silver nanoparticle which synthesized by.

Neoechinorhynchus iraqensis sp. n.(Acanthocephala: Neoechinorhynchidae) from the Freshwater Mullet, Liza abu.

Background: Avascular necrosis (AVN) is defined as cellular death of bone components due to interruption of the blood supply; the bone structures then collapse, resulting in bone destruction, pain, and loss of joint function. AVN is associated with numerous conditions and usually involves the epiphysis of long bones, such as the femoral head. In clinical practice, AVN is most commonly encountered in the hip. Early diagnosis and appropriate intervention can delay the need for joint replacement. However, most patients present late in the disease course. Without treatment, the process is almost always progressive, leading to joint destruction within 5 years.Treatment of a vascular necrosis depends mainly on early diagnosis which mainly base

This work comprises the synthesis of new phenoxazine derivatives containing N-substituted phenoxazine starting from phenoxazine (1). Synthesis of ethyl acetate phenoxazine (2) through the reaction of phenoxazine with ethylchloroacetate, which reacted with hydrazine hydrate to give 10-aceto hydrazide phenoxazine (3), then reacted with formic acid to give 10-[N-formyl acetohydrazide] phenoxazine (4). Reaction of compound (4) with phosphorous pentaoxide or phosphorus pentasulphide to gave 10-[N-methylene-1,3,4-oxadiazole] phenoxazine (5) and 10-[N-methylene-1,3,4-thiadiazole] phenoxazine (6).

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