Miracle-dz/UniFAR-FTUs huggingface dataset:
YouTubers Creativity in Creating Public Awareness of COVID-19 in Indonesia: A YouTube Content Analysis

The Government of Indonesia has designated COVID-19 as a national disaster. Until now, many people are still not fully aware of the risks of COVID-19. On YouTube, the most widely used social media platform in Indonesia, information about COVID-19 is available. However, much misleading information has caused public worry and low public trust in the government in overcoming this pandemic. Popular YouTubers who have many subscribers can reach many viewers. Some of them made videos about COVID-19. This study aims to analyze how individual Indonesian YouTubers present the issue of COVID-19 in creating public awareness in Indonesia through their YouTube video content based on ethos, pathos, and logos. The research used qualitative content analysis methods includes planning, data collection, data analysis, and finally, creating a report and presentation of the result. This study analyzes YouTube video content created by five individual Indonesian YouTubers who have the most subscribers ranking. The unit analysis is video content about COVID-19 of the YouTubers through a visual image, audio, and text. The study results show that YouTubers use credible speakers on the ethos aspect (such as a pulmonologist, government spokesperson for COVID-19, doctors, and public figures) to convey information to their audience. The pathos aspect appears in serious nonverbal messages to influence the emotions of the audience. The logos aspect is verbal messages (arguments and pieces of evidence) that contains information about the characteristics of COVID-19, health protocols during a pandemic, critics to government decision making, and socio-economic impacts of coronavirus pandemic.
Distribution and Diversity of Comammox Nitrospira in Coastal Wetlands of China

Complete ammonia oxidizers (comammox), able to individually oxidize ammonia to nitrate, are considered to play a significant role in the global nitrogen cycle. However, the distribution of comammox Nitrospira in estuarine tidal flat wetland and the environmental drivers affecting their abundance and diversity remain unknown. Here, we present a large-scale investigation on the geographical distribution of comammox Nitrospira along the estuarine tidal flat wetlands of China, where comammox Nitrospira were successfully detected in 9 of the 16 sampling sites. The abundance of comammox Nitrospira ranged from 5.19 × 103 to 8.33 × 104 copies/g, 2.21 to 5.44-folds lower than canonical ammonia oxidizers: ammonia-oxidizing bacteria (AOB) and ammonia-oxidizing archaea (AOA). Phylogenetic analysis based on the alpha subunit of the ammonia monooxygenase encoding gene (amoA) revealed that comammox Nitrospira Clade A, mainly originating from upstream river inputs, accounts for more than 80% of the detected comammox Nitrospira, whereas comammox Nitrospira clade B were rarely detected. Comammox Nitrospira abundance and dominant comammox Nitrospira OTUs varied within the estuarine samples, showing a geographical pattern. Salinity and pH were the most important environmental drivers affecting the distribution of comammox Nitrospira in estuarine tidal flat wetlands. The abundance of comammox Nitrospira was further negatively correlated with high ammonia and nitrite concentrations. Altogether, this study revealed the existence, abundance and distribution of comammox Nitrospira and the driving environmental factors in estuarine ecosystems, thus providing insights into the ecological niches of this recently discovered nitrifying consortium and their contributions to nitrification in global estuarine environments.
Total Sleep Deprivation Impairs Lateralization of Spatial Working Memory in Young Men

Total sleep deprivation (TSD) negatively affects cognitive function. Previous research has focused on individual variation in cognitive function following TSD, but we know less about how TSD influences the lateralization of spatial working memory. This study used event-related-potential techniques to explore asymmetry in spatial-working-memory impairment. Fourteen healthy male participants performed a two-back task with electroencephalogram (EEG) recordings conducted at baseline and after 36 h of TSD. We selected 12 EEG points corresponding to left and right sides of the brain and then observed changes in N2 and P3 components related to spatial working memory. Before TSD, P3 amplitude differed significantly between the left and right sides of the brain. This difference disappeared after TSD. Compared with baseline, P3 amplitude decreased for a duration as extended as the prolonged latency of N2 components. After 36 h of TSD, P3 amplitude decreased more in the right hemisphere than the left. We therefore conclude that TSD negatively affected spatial working memory, possibly through removing the right hemisphere advantage.
LiNbO3-Based SAW Sensors Capable to Measure up to 1100°C High Temperature

