Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Delinquency and Social Relationships of Loners

Little is known about the delinquency and social relations of adolescents who lack close friendships. This study compares the delinquency of “loners” and “nonloners,” explores group differences within the larger social context of peers, family, and school, and assesses the efficacy of loner status as a distinct dimension of peer relationships. The findings indicate that loners are less delinquent than nonloners are, but there are nevertheless negative circumstances associated with being a loner.
Lavandula angustifolia Mill. Essential Oil Exerts Antibacterial and Anti-Inflammatory Effect in Macrophage Mediated Immune Response to Staphylococcus aureus

ABSTRACT Different studies described the antibacterial properties of Lavandula angustifolia (Mill.) essential oil and its anti-inflammatory effects. Besides, no data exist on its ability to activate human macrophages during the innate response against Staphylococcus aureus. The discovery of promising regulators of macrophage-mediated inflammatory response, without side effects, could be useful for the prevention of, or as therapeutic remedy for, various inflammation-mediated diseases. This study investigated, by transcriptional analysis, how a L. angustifolia essential oil treatment influences the macrophage response to Staphylococcus aureus infection. The results showed that the treatment increases the phagocytic rate and stimulates the containment of intracellular bacterial replication by macrophages. Our data showed that this stimulation is coupled with expression of genes involved in reactive oxygen species production (i.e., CYBB and NCF4). Moreover, the essential oil treatment balanced the inflammatory signaling induced by S. aureus by repressing the principal pro-inflammatory cytokines and their receptors and inducing the heme oxygenase-1 gene transcription. These data showed that the L. angustifolia essential oil can stimulate the human innate macrophage response to a bacterium which is responsible for one of the most important nosocomial infection and might suggest the potential development of this plant extract as an anti-inflammatory and immune regulatory coadjutant drug. Abbreviations: CE: controlled environment; EO: essential oil; LA: Lavandula angustifolia Mill. ; MDM: monocyte-derived macrophages; NE: natural environment; Neg-Res: negative responsive donors; TLR-IL1R: Toll-like receptor-interleukin 1 receptor; Pos-Res: positive responsive donor
Localization of Sub/super-synchronous Oscillation Sources in Wind Power Systems Based on Deep Adaptation Network

When persistent sub/super-synchronous oscillations occur in a wind power system, timely and accurate localization of the wind turbine that causes the oscillations is the first and foremost task to suppress the oscillations precisely. However, the scarcity of actual system oscillation data causes a serious data bottleneck in the research of localization based on data-driven technology. Based on this, this paper proposes an online localization method of oscillation source based on deep adaptation network. Through the transfer and generalization between data, the problem of scarcity and difficulty in obtaining the oscillation data of the actual system is solved. The analysis of examples shows that the oscillation source localization method proposed in this paper has better performance in terms of model training speed as well as localization accuracy.
The NS1 Protein of a Human Influenza Virus Inhibits Type I Interferon Production and the Induction of Antiviral Responses in Primary Human Dendritic and Respiratory Epithelial Cells

ABSTRACT The NS1 protein of the influenza A virus is a potent virulence factor that inhibits type I interferon (IFN) synthesis, allowing the virus to overcome host defenses and replicate efficiently. However, limited studies have been conducted on NS1 function using human virus strains and primary human cells. We used NS1 truncated mutant influenza viruses derived from the human isolate influenza A/TX/91 (TX WT, where WT is wild type) to study the functions of NS1 in infected primary cells. Infection of primary differentiated human tracheo-bronchial epithelial cells with an NS1 truncated mutant demonstrated limited viral replication and enhanced type I IFN induction. Additionally, human dendritic cells (DCs) infected with human NS1 mutant viruses showed higher levels of activation and stimulated naïve T-cells better than TX WT virus-infected DCs. We also compared infections of DCs with TX WT and our previously characterized laboratory strain A/PR/8/34 (PR8) and its NS1 knockout strain, deltaNS1. TX WT-infected DCs displayed higher viral replication than PR8 but had decreased antiviral gene expression at late time points and reduced naïve T-cell stimulation compared to PR8 infections, suggesting an augmented inhibition of IFN production and human DC activation. Our findings show that human-derived influenza A viruses have a high capacity to inhibit the antiviral state in a human system, and here we have evaluated the possible mechanism of this inhibition. Lastly, C-terminal truncations in the NS1 protein of human influenza virus are sufficient to make the virus attenuated and more immunogenic, supporting its use as a live attenuated influenza vaccine in humans.
Changing the Conversation about Higher Education ed. by J. Robert Thompson Jr. (review)

