Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding motivation in the self-directedness of first-year teacher students

This article investigated the relation between learning motivation and self-directedness in learning of first-year teacher students. Self-directed learning is characterized as one of the fastest growing areas of research in the past forty years. Self-directed learning can be regarded as an essential skill in the 21st century and it implies that the acquisition of school subject knowledge alone is not sufficient; skills such as critical thinking, resource identification and responsibility to learn should be emphasised. Furthermore, a student with a high level of self-directedness in learning can set educational objectives and also reach these objectives as well as successfully avail themselves of opportunities beyond the boundaries of formal education, which can accordingly lead to career success and economic growth. Although the value of self-directed learning can be overstated, students find it difficult to keep up with the academic pace due to the leap from secondary to tertiary education. It turns out that first-year students qualify on paper for university admission, but in fact they cannot acquire the necessary academic skills (including self-directed learning). Despite the lack of self-directed learning activities in secondary education De Lange et al. (2010) mention several factors that can affect teaching and learning (as well as self-directed learning) of a student, namely a lack of motivation. From the above statement it is evident that a lack of motivation may have an effect on a student’s self-directedness in learning. Learning motivation is the driving force behind the implementation of self-directed learning activities although learning motivation is characterized as one of the most popular research dimensions in the area of self-directed learning. In an attempt to explain the above context and interpret this study, an interpretivistic qualitative research design was followed. The main research findings of this article included that learning motivation is the driving force behind self-directed learning, and therefore it enables students to learn in a self-directed manner.
Effects of Teachers' Information Literacy on Lifelong Learning and School Effectiveness.

Background:The rapid alternation of computers, the Internet, and smart phones has human beings live in the information-built environment. Information technology is also applied to measure the modernization of a nation. A lot of countries even list computer learning as a primary course in the compulsory education, as the one mastering the latest information in the information flood could dominate everything and present better competitiveness in the world. Such a trend reflects on the education, especially the informatized administrative environments and information integrated instruction in schools, because convenient network information enhances interpersonal interaction breaking through traditional restrictions of space and time.Material and methods:Aiming at the teachers of universities in Fujian Province, total 500 copies of questionnaires are distributed, and 276 valid copies are collected, with the retrieval rate 55%. TResults:The research results show significant correlations between 1.information literacy and lifelong learning, 2.lifelong learning and school effectiveness, and 3.information literacy and school effectiveness. This study could help educational sectors better understand the current situation and discover the encountered problems and challenges.Conclusions:Based on such situations, suggestions are provided for education sectors. In addition to the academic contribution, this study also presents the value for practical reference.
Mutational profiling of colorectal cancers with microsatellite instability

Microsatellite instability (MSI) is caused by defective mismatch repair in 15–20% of colorectal cancers (CRCs). Higher mutation loads in tumors with mismatch repair deficiency can predict response to pembrolizumab, an anti-programmed death 1 (PD-1) immune checkpoint inhibitor. We analyzed the mutations in 113 CRCs without MSI (MSS) and 29 CRCs with MSI-High (MSI-H) using the 50-gene AmpliSeq cancer panel. Overall, MSI-H CRCs showed significantly higher mutations than MSS CRCs, including insertion/deletion mutations at repeat regions. MSI-H CRCs showed higher incidences of mutations in the BRAF, PIK3CA, and PTEN genes as well as mutations in the receptor tyrosine kinase families. While the increased mutations in BRAF and PTEN in MSI-H CRCs are well accepted, we also support findings of mutations in the mTOR pathway and receptor tyrosine kinase family genes. MSS CRCs showed higher incidences of mutations in the APC, KRAS and TP53 genes, confirming previous findings. NGS assays may be designed to detect driver mutations for targeted therapeutics and to identify tumors with high mutation loads for potential treatment with immune checkpoint blockade therapies. Further studies may be warranted to elucidate potential targeted therapeutics against mutations in the mTOR pathway and the receptor tyrosine kinase family in MSI-H CRCs as well as the benefit of anti-PD-1 immunotherapy in hypermutated MSS CRCs or other cancers.
Deployment of a Secure Wireless Infrastructure Oriented to Vehicular Networks

