Syftet med den här studien är att redogöra för olika läsinlärningsmetoder samt att undersöka hur verksamma pedagoger resonerar kring de metoder de anser sig använda. För att ta reda på detta används kvalitativa intervjuer med pedagoger verksamma i årskurs 1–3. Studien redogör för en litteraturgenomgång vad gäller läsning och läsinlärningsmetoder. Min teoretiska utgångspunkt har varit ett sociokulturellt perspektiv då intentionen är att dels belysa pedagogernas arbete men också sätta det i ett sammanhang och öka kunskapen kring just metoderna som pedagogerna menar att de använder. Resultatet redovisas utifrån fyra kategorier som framkommit genom meningskoncentrering av intervjuerna. Här sammanfattas pedagogernas svar utifrån, Inledande läsinlärningsarbete, Läsinlärningsmetoder, Bedömning och Stöd och utmaningar. I mina slutsatser framkommer att de metoder jag redogör för i avsnittet Forsknings- och litteraturgenomgång i stort sett överensstämmer med de som pedagogerna uppger sig använda. Pedagogerna berättar om sin beredskap för hur de bemöter sina elever och utifrån det drar jag slutsatsen att pedagoger behöver stor kunskap och erfarenhet för att lyckas i sitt uppdrag att utveckla läsande elever. / The purpose of this study is to describe different reading learning methods as well as study how active educators reason about the methods they consider using. To find out, qualitative interviews are used with educators working in grades 1-3. The study describes a literature review regarding reading and reading methods. My theoretical point of departure has been a socio-cultural perspective, as the intention is to illuminate the work of the educators, but also to put it in context and to increase the knowledge about the methods the educators believe they use. The results are reported on the basis of four categories that have been identified by the focus of the interviews. Here, the teachers' answers are summarized, Initial Reading Learning, Reading Learning Methods, Assessment and Support and Challenges. In my conclusions it appears that the methods I describe in the section Research and Literature review are broadly consistent with those used by the educators. The educators tell us about their readiness for responding to their students, and on this basis, I conclude that educators need great knowledge and experience to succeed in their task of developing reading students.

Autism Spectrum Disorder (ASD) is part of the developmental disabilities. There are numerous symptoms for ASD patients, including lack of abilities in social interaction, communication obstacle and repeatable behaviors. Meanwhile, the rate of ASD prevalence has kept rising by the past 20 years from 1 out of 150 in 2000 to 1 out of 54 in 2016. In addition, the ASD population is quite large. Specifically, 3.5 million Americans live with ASD in the year of 2014, which will cost U.S. citizens $236-$262 billion dollars annually for autism services. So, it is critical to make an accurate diagnosis for preschool age children with ASD, in order to give them a better life. Instead of using traditional ASD behavioral tests, such as ADI-R, ADOS, and DSM-IV, we applied brain MRI images as input to make diagnosis. We revised 3D-ResNet structure to fit 110 preschool children's brain MRI data, along with Convolution 3D and VGG model. The prediction accuracy with raw data is 65.22%. The accuracy is significantly improved to 82.61% by removing the noise around the brain. We also showed the speed of ML prediction is 308 times faster than behavior tests.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / Protein fold recognition is an important task in the biological area. Different machine learning methods such as multiclass classifiers, one-vs-all and ensemble nested dichotomies were applied to this task and, in most of the cases, multiclass approaches were used. In this paper, we compare classifiers organized in tree structures to classify folds. We used a benchmark dataset containing 125 features to predict folds, comparing different supervised methods and achieving 54% of accuracy. An approach related to tree-structure of classifiers obtained better results in comparison with a hierarchical approach. / Revisión por pares

