Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling and Simulation of photovoltaic module considering single-diode equivalent circuit model in MATLAB

This paper presents a photovoltaic (PV) cell to module simulation model using the single-diode five parameter models. The model was implemented in MATLAB software and the results have been compared with the data sheet values and characteristics of the PV module in Standard Test Conditions (STC). Parameters values were extracted using Newton Raphson's method from experimental Current (I)-Voltage (V) characteristics of Solarex MSX60 module. The results obtained are in good agreement with the experimental data provided by manufacturer. The approach can thus, be very useful for researchers or engineers to quickly and easily determine the performance of any photovoltaic module. Among these methods, in view of its quadratic convergence and enhanced accuracy, Newton Raphson's method remains attractive with the number of variables being limited to five parameters and their partial derivatives easily obtainable. In this work, we elaborate a MATLAB script file program, which uses Newton Raphson's method to compute the five parameters of the single diode model of illuminated solar cells. The results obtained by simulation show the consistency between the data and obtained the parameters given by the manufacturer, namely: short circuit current (ISC), open circuit voltage (VOC) and maximum power point (Pmpp). For instance, these parameters can be used for quality control during production or to provide insights into the operation of the devices, thereby leading to improvements in devices.
Joint Models for Extracting Adverse Drug Events from Biomedical Text

Extracting adverse drug events receives much research attention in the biomedical community. Previous work adopts pipeline models, firstly recognizing drug/disease entity mentions and then identifying adverse drug events from drug/disease pairs. In this paper, we investigate joint models for simultaneously extracting drugs, diseases and adverse drug events. Compared with pipeline models, joint models have two main advantages. First, they make use of information integration to facilitate performance improvement; second, they reduce error propagation in pipeline methods. We compare a discrete model and a deep neural model for extracting drugs, diseases and adverse drug events jointly. Experimental results on a standard ADE corpus show that the discrete joint model outperforms a state-of-the-art baseline pipeline significantly. In addition, when discrete features are replaced by neural features, the recall is further improved.
Improving students’ achivement in reading comprehension by using k-w-l (know-want-leraned) strategy in smp muhammadiyah 2 medan in the academic year of 2016-2017

This study is aimed at finding out the impacts of the use of Know-Want Learn strategy in improving the reading comprehension among eight grade students of SMP Muhammadiyah 2 Medan in the academic year of 2016/2017. The subject of this research was eight grade students of SMP Muhammadiyah 2 Medan in the academic years of 2016/2017. It consisted of one class with 30 students as respondents. This research was conducted by using classroom action research. The technique of analyzing data was applied in this research were qualitative and quantitative. The qualitative data were taken from observation sheet, interview, and documentation. The quantitative data were taken from tests. The tests were given to the students in the form of pre-test, post-test I and post-test II. The result of data analysis showed that there was an improvement on the students’ improvement in reading comprehension from each cycle. It was showed from the mean of pre-test which was 42,5, after KWL Strategy was applied in the first cycle, there was an improvement of the result of the students’ mean which was 66,8 and for the second cycle after reflection on the first cycle there was an improvement of students’ mean which was 76,76. Moreover In the pre-test, there were 13,33 % (4 of 30 students) who got score ≥ 68. In the post-test I, there were 43.33%% (13 of 30 students) who got score ≥ 68. In the post-test II, there were 90% (25 of 30 students) who got score ≥ 68
K4, K9 and K18 in human histone H3 are targets for biotinylation by biotinidase