High temperature measurement of the rotating parts in aero-engine system requires wireless passive sensors. Surface acoustic wave (SAW) devices capable of measuring high temperature wirelessly are therefore ideal for these extreme scenarios where wired sensors are not applicable. In this article, SAW sensors built on the LiNbO3 substrate which can measure up to 1100 °C with a good repeatability and endurance are reported. The platinum IDT and LiNbO3 substrate were effectively protected by a SiO2 passivation layer to ensure the functionality and stability of the SAW devices under extremely high temperature. Distinct linearity of f0 vs. temperature, together with the temperature durability of the SAW sensors were verified by conducting various high-temperature RF tests. Such a SAW sensor attached with an embedded near-field antenna was also formulated to enhance the wireless transmission quality for future high-temperature remote sensing system.
Temporal activation of LRH‐1 and RAR‐γ in human pluripotent stem cells induces a functional naïve‐like state

Naïve pluripotency can be established in human pluripotent stem cells (hPSCs) by manipulation of transcription factors, signaling pathways, or a combination thereof. However, differences exist in the molecular and functional properties of naïve hPSCs generated by different protocols, which include varying similarities with pre‐implantation human embryos, differentiation potential, and maintenance of genomic integrity. We show here that short treatment with two chemical agonists (2a) of nuclear receptors, liver receptor homologue‐1 (LRH‐1) and retinoic acid receptor gamma (RAR‐γ), along with 2i/LIF (2a2iL) induces naïve‐like pluripotency in human cells during reprogramming of fibroblasts, conversion of pre‐established hPSCs, and generation of new cell lines from blastocysts. 2a2iL‐hPSCs match several defined criteria of naïve‐like pluripotency and contribute to human–mouse interspecies chimeras. Activation of TGF‐β signaling is instrumental for acquisition of naïve‐like pluripotency by the 2a2iL induction procedure, and transient activation of TGF‐β signaling substitutes for 2a to generate naïve‐like hPSCs. We reason that 2a2iL‐hPSCs are an easily attainable system to evaluate properties of naïve‐like hPSCs and for various applications.
Sleep Quality and Daily Activity Association Assessment From Wearable Device Data

Having good sleep is important to maintain physical and mental health. However, many young adults suffer poor sleep due to work or academic demands. Even more, sleep is associated with stress which is many young adults experienced it. To solve this problem, many epidemiologic studies suggest that exercise may improve sleep quality. On the other hand, several studies show that daily activity has no association with sleep quality. In this study, we tried to find more evidence of whether the daily activity has an association with sleep quality or not. We used a commercially available wearable device to capture the subject's heart rate, step count and deep sleep duration for 7 months. Then we used linear regression, XGBoost, and Long Short-Term Memory to find an association between these variables. The results show that there was no association between these variables.
Active Users Detection Based on Orthogonal Least Squares

The Internet of Things (IoT) is the network of numerous physical users that are able to gather and share electronic information with each other. One of the main challenges of IoT is to detect the active users. We first employ the orthogonal least squares (OLS) algorithm to detect the active users. We then prove that under some conditions on the noise vector <inline-formula> <tex-math notation="LaTeX">$\mathbf {v}$ </tex-math></inline-formula>, OLS can successfully detect all the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> active users from the linear model <inline-formula> <tex-math notation="LaTeX">$\mathbf {y} = \mathbf {H} \mathbf {x}+\mathbf {v}$ </tex-math></inline-formula> in at most <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> iterations provided that <inline-formula> <tex-math notation="LaTeX">$\mathbf {H}$ </tex-math></inline-formula> satisfies the restricted isometry property with <inline-formula> <tex-math notation="LaTeX">$\delta _{K + 1} < 1/\sqrt {K+1}$ </tex-math></inline-formula>. Numerical tests are also performed to show the efficiency and effectiveness of the OLS algorithm.
A Compact, Passive Frequency-Hopping Harmonic Sensor Based on a Microfluidic Reconfigurable Dual-Band Antenna

