed to continuous, winddriven firebrand bombardment generated by the newly developed NIST full-scale Continuous Feed Firebrand Generator (NIST full-scale Continuous Feed Dragon) installed in the Building Research Institute’s (BRI) Fire Research Wind Tunnel Facility (FRWTF). Three different wood deck types were exposed to wind-driven firebrand showers at wind speed of 6 m/s; Western Red Cedar, Douglas-Fir, and Redwood. For each wood deck tested (exposed to a total firebrand mass flux of 17.1 g/m 2 s), firebrands accumulated on the deck surface, and each wood deck type was observed to ignite by flaming ignition. The average time to flaming ignition was 437 s for Cedar, 934 s for Douglas-Fir, and 756 s for Redwood. Therefore, wood decks were observed to be vulnerable to ignition from continuous, wind-driven firebrand showers. Results of these experiments are discussed in detail.
Evaluation of patients admitted to our hospital with a possible diagnosis of COVID-19

We aimed to evaluate the clinical characteristics, laboratory and imaging findings, and outcomes associated with the severity of illness of patients admitted to our hospital with possible COVID-19 diagnosis. The patients admitted to our hospital with a possible diagnosis of COVID-19 between March 25 and December 3, 2020, were evaluated retrospectively. In terms of their clinical, laboratory, imaging findings, and mortality were compared between patients discharged and hospitalized with died and survivors. 12470 patients admitted to our hospital with a possible diagnosis of COVID-19 tested by SARS-CoV-2 RT-PCR. Of those tested, 3116 (24.9%) were positive. Of the patients, 2529 (81.2%) were discharged, 587 (18.8%) hospitalized, and 92 (3%) were died. In the comparison of discharged, and hospitalized groups, a significant difference was found in age, symptoms, comorbid diseases, chest CT, laboratory findings, and mortality (p< 0.05). In the comparison of survivor, and dead, the risk of mortality analysis showed similar characteristics. Older age, male gender, comorbidities, lymphopenia, thrombocytopenia, increased levels of CRP, NLR, D-dimer, ferritin, and chest CT findings were significant risk factors. Of the patients who died, 23 (25%) were female and 69 (75%) were male. The outbreak of COVID-19 is a significantly health problem. We were experienced with high numbers of COVID-19 cases and found that age, symptoms, comorbid diseases, chest CT, laboratory findings of inflammation are significant predictors for admission to hospital. Therefore, these risk factors should consider routinely for patients with a high risk of developing severe and critical diseases by clinicians.
Compact Quad-Band CP Series-Fed Circular Slit Microstrip Array Antenna Using Machine Learning

This article presents the design and characterization of a dual-band, compact, and dual-circularly polarized (CP) traveling wave series-fed circular slit microstrip array (TWSCSMA) antenna operating in quad-band CP at 2.5/3.5/3.8 GHz WiMAX and 5.2 GHz WLAN applications. The radiating structure consists of an annular ring traveling wave series-fed circular slit array elements, rotates in sequence with seven circular quasi-lumped resonators contributes to two-impedance bandwidth (IBW) and quad CP bands. The four split-ring resonators with double-annular rings are introduced on the defected ground plane to improve the impedance matching, IBW, and axial ratio bandwidth (ARBW) effectively. The measured results show that the proposed dual-CP TWSCSMA antenna yields two $S_{11} \leq $ –10 dB IBW of 33.85% and 50.73% and four < 3 dB ARBW of 16.26%, 12.52%, 8.22%, and 13.82% for port 1. Furthermore, modern machine learning regression techniques are used to improve the accuracy of the model performance requirements for the optimal design of the proposed antenna. The TWSCSMA antenna demonstrates a compact, low profile, wide impedance bandwidth, and minimum axial ratio which is making it a good candidate for WiMAX and WLAN applications.
Corneal Regeneration Using Adipose-Derived Mesenchymal Stem Cells

Adipose-derived stem cells are a subtype of mesenchymal stem cell that offers the important advantage of being easily obtained (in an autologous manner) from low invasive procedures, rendering a high number of multipotent stem cells with the potential to differentiate into several cellular lineages, to show immunomodulatory properties, and to promote tissue regeneration by a paracrine action through the secretion of extracellular vesicles containing trophic factors. This secretome is currently being investigated as a potential source for a cell-free based regenerative therapy for human tissues, which would significantly reduce the involved costs, risks and law regulations, allowing for a broader application in real clinical practice. In the current article, we will review the existing preclinical and human clinical evidence regarding the use of such adipose-derived mesenchymal stem cells for the regeneration of the three main layers of the human cornea: the epithelium (derived from the surface ectoderm), the stroma (derived from the neural crest mesenchyme), and the endothelium (derived from the neural crest cells).
Wildfire Identification Based on an Improved Two-Channel Convolutional Neural Network

