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This pilot study examined the effect of participation in an equine-assisted activities (EAA) program on the social functioning of participants with autism spectrum disorder (ASD). Pre- and post-assessments via the Naples Equestrian Challenge Participant Initial Evaluation were completed by a trained Certified Therapeutic Riding Instructor prior to and at the conclusion of a 12 week EAA program. 12 individuals (75% male; M age = 10.8; age range 5 – 20 years) participated. Paired-sample t-tests were conducted to examine the impact of EAA on social functioning. Analyses revealed that involvement in the EAA program resulted in a significant improvement in social functioning, but when grouped by age (5 – 10 years old, 10 – 20 years old) the effects were not significant. Lastly, individual analyses indicated that 75% of the sample had improved social functioning scores after participation in the EAA program. Results support EAA as an effective therapy for persons with ASD.

<p>The dissolved concentration of an active pharmaceutical ingredient in biological fluids is of significant importance for establishing a therapeutic effect in patients. However, the current pharmaceutical landscape is abundant in poorly soluble drugs that require solubility enhancing techniques to enable their administration. A promising technique, with increasing commercial success, is to molecularly mix drug and polymer to create an amorphous solid dispersion (ASD). While these mixtures provide enhanced drug solubility and dissolution in aqueous solutions, the mechanistic processes by which they release drug into solution are not well understood. Some unexplained behaviors include rapid drug release even at the maximum supersaturated concentration and spontaneous formation of drug-rich nanoparticles. These are beneficial for rapidly achieving and maintaining a highly supersaturated drug concentration during absorption, if crystallization is inhibited. However, the phenomena occur at typically low drug loading and are abruptly lost above a certain threshold termed the ‘limit of congruency’ (LoC), which has been reported to vary based on the drug-polymer system. In this research, the mechanisms underpinning ASD release at low and high drug loading were studied, and the factors affecting LoC were mechanistically explored by performing dissolution experiments and utilizing imaging, separation, thermal analysis, and spectroscopy methods to characterize the materials in the presence and absence of water. The results show that ASDs developed a gel layer on the surface when exposed to aqueous solution. This water-rich environment was thermodynamically unstable and phase separated into hydrophilic and hydrophobic phases. The morphology of the hydrophobic phase was directly related to the ASD release behavior, where ASDs below the LoC exhibited a dispersed and stable hydrophobic phase morphology, and ASDs above the LoC displayed a continuous or aggregated morphology. In cases where thermodynamic factors were rate limiting, LoC was inferred from features on the ternary phase diagram. Moreover, drug-polymer interactions and polymer molecular weight were demonstrated to affect the morphology of the hydrophobic phase and ultimately the LoC. The conclusions from this work provide the basis of a theoretical framework for rationally designing ASDs and optimizing their release. </p>

This diploma thesis is focuses on the individualization of the education of pupils with autism spectrum disorder in the education. The diploma thesis is divided into two parts, a theoretical and practical part. The theoretical part of the thesis describes and defines autism spectrum disorders, nemely chilhood autism, atypical autism, Asperger's and Rett's syndrome and disintigration disorder in childhood. The theoretical part od the work is also focused on the principles and methods used in the education of pupils with ASD. The theoretical part also describes support measures that are closely related to the education of students with autism spectrum disorders. The practical part of this diploma thesis is focused mainly on the education of pupils with ASD in compulsory distance education, which occurred as a result of the epidemic of the spread of COVID-19. The theoretical part of this diploma thesis uses the research method of qualitative research. The strategy of semi-structured interviews was used as a research strategy for the processing of qualitative research. The aim of the practical part was to answer research questions that touch on the issue of individualization of teaching pupils with ASD in the framework of regular full-time teaching, and especially in the framework of compulsory...

The purpose of this study was to survey special education teachers and speech therapists in eleven school districts in Northeast Tennessee regarding their knowledge level (etiology and educational programming) of autism spectrum disorder (ASD). The primary focus of the study was to identify effective programs and methods used by special educators in this region, comparing them to the latest techniques and teaching methods prescribed by recent research. In addition, identified weaknesses were used to recommend future training and staff development to enable educators to provide the best possible programs for children with autism. Five hundred fifty-two surveys were disseminated to special education teachers and speech therapists in eleven school districts in Northeast Tennessee. Two hundred ninety-two professionals responded to the survey, resulting in a return rate of 52.9%. Educators were asked to respond to a total of 44 questions (28 true/false items and 16 multiple choice items). The multiple choice items were designed to obtain demographic information, job related characteristics, preparation and experience teaching students with ASD, and professional needs of special educators in this region. The 16 multiple choice items were categorized into knowledge of ASD etiology and ASD educational programming. The results of the study indicate that there were no marked deficits in special educators' knowledge levels (etiology and educational programming) of ASD. However, the scores on educational programming were consistently higher than scores on etiology. There is a need for further training because very few special educators have been trained in research based methods currently used with students diagnosed as ASD.

Background: Autism Spectrum Disorder (ASD) is a complex neurodevelopmen-tal disorder that affects social communication, behavior, and cognitive development.Patients with autism have a variety of difficulties, such as sensory impairments, at-tention issues, learning disabilities, mental health issues like anxiety and depression,as well as motor and learning issues. The World Health Organization (WHO) es-timates that one in 100 children have ASD. Although ASD cannot be completelytreated, early identification of its symptoms might lessen its impact. Early identifi-cation of ASD can significantly improve the outcome of interventions and therapies.So, it is important to identify the disorder early. Machine learning algorithms canhelp in predicting ASD. In this thesis, Support Vector Machine (SVM) and RandomForest (RF) are the algorithms used to predict ASD. Objectives: The main objective of this thesis is to build and train the models usingmachine learning(ML) algorithms with the default parameters and with the hyper-parameter tuning and find out the most accurate model based on the comparison oftwo experiments to predict whether a person is suffering from ASD or not. Methods: Experimentation is the method chosen to answer the research questions.Experimentation helped in finding out the most accurate model to predict ASD. Ex-perimentation is followed by data preparation with splitting of data and by applyingfeature selection to the dataset. After the experimentation followed by two exper-iments, the models were trained to find the performance metrics with the defaultparameters, and the models were trained to find the performance with the hyper-parameter tuning. Based on the comparison, the most accurate model was appliedto predict ASD. Results: In this thesis, we have chosen two algorithms SVM and RF algorithms totrain the models. Upon experimentation and training of the models using algorithmswith hyperparameter tuning. SVM obtained the highest accuracy score and f1 scoresfor test data are 96% and 97% compared to other model RF which helps in predictingASD. Conclusions: The models were trained using two ML algorithms SVM and RF andconducted two experiments, in experiment-1 the models were trained using defaultparameters and obtained accuracy, f1 scores for the test data, and in experiment-2the models were trained using hyper-parameter tuning and obtained the performancemetrics such as accuracy and f1 score for the test data. By comparing the perfor-mance metrics, we came to the conclusion that SVM is the most accurate algorithmfor predicting ASD.

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