This paper describes a testbed scenario deployed over a WiMAX1 [11] based wireless access network using IKEv22 [3] protocol for authentication purposes. OpenIKEv2[8] is the chosen implementation of IKEv2, that provides security to wireless communications, but also allows us to use the EAP3 [6] authentication protocol that lets us reach the main purpose of this testbed: to test the behaviour of recent designed authentication methods in a mobility scenario of vertical and horizontal handovers. These researches are aimed at vehicular networks[1], where mobility is the crucial aspect to be taken into account.
Joint Routing and STDMA-based Scheduling to Minimize Delays in Grid Wireless Sensor Networks

In this report, we study the issue of delay optimization and energy efficiency in grid wireless sensor networks (WSNs). We focus on STDMA (Spatial Reuse TDMA)) scheduling, where a predefined cycle is repeated, and where each node has fixed transmission opportunities during specific slots (defined by colors). We assume a STDMA algorithm that takes advantage of the regularity of grid topology to also provide a spatially periodic coloring ("tiling" of the same color pattern). In this setting, the key challenges are: 1) minimizing the average routing delay by ordering the slots in the cycle 2) being energy efficient. Our work follows two directions: first, the baseline performance is evaluated when nothing specific is done and the colors are randomly ordered in the STDMA cycle. Then, we propose a solution, ORCHID that deliberately constructs an efficient STDMA schedule. It proceeds in two steps. In the first step, ORCHID starts form a colored grid and builds a hierarchical routing based on these colors. In the second step, ORCHID builds a color ordering, by considering jointly both routing and scheduling so as to ensure that any node will reach a sink in a single STDMA cycle. We study the performance of these solutions by means of simulations and modeling. Results show the excellent performance of ORCHID in terms of delays and energy compared to a shortest path routing that uses the delay as a heuristic. We also present the adaptation of ORCHID to general networks under the SINR interference model.
Investigation of Nonreciprocal Dispersion Phenomena in Anisotropic Periodic Structures Based on a Curvilinear FDFD Method

The aim of this paper is the investigation of nonreciprocal phenomena in anisotropically loaded 2-D periodic structures. For this purpose, our well-established 2-D curvilinear finite difference frequency domain method is combined with periodic boundary conditions and extended toward the eigenanalysis of periodic structures loaded with both isotropic and general anisotropic materials. The periodic structures are simulated in a 2-D domain, while uniformity is considered along the third axis. The propagation constant along the third axis can either be zero (in-plane-propagation) or nonzero (out-of-plane propagation). Particular effort was devoted to the identification of the appropriate irreducible Brillouin zone to be scanned during the eigenanalysis. It was herein realized that similar to geometrically artificial crystal anisotropy, the wave propagation directional asymmetries modify the irreducible Brillouin zone in the microwave regime as well. Both gyrotropic and particularly magnetized ferrite as well as full tensor anisotropic (arbitrarily biased ferrite) material loadings are investigated through the eigenanalysis of different periodic structures, including strip grating. Interesting nonreciprocal backward wave and unidirectional phenomena are justified as expected.
An Affine Projection Algorithm With Update-Interval Selection

This paper presents a mean-square deviation (MSD) analysis of the periodic affine projection algorithm (P-APA) and two update-interval selection methods to achieve improved performance in terms of the convergence and the steady-state error. The MSD analysis of the P-APA considers the correlation between the weight error vector and the measurement noise vector. Using this analysis, it is verified that the update interval governs the trade-off between the convergence rate and the steady-state errors in the P-APA. To overcome this drawback, the proposed APAs increase the update interval when the adaptive filter reaches the steady state. Consequently, these algorithms can reduce the overall computational complexity. The simulation results show that the proposed APAs perform better than the previous algorithms.
Epigenetic inactivation of VGF associated with Urothelial Cell Carcinoma and its potential as a non-invasive biomarker using urine

