dataesr/scientific-paragraphs-categorization huggingface dataset:
XSS. Injected JavaScript code can perform all actions on the user's behalf. It can raise new authorized actions and redirect the respective requests to the Mo-bileAuthenticator. However, due to the missing shared secret, it can not sign the requests. So, as long as users do not acknowledge unintended actions, no authorized action can be triggered. The only damage an XSS attacker can cause is a denial-of-service by discarding all signed requests and, thus, preventing intended authorized actions. Session Fixation. The attacker can still get access to the user's account. The login step elevates the session cookie to an authorized state granting access to the owner. However, the attacker can not perform authorized actions because he has no access to the shared secret. No More Password Entry. Felt and Wagner discussed the fact that mobile keyboards actively discourage the usage of complicated, and thus secure, passwords, as the entry of numbers or special characters require cumbersome context switches  [2].
X-ray photoelectron spectroscopy (XPS) is a widely used tool for studying the chemical composition of materials and it is a standard technique in surface science and technology. XPS is particularly useful for characterizing nanostructures such as carbon nanomaterials due to their reduced dimensionality. In order to assign the measured binding energies to specific bonding environments, reference energy values need to be known. Experimental measurements of the core level signals of the elements present in novel materials such as graphene have often been compared to values measured for molecules, or calculated for finite clusters. Here we have calculated core level binding energies for variously functionalized or defected graphene by delta Kohn-Sham total energy differences in the real-space grid-based projector-augmented wave density functional theory code (GPAW).
We will call this a reconstructed single vacancy, or simply SV (V 1  (5) (6) (7) (8) (9)   [9] ). However, the remaining single carbon atom still cannot satisfy its chemically reactive dangling bond, as has been directly observed by scanning tunneling microscopy (STM) in high vacuum  [11]. To address these important systems, and the potential shortcomings of previous studies, we have calculated graphene core level binding energies by using density functional theory implemented with real-space grid-based projector-augmented waves in the GPAW code  [12]. We applied periodic boundary conditions in large unit cells to avoid spurious image interaction effects. Furthermore, we benchmarked select results against allelectron calculations with the FHI-aims code  [13]  to ensure that the projector-augmented waves in GPAW described the core levels of these systems accurately.
We found that the projector-augmented results were in excellent agreement with all-electron calculations. In almost all cases, in which data was available, a good agreement for the C 1s levels with experimental values reported in the literature was also found  [4, [17] [18] [19] [20]. As a further refinement, we considered binding energy contributions arising from up to third nearest neighbors to the functional group or defect, and plotted the resulting line shapes by using experimentally realistic broadenings. In the case of the O 1s level, the line-shape variations of graphene have not been extensively examined in experimental reports, which makes the comparison of the calculated O 1s values to literature data problematic. This is why we have focused our discussion on the C 1s energies. With this caveat, core-hole calculations with the GPAW code are a convenient and valuable tool for simulating the core level binding energies of graphene systems.
Conclusion We have calculated the core level binding energies of both pristine and defective monolayered graphene functionalized with oxygen-and hydrogen-based adsorbates in a large and periodic unit cell. We have shown that the use of the projectoraugmented wave method does not introduce significant errors in the treatment of the core electrons compared to all-electron calculations. The computationally efficient and scalable GPAW code is thus well suited for calculating core level binding energy shifts for graphene-based systems. However, higher levels of theory or more advanced functionals could certainly improve the absolute energy values. Because good agreement was obtained with experimental data found in the literature as far as it was available, we envisage that the calculations presented here will be especially useful for predicting the X-ray photoelectron spectroscopy signatures for novel structures for which such data is not available.
Computational details Density functional theory was used as implemented in the GPAW simulation package  [12]. The projectoraugmented wave method  [29]  was used with frozen core electrons, and exchange and correlation was estimated by the Perdew-Burke-Ernzerhof (PBE) generalized gradient approximation  [30]. Periodic boundary conditions were applied with a Monkhorst-Pack  [31]  k-point mesh up to 5 × 5 × 1 k-points.