generally disjointed. Moreover, use of the term “SSCI syndrome” of the title is inconsistent. Contributing authors use a mix of terms, such as “3-I syndrome,” “I-idolization,” and “publish or perish,” none of which are clearly defined in contrast to one another. The text could have strengthened its argument and increased its potential impact through sufficient organization and collaboration prior to publication. That said, the book is a valuable and timely contribution to knowledge in the face of mounting challenges to academics around the world confronting emerging pressures due to international competition, lack of resources, and the resulting changes in policy and governance. Scholars working in higher education, policymakers, and university students already exposed to the contagion of the SSCI syndrome will find it a useful reference for their research and professional work. However, the lack of cohesion will leave some readers disappointed, as the value of the book is equal to the sum of its parts and no greater.
Diagnostic and prognostic utility of early measurement with high-sensitivity troponin T assay in patients presenting with chest pain

Background: High-sensitivity troponin assays are now available for clinical use. We investigated whether early measurement with such an assay is superior to a conventional assay in the evaluation of acute coronary syndromes. Methods: Patients presenting to an emergency department with chest pain who did not have ST-segment elevation were prospectively recruited from November 2007 to December 2010. Patients underwent serial testing with a conventional cardiac troponin I assay. Samples were also obtained at presentation and two hours later for measurement of troponin T levels using a high-sensitivity assay. The primary outcome was diagnosis of myocardial infarction on admission; secondary outcomes were death, myocardial infarction and heart failure at one year. Results: Of the 939 patients enrolled in the study, 205 (21.8%) had myocardial infarction. By two hours after presentation, the high-sensitivity troponin T assay at the cut-off point of the 99th percentile of the general population (14 ng/L) had a sensitivity of 92.2% (95% confidence interval [CI] 88.1%–95.0%) and a specificity of 79.7% (95% CI 78.6%–80.5%) for the diagnosis of non–ST-segment myocardial infarction. The sensitivity of the assay at presentation was 100% among patients who presented four to six hours after symptom onset. By one year, the high-sensitivity troponin T assay was found to be superior than the conventional assay in predicting death (hazard ratio [HR] 5.4, 95% CI 2.7–10.7) and heart failure (HR 27.8, 95% CI 6.6–116.4), whereas the conventional assay was superior in predicting nonfatal myocardial infarction (HR 4.0, 95% CI 2.4–6.7). Interpretation: The high-sensitivity troponin T assay at the cut-off point of the 99th percentile was highly sensitive for the diagnosis of myocardial infarction by two hours after presentation and had prognostic utility beyond that of the conventional assay. To rule out myocardial infarction, the optimal time to test a second sample using the high-sensitivity troponin T level may be four to six hours after symptom onset, but this finding needs verification in future studies before it can become routine practice.
Design of a High-Performance Scalable CDMA Router for On-Chip Switched Networks

Performance results and synthesized area overhead for a code division multiple access (CDMA) router intended for network-on-chip (NoC) applications are presented. Specific architectural block diagrams of the main components of the router are given and synthesis results are provided for 0.18 micron and 0.25 micron structured ASIC libraries. Post-synthesis VHDL simulations verify the functionality of the router and provide values for packet transmission latency and throughput as functions of the payload size. The router can be used to construct star+star and star+mesh network architectures which can be scaled to meet the needs of high-performance applications.
A Comparison of Two-Group Classification Methods

The statistical classification of N individuals into G mutually exclusive groups when the actual group membership is unknown is common in the social and behavioral sciences. The results of such classification methods often have important consequences. Among the most common methods of statistical classification are linear discriminant analysis, quadratic discriminant analysis, and logistic regression. However, recent developments in the statistics literature have brought new and potentially more flexible classification models to the forefront. Although these new models are increasingly being used in the physical sciences and marketing research, they are still relatively little used in the social and behavioral sciences. The purpose of this article is to provide a comparison of these modern methods with the classical methods widely used in situations that are relevant in the social and behavioral sciences. This study uses a large-scale Monte Carlo simulation study for the comparisons, as analytic comparisons are often not tractable. Results indicate that classification and regression trees generally produced the highest classification accuracy of all techniques tested, though study design characteristics such as sample size and model complexity can greatly influence optimal choice or effectiveness of statistical classification method.
Narrative Comprehension Skills in 5-Year-Old Children: Correlational Analysis and Comprehender Profiles