The identification of wildfires is a very complex task due to their different shapes, textures, and colours. Traditional image processing methods need to manually design feature extraction algorithms based on prior knowledge, and because fires at different stages have different characteristics, manually designed feature extraction algorithms often have insufficient generalization capabilities. A convolutional neural network (CNN) can automatically extract the deeper features of an image, avoiding the complexity and blindness of the feature extraction phase. Therefore, a wildfire identification method based on an improved two-channel CNN is proposed in this paper. Firstly, in order to solve the problem of the insufficient dataset, the dataset is processed by using PCA_Jittering, transfer learning is used to train the model and then the accuracy of the model is improved by using segmented training. Secondly, in order to achieve the effective coverage of the model for fire scenes of different sizes, a two-channel CNN based on feature fusion is designed, in which the fully connected layers are replaced by a support vector machine (SVM). Finally, in order to reduce the delay time of the model, Lasso_SVM is designed to replace the SVM in the original model. The results show that the method has the advantages of high accuracy and low latency. The accuracy of wildfire identification is 98.47% and the average delay time of fire identification is 0.051 s/frame. The wildfire identification method designed in this paper improves the accuracy of identifying wildfires and reduces the delay time in identifying them.
High-resolution and Multitemporal Impervious Surface Mapping in the Lancang-Mekong Basin with Google Earth Engine

High-resolution and multitemporal impervious surface maps on large scales are crucial for environmental and socioeconomic studies. However, recently available multitemporal impervious surface maps of the Lancang-Mekong basin were limited at 30-m resolution with considerably low accuracy. Hence, the development of up-to-date, accurate, and multitemporal impervious surface maps with the 10-m resolution is urgently needed. In this article, a machine learning framework is demonstrated by fusing Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral Sensor images 15 to map and study the annual dynamics of impervious surfaces in the Lancang-Mekong basin from 2016 to 2021 facilitated by Google Earth Engine. Moreover, a train sample migration strategy is proposed to automate impervious surface mapping for various time periods eliminating the need to collect additional train samples from this vast study area. Finally, qualitative and quantitative assessments are conducted using test samples from Google Earth and four existing state-of-the-art datasets. The result shows that the overall accuracy and Kappa of the final impervious surface maps range from 91.45% to 92.44% and 0.829 20 to 0.848, respectively, which demonstrates the feasibility and reliability of the proposed method and results. The LMISD is freely available from https://doi.org/10.5281
Spatial-Net for Human-Object Interaction Detection

Human-object interaction (HOI) detection is the detection of a human’s relationship with an object in still images and videos. The majority of HOI detection methods rely on appearance features as the primary feature for detecting the relationship between humans and objects. Furthermore, the model’s performance is affected by the abundance of false-positive pairs generated by the image’s non-interactive human-object pairs and human-object mis-grouping. In this paper, we propose “Spatial-Net”, a new HOI detection approach in still images. In the proposed approach, the HOI problem is divided into two main tasks, namely pair-prediction and global-rejection. In the pair-prediction task, the spatial relationship is adopted to predict the human-object interaction for each human-object pair using spatial features that contains spatial map which is a single channel image that represents human-object pairs including body parts and object masks, relative geometry features such as relative size, relative distance, and intersection-over-union between body part and objects, and weighted distance that is used as body part attention deterministic model. In the global-rejection task, an augmented model is employed to reject false positive pairs. We use the Hungarian matching technique to assign human-object pairs for each action and human-centric model to reject the non-interaction human-object pairs according to semantic co-occurrence between human and object. The experimental results on the V-COCO dataset demonstrate that the proposed Spatial-Net outperforms many state-of-the-art HOI models with less inference time.
Smart load management of distribution-class toroidal transformers using a dynamic thermal model

Thermal behaviour is a prime factor in the accurate performance assessment of power transformers as well as in the prediction of their life expectancy. This study presents a computer modelling tool based on an electro-thermal equivalent circuit of transformers that is able to predict the hot-spot temperature and average surface temperatures for all internal layers of distribution-class toroidal transformers. Temperature is the limiting factor that prevents running transformers for hours or days in overload conditions. The modelling tool presented in this study is capable to identify the safe maximum overload current and duration that a transformer can handle without introducing damage or loss of life. The model is helpful to predict the short-term (few hours) and long-term (few days) overload capabilities of transformers. The electro-thermal model can also be used as a tool to optimise the design and evaluate the performance of transformers. This study is specifically focused on the implementation of the proposed method on dry-type distribution-grade toroidal transformers. The model is built using circuit components (lumped R and C ) obtained from the thermal-electrical analogy. The model is validated with numerous finite-element method simulations and laboratory tests with transformers of various power ratings.
Semi-Supervised Node Classification on Graph via Inconsistent Nodes Correction

