A comparison of the antimicrobial efficacy of silver diamine fluoride and silver nitrate: an ex vivo study

A comparison of the antimicrobial efficacy of silver diamine fluoride and silver nitrate on various cariogenic bacteria: an ex vivo study By: Reham AlNajjar, D.D.S. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Dentistry at Virginia Commonwealth University. Virginia Commonwealth University, 2019 Thesis Advisor: William Dahlke, D.M.D., Associate Professor and Chair of Pediatric Dentistry, School of Dentistry Purpose: The use of silver-based antimicrobials is an emerging method for the treatment of dental caries. In this study, the authors compare the efficacy of the two most prominent silver- based therapeutics, silver diamine fluoride (SDF) and silver nitrate (AgNO3), on cariogenic and non-cariogenic multispecies biofilms. Currently there is a lack of studies comparing the efficacy of SDF to AgNO3. Methods: Plaque samples from anterior and posterior tooth sites from children presenting both with early childhood caries and caries-free children were collected, pooled, and utilized to create four ex vivo biofilm systems in artificial saliva. SDF and AgNO3 were administered to these biofilms and bacterial survival was quantified and compared to untreated controls. Results: Each of the four pooled sample types was applied to plates coated in artificial saliva + 1% sucrose. Both SDF and AgNO3 were very effective against plaque derived biofilms when compared to untreated biofilms (P0.05) in the potency of each compound. Conclusions: SDF and AgNO3 significantly inhibit ex vivo cariogenic and non-cariogenic biofilms at similar levels

"Impact of Internships on Students Personal, Interpersonal, Academic, Occupational and Civic Characteristics in Turkish Academic Institutions"

The research which titled (Impact of internships on students personal, Interpersonal, Academic, occupational and civic characteristics in Turkish Academic Institutions) aims to identifying the impact of Impact of internships on students personal, Interpersonal, Academic, occupational and civic characteristics in Turkish Academic Institutions It aims at providing quantitative data that shows that joining an internship program could affect students in personal, interpersonal, academic, occupational and civic areas This research adapted to prove by evidence how would internships affect the students, and show the extent of the impact and answering by numbers how does internship affect a student in details in every aspect.  The researcher adapted a descriptive analytical approach which depends on data collection, analysis using SPSS and interpretation of the results to determine the variables mentioned. An accepted measurement tools were adapted, and modified to suit the purpose of the study. The results also proved that Participation in a student’s internship have an overall impact on the five variables mentioned. The research has presented some recommendations concerning applying learning program which could be more effective if it has been taken into consideration

Autism Spectrum Disorder Classification via Local and Global Feature Representation of Facial Image

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social communication and interaction. Early diagnosis of ASD can mitigate the severity and help with ideal treatment direction. Computer vision-based methods with traditional machine learning and deep learning are employed in the literature for automatic diagnosis. Recently, deep learning with a facial image-based ASD classification has gained interest due to its ease of collection and non-invasiveness. We observed that the existing approaches utilized either local or global features of facial images to diagnose ASD. However, its important to consider both local and global features to obtain fine-grained details and larger contextual information for accurate detection and classification. This paper proposes a sequencer-based patch-wise Local Feature Extractor along with a Global Feature Extractor. Finally, the features from these modules are aggregated to obtain the final feature for the classification of ASD. Experiments on a publicly available Autism Facial Image Dataset demonstrate that our proposed framework achieves state-of-the-art performance. We achieved accuracy, precision, recall, and F1-score of 94.7%, 94.0%, 95.3%, and 94.6%, respectively

E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis

Rice is one of the most important crops worldwide. Diseases of the rice plant can drastically reduce crop yield and even lead to complete loss of production. Early diagnosis can reduce the severity and help efforts to establish effective treatment and reduce the usage of pesticides. Traditional machine learning approaches have already been employed for automatic diagnosis. However, they heavily rely on manual preprocessing of images and handcrafted features, which is challenging, time-consuming, and may require domain expertise. Recently, a single end-to-end deep learning (DL)-based approach was employed to diagnose rice diseases. However, it is not highly robust, nor is it generalizable to every dataset. Hence, we propose a novel end-to-end training of convolutional neural network (CNN) and attention (E2ETCA) ensemble framework that fuses the features of two CNN-based state-of-the-art (SOTA) models along with those of an attention-based vision transformer model. These fused features are utilized for diagnosis by the addition of an extra fully connected layer with softmax. The whole procedure is performed end-to-end, which is very important for real-world applications. Additionally, we feed the extracted features into a traditional machine learning approach support vector machine for classification and further analysis. To verify the effectiveness of our proposed E2ETCA framework, we demonstrate it on three publicly available datasets: the Mendeley Rice Leaf Disease Image Samples dataset, the Kaggle Rice Diseases Image dataset, the Bangladesh Rice Research Institute dataset, and a combination of these three datasets. On the basis of various evaluation metrics (accuracy, precision, recall, and F1-score), our proposed E2ETCA framework exhibits superior performance to existing SOTA approaches for rice disease diagnosis, which can also be generalizable in similar other domains