ABSTRACT This study was designed to examine whether a variety of cognitive and linguistic factors theoretically considered to be predictive of reading comprehension skills in elementary school children were also predictive of listening comprehension skills in 131 five-year-old children. The results showed that the predictors of young children's listening comprehension performance are indeed similar to those identified in the literature as predictive of reading comprehension performance. The involvement of sentence comprehension skills, updating capacities, vocabulary, and syntactic and morphological knowledge was therefore confirmed in young children. Furthermore, a cluster analysis provided evidence of early profiles of comprehenders who differed in their ability to respond to either literal or inferential questions in a comprehension test. The skills that make it possible to distinguish between these profiles are identified.
Reduced Spread Risk of COVID-19 based on Tracking System Monitoring in WSNs

The phrase "COVID-19" has become the most popular and highly searched term on Google since its emergence in November 2019. The world must combat this pandemic. With advancements in mobile technology and sensors, Developing Internet of Things (IoT)-based healthcare solutions is now feasible. Proactive as well as preventative, these new systems, are in contrast to the traditional reactive healthcare systems. In this study, a paradigm based on the Internet of Things is proposed for detecting COVID-19 suspects. The framework presented in this research focuses on predicting COVID-19 in its early stages. To determine whether the virus is present, it uses health measurements collected from sensors and other Internet of Things devices. By gathering symptom data related to COVID-19, the behavior of the virus can be better understood. Four machine learning models are introduced in this paper: CatBoost, Decision Tree, Random Forest, and Logistic Regression. These models are used to create a Diagnostic Learning Classifier (DLC) model. The top three performing models, Decision Tree, Logistic Regression, and CatBoost, are combined using a weighted average or majority voting approach, depending on the nature of the problem. The DLC model is compared to base models such as Random Forest, Decision Tree, Logistic Regression, and CatBoost, using multiple evaluation criteria. The goal of this ensemble model is to increase forecast accuracy by utilizing the variety and advantages of the chosen models. It delivers great diagnostic accuracy when used as a machine learning model on the gathered data. Notably, the DLC classifier achieves an accuracy of 91%, followed by Random Forest (86%), CatBoost (85%), Logistic Regression (84%), and Decision Tree (82%). These results demonstrate the effectiveness of the voting classifier in efficiently managing COVID-19 by accurately diagnosing patients and notifying health authorities of suspected cases.
Boys' underachievement and the management of teacher accountability

This paper investigates the ways in which discourses of teacher accountability were negotiated by teachers within hearings held for the Australian Inquiry into Boys' Education. In the Inquiry context, dominant interpretative repertoires positioned teachers as obliged to ‘acknowledge’, ‘address’ and ‘actively respond’ to an essentialised version of male students' ‘learning needs’. These repertoires functioned to equate the provision of ‘boy-friendly’ interventions with ‘professional’ classroom practice – suggesting significant discursive constraints on teachers' capacity to make sense of boys' underperformance outside of this dominant framework. Attention is paid to the ways in which these repertoires supported teachers to manage a positive identity in a context where they were positioned as largely responsible for male students' underperformance. At the same time, such constructions are interrogated with regard to their broader function in naturalising the surveillance of teachers, and reproducing boys' underachievement as something for which (particularly female) teachers have to account.
A taxonomy of multicast data origin authentication: Issues and solutions

Multicasting is an efficient communication mechanism for group-oriented applications such as videoconferencing, broadcasting stock quotes, interactive group games, and video on demand. The lack of security obstructs a large deployment of this efficient communication model. This limitation motivated a host of research works that have addressed the many issues relating to securing the multicast, such as confidentiality, authentication, non-repudiation, integrity, and access control. Many applications, such as broadcasting stock quotes and video-conferencing, require data origin authentication of the received traffic. Hence, data origin authentication is an important component in the multicast security architecture. Multicast data origin authentication must take into consideration the scalability and the efficiency of the underlying cryptographic schemes and mechanisms, because multicast groups can be very large and the exchanged data is likely to be heavy in volume (streaming). Besides, multicast data origin authentication must be robust enough against packet loss because most multicast multimedia applications do not use reliable packet delivery. Therefore, multicast data origin authentication is subject to many concurrent and competitive challenges, when considering these miscellaneous application-level requirements and features. In this article we review and classify recent works dealing with the data origin authentication problem in group communication, and we discuss and compare them with respect to some relevant performance criteria.
A novel codebook representation method and encoding strategy for bag-of-words based acoustic event classification