This article is a work-in-progress paper. In traditional graph convolutional networks (GCNs), node feature aggregation in graph convolutional learning is guided only by topology graphs. In reality, both network topology and node features offer unique and valuable information. Using topology alone cannot obtain fully and completely accurate neighborhood information. This paper advo-cates a representation learning method with output correction for GCNs (OC-GCN), simultaneously using topological structure and node features information. Specifically, we use two GCN encoders to extract node embeddings in feature space and topology space. Then, determine inconsistent nodes by the pseudo-labels generated by the two models. Finally, we use the representations of consistent neighbors to regenerate representations of inconsistent nodes in feature and topology views. Our experiments have shown that the OC-GCN can significantly improve the classification accuracy of inconsistent nodes in feature and topology views. We conducted extensive experiments on the benchmark datasets and demonstrated that OC-GCN is substantially better than state-of-the-art baselines at different label rates.
Employing Blockchain Properties for Transparent Databases

Both transparency and privacy are important properties in most any information-management application, yet they are in many ways inherently opposed to each other. The tradeoff between these two goals is typically intermediated by a trusted system or organization. The resulting tradeoff is only as meaningful as the level of trust in the intermediary. Blockchains provide a decentralized single source of truth that allows for the reduction or elimination of the need for trust. This paper explores the options in trading off transparency and privacy in a blockchain database. It also considers the closely-related concepts of accountability and auditability and explores how cryptographic techniques allow the disclosures needed for regulatable information systems while preserving a high degree of privacy.
Discovering the True Schumpeter-New Insights into the Finance and Growth Nexus

Joseph A. Schumpeter is one of the most famous economists of the 20th century and the ’patron saint’ of the finance and growth literature. We have discovered that the prevailing literature has, however, misinterpreted Schumpeter, which leads to puzzling empirical results and difficulties in explaining even fundamental relationships. We argue that this is due to a misrepresentation of the role of banks and liquidity creation and the role of household saving. After a critical discussion of the literature, we provide our own empirical analysis using a panel of 43 countries to explore the relationships between the important variables of the finance and growth literature. Our empirical analysis above all supports Schumpeter’s view that credit growth supports GDP growth while saving is irrelevant for credit growth and GDP growth. In sum, a correct interpretation of Schumpeter helps to overcome the theoretical and empirical challenges which confront the prevailing literature.
The FTC of multiphase motors is of a high-level searching algorithm for the optimal current

The decoupled model of Five-Phase Permanent Magnet Synchronous Motors (5Ph PMSMs) under a two-phase open circuit has been developed recently, yet the corresponding PWM has not been fully exploited. The Pulse Width Modulation (PWM), which is straightforward for a balanced system, can be problematic to fit a 5Ph PMSM under faulty conditions. During the open faults, the neutral voltage is drifting over the mid-point of DC bus, thus, the PWM typically designed for a balanced system always fails. In this paper, an affine transformation is first presented to offset the drifting neutral effect, and only then PWMs can be effective. A quasi-sinusoidal PWM, a Space Vector PWM, and a min-max Carrier-based PWM are presented and compared experimentally. Each method has its own merits and demerits, and their effectiveness or rather the proposed affine transformation is validated by the experimental results.
Exploring Symmetric Encryption Using Large-Scale Archetypes

Many researchers would agree that, had it not been for the transistor, the improvement of IPv7 might never have occurred. It might seem per-verse but often conﬂicts with the need to provide multi-processors to end-users. Given the trends in autonomous communication, mathematicians particularly note the construction of thin clients. Here, we motivate a novel solution for the synthesis of the memory bus (POPET), which we use to argue that the memory bus and 802.11b can interact to ﬁx this question.
A Review of Rainfall Erosivity as a Natural Factor of Gully Erosion

— In this 21 st century which is climate change-driven with more extreme rainfall events, gully erosion is increasingly becoming a global environmental problem influenced by both natural and anthropogenic factors. This paper is a literature review of rainfall erosivity as one of the natural factors of gully erosion. The central objective of the paper is to show in a global context how rainfall erosivity has influenced gully erosion. The method used is a review of academic/journal articles, textbooks, internet materials, conference papers and publicly available materials on gully erosion and rainfall erosivity as one of its factors. Previous authors whose works were reviewed on rainfall erosivity as a factor of gully erosion have a convergent view that rainfall is the primary cause of water-induced erosion and its power to do so is known as erosivity. They were of the view that a more intense rainfall of short duration can cause gully erosion more than a less intense one for a longer period of time. The authors also have a unity of opinion that areas with high rainfall regime such as the tropics are more prone to soil erosion than areas with low rainfall events. Recommendations to reduce the influence of rainfall as a factor of soil erosion include planting of trees and grasses that reduce the impact of rainfall to detach soil particles, conservation practices such as terracing, strip cropping and contour ploughing, a shift from rain-fed agriculture to dry season farming and grants to the affected people and landholders to manage gully erosion using adaptive measures based on their indigenous knowledge.
Employee's Attitude toward Electronic Administration Adoption: A Case Study of Al-Mustansiriyah University

