Miracle-dz/UniFAR-FTUs huggingface dataset:
A risk-centric defensive architecture for threat modelling in e-government application

To improve the security of an e-government, software engineering plays a vital role. During the application development for an e-government, there exist several risks. To analyse those risks, threat modelling methodology which is defined as the process to understand and address the threats of an application. Threat modelling is used to determine security controls and countermeasures for the targeting attacks. This paper describes an approach to identify how far the attack penetrates in risk layers and how the model defends from an attacker in e-government systems. The relevant attacks are retrieved from the attack pattern information is gathered from MITRE's common attack pattern enumeration and classification (CAPEC) security source. This architecture dynamically identifies the risk severity and prioritises the risk in a single step. An attack pattern applied to a risk-centric defensive architecture model to identify threat severity and also it is prioritised based on its impact. We validate risk-centric defensive architecture model by implementing it in a tool based on data flow diagrams (DFDs), from the Microsoft security development methodology.
Analyzing AbrB-Knockout Effects through Genome and Transcriptome Sequencing of Bacillus licheniformis DW2

As an industrial bacterium, Bacillus licheniformis DW2 produces bacitracin which is an important antibiotic for many pathogenic microorganisms. Our previous study showed AbrB-knockout could significantly increase the production of bacitracin. Accordingly, it was meaningful to understand its genome features, expression differences between wild and AbrB-knockout (ΔAbrB) strains, and the regulation of bacitracin biosynthesis. Here, we sequenced, de novo assembled and annotated its genome, and also sequenced the transcriptomes in three growth phases. The genome of DW2 contained a DNA molecule of 4,468,952 bp with 45.93% GC content and 4,717 protein coding genes. The transcriptome reads were mapped to the assembled genome, and obtained 4,102∼4,536 expressed genes from different samples. We investigated transcription changes in B. licheniformis DW2 and showed that ΔAbrB caused hundreds of genes up-regulation and down-regulation in different growth phases. We identified a complete bacitracin synthetase gene cluster, including the location and length of bacABC, bcrABC, and bacT, as well as their arrangement. The gene cluster bcrABC were significantly up-regulated in ΔAbrB strain, which supported the hypothesis in previous study of bcrABC transporting bacitracin out of the cell to avoid self-intoxication, and was consistent with the previous experimental result that ΔAbrB could yield more bacitracin. This study provided a high quality reference genome for B. licheniformis DW2, and the transcriptome data depicted global alterations across two strains and three phases offered an understanding of AbrB regulation and bacitracin biosynthesis through gene expression.
Control charts for monitoring accumulating adverse event count frequencies from single and multiple blinded trials

Conventional practice monitors accumulating information about drug safety in terms of the numbers of adverse events reported from trials in a drug development program. Estimates of between‐treatment adverse event risk differences can be obtained readily from unblinded trials with adjustment for differences among trials using conventional statistical methods. Recent regulatory guidelines require monitoring the cumulative frequency of adverse event reports to identify possible between‐treatment adverse event risk differences without unblinding ongoing trials. Conventional statistical methods for assessing between‐treatment adverse event risks cannot be applied when the trials are blinded. However, CUSUM charts can be used to monitor the accumulation of adverse event occurrences. CUSUM charts for monitoring adverse event occurrence in a Bayesian paradigm are based on assumptions about the process generating the adverse event counts in a trial as expressed by informative prior distributions. This article describes the construction of control charts for monitoring adverse event occurrence based on statistical models for the processes, characterizes their statistical properties, and describes how to construct useful prior distributions. Application of the approach to two adverse events of interest in a real trial gave nearly identical results for binomial and Poisson observed event count likelihoods. Copyright © 2016 John Wiley & Sons, Ltd.
Pitfalls in the measurement of muscle mass: a need for a reference standard

All proposed definitions of sarcopenia include the measurement of muscle mass, but the techniques and threshold values used vary. Indeed, the literature does not establish consensus on the best technique for measuring lean body mass. Thus, the objective measurement of sarcopenia is hampered by limitations intrinsic to assessment tools. The aim of this study was to review the methods to assess muscle mass and to reach consensus on the development of a reference standard.
Measuring Oxytocin and Vasopressin: Bioassays, Immunoassays and Random Numbers