The bag-of-words (BoW) model has been widely used for acoustic event classification (AEC). The performance of the BoW based AEC model is much influenced by "codebook construction" and "histogram generation". The common approaches for constructing the codebook and generating the histogram are the K-means and vector quantization encoding (VQE) respectively. However, they have some inherent disadvantages which pose negative effects on the AEC performance. In this paper, for the BoW based AEC problem, we propose a novel method to construct the codebook and generate the histogram. The self-organizing feature map (SOFM) network is utilized for codebook construction, which can ameliorate the local optimization problem. In addition, an N-Competition encoding strategy is proposed for histogram generation, and the robustness to the boundary points is improved. Experimental result shows that, the proposed method can achieve average 2.4% improvement in accuracy over the traditional BoW based method. Experimental analysis denote that our proposed approach can obtain robust boundary points and effective codebook.
Older-users' acceptance of smartcard payment systems: An investigation of an old-street venders

Taipei city EASYCARD Corp. continues to explore avenues to integrate more services and plans to extend the use of its smart card to other business activities beyond transportation systems, such as smart card use in the payment systems of small-value purchases. The ultimate aim is to allow people to travel around Taiwan with only one card. However, despite the many lessons that can be learned from the experiences of those company initiatives, there are still various issues pertaining to the implementation of their new services. Based on the Unified Theory of Acceptance and Use of technology (UTAUT) model, this paper specifically investigates planned usage of the Easycard payment systems between `oldstreet' venders and their customers. Analysis of the results indicates that there is significant difference between the venders and customers' perception in the behavioral intentions to use smart card payment systems services. It is implicated that females, older and lower- educated users' lack influence on the new technology's performance expectancy, lack facilitating conditions, and lack social influence, so as to affect their intension to use technology.
Adversarial Learning with Bayesian Hierarchical Mixtures of Experts

Many data mining applications operate in adversarial environment, for example, webpage ranking in the presence of web spam. A growing number of adversarial data mining techniques are recently developed, providing robust solutions under specific defense-attack models. Existing techniques are tied to distributional assumptions geared towards minimizing the undesirable impact of given attack models. However, the large variety of attack strategies renders the adversarial learning problem multimodal. Therefore, it calls for a more flexible modeling ideology for equivocal input. In this paper we present a Bayesian hierarchical mixtures of experts for adversarial learning. The technique groups data into soft partitions and fits simple function approximators, referred to as “experts”, within each. Experts are ranked using gating functions for each input. Ambiguous input is predicted competitively by multiple experts, while unambiguous input is effectively predicted by a single expert. Optimal attacks minimizing the likelihood of malicious data are modeled interactively at both expert and gating levels in the learning hierarchy. We demonstrate that our adversarial hierarchicalmixtures-of-experts learning model is robust against adversarial attacks on both artificial and real data.
A 76-107-GHz Low-Noise Amplifier with 17-dB Gain and 6.3-dB NF in 40-nm CMOS

This paper presents a compact W-band differential low-noise amplifier (LNA) in 40-nm CMOS. The LNA adopts three-stage differential common source topology with transformer-based magnetically coupled resonator (MCR) matching network and neutralization capacitors. The post-layout simulation results show that the proposed LNA achieves a 3-dB bandwidth of 31 GHz from 76 GHz to 107 GHz, a 17-dB power gain at 96 GHz, and a 6.3-dB noise figure (NF) at 87 GHz. The LNA consumes 42.4 mW at a 1.1 V power supply with a compact core area of 0.05 mm2.
Document clustering using nonnegative matrix factorization q

A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies. 2004 Elsevier Ltd. All rights reserved.
The Electoral Terrorist: Terror Groups and Democratic Participation

Why do some terrorist groups participate in the electoral process but not others? If elections provide some strategic or tactical benefit then we would expect other groups to emulate that strategy. However, we see variation in the adoption of an electoral strategy by terrorist groups. I argue that involvement in territorial disputes and group competition determine whether terrorist groups embrace an electoral strategy. Conflicts involving territorial disputes are more likely to see terrorist groups contest elections because electoral participation may aid in the creation of the independent or autonomous territory they desire. Increased group competition changes the number of actors, which impacts the level and distribution of resources (supporters, finance, and arms) involved in the conflict. When multiple terrorist groups compete, groups are motivated to participate in elections in response to new competitive pressures. This argument is tested using a large-n dataset of 89 terrorist groups in existence during the years 1968–2006 and a case study of Hamas's decision to contest elections.