<p>Anaerobic digestion uses a mixed, microbial community to convert organic wastes to biogas, thereby generating a clean renewable energy and reducing greenhouse gas emissions. However, few studies have quantified the relationship between waste composition and the subsequent physical and chemical changes in the digester. This Ph.D. dissertation aimed to gain new knowledge about how these differences in waste composition ultimately affect digester function. This dissertation examined three areas of digester function: (1) hydrogen sulfide production, (2) digester foaming, and (3) methane yield. </p> <p>To accomplish these aims, a variety of materials from four different large-scale field digesters were collected at different time points and from different locations within the digester systems, including influent, liquid in the middle of the digesters, effluent, and effluent after solids separation. The materials were used for biochemical methane potential (BMP) tests in 43 lab-scale lab-digester groups, each containing triplicate or duplicate digesters. The materials from field digesters and the effluents from the lab-digesters were analyzed for an extensive set of chemical and physical characteristics. The three areas of digester function were examined with the physical and chemical characteristics of the digester materials and effluents, and the BMP performances. </p> <p>Hydrogen sulfide productions in the lab-digesters ranged from non-detectable to 1.29 mL g VS^-1. Higher H₂S concentrations in the biogas were observed within the first ten days of testing. The initial Fe(II) : S ratio and OP concentrations had important influences on H₂S productions. Important parameters of digester influents related to digester foaming were the ratios of Fe(II) : S, Fe(II) : TP, and TVFA : TALK; and the concentrations of Cu. Digesters receiving mixed waste streams could be more vulnerable to foaming. The characteristics of each waste type varied significantly based on substrate and inoculum type, and digester functioning. The influent chemical characteristics of the waste significantly impacted all aspects of digester function. Using multivariate statistics and machine learning, models were developed and the prediction of digester outcomes were simulated based on the initial characteristics of the waste types. </p>

<p>Post-disaster temporary housing has been a significant challenge for the emergency management group and industries for many years. According to reports by the Department of Homeland Security (DHS), housing in states and territories is ranked as the second to last proficient in 32 core capabilities for preparedness.The number of temporary housing required in a geographic area is influenced by a variety of factors, including social issues, financial concerns, labor workforce availability, and climate conditions. Acknowledging and creating a balance between these interconnected needs is considered as one of the main challenges that need to be addressed. Post-disaster temporary housing is a multi-objective process, thus reaching the optimized model relies on how different elements and objectives interact, sometimes even conflicting, with each other. This makes decision making in post-disaster construction more restricted and challenging, which has caused ineffective management in post-disaster housing reconstruction.</p> <p>Few researches have studied the use of Artificial Intelligence modeling to reduce the time and cost of post-disaster sheltering. However, there is a lack of research and knowledge gap regarding the selection and the magnitude of effect of different factors of the most optimized type of Temporary Housing Units (THU) in a post-disaster event.</p> The proposed framework in this research uses supervised machine learing to maximize certain design aspects of and minimize some of the difficulties to better support creating temporary houses in post-disaster situations. The outcome in this study is the classification type of the THU, more particularly, classifying THUs based on whether they are built on-site or off-site. In order to collect primary data for creating the model and evaluating the magnitude of effect for each factor in the process, a set of surveys were distributed between the key players and policymakers who play a role in providing temporary housing to people affected by natural disasters in the United States. The outcome of this framework benefits from tacit knowledge of the experts in the field to show the challenges and issues in the subject. The result of this study is a data-based multi-objective decision-making tool for selecting the THU type. Using this tool, policymakers who are in charge of selecting and allocating post-disaster accommodations can select the THU type most responsive to the local needs and characteristics of the affected people in each natural disaster.