GPAW core-hole calculation The total energy of a system before photoemission is a sum of the energy of the X-ray photon, hν, and the energy of the target system in its initial state, E i. After the photoemission, the total energy is equal to the kinetic energy of the emitted photelectron, E k , plus that of the ionized system in its final state, E f. We thus have hν + E i = E k + E f. The binding energy, E b , of the 1s electron is given by the difference between the energies of the X-ray photon and the emitted photelectron: E b = hν -E k , which leads to E b = E f -E i , the difference between final and initial energies of the target system. For the DFT calculations, we used the real-space grid-based projector-augmented wave (GPAW) code  [12]. Recently, corehole calculations that utilize a delta Kohn-Sham (∆K-S) total energy differences method were implemented into GPAW by Ljungberg et al.
FHI-aims all-electron calculations Finally, to confirm that the use of the projectors did not introduce errors in the treatment of the core level energies, we performed additional calculations for selected systems using the all-electron code FHI-aims  [13] , also with the PBE functional, and compared the C 1s energy values to the corresponding GPAW calculation. The core level energies were calculated by comparing the relaxed total energies of a system with or without a core hole -described by an explicitly empty 1s core orbital in the case of FHI-aims -on an atom of interest.
Acknowledgements We thank  Jani Kotakoski ,  Jussi Enkovaara ,  Arkady Krasheninnikov ,  Duncan Mowbray  and  Georgina Ruiz-Soria  for helpful discussions. T. S. was supported by the  Austrian Science Fund (FWF)  through grant M  1497-N19 , by the  Finnish Cultural Foundation , and by the  Walter Ahlström Foundation. Generous computational resources from  CSC-IT Center for Science in Espoo, Finland  are gratefully acknowledged.
The research will be conducted with assistance of a finite elements software ABAQUS® for simulating the model along with its parameters and mathematical software Matlab® for interpolating data obtained from the simulation. Firstly, the model will be validated by comparing it to experimental result of research conducted by Bakhiet[1], then its boundary conditions and mesh will be used to next model simulations. Secondly, the parameters of the assembly will be varied such as bolt positions on the part and the modulus Young ratio of bolt and parts. Finally, both assemblies are to be compared (e. g. with normal and slotted hole) and then the assembly's critical value is to be found by using dimensionless parameters developed by Turgeon and Vadean[2]. From research conducted, we could conclude that the behavior of the assembly, particularly at its fatigue stress produced at the bolt, changes significantly with the variation of the bolt position and also the modulus Young ratio of the bolt and part.
D. Finite Elements Model In this research, we will use ABAQUS software in order to model and calculate with given parameters. Principally, there are four important components used: 1. The slotted holed part 2. The normal holed part 3. The bolt 4. The load support, this has the objective to avoid the element distortion while load is being applied Taking into account the symmetry of the assembly, only a half of the assembly is modelled in order to reduce the calculation time. It is clear that the reduction of calculation time has always been the objective at finite element simulation study. 1) Parameters Explanation a. m :distance between the bolt's axis and the end of the part which is not being applied by the force b. n: distance between axis of the bolt and the end of part where load is being applied (force eccentricity) c.
5) Collecting Data Points The stress is read at three control points of bolt, which are (1) the point that is located the furthest with respect to loading point, (2) the point that is located the neares with respect to loading point, and (3) the point which is located perpendicular the line between number 1 and 2. These three points could be seen from figure  11. After having collected data from three control points, normal and bending stress are to be calculated by these following equations : (13) (  14 ).
Generally, a lower modulus Young ratio will always result in the augmentation of fatigue resistance of the assembly. From those three figures (figure 21-23), there is a decreasing trend of maximum external force could be rstrained when we increase the modulus Young ratio. Therefore, on the design process, one should take into account these ratio and minimize this value as lower as possible. E. The data interpolation in order to visualize the evolution of maximal external force value By using the Matlab® the evolution curve of maximum external force could be achieved. Firstly, the curves of each bolt position are plotted. Afterwards, values from each bolt variation are interpolated and finally will form a surface such as shown on the figure 24, 25, and 26. The objective of this interpolation is to facilitate the reading of maximum external force could be restrained by the assembly for each variation of modulus Young ratio.