Histones are modified post‐translationally, e.g. by methylation of lysine and arginine residues, and by phosphorylation of serine residues. These modifications regulate processes such as gene expression, DNA repair, and mitosis and meiosis. Recently, evidence has been provided that histones are also modified by covalent binding of the vitamin biotin. The aims of this study were to identify biotinylation sites in histone H3, and to investigate the crosstalk among histone biotinylation, methylation and phosphorylation. Synthetic peptides based on the sequence of human histone H3 were used as substrates for enzymatic biotinylation by biotinidase; biotin in peptides was probed using streptavidin peroxidase. These studies provided evidence that K4, K9 and K18 in histone H3 are good targets for biotinylation; K14 and K23 are relatively poor targets. Antibodies were generated to histone H3, biotinylated either at K4, K9 or K18. These antibodies localized to nuclei in human placental cells in immunocytochemistry and immunoblotting experiments, suggesting that lysines in histone H3 are biotinylated in vivo. Dimethylation of R2, R8 and R17 increased biotinylation of K4, K9 and K18, respectively, by biotinidase; phosphorylation of S10 abolished biotinylation of K9. These observations are consistent with crosstalk between biotinylation of histones and other known modifications of histones. We speculate that this crosstalk provides a link to known roles for biotin in gene expression and cell proliferation.
Tetrahydrobiopterin and Nitric Oxide : Mechanistic and Pharmacological Aspects 2003

Experimental Biology and Medicine Bernd Mayer Ernst R. Werner, Antonius C.F. Gorren, Regine Heller, Gabriele Werner-Felmayer and Tetrahydrobiopterin and Nitric Oxide: Mechanistic and Pharmacological Aspects 2003, 228:1291-1302. Experimental Biology and Medicine http://ebm.rsmjournals. com/content/228/11/1291 Updated information and services can be found at: http://ebm.rsmjournals. com/content/228/11/1291#BIBL This article cites 152 articles, 63 of which can be accessed free at: http://ebm.rsmjournals. com/content/228/11/1291#otherarticles 20 online articles that cite this articles can be accessed at:
Analysis of Differential Prediction of Law School Performance by Racial / Ethnic Subgroups Based on 2005 – 2007 Entering Law School Classes

The Law School Admission Council (LSAC) is a nonprofit corporation that provides unique, state-of-the-art admission products and services to ease the admission process for law schools and their applicants worldwide. More than 200 law schools in the United States, Canada, and Australia are members of the Council and benefit from LSAC's services. (CAS), and other matters may change without notice at any time. Up-to-date LSAC policies and procedures are available at LSAC.org, or you may contact our candidate service representatives. Executive Summary In the law school admission process, it is essential that the criteria used for admission are fair to all subgroups in the applicant population. One method used to evaluate the fairness of the admission process is to compare the predicted and actual first-year averages (FYAs) within individual law schools for various subgroups of the applicant population. The current study was designed to address questions of differential prediction of law school grades for various racial/ethnic subgroups. The sample used in this study was drawn from the 2005, 2006, and 2007 entering law school classes, using data that were available from the Law School Admission Council (LSAC)-sponsored correlation studies. The study examined results for three racial/ethnic minority subgroups and the nonminority (White) subgroup. Data were analyzed from 170 law schools, each of which over the 3-year period enrolled 10 or more first-year students who identified themselves as Asian American, Black, or Latino and 10 or more first-year students who identified themselves as White. Statistical analyses were used to predict FYAs using Law School Admission Test (LSAT) score alone, undergraduate grade point average (UGPA) alone, and the best predictive combination of LSAT score and UGPA. Analyses were carried out separately for all individual law schools included in the study, resulting in three prediction equations for each law school. The results of the analyses indicate that FYA tended to be, on average, slightly overpredicted (i.e., predicted FYAs exceeded actual FYAs) for all three of the racial/ethnic minority subgroups studied here, with Black law students exhibiting the most overprediction and Asian American law students exhibiting the least overprediction. The use of a combination of both LSAT scores and UGPAs provided the least amount of overprediction for racial/ethnic minority subgroups on the school level compared to the use of either single predictor alone. Overall, these results do not support the concern that the LSAT score or the traditional combination of LSAT score and UGPA …
Algebraic Signal Processing Theory