In this review, we consider the ways in which vasopressin and oxytocin have been measured since their first discovery. Two different ways of measuring oxytocin in widespread use currently give values in human plasma that differ by two orders of magnitude, and the values measured by these two methods in the same samples show no correlation. The notion that we should accept this seems absurd. Either one (or both) methods is not measuring oxytocin, or, by ‘oxytocin’, the scientists that use these different methods mean something very different. If these communities are to talk to each other, it is important to validate one method and invalidate the other, or else to establish exactly what each community understands by ‘oxytocin’. A similar issue concerns vasopressin: again, different ways of measuring vasopressin give values in human plasma that differ by two orders of magnitude, and it appears that the same explanation for discrepant oxytocin measurements applies to discrepant vasopressin measurements. The first assays for oxytocin and vasopressin measured biological activity directly. When immunoassays were introduced, they encountered problems: high molecular weight factors in raw plasma interfered with the binding of antibodies to the hormones, leading to high and erroneous readings. When these interfering factors were removed by extraction of plasma samples, immunoassays gave measurements consistent with bioassays, with measures of turnover and with the sensitivity of target tissues to exogenous hormone. However, many recent papers use an enzyme‐linked immunoassay to measure plasma levels without extracting the samples. Like the first radioimmunassays of unextracted plasma, this generates impossibly high and wholly erroneous measurements.
Artistic brushstroke representation and animation with disk b-spline curve

A novel modeling and representation approach for brushstroke and animation is proposed in this paper based on disk B-spline curve (DBSC). This method represents not only the 2D region of the stroke but also its centerline so that various attributes like scalar and vector field can be applied to the stroke. Given two or more animation drawings represented by DBSC, the intermediate frames can be automatically generated to form a smoother sequence, either linearly or non-linearly. Moreover, compared to other models like polygon and triangle mesh, DBSC allows more flexible manipulation of brushstroke. With smaller dataset, DBSC has advantages to store and transmit the model across the network thus may be suitable to online games and animation.
Modelling and control of the Modular Multilevel Matrix Converter and its application to Wind Energy Conversion Systems

In the last past years, some countries are enforcing stringent grid codes to regulate the connection of Wind Energy Conversion Systems (WECSs) to the electrical network, mainly because of the high penetration of electric power from this renewable source. Additionally, the trend of wind turbines has shown an ongoing power rating growth, reaching sizes up to 10 MW. Multilevel converters have appeared as a solution for large WECSs, due to its high reliability, controllability and the capability to reach high power ratings. This paper presents a control strategy for the application of the Modular Multilevel Matrix Converter in Multimegawatts Wind Turbines. Extensive computer simulations and a downscaled laboratory prototype, with twenty-seven power cells, are presented to validate the effectiveness of the proposed control system.
Lipophilicity and biomimetic properties to support drug discovery

ABSTRACT Introduction: Lipophilicity, expressed as the octanol-water partition coefficient, constitutes the most important property in drug action, influencing both pharmacokinetic and pharmacodynamics processes as well as drug toxicity. On the other hand, biomimetic properties defined as the retention outcome on HPLC columns containing a biological relevant agent, provide a considerable advance for rapid experimental – based estimation of ADME properties in early drug discovery stages. Areas covered: This review highlights the paramount importance of lipophilicity in almost all aspects of drug action and safety. It outlines problems brought about by high lipophilicity and provides an overview of the drug-like metrics which incorporate lower limits or ranges of logP. The fundamental factors governing lipophilicity are compared to those involved in phospholipophilicity, assessed by Immobilized Artificial Membrane Chromatography (IAM). Finally, the contribution of biomimetic properties to assess plasma protein binding is evaluated. Expert opinion: Lipophilicity and biomimetic properties have important distinct and overlapping roles in supporting the drug discovery process. Lipophilicity is unique in early drug design for library screening and for the identification of the most promising compounds to start with, while biomimetic properties are useful for the experimentally-based evaluation of ADME properties for the synthesized novel compounds, supporting the prioritization of drug candidates and guiding further synthesis.
Cognitive Performance Associated With Self-care Activities in Mexican Adults With Type 2 Diabetes

Purpose The purpose of this study was to determine the effect of memory-learning on self-care activities in adults with type 2 diabetes moderated by previous education/ understanding in diabetes and to explore the explicative capacity of age, gender, schooling, diabetes duration, and glycemic control in memory-learning. Methods A descriptive correlational study was conducted in a randomized sample of 105 Mexican adult patients with type 2 diabetes at a community-based outpatient clinic. Evaluation measures included the Wechsler Memory Scale for memory-learning; 2 questionnaires for self-care activities and previous education/understanding in diabetes, respectively; and glycosylated hemoglobin for glycemic control. Multiple linear regression analysis was used to examine the effect of memory-learning on self-care activities and the moderator capacity of previous education/understanding on diabetes. Multivariate analysis was used to identify the capacity of age, schooling, diabetes duration, and glycemic control in memory-learning types. Results A significant positive effect of memory-learning on self-care activities was found. Education/understanding in diabetes moderated the relationship between immediate and delayed memory-learning and self-care in glucose monitoring and diet. Gender, schooling, and the gender-glycemic control interaction explained memory-learning performance. Conclusions Immediate and delayed verbal and visual memory-learning were important for the patient to carry out self-care activities, and this relationship can be moderated by previous education/understanding in diabetes. These findings suggest potential benefits in emphasizing cognitive strategies to promote relearning of self-care behaviors in persons who live with diabetes.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals

Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology, and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired, and thus, how to recover the full signal becomes an active research topic, but existing approaches cannot efficiently recover <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>-dimensional exponential signals with <inline-formula><tex-math notation="LaTeX">$N\geq 3$</tex-math></inline-formula>. In this paper, we study the problem of recovering <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>-dimensional (particularly <inline-formula><tex-math notation="LaTeX">$N\geq 3$</tex-math></inline-formula>) exponential signals from partial observations, and formulate this problem as a low-rank tensor completion problem with exponential factor vectors. The full signal is reconstructed by simultaneously exploiting the CANDECOMP/PARAFAC tensor structure and the exponential structure of the associated factor vectors. The latter is promoted by minimizing an objective function involving the nuclear norm of Hankel matrices. Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.
Active damping of a 6th order 3LC output filter with state-feedback

Standard solution to reduce switching ripple in power converters is to employ LC(L) output filters, with higher attenuation requiring higher switching frequency and/or larger filter size. High switching frequency can increase the converter losses, whereas large filter reduces the bandwidth and increases cost of the power converter. The alternative path of increasing the filter order is taken in this paper. To be more exact, a 6th order filter consisting of 3 series connected LC blocks is employed to achieve very high ripple attenuation and increased converter bandwidth without increasing the switching frequency. In contrast to previously proposed high-order filter solutions active damping is employed yielding no penalty to the converter efficiency. The active damping is achieved via state-feedback control, i.e., Linear Quadratic Regulator, with the state information reconstructed only from capacitor voltage measurements with a Luenberger observer. The complete design is evaluated in simulations and validated on the experimental synchronous buck converter achieving in excess of -80 db ripple attenuation in one decade.
Epstein-Barr Virus BART9 miRNA Modulates LMP1 Levels and Affects Growth Rate of Nasal NK T Cell Lymphomas

Nasal NK/T cell lymphomas (NKTCL) are a subset of aggressive Epstein-Barr virus (EBV)-associated non-Hodgkin's lymphomas. The role of EBV in pathogenesis of NKTCL is not clear. Intriguingly, EBV encodes more than 40 microRNAs (miRNA) that are differentially expressed and largely conserved in lymphocryptoviruses. While miRNAs play a critical role in the pathogenesis of cancer, especially lymphomas, the expression and function of EBV transcribed miRNAs in NKTCL are not known. To examine the role of EBV miRNAs in NKTCL, we used microarray profiling and qRT-PCR to identify and validate expression of viral miRNAs in SNK6 and SNT16 cells, which are two independently derived NKTCL cell lines that maintain the type II EBV latency program. All EBV BART miRNAs except BHRF-derived miRNAs were expressed and some of these miRNAs are expressed at higher levels than in nasopharyngeal carcinomas. Modulating the expression of BART9 with antisense RNAs consistently reduced SNK6 and SNT16 proliferation, while antisense RNAs to BARTs-7 and -17-5p affected proliferation only in SNK6 cells. Furthermore, the EBV LMP-1 oncoprotein and transcript levels were repressed when an inhibitor of BART9 miRNA was transfected into SNK6 cells, and overexpression of BART9 miRNA increased LMP-1 protein and mRNA expression. Our data indicate that BART9 is involved in NKTCL proliferation, and one of its mechanisms of action appears to be regulating LMP-1 levels. Our findings may have direct application for improving NKTCL diagnosis and for developing possible novel treatment approaches for this tumor, for which current chemotherapeutic drugs have limited effectiveness.
Technique for Deep Placement of Coated Urea Fertilizer in Soybean Cultivation

The objective of this study was to increase the productivity of soybean seeds by the application of controlled release nitrogen fertilizers (coated urea). Based on the results obtained in field experiments carried out for 3 years (1989-1991), deep placement of coated urea consistently enabled to increase the seed yield by about 10-20% over the conventional treatment. Deep placement of coated urea did not depress the N2 fixation activity throughout the growth stages until maturity, though N from fertilizer was actively absorbed. The basal deep placement of coated urea for soybean cultivation enabled to supply N from the lower part of roots without concomitant depression of N2 fixation, and consequently seed yield increased. High recovery rate (over 60%) of fertilizer N was also advantageous from economical viewpoints. Discipline: Soils, fertilizers and plant nutrition/Crop production Additional key words: N2 fixation, ureide, root growth, Rb uptake
Structure and property of bamboo fiber.

