In high-dimensional semiparametric regression, balancing accuracy and interpretability often requires combining dimension reduction with variable selection. This study intro- duces two novel methods for dimension reduction in additive partial linear models: (i) minimum average variance estimation (MAVE) combined with the adaptive least abso- lute shrinkage and selection operator (MAVE-ALASSO) and (ii) MAVE with smoothly clipped absolute deviation (MAVE-SCAD). These methods leverage the flexibility of MAVE for sufficient dimension reduction while incorporating adaptive penalties to en- sure sparse and interpretable models. The performance of both methods is evaluated through simulations using the mean squared error and variable selection cri

The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

: In this study, a linear synchronous machine is compared with a linear transverse flux machine. Both machines have been designed and built with the intention of being used as the power take off in a free piston engine. As both topologies are cylindrical, it is not possible to construct either using just flat laminations and so alternative methods are described and demonstrated. Despite the difference in topology and specification, the machines are compared on a common base in terms of rated force and suitability for use as a generator. Experience gained during the manufacture of two prototypes is described.

In the 1970s, the world knew the long-tailed nesokia Nesokia bunnii (Khajuria, 1981) (Rodentia, Muridae) from the Mesopotamian marshes of Garden of Eden in Southern Iraq. This distinct rodent was known from only five voucher specimens collected at the confluence of Tigris and Euphrates Rivers in southern Iraq while its occurrence in Southwestern Iran had
never been reported. In the 1990s, a large extent of its natural habitat was catastrophically desiccated and the animal was last seen in the 1970s. Since then, the status of this elusive rodent was shrouded in mystery. In 2007, an extraordinary photograph of a carcass of this species came to the light from Hawizeh Marsh which was interpreted as concrete evidence of the species’ pers

Metal complexes chrome(III), manganese(II), iron(III), cobalt(II), nickel(II), cupper(II) and zinc(II) with diazonium of 3-amino-2-chloropyridine of general formula [2-Cl-C₅H₃N≡N]_n[MX_m], where n=2 or 3 for divalent and trivalent metal, m= 4 or 6  were synthesized. The complexes have been characterized by flame atomic absorption, (C.H.N), molar conductance, magnetic susceptibility UV-vis spectra, infrared spectra,¹H-NMR spectroscopy and thermo gravimetric analysis (TGA and DTA). The measurements showed that the divalent metal ion complexes (M²⁺) have (1:2) M:L ratio with tetrahedral geometry around metal ions while the trivalent metal ions (M³⁺) formed (1:3) m

Aim: The aim of this study was to investigate babesiosis in dogs of different breeds and ages and of both sexes in Baghdad Province by molecular detection of Babesia canis using conventional polymerase chain reaction (PCR) and sequencing followed by phylogenetic analyses. Materials and Methods: Blood samples were collected from 310 dogs of different ages and breeds, and of both sexes in different areas of Baghdad Province from December 2018 to September 2019; during clinical examinations, body temperature, pulse, respiratory rate, and signs of diseases were recorded. PCR was used to amplify a specific 450-bp fragment of the 18S rRNA gene of B. canis. PCR products were sequenced, and MEGA 6.0 software was used for analysis. Chi-squar

Rutting in asphalt mixtures is a very common type of distress. It occurs due to the heavy load applied and slow movement of traffic. Rutting needs to be predicted to avoid major deformation to the pavement. A simple linear viscous method is used in this paper to predict the rutting in asphalt mixtures by using a multi-layer linear computer programme (BISAR). The material properties were derived from the Repeated Load Axial Test (RLAT) and represented by a strain-dependent axial viscosity. The axial viscosity was used in an incremental multi-layer linear viscous analysis to calculate the deformation rate during each increment, and therefore the overall development of rutting. The method has been applied for six mixtures and at different tem

Necessary and sufficient conditions for the operator equation I AXAX n*, to have a real positive definite solution X are given. Based on these conditions, some properties of the operator A as well as relation between the solutions X andAare given.