Evaluation of an adaptive and intelligent educational hypermedia for enhanced individual learning of mathematics: A qualitative study

The purpose of this study is to design a personalized adaptive and intelligent web based tutoring system based on learning style and expert system named UZWEBMAT and to evaluate its effects on 10th grade students’ learning of the unit of probability. In the study, initially, learning objects were prepared in three different ways in relation to three sub-learning areas of Visual–Auditory–Kinesthetic (VAK) learning style for each subject of the probability unit. These were appropriate for secondary school mathematics curricula. Then, they were transferred into the digital environment. Each student’s dominant learning style determines the content to which s/he will be directed since s/he is directed to the content that is appropriate for his/her learning style. The course to be followed by the students within UZWEBMAT and their browsing around the pages are decided by expert system integrated into the system. This expert system sets the situations in which s/he will get solution supports and the course s/he will follow in accordance with the performance of the student. Hereby, each student may follow a different course, and the solution supports s/he will get may also differ highlighting the individual learning. The sample of the study consists of 81 10th grade students and 3 mathematics teachers from two high schools in Trabzon, Turkey. Qualitative data were obtained both from the teachers and students participating in the study in order to answer the research questions about the implementation and evaluation of UZWEBMAT for mathematics teaching in a high school classroom. Obtained data were analyzed using qualitative data analysis methods. According to the results of the present study, positive opinions of students and teachers such as taking into account the individual learning differences and deriving mathematical relations and formulas through exploration became prominent. In addition, there were also other positive opinions of students and teachers such as providing permanent learning and introducing learning responsibility to the students. In this sense, it was concluded that UZWEBMAT is a beneficial instrument for both students and teachers.     

Calculations of the Elastic Scattering Properties of Ultra-cold Francium Isotopes

The calculations of the elastic scattering properties of ultra-cold francium isotopes are reported in detail. A parametric model potential for the triplet molecular states of the Fr₂ is represented, and the scattering lengths a_t and the effective range r_e are calculated using WKB and Numerov methods for Fr--Fr in the triplet state. The convergence of these scattering properties depending on a K⁰ parameter and core radius is also investigated using two methods: Quantum Defect Theory and analytical formula of Symskoviski.     

Application of InP Neural Network to ECG BeatClassification

This paper presents an application of a hybrid neural network structure to the classification of the electrocardiogram (ECG) beats. Three different feature extraction methods are comparatively examined: discrete cosine transform, wavelet transform and a direct method. Classification performances, training times and the numbers of nodes of Kohonen network, Restricted Coulomb Energy (RCE) network and the hybrid neural network are presented. To increase the classification performance and to decrease the number of nodes, the hybrid neural network is trained by Genetic Algorithms (GAs). Ten types of ECG beats obtained from the MIT-BIH database and from a real-time ECG measurement system are classified with a success of 98% by using the hybrid neural network structure and discrete cosine transform together.     

On periodic resource scheduling for continuous-media databases

The Enhanced Pay-Per-View (EPPV) model for providing continuous-media services associates with each continuous-media clip a display frequency that depends on the clip's popularity. The aim is to increase the number of clients that can be serviced concurrently beyond the capacity limitations of available resources, while guaranteeing a constraint on the response time. This is achieved by sharing periodic continuous-media streams among multiple clients. The EPPV model offers a number of advantages over other data-sharing schemes (e.g., batching), which make it more attractive to large-scale service providers. In this paper, we provide a comprehensive study of the resource-scheduling problems associated with supporting EPPV for continuous-media clips with (possibly) different display rates, frequencies, and lengths. Our main objective is to maximize the amount of disk bandwidth that is effectively scheduled under the given data layout and storage constraints. Our formulation gives rise to -hard combinatorial optimization problems that fall within the realm of hard real-time scheduling theory. Given the intractability of the problems, we propose novel heuristic solutions with polynomial-time complexity. We also present preliminary experimental results for the average case behavior of the proposed scheduling schemes and examine how they compare to each other under different workloads. A major contribution of our work is the introduction of a robust scheduling framework that, we believe, can provide solutions for a variety of realistic EPPV resource-scheduling scenarios, as well as any scheduling problem involving regular, periodic use of a shared resource. Based on this framework, we propose various interesting research directions for extending the results presented in this paper. Received June 9, 1998 / Accepted October 13, 1998     

Mode competition using TE03 gyrotron cavities

The gyrotron is a powerful source of millimetre wave radiation. Fusion applications require more power per tube than is currently available This in turn means that the resonator must be highly overmoded, with a dense mode spectrum, which leads to mode competition. The influence of external parameters, such as the applied magnetic field, electron beam, and resonator geometry, on mode competition in tubes designed to operate at 150 and 140 GHz in the TE⁰³ mode is investigated theoretically and experimentally. It can be shown that even when the mode spectrum is fairly dense, single mode operation of a gyrotron is possible. The influence of startup conditions on output power is investigated in the 150 GHz experiment. In some cases, modes with an axial index of 2 were excited.     

Technical note Seafloor characterization through side scan sonar image processing

This paper presents the most suitable image processing techniques being used at present for recognising features either lying on the seafloor or floating in the water column. The preprocessing methodology is already incorporated in the sonar system, and as we cannot access the raw data, it is not included in this paper. The studied images correspond to the Cabrera Archipelago, in the Mediterranean Sea.     

Diagonal stabilization of linear multivariable systems

A new procedure is presented for the synthesis of diagonal compensators for N × N linear multivariable systems that are free of fixed modes with respect to constant diagonal output feedbacks. The synthesis procedure employs simple polynomial algebra and it is in the form of an N-step algorithm. The geometric configurations 2N- and 2^N-cells in N-space are shown to be especially suitable for visualizing diagonal feedback and aiding the application of the synthesis algorithm.     

A new feature encoding scheme for HIV-1 protease cleavage site prediction

HIV-1 protease has been the subject of intense research for deciphering HIV-1 virus replication process for decades. Knowledge of the substrate specificity of HIV-1 protease will enlighten the way of development of HIV-1 protease inhibitors. In the prediction of HIV-1 protease cleavage site techniques, various feature encoding techniques and machine learning algorithms have been used frequently. In this paper, a new feature amino acid encoding scheme is proposed to predict HIV-1 protease cleavage sites. In the proposed method, we combined orthonormal encoding and Taylor’s venn-diagram. We used linear support vector machines as the classifier in the tests. We also analyzed our technique by comparing some feature encoding techniques. The tests are carried out on PR-1625 and PR-3261 datasets. Experimental results show that our amino acid encoding technique leads to better classification performance than other encoding techniques on a standalone classifier.