The first-principles methods such as density functional theory (DFT) often produce quantitative predictions for physics and chemistry of materials with explicit descriptions of electron’s behavior. We were able to provide information of electronic structures with chemical doping and metal-insulator transition of rare-earth nickelates that cannot be easily accessible with experimental characterizations. Moreover, combining with mean-field microkinetic modeling, we utilized the DFT energetics to model water gas shift reactions catalyzed by Fe3O4at steady-state and determined favorable reaction mechanism. However, the high computational costs of DFT calculations make it impossible to investigate complex chemical processes with hundreds of elementary steps with more than thousands of atoms for realistic systems. The study of molecular high energy (HE) materials using the reactive force field (ReaxFF) has contributed to understand chemically induced detonation process with nanoscale defects as well as defect-free systems. However, the reduced accuracy of the force fields canalso lead to a different conclusion compared to DFT calculations and experimental results. Machine learning force field is a promising alternative to work with comparable simulation size and speed of ReaxFF while maintaining accuracy of DFT. In this respect, we developed a neural network reactive force field (NNRF) that was iteratively parameterized with DFT calculations to solve problems of ReaxFF. We built an efficient and accurate NNRF for complex decomposition reaction of HE materials such as high energy nitramine 1,3,5-Trinitroperhydro-1,3,5-triazine (RDX)and predicted consistent results for experimental findings. This work aims to demonstrate the approaches to clarify the reaction details of materials using the first-principles methods and machine learning force fields to guide quantitative predictions of complex chemical process.

Active learning methodologies (ALM) are associated with student success, but little research on this topic has been pursued at the community college level. At a local community college, students in science, technology, engineering, and math (STEM) courses exhibited lower than average grades. The purpose of this study was to examine whether the use of ALM predicted STEM course grades while controlling for academic discipline, course level, and class size. The theoretical framework was Vygotsky's social constructivism. Descriptive statistics and multinomial logistic regression were performed on data collected through an anonymous survey of 74 instructors of 272 courses during the 2016 fall semester. Results indicated that students were more likely to achieve passing grades when instructors employed in-class, highly structured activities, and writing-based ALM, and were less likely to achieve passing grades when instructors employed project-based or online ALM. The odds ratios indicated strong positive effects (greater likelihoods of receiving As, Bs, or Cs in comparison to the grade of F) for writing-based ALM (39.1-43.3%, 95% CI [10.7-80.3%]), highly structured activities (16.4-22.2%, 95% CI [1.8-33.7%]), and in-class ALM (5.0-9.0%, 95% CI [0.6-13.8%]). Project-based and online ALM showed negative effects (lower likelihoods of receiving As, Bs, or Cs in comparison to the grade of F) with odds ratios of 15.7-20.9%, 95% CI [9.7-30.6%] and 16.1-20.4%, 95% CI [5.9-25.2%] respectively. A white paper was developed with recommendations for faculty development, computer skills assessment and training, and active research on writing-based ALM. Improving student grades and STEM course completion rates could lead to higher graduation rates and lower college costs for at-risk students by reducing course repetition and time to degree completion.

<div> <div> <div> <p>Understanding protein-protein interactions (PPIs) in a cell is essential for learning protein functions, pathways, and mechanisms of diseases. This dissertation introduces the computational method to predict PPIs. In the first chapter, the history of identifying protein interactions and some experimental methods are introduced. Because interacting proteins share similar functions, protein function similarity can be used as a feature to predict PPIs. NaviGO server is developed for biologists and bioinformaticians to visualize the gene ontology relationship and quantify their similarity scores. Furthermore, the computational features used to predict PPIs are summarized. This will help researchers from the computational field to understand the rationale of extracting biological features and also benefit the researcher with a biology background to understand the computational work. After understanding various computational features, the computational prediction method to identify large-scale PPIs was developed and applied to Arabidopsis, maize, and soybean in a whole-genomic scale. Novel predicted PPIs were provided and were grouped based on prediction confidence level, which can be used as a testable hypothesis to guide biologists’ experiments. Since affinity chromatography combined with mass spectrometry technique introduces high false PPIs, the computational method was combined with mass spectrometry data to aid the identification of high confident PPIs in large-scale. Lastly, some remaining challenges of the computational PPI prediction methods and future works are discussed. </p> </div> </div> </div>