ACKNOWLEDGEMENTI would like to express my gratitude to my supervisor in INSA Toulouse Monsieur Alain Daidié who had guided me in this research in spite of my different study background with regard to the subject of the research. I also would like to thank Sepuluh Nopember Institute of Technology (ITS) who had given a golden chance to continue my master study at Université de Touluse III (Université Paul Sabatier) along with Program Beasiswa Unggulan Kementrian Pendidikan Nasional Republik Indonesia which has funded my study abroad.
Estimating the Effective Stiffness of the spindle and its resistance to primary-mode buckling As the spindle's cross-sections have striking geometric properties that appear to enhance its mechanical properties, an open question concerns whether the length and number of spindle microtubules also increase the critical force that the structure can tolerate before buckling. Since the Euler-Bernoulli beam theory only applies to prismatic beams with a constant transverse organisation, it is also unclear whether the local design rules for microtubule packing endow the non-prismatic spindles with high critical forces  (Landau et al. , 1986; Gere and Goodno, 2012). To address these questions, we constructed a simplified computational model of each anaphase B spindle using the Cytosim simulation software  (Nedelec and Foethke, 2007).
The Argon Krypton line laser was used at wavelength of 488 nm for GFP signal detection. Images were collected using a 63× oil immersion objective (Plan-Apochromat, NA 1. 4) coupled to a Hamamatsu C9100-50 EMCCD camera (Hamamatsu, Japan) with a pixel size of 8 μm. The microscope was controlled with the Ultraview acquisition software (Perkin Elmer, Foster City, CA US). All experiments were performed inside a climate control box (EMBL, Heidelberg, Germany) at a constant temperature of 28°C. Live-cell imaging of fission yeast spindles was carried out using spinning disk confocal microscopy from 15 focal planes separated by a distance of 0. 5 μm, a set-up similar to that used to quantify the cellular abundance of proteins localised to the cytokinetic ring in fission yeast  (Wu and Pollard, 2005).
Erosion and dilation operations were then used to remove isolated bright pixels and smooth the cellular outlines. Apart from deconvolution, all analyses of images were carried out using Matlab (The MathWorks inc. ). Scripts for performing image analysis are provided in Source Code 1. The tracking of SPB positions was performed using the μ-Track image analysis package  (Jaqaman et al. , 2008). The splitting of a single SPB into two distinct and persistent tracks was used to identify mitotic events in a field of asynchronously dividing cells. Each mitotic event was then mapped to a single segmented cell that was used in subsequent analyses. The background signal was estimated by fitting a histogram to the pixel intensities in each field of view. The background signal was subtracted from the pixel intensity measurements to obtain an absolute estimate of fluorescent intensity.
The samples were then kept for 24 hr more at 20°C under UV light. Tilt-series of relevant areas were acquired from ±60° at 1° intervals in a Tecnai F30/F20/ T12 (FEI, Oregon USA) at the magnification of 15,500×/14,500×/11,000×, respectively using the SerialEM software. Tilt-series were reconstructed, joined in the cases where spindles spanned several adjacent subsections, and tracked manually using the software package IMOD  (Kremer et al. , 1996). In the electron tomograms, microtubules appear as cylindrical tubes with a diameter of around 18 nm compared with the 25 nm expected for 13-protofilament microtubules. The microtubule shrinkage is caused by the freeze-substitution methods used to fix the cells, but other aspects of the microtubule structure appeared unperturbed. All measurements were thus scaled uniformly by γ = 25/18 to obtain more accurate estimates of the spindles' physical properties.
Computational models of yeast spindles The response of the spindle to compressive forces was investigated using the cytoskeletal modelling software Cytosim, which solves the over-damped Langevin equations of cytoskeletal filaments using an implicit numerical integration scheme  (Nedelec and Foethke, 2007). The code was compiled and run on the EMBL High Performance Computing cluster, with jobs submitted to the Platform LSF scheduler using custom Python scripts. Simulation results were analysed using Matlab (The MathWorks inc. ). The simulations were designed to reproduce the morphology and biophysical characteristics of each spindle sufficiently closely to estimate the spindle's critical force. The SPBs were modelled as cylindrical elastic solids, associated with a scalar drag coefficient. Each microtubule was connected to the SPB by a pair of Hookean springs.
