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 present study is concerned with the writer's ideologies towards violence against women. The study focuses on analyzing violence against women in English novel to see the extent the writers are being affected and influenced by their genders. It also focuses on showing to what extent the writer's ideologies are reflected in their works. Gender influences social groups ideologies; therefore, when a writer discusses an issue that concerns the other gender, they will be either subjective or objective depending on the degree of influence, i.e., gender has influenced their thoughts as well as behaviors. A single fact may be presented differently by different writers depending on the range of a

Introduction: Methadone hydrochloride (MDN) is an effective pharmacological substitution treatment for opioids dependence, adopted in different countries as methadone maintenance treatment (MMT) programmes. However, MDN can exacerbate the addiction problem if it is abused and injected intravenously, and the frequent visits to the MMT centres can reduce patient compliance. The overall aim of this study is to develop a novel extended-release capsule of MDN using the sol-gel silica (SGS) technique that has the potential to counteract medication-tampering techniques and associated health risks and reduce the frequent visits to MMT centres. Methods: For MDN recrystallisation, a closed container method (CCM) and hot-stage method (HSM) were conduc

Researchers are interested in the issue of children abuse and they look for its cause in the past and present. Their interest is limited to identifying penal liability which is caused by children abuse away from focusing on civil liability. So, the study is going to clarify the parents' responsibility for children abuse rather than civil liability of the medic in case he wouldn’t notify the authorities about the case according to the American law rules and the attitude of Iraqi law rather than some judicial application of civil cases that were exposed to American judiciary concerning children abuse.

In this review of literature, the light will be concentrated on the local drugs delivery systems for treating the periodontal diseases. Principles, types, advantages and indications of each type will be discussed in this paper.

Background: Congenital club foot is a complex deformity of foot .It is a collection of different abnormalities, with different etiologies. Consequently, Severity varies with   difficulties in evaluating treatment strategies with outcome results. The treatment of congenital club foot remains controversial. Usually, the orthopedist's goal is to obtain anatomically and functionally normal feet in all patients.                                Objective: To asses short term follow up result of conservatively treated club feet in relation to the age of initial casting by Ponseti technique.           Methods :A cross sectional observational study with some comparative content done in Al-kindy

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Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D