The chemical composition, fiber characteristics, crystalline structure, mechanical properties and thermal behavior of the five species of bamboo (Phyllostachys edulis cv.Pachyloe, Bambusa tootisk, Arundinaia amabilis, B.vulgaris cv. Vittata, and Dendrocalamus affinis) were studied with IR, X-ray, DSC and chemical analyses. The results indicated that the benzene-ethanol extractive content of bamboo was higher than that of wood, the content of lignin and the content of pentosan were 19.1% - 25.3% and 14.9% - 22.6%, respectively. Except for Ph. edulis cv. Pachyloe, the cellulose content in the other 4 species was over 50%. The average length of fiber of D. affinis was the longest (1.861 mm). The length/ width ratio of bamboo fiber was also higher than that of wood, and it was the highest for B.tootisk, which reached 142. The results of IR and X-ray confirmed that crystalline structure of bamboo fibers was cellulose, and A. amabilis had the highest crystallinity index. The differential thermal analysis (DSC) can be one of the methods to identify various fiber materials. After steam explosive pretreatment, cellulose content of D. affinis had increased from 50.5% to 69.2%, the crystallinity index had increased and the breaking tenacity of bamboo fibers was suitable for producing textile products, indicating that steam explosion is an useful pretreatment for bamboo fibers made into composite materials and textiles.
Yimeng Liu's dissertation

Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other fatal diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. Thus a common problem of interest is to test the equality of the QRL between two populations. In the first chapter of this dissertation, we propose two classes of tests to compare two QRLs; one class is based on the difference between two estimated QRLs, and the other is based on the estimating function of the QRL, where the estimated QRL from one sample is plugged into the QRL-estimating-function of the other sample. We outline the asymptotic properties of the proposed test statistics. Simulation studies demonstrate that the proposed tests produced Type I errors closer to the nominal level and are superior to some existing tests based on both Type I error and power. Our proposed statistics are also computationally less intensive and more straightforward compared to tests based on the confidence intervals. Moreover, for experimental designs, such as for randomized control trials, there is often no interest or need to adjust for the confounding factors due to the nature of the randomization. However, when there is missing data, adding the covariate information can help in improving the efficiency of estimators, e.g., the estimated treatment effect. In the second chapter, we propose an augmented inverse probability weighting estimator (AIPW) for QRL by incorporating the auxil-
Power sharing enhancement for Islanded microgrid based on state estimation of PCC rms-voltage

It is well known that the Conventional Droop method is commonly used to share real and reactive power between distributed Generation interfaced converters (DICs) based Islanded microgrid. The share of power is done autonomously without communication between Converters. Despite the lot of desirable features the Droop method had, there exist some limitations concerning the accuracy of the reactive power shared between converters when line impedance mismatches exist. This paper presents the droop control law for islanded microgrid, and proposes an online state estimation of the point of common coupling (PCC) voltage, which is then used as a feedback signal for the Droop control to accurately share the power between parallel converters. Thus the proposed method keeps the advantages of the Droop control method to manage the power between distributed generations in autonomous fashion without additional communication links.
Energy consumption reduction by integrating Wireless Sensors and Actuators Networks Supervisory Controller with the Cloud Computing

In this paper we present a new framework for energy consumption conservation in green smart building. We propose an energy efficient smart wireless control system that integrates Wireless Sensors Actuators Networks (WSAN), Machine Learning and Cloud Computing. Moving WSANs Supervisory controlled by Petri nets system to the cloud have many advantages such as scalability, and availability. The first component of our proposed framework is Petri Nets supervisory controller which is implemented to monitor and control the entire system. Moreover, fault-tolerant algorithms could be developed and deployed with the supervisory system. The second component of our framework is the machine learning scheme that is responsible of processing and manipulating users preferences based on Spiking Neural Networks. This is very crucial as it considers behavioral preferences of tenants of building called user preferences. Finally integrating all components of the framework and deploy it on the cloud environment gives WSAN the benefits of using cloud resources and make the system highly available, scalable and fault-tolerant.
Object Attention Patches for Text Detection and Recognition in Scene Images using SIFT

Natural urban scene images contain many problems for character recognition such as luminance noise, varying font styles or cluttered backgrounds. Detecting and recognizing text in a natural scene is a difficult problem. Several techniques have been proposed to overcome these problems. These are, however, usually based on a bottom-up scheme, which provides a lot of false positives, false negatives and intensive computation. Therefore, an alternative, efficient, character-based expectancy-driven method is needed. This paper presents a modeling approach that is usable for expectancy-driven techniques based on the well-known SIFT algorithm. The produced models (Object Attention Patches) are evaluated in terms of their individual provisory character recognition performance. Subsequently, the trained patch models are used in preliminary experiments on text detection in scene images. The results show that our proposed model-based approach can be applied for a coherent SIFT-based text detection and recognition process.