Background: Autism spectrum disorder (ASD) is characterized by impairments in social interaction and communication, restricted patterns of behavior, and unusual sensory sensitivities. Saliva may provide an easily accessible sample for analysis. Some salivary constituents levels altered in adolescents with ASD including antioxidants . This study aimed to investigate salivary physicochemical characteristic in relation to oral health status among adolescent with ASD. Materials and methods: Two groups were included in this study: forty institutionalized autistic adolescents and forty apparently healthy school adolescents with age range (12-15 years old, only males) selected randomly from Baghdad. Each group subdivided into two groups according to the severity of dental caries: caries free group (20 child, DMFT=0) and high caries group (20 child, DMFT≥6). Decayed, missing and filled surfaces (DMFS), plaque (PlI), Gingival (GI) and calculus (CI) indices were used to measure oral health status for both groups. Copper (Cu), zinc (Zn) and thiocyanate (SCN) in saliva measured by atomic absorption spectrophotometer. Salivary alpha amylase (sAA) and glutathione (GSH) assessed by enzyme-linked immunosorbent assay (ELISA). Salivary pH and flow rate were measured directly. The data of current study was analyzed using SPSS version 21. Results: A higher value of salivary pH, flow rate, sAA, SCN, Cu and Zn were found among study group than control group with significant difference, also higher in caries free subgroup than high caries subgroup. While GSH was higher in control group than study group. Moderate negative correlations between sAA, Cu, Zn and PlI, CI, GI with highly significant and salivary pH correlate moderately with PlI and CI with highly significant. Conclusion: There is alteration in salivary constituents levels which related to oral health status in adolescents with ASD and can act as adjunctive diagnostic aid for diagnosing autism.
The aim of the study is to reveal the effect of the constructivist learning Model on the achievement and reflective thinking of the fifth grade literary Preparatory students in History subject. A random sample was chosen which consisted of 64 students divided into experimental and control groups, each group consisted of 32 students. The experimental group was taught via the constructivist learning model, and the control group was taught via the traditional method. The experiment was lasted for Eight weeks, each week taught two lessons. The researcher adopted the experimental design with partial control. The two groups were equalized statistically. The researcher used two instruments, the achievement test and the reflective thinking test.
... Show MoreThree-dimensional nonlinear thermal numerical simulations are conducted for the friction stir welding (FSW) of AA 7020-T53. Three welding cases with tool (rotational and travel) speeds of 900rpm-40mm/min, 1400rpm-16mm/min and 1400rpm-40mm/in are analyzed. The objective is to study the variation of transient temperature in a friction stir welded plate of 5mm workpiece thickness. Based on the experimental records of transient temperature at several specific locations during the friction stir welding process for the AA 7020-T53, thermal numerical simulation is developed. The numerical results show that the temperature field in the FSW process is symmetrically distributed with respect to the welding line, increasing travel speed decreasing tran
... Show MoreThe fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature‐based datasets often perform poorly in ZD detection. A new technique for detecting zero‐day (ZD) attacks in IoT‐based Conventional Spiking Neural Networks (CSNN), termed ZD‐CSNN, is proposed. The model comprises three key levels: (1) Data Pre‐processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and t
This research presents a numerical study to simulate the heat transfer by forced convection as a result of fluid flow inside channel’s with one-sided semicircular sections and fully filled with porous media. The study assumes that the fluid were Laminar , Steady , Incompressible and inlet Temperature was less than Isotherm temperature of a Semicircular sections .Finite difference techniques were used to present the governing equations (Momentum, Energy and Continuity). Elliptical Grid is Generated using Poisson’s equations . The Algebraic equations were solved numerically by using (LSOR (.This research studied the effect of changing the channel shapes on fluid flow and heat transfer in two cases ,the first: cha
... Show MorePlantation of humic acid nanoparticles on the inert sand through simple impregnation to obtain the permeable reactive barrier (PRB) for treating of groundwater contaminated with copper and cadmium ions. The humic acid was extracted from sewage sludge which is byproduct of the wastewater treatment plant; so, this considers an application of sustainable development. Batch tests signified that the coated sand by humic acid (CSHA) had removal efficiencies exceeded 98 % at contact time, sorbent dosage, and initial pH of 1 h, 0.25 g/50 mL and 7, respectively for 10 mg/L initial concentration and 200 rpm agitation speed. Results proved that physicosorption was the predominant mechanism for metals-CSHA interaction because the sorption data followed
... Show MoreA robust and sensitive analytical method is presented for the extraction and determination of six pharmaceuticals in freshwater sediments.
This study proposes a hybrid predictive maintenance framework that integrates the Kolmogorov-Arnold Network (KAN) with Short-Time Fourier Transform (STFT) for intelligent fault diagnosis in industrial rotating machinery. The method is designed to address challenges posed by non-linear and non-stationary vibration signals under varying operational conditions. Experimental validation using the FALEX multispecimen test bench demonstrated a high classification accuracy of 97.5%, outperforming traditional models such as SVM, Random Forest, and XGBoost. The approach maintained robust performance across dynamic load scenarios and noisy environments, with precision and recall exceeding 95%. Key contributions include a hardware-accelerated K
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