We propose here a fully-passive wireless liquid sensor using a harmonic transponder, which comprises a dual-band microstrip antenna reconfigured by different types of liquids injected in a fluidic cavity. Different from traditional radio-frequency (RF) backscatter sensors, the proposed harmonic-transponder sensor (or harmonic sensor) receives frequency-hopped RF monotones and backscatters their second harmonics, with the peak frequency shifted by dielectric properties of liquid mixtures. This microstrip antenna has a hybrid-feed structure, of which an outer split-ring patch exhibits a narrow-band TM<sub>310</sub> mode at the fundamental frequency (<inline-formula> <tex-math notation="LaTeX">${f}_{0}$ </tex-math></inline-formula>) and an inner elliptical patch displays a wideband resonance centered at the second-harmonic frequency (<inline-formula> <tex-math notation="LaTeX">$2{f}_{0}$ </tex-math></inline-formula>), achieved with hybridization of TM<inline-formula> <tex-math notation="LaTeX">$_{e110}$ </tex-math></inline-formula> and TM<inline-formula> <tex-math notation="LaTeX">$_{o110}$ </tex-math></inline-formula> modes. In particular, the outer split-ring patch is loaded with a fluidic channel system to tune the resonance frequency of the TM<inline-formula> <tex-math notation="LaTeX">$_{3}10$ </tex-math></inline-formula> mode (<inline-formula> <tex-math notation="LaTeX">${f}_{0}$ </tex-math></inline-formula>). We demonstrate that the type of liquid mixture filling in the fluidic cavity can be clearly perceived by reading the peak received signal strength indicator (RSSI) in the spectrum of second harmonics. Our results show the potential for deploying this passive wireless sensor in noisy environments that include clutters, multiple reflections, jamming, and crosstalks.
IT Governance Training for Small Medium Enterprises

The aim of this research is to develop the IT governance training model for SMEs - The research carries out a series of observations on IT implementation in SMEs, where the level of SME implementation can be grouped into offline, basic, intermediate and advance levels and this research observes SME implementation at intermediate and advance levels. The low level of SMEs knowledge on IT Governance results in a low level of effectiveness of IT use in SMEs so that organizations cannot evaluate the benefits of using IT in organizations - research methods carried out through observation to the SMEs about the use of IT to support business processes and evaluate the effectiveness of IT use. The results of this research are training models for SMEs to implement IT Governance or Information Technology Governance Training SME (ITGTSme). Practical implications are the resulting training models that can be used for SMEs to gain knowledge about the implementation of IT governance through training. The paper contributes to the improvement of SMEs' knowledge especially knowledge of IT Governance implementation and encourages SMEs who have not used IT fully to be able to use IT better.
FPGA-Based Network Traffic Classification Using Machine Learning

Real-time classification of internet traffic is critical for the efficient management of networks. Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. In this article, the suitability of packet-level and flow-level features is validated using stepwise regression and random forest feature selection. Moreover, the optimal percentage of packets considered within a flow while extracting flow-level features is determined. Several experiments are conducted using naïve Bayes, support vector machine, $k$ -nearest neighbor, random forest, and artificial neural networks on the University of Brescia (UNIBS) and the University of New Brunswick (UNB) datasets, which are both publicly available. The performed experiments show that 60% of flow packets are a good compromise that ensures high performance in the least processing time. The results of the conducted experiments indicate that random forest outperforms other algorithms achieving a maximum accuracy of 98.5% and an F-score of 0.932. Further, and since software-based classifiers cannot meet the anticipated real-time requirements, we propose a Field-Programmable Gate Array (FPGA) based random forest implementation that utilizes a highly pipelined architecture to accelerate such a time-consuming task. The proposed design achieves an average throughput of 163.24 Gbps, exceeding throughputs of reported hardware-based classifiers that use comparable approaches, which in turn ensures the continuity of real-time traffic classification at congested data centers.
Patterns of CSF Inflammatory Markers in Non-demented Older People: A Cluster Analysis