Background: To identify new epigenetic markers and further characterize Urothelial Cell Carcinoma (UCC), we tested the promoter methylation (PM) status of 19 genes previously identified as cancer specific methylated genes in other solid tumors. Methods: We used bisulfite sequencing, methylation specific PCR and quantitative methylation specific PCR (QMSP) to test the PM status of 19 genes in urothelial cancer cell lines. Results: Among the 19 genes tested, VGF was found to be completely methylated in several UCC cell lines. VGF QMSP analysis showed that methylation values of almost all the primary 19 UCC tissues were higher than the paired normal tissues (P=0.009). In another cohort, 12/35 (34.3%) of low grade UCC cases displayed VGF methylation. As a biomarker for non-invasive detection of UCC, VGF showed a significantly higher frequency of methylation in urine from UCC cases (8/20) compared to controls (1/20) (P=0.020). After treatment of cell lines with 5-Aza-2'-deoxycytidine, VGF was robustly re-expressed. Forced expression of VGF in bladder cancer cell lines inhibited cell growth. Conclusion: Selection of candidates from genome-wide screening approach in other solid tumors successfully identified UCC specific methylated genes.
Can MPI Benefit Hadoop and MapReduce Applications?

The Message Passing Interface (MPI) standard and its implementations (such as MPICH and OpenMPI) have been widely used in the high-performance computing area to provide an efficient communication infrastructure. This paper investigates whether MPI can be adapted to the data intensive computing area to substantially speed up Hadoop and MapReduce applications, by reducing communication overheads. Three specific issues are studied. First, is the potential for reducing communication overheads significant, if MPI is used? Second, what are the main technical challenges to adapt MPI to Hadoop? Third, what are the minimal extensions to the MPI standard that can help alleviate the challenges while promise to significantly improve performance? To answer the first question, we identify important and basic communication primitives in both MPI and Hadoop, and make fair comparisons of their performance through experiments. The results show that the potential for improvement could be high. To answer the second and the third questions, we analyze the Hadoop code base to identify communication related programmers' needs. Furthermore, we propose a minimal interface extension to the MPI standard (only one pair of library calls are added), which capture the key-value pair nature commonly found in data intensive computing. This extension is implemented in a prototype library called MPI-D. Benchmark tests based on simulation show that Hadoop augmented with MPI-D could significantly speed up MapReduce application performance.
Accounting and Accountability in Content Distribution Architectures

Many content distribution architectures offer desirable elements that lead to less bandwidth usage, reduced network congestion, higher content availability, and reduced costs. However, their level of adoption and utilization in commercial environments depends on the level of content accounting and accountability they offer. Content accounting refers to any information that a content distributor needs to track relating to the delivery of content to its intended consumers. In contrast, content accountability refers to the willingness of the communicating infrastructure to produce accurate and verifiable information about the content distribution process. This article surveys existing and proposed future content delivery architectures detailing their methodologies for providing efficient low-cost content distribution, content accounting, and accountability across trustworthy and untrustworthy infrastructures. We use two methods to help identify the drawbacks and merits of these architectures. The first is a taxonomy for accounting information based on our analysis of logging information gathered from the surveyed systems. The second is a generic model for content distribution based on a synthesis of desirable elements from the surveyed architectures. These methods help highlight key architectural elements required for efficient low-cost content distribution. Finally, we identify outstanding challenges related to establishing accountability in accounting information produced for content distributed across trusted and untrusted infrastructures.
Toxicity Assessment of Iron Oxide Nanoparticles in Zebrafish (Danio rerio) Early Life Stages