This paper presents an algebraic theory of linear signal processing. At the core of algebraic signal processing is the concept of a linear signal model defined as a triple (A, M, phi), where familiar concepts like the filter space and the signal space are cast as an algebra A and a module M, respectively, and phi generalizes the concept of the z-transform to bijective linear mappings from a vector space of, e.g., signal samples, into the module M. A signal model provides the structure for a particular linear signal processing application, such as infinite and finite discrete time, or infinite or finite discrete space, or the various forms of multidimensional linear signal processing. As soon as a signal model is chosen, basic ingredients follow, including the associated notions of filtering, spectrum, and Fourier transform. The shift operator is a key concept in the algebraic theory: it is the generator of the algebra of filters A. Once the shift is chosen, a well-defined methodology leads to the associated signal model. Different shifts correspond to infinite and finite time models with associated infinite and finite z-transforms, and to infinite and finite space models with associated infinite and finite C-transforms (that we introduce). In particular, we show that the 16 discrete cosine and sine transforms are Fourier transforms for the finite space models. Other definitions of the shift naturally lead to new signal models and to new transforms as associated Fourier transforms in one and higher dimensions, separable and non-separable. We explain in algebraic terms shift-invariance (the algebra of filters A is commutative), the role of boundary conditions and signal extensions, the connections between linear transforms and linear finite Gauss-Markov fields, and several other concepts and connections.
Gene Expression Profiling of Early Hepatic Stellate Cell Activation Reveals a Role for Igfbp3 in Cell Migration

Background Scarring of the liver is the result of prolonged exposure to exogenous or endogenous stimuli. At the onset of fibrosis, quiescent hepatic stellate cells (HSCs) activate and transdifferentiate into matrix producing, myofibroblast-like cells. Aim and methods To identify key players during early HSC activation, gene expression profiling was performed on primary mouse HSCs cultured for 4, 16 and 64 hours. Since valproic acid (VPA) can partly inhibit HSC activation, we included VPA-treated cells in the profiling experiments to facilitate this search. Results Gene expression profiling confirmed early changes for known genes related to HSC activation such as alpha smooth muscle actin (Acta2), lysyl oxidase (Lox) and collagen, type I, alpha 1 (Col1a1). In addition we noticed that, although genes which are related to fibrosis change between 4 and 16 hours in culture, most gene expression changes occur between 16 and 64 hours. Insulin-like growth factor binding protein 3 (Igfbp3) was identified as a gene strongly affected by VPA treatment. During normal HSC activation Igfbp3 is up regulated and this can thus be prevented by VPA treatment in vitro and in vivo. siRNA-mediated silencing of Igfbp3 in primary mouse HSCs induced matrix metalloproteinase (Mmp) 9 mRNA expression and strongly reduced cell migration. The reduced cell migration after Igfbp3 knock-down could be overcome by tissue inhibitor of metalloproteinase (TIMP) 1 treatment. Conclusion Igfbp3 is a marker for culture-activated HSCs and plays a role in HSC migration. VPA treatment prevents Igfbp3 transcription during activation of HSCs in vitro and in vivo.
Direct simulation of evolution and control of three-dimensional instabilities in attachment-line boundary layers

The spatial evolution of three-dimensional disturbances in an attachment-line boundary layer is computed by direct numerical simulation of the unsteady, incompressible Navier–Stokes equations. Disturbances are introduced into the boundary layer by harmonic sources that involve unsteady suction and blowing through the wall. Various harmonic-source generators are implemented on or near the attachment line, and the disturbance evolutions are compared. Previous two-dimensional simulation results and nonparallel theory are compared with the present results. The three-dimensional simulation results for disturbances with quasi-two-dimensional features indicate growth rates of only a few percent larger than pure two-dimensional results; however, the results are close enough to enable the use of the more computationally efficient, two-dimensional approach. However, true three-dimensional disturbances are more likely in practice and are more stable than two-dimensional disturbances. Disturbances generated off (but near) the attachment line spread both away from and toward the attachment line as they evolve. The evolution pattern is comparable to wave packets in flat-plate boundary-layer flows. Suction stabilizes the quasi-two-dimensional attachment-line instabilities, and blowing destabilizes these instabilities; these results qualitatively agree with the theory. Furthermore, suction stabilizes the disturbances that develop off the attachment line. Clearly, disturbances that are generated near the attachment line can supply energy to attachment-line instabilities, but suction can be used to stabilize these instabilities.
Roles of d-Amino Acids on the Bioactivity of Host Defense Peptides

