qasim kadhim

Tissue engineering benefits from electrospun scaffolds, particularly as drug carriers and reconstructive materials for orthopedic implants, as well as many other uses obtaining a large number of publications in a short period in the region through the production of complex scaffolds, the development of new nanotechnology processes, and improvement of imaging methods. Labeling these materials has become critical to achieving accurate and satisfactory results. This is an excellent method for mimicking the extracellular matrix of bone using biodegradable and biocompatible polymers for bone restoration. In this project, electrospinning of a PMMA: PVA scaffold is used. These composite fibers had a clear and continuous shape when examined under a scanning electron microscope (SEM), and their components were identified using (FTIR). Experiments revealed that this characterization of significant effects in the electrospinning method for biomedical applications plays an important role in producing implant coating materials for bone reconstruction.

Cognitive diagnostic models (CDMs) have received much interest within the field of language testing over the last decade due to their great potential to provide diagnostic feedback to all stakeholders and ultimately improve language teaching and learning. A large number of studies have demonstrated the application of CDMs on advanced large-scale English proficiency exams, such as IELTS, TOEFL, MELAB, and ECPE. However, too little attention has been paid to the utility of CDMs on elementary and intermediate high-stakes English exams. The current study aims to diagnose the reading ability of test takers in the B1 Preliminary test, previously known as the Preliminary English Test (PET), using the generalized deterministic input, noisy, “and” gate (G-DINA; de la Torre, 2011) model. The G-DINA is a general and saturated model which allows attributes to combine in both compensatory and non-compensatory relationships and each item to select the best model. To achieve the purpose of the study, an initial Q-matrix based on the theory of reading comprehension and the consensus of content experts was constructed and validated. Item responses of 435 test takers to the reading comprehension section of the PET were analyzed using the “G-DINA” package in R. The results of attribute profiles suggested that lexico-grammatical knowledge is the most difficult attribute, and making an inference is the easiest one.

A C-Test is a gap-filling test for measuring language competence in the first and second language. C-Tests are usually analyzed with polytomous Rasch models by considering each passage as a super-item or testlet. This strategy helps overcome the local dependence inherent in C-Test gaps. However, there is little research on the best polytomous Rasch model for C-Tests. In this study, the Rating Scale Model (RSM) and the Partial Credit Model (PCM) for analyzing C-Tests were compared. To this end, a C-Test composed of six passages with both RSM and PCM was analyzed. The models were compared in terms of overall fit, individual item fit, dimensionality, test targeting, and reliability. Findings showed that, although the PCM has a better overall fit compared to the RSM, both models produce similar test statistics. In light of the findings of the study, the choice of the best Rasch model for C-Tests will be discussed.