Acknowledgements We thank  Phong Tran  for help in initiating the project. We thank  Ken Sawin  and  Fred Chang  for providing yeast strains, and  Damian Brunner  and members of his lab for yeast strains and technical help. We thank  Carl Zeiss  and  PerkinElmer  for continuous support of the  EMBL Advanced Light Microscopy Facility. We would like to thank our colleagues  Jan Ellenberg ,  Darren Gilmour ,  Marko Kaksonen ,  Robert Mahen ,  Marcel Janson  and  Jordan Raff  for critical reading of the manuscript. Research in the Nedelec lab is supported by  BioMS  and the  European Commission Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements no. 241548. and no. 258068. JJW acknowledges financial support from the  VW  foundation within the program;  Computational Soft Matter and Biophysics  (Grant No.
3. a. The second explored subject is concerning interweaving human and tools for production of documents bearing knowledge. The generic name imagined for this dynamic system was attic of knowledge. 4. b. For this experiment we have studied the hypermodern episteme from three books: "Words and Things" of Michel Foucault  (Foucault, 1966) , "We Have Never Been Modern" by Bruno Latour  (Latour, 2006) , and from Peter Sloterdijk "Spheres trilogy"  (Sloterdijk, 2003 (Sloterdijk, , 2005)). This method was applied to the manufacture of an Online Attic of Knowledge on building earthen-clay by a collaborative practice with a STICA (socio-technical of information and communication arrangement). Artifacts of this research were two prototypes Attics of knowledge, an application in PHP MySQL JavaScript for dialogue between authors and users of documents describing the intertwining between humans and tools during the collaborative process.
Acknowledgments I must thank the doctors  Herbaux, Bois, Perrin ,  Maurel  and future one, Déprez (who will defend his thesis on September 25 this year) for allowing me to direct their work of doctoral research. They helped to gradually build what was a simple intuition and intention fifteen years ago. More than a scientific experiment, it was a real human adventure. I hope I have translated the most of their experience. Special thanks to  Prof Joseph Kouneiher ,  Head of the Sciences and Technologies Department, University of Nice Sophia Antipolis/ESPE. www. ccsenet. org/cisComputer and  Information Science Vol. 7, No. 4; 2014.
With the incorporation of information from the MetaCrop and MaizeCyc databases, this updated model spans 5,824 genes, 8,525 reactions, and 9,153 metabolites, an increase of approximately 4 times the size of the earlier iRS1563 model. Transcriptomic and proteomic data have also been used to introduce regulatory constraints in the model to simulate an N-limited condition and mutants deficient in glutamine synthetase, gln1-3 and gln1-4. Model-predicted results achieved 90% accuracy when comparing the wild type grown under an N-complete condition with the wild type grown under an N-deficient condition.
Maize (Zea mays), also known as corn, is an essential dual-use food and energy crop. Maize production is increasing at the greatest rate among all cereals, with a worldwide trend of 0. 06 tons ha 21 year 21  (Leveau et al. , 2011)  and a record 877 million tons produced in the 2011-2012 fiscal year  (International Grains Council, 2013). With the recent completion of the maize genome in 2009 along with the creation and curation of databases such as MaizeGDB in 2011  (Schaeffer et al. , 2011) , MaizeCyc in 2013  (Monaco et al. , 2013) , and MetaCrop 2. 0 in 2012  (Schreiber et al. , 2012) , there is a need for an updated genome-scale metabolic model (GSM;  Saha et al. , 2011)  that will integrate all newly available information from diverse sources. The integration of this information with experimental transcriptomic data, proteomic data, and biomass composition measurements obtained with wildtype plants grown under optimal nitrogen (N + WT) conditions and limited nitrogen (N 2 WT) conditions  (Amiour et al.