— The advancement of the information and communications technology has helped almost all governments across the world as they have exploited these technologies for delivering services to their citizens. However, this phenomenon may face several challenges and barriers that lead to the failure in its adoption, use, or continuous usage. In the Arab countries, the rate of failure in the use of electronic services is high in the public sector. Therefore, previous studies have concentrated on this critical issue and highlighted on the citizens’ perspective and ignored the perspective of employees in the government organizations. In addition, very few previous studies dealt with the quality of the services based on the employees’ viewpoint. Thus, based on the arguments that have been stated earlier, this preliminary study strives to identify the factors that may affect the electronic administrative adoption according to the employees’ perspective. With regard to the data collection, the quantitative method, self-administered questionnaires will be distributed among the staff of the Al-Mustansiriyah University, Iraq. With regard to data analysis, a technique identified in analysing the data gathered from the key respondents (employees) is the partial least squares – structural equation modelling. In fact, this research strives to enrich the literature by adding more information about the factors that may hinder the adoption of modern technologies in general and electronic administration in particular. With regard to the Al-Mustansiriyah University, the present study is considered as the first study conducted in this area; therefore the outputs will assist the government to remedy these obstacles before beginning any project in the public sector including the use of ICT instead of the conventional manner. The result of the analysis showed that certain aspects such as system quality, the trust in the organization as well as the usefulness have been seen to act as major factors which contribute impacts to the adoption of e-administration services by the employees in universities.
Status of lymphatic filariasis in Afikpo North L.G.A., Ebonyi State, Nigeria

— The status of lymphatic filariasis in Afikpo North L.G. A. , Ebonyi State, Nigeria was investigated among seven hundred and eighty one (781) subjects from thirty (30) villages, using the following Parasitological methods; the Wet Smear preparation, the Knott’s concentration Technique and the Immunochromatographic Card Test (ICT). From the study, the following prevalence rates of Wuchereria bancrofti were obtained, wet smear preparation (1.66%), the knott’s concentration technique (5.38%) and the ICT method (21.13%). Subjects within the age group 58-62 years had the highest prevalence of 43.48%, using the ICT method. Males were more infected 85(21.85%) than female 80(20.41%). There was no statistically significant difference in the prevalence of infection by gender using ICT cards (P>0.05) at 2 tailed difference of . 780. With the Knott’s concentration technique, subjects within 58 – 62 years had the highest prevalence of 26.09%. Males were also more infected 24(6.16%) than females 18(4.59%). The prevalence of hydrocoele was found to be 8.23%. Hydrocoele was more frequent among older people of 53 years and above than the younger ones below the age of 27 years. The prevalence of lymphoedema was 8.88%. Males were more affected with prevalence rate of 5.55% than females (3.33%). Knott’s
Comparative Study of Various Approaches For Joint Demosaicing And Subpixel Based Downsampling In Bayer Images

 Abstract — A portable device such as a digital camera with a single sensor and Bayer color ﬁlter array (CFA) requires demosaicing to reconstruct a full color image. To display a high resolution image on a low resolution LCD screen of the portable device, it must be down-sampled. The two steps, demosaicing and down-sampling, inﬂuence each other. On one hand, the color artifacts introduce d in demosaicing may be magniﬁed when followed by down-sampling; on the other hand, the detail removed in the down-sampling cannot be recovered in the demosaicing. Therefore, it is very important to consider simultaneous demosaicing and down-sampling. In this paper, a fast frequency domain analysis approach and an adaptive approach for joint demosaicing and subpixel-based down-sampling of single sensor Bayer images is described. In frequency domain approach for joint demosaicing and subpixel-based down-sampling (FFAJDSD), we integrate demosaicing into down-sampling by directly performing subpixel based down-sampling in the Bayer domain.In Adaptive Joint Demosaicing and Subpixel based Down-sampling scheme (AJDSD),subpixel-based down-sampling is adaptively and directly applied in Bayer domain, without the process of demosaicing.When compared with conventional methods, these two approaches achieve superior performance improvements.