Host defense peptides (HDPs) are positively-charged and amphipathic components of the innate immune system that have demonstrated great potential to become the next generation of broad spectrum therapeutic agents effective against a vast array of pathogens and tumor. As such, many approaches have been taken to improve the therapeutic efficacy of HDPs. Amongst these methods, the incorporation of d-amino acids (d-AA) is an approach that has demonstrated consistent success in improving HDPs. Although, virtually all HDP review articles briefly mentioned about the role of d-AA, however it is rather surprising that no systematic review specifically dedicated to this topic exists. Given the impact that d-AA incorporation has on HDPs, this review aims to fill that void with a systematic discussion of the impact of d-AA on HDPs.
pyBioSig: optimizing group discrimination using genetic algorithms for biosignature discovery

In medical sciences, a biomarker is "a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention". Molecular experiments are providing rapid and systematic approaches to search for biomarkers, but because single-molecule biomarkers have shown a disappointing lack of robustness for clinical diagnosis, researchers have begun searching for distinctive sets of molecules, called "biosignatures". However, the most popular statistics are not appropriate for their identification, and the number of possible biosignatures to be tested is frequently intractable. In the present work, we developed a "multivariate filter" using genetic algorithms (GA) as a feature (gene) selector to optimize a measure of intra-group cohesion and inter-group dispersion. This method was implemented using Python and R (pyBioSig, available at this https URL under LGPL) and can be manipulated via graphical interface or Python scripts. Using it, we were able to identify putative biosignatures composed by just a few genes and capable of recovering multiple groups simultaneously in a hierarchical clustering, even ones that were not recovered using the whole transcriptome, within a feasible length of time using a personal computer. Our results allowed us to conclude that using GA to optimize our new intra-group cohesion and inter-group dispersion measure is a clear, effective, and computationally feasible strategy for the identification of putative "omical" biosignatures that could support discrimination among multiple groups simultaneously.
Automatic facial expression recognition on a single 3D face by exploring shape deformation

Facial expression recognition has many applications in multimedia processing and the development of 3D data acquisition techniques makes it possible to identify expressions using 3D shape information. In this paper, we propose an automatic facial expression recognition approach based on a single 3D face. The shape of an expressional 3D face is approximated as the sum of two parts, a basic facial shape component (BFSC) and an expressional shape component (ESC). The BFSC represents the basic face structure and neutral-style shape and the ESC contains shape changes caused by facial expressions. To separate the BFSC and ESC, our method firstly builds a reference face for each input 3D non-neutral face by a learning method, which well represents the basic facial shape. Then, based on the BFSC and the original expressional face, a facial expression descriptor is designed. The surface depth changes are considered in the descriptor. Finally, the descriptor is input into an SVM to recognize the expression. Unlike previous methods which recognize a facial expression with the help of manually labeled key points and/or a neutral face, our method works on a single 3D face without any manual assistance. Extensive experiments are carried out on the BU-3DFE database and comparisons with existing methods are conducted. The experimental results show the effectiveness of our method.
Swarm contours: A fast self-organization approach for snake initialization

