See Fig br FTIR spectras of polymer See Fig

See Fig. 2.
FTIR spectras of polymer See Fig. 3 .
Experimental design, materials and methods FTIR measurements were carried out by Spectrum Two model of Perkin Elmer FTIR spectrophotometer at 4cm-1 resolution in ATR mode using a ceramic light source, KBr/Ge beam splitter, and a LiTaO3 detector. The spectra of cyanuric chloride, EDA, TETA, PEHA polymers and Au(III) adsorbed EDA, TETA, PEHA polymers were scanned between 600 and 4000cm−1 for four times. The thermal stability behavior of the polymers was carried out by heating from 298K (25οC) to 1073K (800οC) at 40/10K/min in nitrogen nmda receptor antagonist by using NETZSCH -STA 449F1 with thermocouples in aluminum pot after milling of the polymers (51.47mg). BET surface area and total pore volume measurements were determined by Gemini 2390 VII using 0.45g sample. In order to optimization of the effecting factors to the Au(III) adsorption, 10mg of EDA, TETA, PEHA polymers was used. The pH and pCl of Au(III) solutions (25 and 50mg/L concentration) was adjusted to desired value and batch adsorption experiments were carried out by constant stirring rate using mechanical stirrer or orbital shaker. While only kinetic experiments were carried out by 1000mL of the solution, all the others were studied using 50mL. All samples taken for measurements were centrifuged and filtered. Au(III) concentrations in the filtered solutions were measured using Shimadzu 6711F flame atomic adsorption spectrometer (FAAS) . Before measurements, FAAS was calibrated by using 0, 4, 8, 12, 16 and 20mg/L Au (III) standard solutions and then, the samples were analyzed. Au(III) measurements were carried out by using air-acetylene flame at 242.8nm and 0.5nm of slit width. The amount of Au(III) adsorbed on polymers (qe) was calculated by mass balance as follows:where qe is the equilibrium sorption capacity in mg/g; V the volume (L) of the solution; w the weight (g) of the polymer; Ce and C0 are the equilibrium and initial concentrations of Au(III) (mg/L), respectively. All experiments were performed twice.
Acknowledgements
Data The corpus is collected mostly from Islamic classical books [14], and using semi-automatic web crawling process. The Modern Standard Arabic texts crawled from the Internet represent 1.15% of the corpus, about 867,913 words, while the most part is collected from Shamela Library, which represent 98.85%, with 74,762,008 words contained in 97 books (cf. Table 1).
Experimental design, materials and methods The process of text vocalization is a hard task to accomplish, however, there are limited vocalized texts, mainly, in learning Arabic language for beginners, or in specific-domains texts like religious texts i.e. Quranic and Hadith scripts. For these reasons, obtaining vocalized texts is considered as very hard task to accomplish [15,16]. The only resources available to obtain vocalized texts are those religious texts [17], which are often written in classical Arabic, or as new textual scripts written by modern authors who usually use a classical language in general. The classical Arabic language is a bit different from modern standard Arabic, in terms of grammars, vocabularies and semantic [18]. However, below is a list of available vocalized resource: To overcome this issue, we managed to use Google verbatim search to find diacritized texts, we have used Google to find diacritics texts without significant keywords to retrieve general texts without any specific keywords, we used most frequent diacritized words [19] which are considered as stop words i.e., . However, we used vocalized stop words as Linker scanner mutations are not ignored in verbatim search, in case if the writer vocalize them, most probable that the other words in text are vocalized. The extraction process:
Acknowledgments
Description of data The initial release of CapriDB contains 40 watertight textured mesh models of the objects listed in Table 1 and depicted in Fig. 1. Mesh models are stored in Wavefront OBJ format, a mesh to texture mapping is provided in MDL format and an associated texture file is stored as a JPEG image for each object. The objects for the 2015 IEEE ICRA Amazon Picking Challenge are also included in the database. Table 1 lists the physical dimensions of these objects, their weight and original material as well as additional notes which will also be stored in CapriDB. In addition, the initial database release contains the original photos (approx. 40 per object) used to construct the mesh approximation in JPEG format. To facilitate performance evaluation on the applications of the database, we also include reference images (in JPEG) and associated tracking boundaries (overlaid JPEG based on object poses acquired from the tracker) for each object as in Fig. 2 to test and compare other tracking methodologies. Fig. 3 shows how the database and interactive tracking could be used for an example benchmarking approach using a pre-defined scene layout. The included scenes and object poses can be used as ground truth to set up a system using these object models and the tracker. More information about the tracker׳s accuracy can be found in [1].