In this work, we describe the reconstruction of a second-generation maize leaf model and the incorporation of omics data into the model with the goal of improving the understanding of N metabolism. Both the primary and secondary metabolic pathways of maize are included, by combining information from MetaCrop  (Schreiber et al. , 2012) , MaizeCyc  (Monaco et al. , 2013) , and the earlier iRS1563  (Saha et al. , 2011)  models. In comparison with the iRS1563 model, this second-generation model spans an additional 4,261 genes and 6,540 reactions. The increased number of genes and reactions enables the inclusion of additional pathways such as fructan biosynthesis, siroheme biosynthesis, and ubiquniol-9 biosynthesis. The model accounts for the two major cell types in the leaf (i. e. the bundle sheath and mesophyll cells).
Development of the Second-Generation Maize Leaf Model The second-generation maize leaf model was developed using a combination of gene, protein, and reaction information from the previously developed maize model iRS1563  (Saha et al. , 2011) , biological databases such as the Kyoto Encyclopedia of Genes and Genomes  (Kanehisa et al. , 2014) , MaizeCyc  (Monaco et al. , 2013) , and Meta-Crop  (Schreiber et al. , 2012) , as well as published literature sources. The model contains 5,824 genes and 8,525 reactions, a significant increase from the iRS1563 model, which contained 1,563 genes and 1,985 reactions. The second-generation maize model is split into two cell types (i. e. the bundle sheath and mesophyll cells). The bundle sheath cell is further divided into seven compartments, while the mesophyll cell contains six compartments (Fig.
, 2013). Due to the incomplete information available in published literature or databases regarding possible alternative routes of the production and degradation of a specific metabolite, regulating reactions that are essential to the model will restrict biomass synthesis. Based on experimental evidence, the fluxes through 83 reactions in the N + WT condition, 20 in the N 2 WT condition, 100 in the gln1-3 mutant, and nine in the gln1-4 mutant were restricted. The reactions regulated in the N + WT condition mainly correspond to reactions known to occur only under stress and are expressed at a low level in comparison with the N 2 WT and mutant conditions. Reactions that have been down-regulated based on omics data are indicated in the model file (Supplemental Table  S3 ). N perturbations within the leaf tissue were modeled by combining the incorporation of transcriptomic and proteomic data with the unique biomass composition for each condition.
Comparison of Model Predictions with Metabolomic Data The metabolomic data were compared with flux predictions within the model in each of the various N background conditions. The increasing or decreasing trend of the metabolite concentration, displayed in Figure  3 , was qualitatively compared with the change in the flux-sum range determined by the model, as displayed in Figure  4. The flux sum is a measure of the amount of flow through the reactions associated with either the production or consumption of the metabolite. A variability analysis of the flux sum was performed, and flux-sum ranges, normalized by the biomass rate, that do not overlap between the N background condition and the N + WT condition were analyzed. An increase/decrease in the flux sum (i. e. used as a proxy for the metabolite pool) of a metabolite between the N 2 WT condition and the N + WT condition and between the two GS mutants and the N + WT condition was compared with the metabolite concentration changes.
RNA and DNA Preparation Total RNA was extracted as described by  Verwoerd et al. (1989)  from leaves that had been stored at 280°C. Total RNAs (50 mg) for transcriptome and quantitative real-time (qRT)-PCR studies were treated and prepared as described previously by  Amiour et al. (2012). Reverse transcription reactions and quantitative first strands were synthesized according to  Amiour et al. (2012). Primers for qRT-PCR and reverse transcription-PCR cloning were designed from bacterial artificial chromosome sequences found in the public maize genome databases (Maizesequence. org, PlantGDB, and GenBank). The sequences of the primers used in reverse transcription-PCR and qRT-PCR are presented in Supplemental Table  S1.
Statistical Analysis of Maize Complementary DNA Microarray Data Statistical significance for differentially expressed genes was evaluated with statistical group comparisons using multiple testing procedures. The following two gene selection approaches were applied: the significance analysis of microarrays  (Tusher et al. , 2001)  permutation algorithm and a P value ranking strategy using both z statistics in ArrayStat 1. 0 software (Imaging Research) and moderated t statistics using a moderated Student's t test available in MAnGO tools (http://bioinfome. cgm. cnrs-gif. fr) and BRBArrayTools version 3. 2. 3 (Korn et al. , 2002). For multiple testing corrections, the false discovery rate procedure was used  (Benjamini and Hochberg, 1995). Statistical tests were computed and combined for each probe set using the log-transformed data.