Department of Computer Science, University ofIllinois at Urbana Champaign, Urbana, Illinois 61801This paper was submitted as an invited paper resulting from the “Understanding Complex Systems”conference held at the University of Illinois–Urbana Champaign, May 2005Received October 17, 2005; revised April 5, 2006; accepted May 2, 2006A major obstacle in real-time performance of a visual tracking system is its initialization phase. Inspired bysocial behavior in ﬁsh and ant groups, a fast self-organization approach to active-contour initialization isproposed. Contours are emerged during two separate phases of aggregation and self-assembly. Aggregation isachieved by a superposition of simpler behaviors, hunting, avoidance, and opportunism. Self-assembly, whichconstitutes the explicit contour formation, occurs by mating behavior when the swarm becomes quite stable.Experiments indicate that the proposed method outperforms exhaustive image search for ﬁnding contours inhigh resolutions. © 2006 Wiley Periodicals, Inc. Complexity 12: 41–52, 2006
The Role of Metformin in Controlling Oxidative Stress in Muscle of Diabetic Rats

Metformin can act in muscle, inhibiting the complex I of the electron transport chain and decreasing mitochondrial reactive oxygen species. Our hypothesis is that the inhibition of complex I can minimize damage oxidative in muscles of hypoinsulinemic rats. The present study investigated the effects of insulin and/or metformin treatment on oxidative stress levels in the gastrocnemius muscle of diabetic rats. Rats were rendered diabetic (D) with an injection of streptozotocin and were submitted to treatment with insulin (D+I), metformin (D+M), or insulin plus metformin (D+I+M) for 7 days. The body weight, glycemic control, and insulin resistance were evaluated. Then, oxidative stress levels, glutathione antioxidant defense system, and antioxidant status were analyzed in the gastrocnemius muscle of hypoinsulinemic rats. The body weight decreased in D+M compared to ND rats. D+I and D+I+M rats decreased the glycemia and D+I+M rats increased the insulin sensitivity compared to D rats. D+I+M reduced the oxidative stress levels and the activity of catalase and superoxide dismutase in skeletal muscle when compared to D+I rats. In conclusion, our results reveal that dual therapy with metformin and insulin promotes more benefits to oxidative stress control in muscle of hypoinsulinemic rats than insulinotherapy alone.
We Did See This Coming: Response to, We Should Have Seen This Coming, by D. Sam Schwarzkopf

We appreciate the effort by Schwarzkopf to examine alternative explanations for predictive anticipatory activity (PAA) or presentiment (for first response, see: Schwarzkopf 2014a; for additional response, see: Schwarzkopf 2014b, for original article, see: Mossbridge et al. 2014). These commentaries are a laudable effort to promote collegial discussion of the controversial claim of presentiment, whereby physiological measures preceding unpredictable emotional events differ from physiological measures preceding calm or neutral events (Mossbridge et al., 2012; Mossbridge et al., 2014). What is called truth at any given time in science has achieved that status through a continuous process of measurement and interpretation based on the current knowledge at hand. Here we address six points in his original commentary (Schwarzkopf 2014a), though our responses are informed by the points he made in his his supplementary commentary (Schwarzkopf 2014b). We hope our responses will help Schwarzkopf and others understand our interpretation of these data.
Collaborative Reasoning With Strategy Instruction for Opinion Writing in Primary Grades: Two Cycles of Design Research

Abstract Argumentation is challenging for young learners. We used design research to develop an intervention that was based on principles of dialogic argumentation and strategy instruction and used genre elements to guide planning and evaluation for revision. Instruction included oral discussion of children’s books, written responses to reading, and the writing of argumentative essays. We based professional development (PD) on the principles of practice-based PD. The study included 2 cycles of design research with implementation of PD and the instructional approach. Cycle 1 included kindergarten and 1st grade (7 weeks, 168 students, 10 teachers); Cycle 2 included kindergarten, 1st grade, and 2nd grade (5 weeks, 229 students, 12 teachers). We made improvements to instruction and PD between cycles based on the design research. Pretest–posttest comparisons showed improvements in quality in both cycles and more genre elements in Cycle 2. Teachers responded positively to PD. We discuss limitations and implications for future research.