Objective In this study, we aimed to examine if patterns of CSF inflammatory markers are correlated with global cognition, episodic memory, hippocampal volume, and CSF AD-related pathologies among non-demented older people. Methods We included 217 non-demented older individuals, including 87 subjects with normal cognition (NC) and 130 subjects with mild cognitive impairment (MCI) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study. Hierarchical cluster analysis including nine inflammatory markers in CSF [Tumor necrosis factor-α(TNF-α), TNF-R1, TNF-R2, transforming growth factor-β1 (TGF-β1), TGF-β2, TGF-β3, Interleukin-21 (IL-21), IL-6, and IL-7] was conducted. Results We identified two clusters among non-demented older people based on nine inflammatory markers in CSF. Compared to the first cluster, the second cluster showed significantly higher levels of CSF inflammatory markers (TNF-R1, TNF-R2, TGF-β1, TGF-β3, and IL-6). Further, the second cluster was also associated with higher levels of t-tau and p-tau levels in CSF. Conclusion We observed a subgroup of non-demented older people characterized by increased levels of inflammatory markers in CSF. Further, this subgroup showed higher levels of t-tau and p-tau levels in CSF.
In-Ear Accelerometer-Based Sensor for Gait Classification

For several years, the detection of gait has been popularly implemented using wearable sensors, especially in the sports and medical areas. They are unobtrusive devices which allow to monitor individuals without the need of any ambulatory technology. Despite the fact, the optimal location of the sensor remains uncertain and dependent on the type of measurement. Ear-worn sensors provide a tactical position, robust against movement, that might be significant for gait classification. The purpose of this paper is to demonstrate the accuracy and reliability of in-ear accelerometer sensor to perform gait classification, between the activities walking and running. The data was collected from fourteen participants using an in-ear sensor called ‘Cosinuss° One’, which contains a three-dimensional accelerometer sensor. The main characteristics between these two activities were detected using 17 time domain features, as for instance the maximums and standard deviations of the 3-axes, and 3 different window sizes were evaluated: 3.75s, 2s and 1s. Support vector machine (SVM) and ${k}$ -nearest neighbors (KNN) classifiers were implemented and later compared. The total number of features was reduced to 6 for SVM and 12 for KNN preserving the same results. An accuracy over 99% for both classifiers was achieved, outperforming most of the previous studies.
IPDroid: Android Malware Detection using Intents and Permissions

With the increasing popularity of Android smart-phones over the years, the number of malware attacks on Android has also increased. Around 26 million malware samples were detected on the Android platform in the year 2018 and those samples pose serious threats such as financial loss and information leakage. Hence, stronger security solutions need to be developed to detect such threats. Several static Android malware detection techniques exist in the literature that analyze manifest file components such as permissions or intents. However, to the best of our knowledge, none of them have aimed to find the best set of permissions and intents combined that could give better accuracy. In this paper, we use Information Gain to rank the permissions and intents intending to find the best set of permissions and intents that can detect Android malware with better accuracy. We propose a novel algorithm to find the best set by applying several machine learning algorithms such as Random Forest, SVM, and Naive Bayes. The experimental results demonstrate that the best set consisted of 37 features, i.e., 20 intents and 17 permissions, and Random Forest classifier gave the best accuracy of 94.73%.
A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

The development of computer and network technology has provided convenience to our daily life, however, attack and intrusion in network emerge endlessly. Intrusion Detection System (IDS) has been developed to confront network attacks. As a result, the research of IDS is one of the most popular fields in recent years. This paper proposes a Gradient Boosting Decision Tree (GBDT)-paralleled quadratic ensemble learning method for intrusion detection system. We use GBDT to deal with the spatial part of traffic data and use Gated Recurrent Unit (GRU) model with special modification for network traffic to deal with temporal data. Then, in order to combine the spatial feature and temporal feature, we fuse GBDT model and GRU model to make a quadratic ensemble model as our final intrusion detection system. The experimental results based on CICIDS2017 dataset show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods. The accuracies of detecting benign, port scan, Distributed Denial of Service (DDoS), infiltration and web attack traffic are up to 99.9%, 99.9%, 99.9%, 99.9%, and 99.9%, respectively. We also use our method in Information-Centric Networking (ICN) dataset and the results show our method achieves much better performance compared with existing methods.
Resistance Gene Analogs in the Brassicaceae: Identification, Characterization, Distribution, and Evolution1[OPEN]

