This paper study two stratified quantile regression models of the marginal and the conditional varieties. We estimate the quantile functions of these models by using two nonparametric methods of smoothing spline (B-spline) and kernel regression (Nadaraya-Watson). The estimates can be obtained by solve nonparametric quantile regression problem which means minimizing the quantile regression objective functions and using the approach of varying coefficient models. The main goal is discussing the comparison between the estimators of the two nonparametric methods and adopting the best one between them

A substantial portion of today’s multimedia data exists in the form of unstructured text. However, the unstructured nature of text poses a significant task in meeting users’ information requirements. Text classification (TC) has been extensively employed in text mining to facilitate multimedia data processing. However, accurately categorizing texts becomes challenging due to the increasing presence of non-informative features within the corpus. Several reviews on TC, encompassing various feature selection (FS) approaches to eliminate non-informative features, have been previously published. However, these reviews do not adequately cover the recently explored approaches to TC problem-solving utilizing FS, such as optimization techniques.

    The results revealed that the incidence of Rhizoctonial damping-off of tomato was 65% and 67% in both rotations. Substrates of pine leaf litter and mushcom 2 suppressed infection reaching 59 and 60%. Mushcom1 restricted disease occurrence to 53%. In contrast, formulated Th + B. subtillus revealed a noticeable disease reduction reaching 33.16%, due to nutrients incited from mushroom thallus. The highest occurrence of damping-off (92 and 94 %) was found in control (sandy loam soil) during rotations. However, partial suppressive of Trichoderma spp. against R. solani was detected in different substrates. Mortality was 90% in control (non-amended soil). Finally, a comparable reduction of

This work revealed the spherical aromaticity of some inorganic E4 cages and their protonated E4H+ ions (E=N, P, As, Sb, and Bi). For this purpose, we employed several evaluations like (0D-1D) nucleus independent chemical shift (NICS), multidimensional (2D-3D) off-nucleus isotropic shielding σiso(r), and natural bond orbital (NBO) analysis. The magnetic calculations involved gauge-including atomic orbitals (GIAO) with two density functionals B3LYP and WB97XD, and basis sets of Jorge-ATZP, 6-311+G(d,p), and Lanl2DZp. The Jorge-ATZP basis set showed the best consistency. Our findings disclosed non-classical aromatic characters in the above molecules, which decreased from N to Bi cages. Also, the results showed more aromaticity in E4 than E4H+

Loanwords are the words transferred from one language to another, which become essential part of the borrowing language. The loanwords have come from the source language to the recipient language because of many reasons. Detecting these loanwords is complicated task due to that there are no standard specifications for transferring words between languages and hence low accuracy. This work tries to enhance this accuracy of detecting loanwords between Turkish and Arabic language as a case study. In this paper, the proposed system contributes to find all possible loanwords using any set of characters either alphabetically or randomly arranged. Then, it processes the distortion in the pronunciation, and solves the problem of the missing lette

Deconstruction theory is a theory that appeared After construction theory, and it tends, through some key principles, to reach the purposive and the main meaning of the text by the means of different perspectives. In other words, deconstruction is a critical literary theory and a contemporary philosophical approach that work together to reach exact concept of the text, and this is achieved through reading and analyzing the text. Therefore, deconstruction has specified some principles so as to reach the exact meaning of the text through these different principles.

پێشەكی:

تیۆری هەڵوەشاندنەوە تیۆرێكە پاش بوونیادگەری سەریهەڵداوە و دەیەوێت لەڕ

Sewer systems are used to convey sewage and/or storm water to sewage treatment plants for disposal by a network of buried sewer pipes, gutters, manholes and pits. Unfortunately, the sewer pipe deteriorates with time leading to the collapsing of the pipe with traffic disruption or clogging of the pipe causing flooding and environmental pollution. Thus, the management and maintenance of the buried pipes are important tasks that require information about the changes of the current and future sewer pipes conditions. In this research, the study was carried on in Baghdad, Iraq and two deteriorations model's multinomial logistic regression and neural network deterioration model NNDM are used to predict sewers future conditions. The results of the