Iron oxide nanoparticles have been explored recently for their beneficial applications in many biomedical areas, in environmental remediation, and in various industrial applications. However, potential risks have also been identified with the release of nanoparticles into the environment. To study the ecological effects of iron oxide nanoparticles on aquatic organisms, we used early life stages of the zebrafish (Danio rerio) to examine such effects on embryonic development in this species. The results showed that ≥10 mg/L of iron oxide nanoparticles instigated developmental toxicity in these embryos, causing mortality, hatching delay, and malformation. Moreover, an early life stage test using zebrafish embryos/larvae is also discussed and recommended in this study as an effective protocol for assessing the potential toxicity of nanoparticles. This study is one of the first on developmental toxicity in fish caused by iron oxide nanoparticles in aquatic environments. The results will contribute to the current understanding of the potential ecotoxicological effects of nanoparticles and support the sustainable development of nanotechnology.
An empirical examination of corporate websites as a voluntary disclosure medium

Standards-setters, regulators and academics believe that corporate websites may enhance the flow of voluntary disclosure to the capital market and other stakeholders. Management’s use of corporate websites for investor relations purposes is a common practice, yet we know little about how these websites affect investors. In this study, we analyse seven corporate websites disclosures categories to examine their predictive ability and their value relevance. The results show that the key non-financial statistics, projected information, information on intangible assets, social and environmental information, are associated with future revenue, future earnings and contemporaneous stock return. The paper contributes to the growing literature on websites disclosure and more generally to the literature on voluntary and strategic disclosure.
Identification of Zebrafish Fxyd11a Protein that is Highly Expressed in Ion-Transporting Epithelium of the Gill and Skin and its Possible Role in Ion Homeostasis

FXYD proteins, small single-transmembrane proteins, have been proposed to be auxiliary regulatory subunits of Na+–K+-ATPase and have recently been implied in ion osmoregulation of teleost fish. In freshwater (FW) fish, numerous ions are actively taken up through mitochondrion-rich cells (MRCs) of the gill and skin epithelia, using the Na+ electrochemical gradient generated by Na+–K+-ATPase. In the present study, to understand the molecular mechanism for the regulation of Na+–K+-ATPase in MRCs of FW fish, we sought to identify FXYD proteins expressed in MRCs of zebrafish. Reverse-transcriptase PCR studies of adult zebrafish tissues revealed that, out of eight fxyd genes found in zebrafish database, only zebrafish fxyd11 (zfxyd11) mRNA exhibited a gill-specific expression. Double immunofluorescence staining showed that zFxyd11 is abundantly expressed in MRCs rich in Na+–K+-ATPase (NaK-MRCs) but not in those rich in vacuolar-type H+-transporting ATPase. An in situ proximity ligation assay demonstrated its close association with Na+–K+-ATPase in NaK-MRCs. The zfxyd11 mRNA expression was detectable at 1 day postfertilization, and its expression levels in the whole larvae and adult gills were regulated in response to changes in environmental ionic concentrations. Furthermore, knockdown of zFxyd11 resulted in a significant increase in the number of Na+–K+-ATPase–positive cells in the larval skin. These results suggest that zFxyd11 may regulate the transport ability of NaK-MRCs by modulating Na+–K+-ATPase activity, and may be involved in the regulation of body fluid and electrolyte homeostasis.
A combination of methylprednisolone and quercetin is effective for the treatment of cardiac contusion following blunt chest trauma in rats

Cardiac contusion is a potentially fatal complication of blunt chest trauma. The effects of a combination of quercetin and methylprednisolone against trauma-induced cardiac contusion were studied. Thirty-five female Sprague-Dawley rats were divided into five groups (n=7) as follows: sham, cardiac contusion with no therapy, treated with methylprednisolone (30 mg/kg on the first day, and 3 mg/kg on the following days), treated with quercetin (50 mg·kg−1·day−1), and treated with a combination of methylprednisolone and quercetin. Serum troponin I (Tn-I) and tumor necrosis factor-alpha (TNF-α) levels and cardiac histopathological findings were evaluated. Tn-I and TNF-α levels were elevated after contusion (P=0.001 and P=0.001). Seven days later, Tn-I and TNF-α levels decreased in the rats treated with methylprednisolone, quercetin, and the combination of methylprednisolone and quercetin compared to the rats without therapy, but a statistical significance was found only with the combination therapy (P=0.001 and P=0.011, respectively). Histopathological degeneration and necrosis scores were statistically lower in the methylprednisolone and quercetin combination group compared to the group treated only with methylprednisolone (P=0.017 and P=0.007, respectively). However, only degeneration scores were lower in the combination therapy group compared to the group treated only with quercetin (P=0.017). Inducible nitric oxide synthase positivity scores were decreased in all treatment groups compared to the untreated groups (P=0.097, P=0.026, and P=0.004, respectively). We conclude that a combination of quercetin and methylprednisolone can be used for the specific treatment of cardiac contusion.
A Method for Evaluation of the Performance of Requirements Inspections