A resource of resistance gene analogs within Brassicaceae species identifies functional resistance genes to improve plant breeding programs. The Brassicaceae consists of a wide range of species, including important Brassica crop species and the model plant Arabidopsis (Arabidopsis thaliana). Brassica spp. crop diseases impose significant yield losses annually. A major way to reduce susceptibility to disease is the selection in breeding for resistance gene analogs (RGAs). Nucleotide binding site-leucine rich repeats (NLRs), receptor-like kinases (RLKs), and receptor-like proteins (RLPs) are the main types of RGAs; they contain conserved domains and motifs and play specific roles in resistance to pathogens. Here, all classes of RGAs have been identified using annotation and assembly-based pipelines in all available genome annotations from the Brassicaceae, including multiple genome assemblies of the same species where available (total of 32 genomes). The number of RGAs, based on genome annotations, varies within and between species. In total 34,065 RGAs were identified, with the majority being RLKs (21,691), then NLRs (8,588) and RLPs (3,786). Analysis of the RGA protein sequences revealed a high level of sequence identity, whereby 99.43% of RGAs fell into several orthogroups. This study establishes a resource for the identification and characterization of RGAs in the Brassicaceae and provides a framework for further studies of RGAs for an ultimate goal of assisting breeders in improving resistance to plant disease.
Performance Evaluation of Anomaly Detection in Imbalanced System Log Data

An administrator needs to examine operating system log files for any anomalous events. In real-life log data, the number of anomalies is often smaller than the normal ones. This imbalance situation affects the performance of the anomaly detectors because a large number of normal events feed the training of the classifier. In this paper, we evaluate popular machine learning methods and consider this problem of data imbalance. We compare data oversampling and undersampling approaches before inputting them to the classifier. Experimental results demonstrate that by taking data imbalance into consideration, there is an improvement in the method performance in terms of precision and recall scores.
CCR2 Mediates Chronic LPS-Induced Pulmonary Inflammation and Hypoalveolarization in a Murine Model of Bronchopulmonary Dysplasia

The histopathology of bronchopulmonary dysplasia (BPD) includes hypoalveolarization and interstitial thickening due to abnormal myofibroblast accumulation. Chorioamnionitis and sepsis are major risk factors for BPD development. The cellular mechanisms leading to these lung structural abnormalities are poorly understood. We used an animal model with repeated lipopolysaccharide (LPS) administration into the airways of immature mice to simulate prolonged airway exposure to gram-negative bacteria, focusing on the role of C-C chemokine receptor type 2-positive (CCR2+) exudative macrophages (ExMf). Repetitive LPS exposure of immature mice induced persistent hypoalveolarization observed at 4 and 18 days after the last LPS administration. LPS upregulated the expression of lung pro-inflammatory cytokines (TNF-α, IL-17a, IL-6, IL-1β) and chemokines (CCL2, CCL7, CXCL1, and CXCL2), while the expression of genes involved in lung alveolar and mesenchymal cell development (PDGFR-α, FGF7, FGF10, and SPRY1) was decreased. LPS induced recruitment of ExMf, including CCR2+ ExMf, as well as other myeloid cells like DCs and neutrophils. Lungs of LPS-exposed CCR2−/− mice showed preserved alveolar structure and normal patterns of α-actin and PDGFRα expression at the tips of the secondary alveolar crests. Compared to wild type mice, a significantly lower number of ExMf, including TNF-α+ ExMf were recruited to the lungs of CCR2−/− mice following repetitive LPS exposure. Further, pharmacological inhibition of TLR4 with TAK-242 also blocked the effect of LPS on alveolarization, α-SMA and PDGFRα expression. TNF-α and IL-17a induced α-smooth muscle actin expression in the distal airspaces of E16 fetal mouse lung explants. In human preterm lung mesenchymal stromal cells, TNF-α reduced mRNA and protein expression of PDGFR-α and decreased mRNA expression of WNT2, FOXF2, and SPRY1. Collectively, our findings demonstrate that in immature mice repetitive LPS exposure, through TLR4 signaling increases lung inflammation and impairs lung alveolar growth in a CCR2-dependent manner.