Image hiding in audio file using chaotic method

In this paper, we propose an efficient image hiding method that combines image encryption and chaotic mapping to introduce adaptive data hiding for improving the security and robustness of image data hiding in cover audio. The feasibility of using chaotic maps to hide encrypted image in the high frequency band of the audio is investigated. The proposed method was based on hiding the image data in the noisiest part of the audio, which is the high frequency band that was extracted by the zero crossing filter. Six types of digital images were used, each of size fit the length of used audio, this to facilitate the process of hiding them among the audio samples. The input image was encrypted by a one-time pad method, then its bits were hidden in the audio by the chaotic map. The process of retrieving the image from the audio was in the opposite way, where the image data was extracted from the high frequency band of the audio file, and then the extracted image was decrypted to produce the retrieved image. Four qualitative metrics were used to evaluate the hiding method in two paths: the first depends on comparing the retrieved image with the original image, while the second depends on comparing the audio containing the image data with the original audio once, and another time by comparing the cover audio with the original audio. The results of the quality metrics proved the efficiency of the proposed method, and it showed a slight and unnoticed effect between the research materials, which indicates the success of the hiding process and the validity of the research path

Vocational Interests of Middle and High School Students in the UAE

This paper investigated the vocational interests of students in the UAE and determined its relationship to gender and grade level. The Emirates Scale for Vocational Interests- Revised (ESVI-R) was used to attain the goal of the investigation. The ESVE-R was administered to 1920 students in the different emirates who were in the 9th, 10th, 11th, and 12th grades. Accordingly, the sample was comprised of 866 (45.1%) male and 1054 (54.9%) female students. The data were analyzed using the appropriate descriptive and inferential statistical protocols. The results showed that there are significant gender differences in most of the subscales of ESVI-R whereas there are significant differences in some of the subscales according to students' grade level. The study concluded that the vocational interests of the students demonstrated stability along grade levels and that there was a notable shift in the vocational interests of female students in the trajectory of seeking an equal opportunity with their male counterparts. Keywords: Vocational interests, gender differences, grade level, UAE school students

Vocational Interests of Middle and High School Students in the UAE

This paper investigated the vocational interests of students in the UAE and determined its relationship to gender and grade level. The Emirates Scale for Vocational Interests- Revised (ESVI-R) was used to attain the goal of the investigation. The ESVE-R was administered to 1920 students in the different emirates who were in the 9th, 10th, 11th, and 12th grades. Accordingly, the sample was comprised of 866 (45.1%) male and 1054 (54.9%) female students. The data were analyzed using the appropriate descriptive and inferential statistical protocols. The results showed that there are significant gender differences in most of the subscales of ESVI-R whereas there are significant differences in some of the subscales according to students' grade level. The study concluded that the vocational interests of the students demonstrated stability along grade levels and that there was a notable shift in the vocational interests of female students in the trajectory of seeking an equal opportunity with their male counterparts. Keywords: Vocational interests, gender differences, grade level, UAE school students

K-Means clustering of optimized wireless network sensor using genetic algorithm

Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby

Aqueous Solution Equilibria and Spectral Features of Copper Complexes with Tripeptides Containing Glycine or Sarcosine and Leucine or Phenylalanine

Copper(II) complexes of glycyl-L-leucyl-L-histidine (GLH), sarcosyl-L-leucyl-L-histidine (Sar-LH), glycyl-L-phenylalanyl-L-histidine (GFH) and sarcosyl-L-phenylalanyl-L-histidine (Sar-FH) have potential anti-inflammatory activity, which can help to alleviate the symptoms associated with rheumatoid arthritis (RA). From pH 2–11, the MLH, ML, MLH-1 and MLH-2 species formed. The combination of species for each ligand was different, except at the physiological pH, where CuLH-2 predominated for all ligands. The prevalence of this species was supported by EPR, ultraviolet-visible spectrophotometry, and mass spectrometry, which suggested a square planar CuN4 coordination. All ligands have the same basicity for the amine and imidazole-N, but the methyl group of sarcosine decreased the stability of MLH and MLH-2 by 0.1–0.34 and 0.46–0.48 log units, respectively. Phenylalanine increased the stability of MLH and MLH-2 by 0.05–0.29 and 1.19–1.21 log units, respectively. For all ligands, 1H NMR identified two coordination modes for MLH, where copper(II) coordinates via the amine-N and neighboring carbonyl-O, as well as via the imidazole-N and carboxyl-O. EPR spectroscopy identified the MLH, ML and MLH-2 species for Cu-Sar-LH and suggested a CuN2O2 chromophore for ML. DFT calculations with water as a solvent confirmed the proposed coordination modes of each species at the B3LYP level combined with 6-31++G**