Att lära sig läsa är en väsentlig del i dagens skola och samhälle. Därför läggs mycket fokus på just läsundervisning i de tidiga åren. Lärares kunskap och didaktik har stor betydelse för att främja elevernas läsinlärning. Det krävs en förståelse för läsprocessens komplexitet och vilka förmågor hos eleverna som är viktiga att utveckla. Följaktligen är studiens syfte att undersöka hur sex lärare på tre olika skolor uppfattar att en god läsundervisning etableras. Undersökningen vill belysa lärarens syn på vilka förmågor som är viktiga att utveckla i den tidiga läsundervisningen, vilka läsinlärningsmetoder som används och hur. Studien tar ansats i den sociokulturella teorin, där den proximala utvecklingszonen stöttar inlärning. Läraren behöver ha läsinlärningskunskaper för att kunna ta reda på var eleven befinner sig i sin utveckling och för att stötta hen vidare till en högre nivå i kunskapstrappan. Undersökningen genomförs med individuella semistrukturerade intervjuer med samtliga lärare. De semistrukturerade intervjuerna används för att samtalet ska vara någorlunda öppet men ändå följa en struktur. Resultatet visade att läsmotivation, avkodning och språkförståelse behövs för en effektiv läsinlärning. I studien framträder flera läsinlärningsmetoder, tre syntetiska och en analytisk. De tre syntetiska metoderna fokuserar mer på avkodning medan den analytiska metoden börjar i förståelsen. Undersökningen visar att det är givande att arbeta med flera metoder eftersom man dels kan anpassa undervisningen på olika nivåer, dels för att de olika metoderna passar olika elever. Genom varierande arbetssätt och läsinlärningsmetoder gynnas fler elever av läsundervisningen, vilket möjliggör att fler barn lär sig läsa. / Learning to read is an essential part of today`s school and society. Therefore, a lot of focus is placed on reading teaching in the early years. Teachers´ knowledge and didactics are great importance in promoting students´ learning to read. It requires an understanding of the complexity of the reading process and which abilities of the student are important to develop. Consequently, the aim of the study is to investigate how six teachers at three different schools perceive that good reading teaching is established. The survey wants to shed light om the teacher`s view of which abilities are important for pupils to develop in early reading teaching, which reading learning methods are used and how. The study takes an approach in the sociocultural theory, where the proximal development zone supports learning. The teacher needs to have reading learning skills to be able to find out where the student is in his development and support him further to a higher level on the knowledge ladder. The survey is carried out with individual semi-structured interviews with all teachers. The semi-structured interviews are used so that the conversation is reasonably open but still follows a structure. The results showed that reading motivation, decoding and language comprehension are needed for effective reading learning. Several reading learning methods appear in the study, three synthetic and one analytic. The three synthetic methods focus more on decoding while the analytical one begins in understanding. The survey shows that it is rewarding to work with several methods, partly because you can adapt the teaching at different levels, partly because the different methods suit different students. By using more varied working methods and reading learning methods, the teaching of reading benefits more students, which enables more children to learn to read.

Meaningful learning requires active teaching and learning approaches. Thus, with a specific focus on Mathematics teaching at university in Oramia, the study aimed to: • examine the extent to which active learning/student-centered approaches were implemented; • assess the attitudes of university lecturers towards active-learning; • investigate whether appropriate training and support have been provided for the implementation of an active learning approaches • assess the major challenges that hinder the implementation of active learning approaches and • recommend ways that could advance the use of active learning approaches in Mathematics teaching at university. A mixed-methods design was used. Among the six universities in the Oromia Regional State of Ethiopia, two of the newly established universities (younger than 5 years) and two of the old universities (15 years and older) were involved in the study. A total of 84 lecturers participated in the study and completed questionnaires. This was complemented by a qualitative approach that used observation checklists and interviews for data gathering: 16 lessons were observed while the lecturers taught their mathematics classes (two lecturers from each of the four sample universities were twice observed). In addition, semi-structured interviews were conducted with four mathematics department heads and eight of the observed lecturers. The study adhered to ethical principles and to applied several techniques to enhance the validity/trustworthiness of the findings. / Psychology of Education / D. Ed. (Psychology of Education)