A new improved model is introduced for the performance of inspectors of user requirements documents. The model, which is based on a linearity assumption employs only two non-dimensional metrics in the [0;1] interval. They characterise the error detection ability of the inspectors and their domain knowledge level. It is our experience that one learns to reason with these two metrics. The application of the model is demonstrated with an analysis of which kind of inspectors, low salaried students or experienced high salaried engineers, will perform a specified inspection most economically.
Observer for Linear Time Invariant Systems with Unknown Inputs based on the Hierarchical Super-Twisting Concept

We use an algorithm, which allows to find the strong observable space, to design a hierarchical observer providing the exact reconstruction of the state components. In order to design the state estimation, we use the super twisting (second order sliding mode) algorithm. The continuity of the super twisting output injection allows to reconstruct in each step a vector formed by a matrix pre-multiplied by the state vector, all this in a finite time. The sequential application of this procedure finally leads to the obtaining of the state vector which can be represented by an auxiliary state vector plus the equivalent super twisting output injection. It is assumed only that the system has no zeros and the unknown inputs are bounded. A numerical example illustrates the efficiency of the suggested technique
Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks

We propose deep distributed recurrent Q-networks (DDRQN), which enable teams of agents to learn to solve communication-based coordination tasks. In these tasks, the agents are not given any pre-designed communication protocol. Therefore, in order to successfully communicate, they must first automatically develop and agree upon their own communication protocol. We present empirical results on two multi-agent learning problems based on well-known riddles, demonstrating that DDRQN can successfully solve such tasks and discover elegant communication protocols to do so. To our knowledge, this is the first time deep reinforcement learning has succeeded in learning communication protocols. In addition, we present ablation experiments that confirm that each of the main components of the DDRQN architecture are critical to its success.
Hypoxia Induces PDK4 Gene Expression through Induction of the Orphan Nuclear Receptor ERRγ

Multiple cellular signaling pathways that control metabolism and survival are activated when cell are incubated under hypoxic conditions. Activation of the hypoxia inducible factor (HIF)-1 promotes expression of genes that increase the capacity to cope with the stress imposed by a reduced oxygen environment. Here we show that the orphan nuclear receptor estrogen related receptor γ (ERRγ) plays a critical role in hypoxia–mediated activation of pyruvate dehydrogenase kinase 4 (PDK4) gene expression. ERRγ mRNA and protein levels were increased by hypoxia or desferrioxamine (DFO) treatment in hepatoma cell lines. Co-expression of HIF-1α and β increased ERRγ promoter activity as well as mRNA expression, while knockdown of endogenous HIF-1α reduced the hypoxia-mediated induction of ERRγ. In addition, hypoxia also increased the promoter activity and mRNA level of PDK4 in HepG2 cells. Adenovirus mediated-overexpression of ERRγ specifically increased PDK4 gene expression, while ablation of endogenous ERRγ significantly decreased hypoxia-mediated induction of PDK4 gene expression. Finally, GSK5182, an inverse agonist of ERRγ, strongly inhibited the hypoxia-mediated induction of PDK4 protein and promoter activity. Regulation of the transcriptional activity of ERRγ may provide a therapeutic approach for the regulation of PDK4 gene expression under hypoxia.