The Effect of History Teaching Supported by Dramatization Technique on Students’ Achievement and Permanence Level

The aim of this study is to determine the effect of history teaching supported by dramatization technique on students’ success and permanence in history education in comparison with the traditional teaching. In this research, the semi experimental method which was practiced by pre-test, post-test and permanence test were used and it was studied on two different student groups. One of them is control group tested with lecture method and the other one is experimental group tested by dramatization technique. Tested group consists of 63 students who studying in classes 10-A and 10-B of a vocational high school of Kırıkkale. This study is limited with units of “From Principality to State” in 10th grade students. During this experiment containing retention test were applied to experimental and control groups and the results were analyzed by the program SPSS 16.0. According to the findings obtained, in history lesson: there is a significant difference between the points of success and permanence of the group which is tested by dramatization technique and the group which is tested by teacher centered teaching. In other words, the dramatization technique, which actively involved students in the learning environment, was found to be more effective in the educational success of students and permanence of the information they learned compared to the teacher centered approach on the transfer of historical information to the students

Initial bounds for certain subclasses of generalized salagean type Bi-univalent functions associated with the Horadam polynomials

This study proposes the use of Horadam polynomials which are known as their special cases, such as the Fibonacci polynomials, the Lucas polynomials, the Pell polynomials, the Pell-Lucas polynomials, and Chebyshev polynomials of the second kind. The aim of this study is to introduce a new subclass of generalized Sǎlǎgean type bi-univalent functions using Hadamard product and defined by Horadam polynomials ()nx in the open unit disk {z:1}.UCz We obtained coefficient estimates of the Taylor-Maclaurin 2a and 3a for functions f belonging to this newly defined subclass. Fekete-Szegö inequalities were also studied. Moreover, we give some interesting results using the relation between Sǎlǎgean’s differential operator and generalized Sǎlǎgean differential operator

Parasite motility is critical for virulence of African trypanosomes.

African trypanosomes, Trypanosoma brucei spp., are lethal pathogens that cause substantial human suffering and limit economic development in some of the world's most impoverished regions. The name Trypanosoma ("auger cell") derives from the parasite's distinctive motility, which is driven by a single flagellum. However, despite decades of study, a requirement for trypanosome motility in mammalian host infection has not been established. LC1 is a conserved dynein subunit required for flagellar motility. Prior studies with a conditional RNAi-based LC1 mutant, RNAi-K/R, revealed that parasites with defective motility could infect mice. However, RNAi-K/R retained residual expression of wild-type LC1 and residual motility, thus precluding definitive interpretation. To overcome these limitations, here we generate constitutive mutants in which both LC1 alleles are replaced with mutant versions. These double knock-in mutants show reduced motility compared to RNAi-K/R and are viable in culture, but are unable to maintain bloodstream infection in mice. The virulence defect is independent of infection route but dependent on an intact host immune system. By comparing different mutants, we also reveal a critical dependence on the LC1 N-terminus for motility and virulence. Our findings demonstrate that trypanosome motility is critical for establishment and maintenance of bloodstream infection, implicating dynein-dependent flagellar motility as a potential drug target

Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors

Monitoring the operation of a graphene transistor in an integrated circuit by XPS

One of the transistors in an integrated circuit fabricated with graphene as the current controlling element, is investigated during its operation, using a chemical tool, XPS. Shifts in the binding energy of C1s are used to map out electrical potential variations, and compute sheet resistance of the graphene layer, as well as the contact resistances between the metal electrodes. Measured shifts depend on lateral positions probed, as well as on polarity and magnitude of the gate-voltage. This non-contact and chemically specific characterization can be pivotal in diagnoses. © 2016 Elsevier B.V

X-ray photoelectron spectroscopy for identification of morphological defects and disorders in graphene devices

The progress in the development of graphene devices is promising, and they are now considered as an option for the current Si-based electronics. However, the structural defects in graphene may strongly influence the local electronic and mechanical characteristics. Although there are well-established analytical characterization methods to analyze the chemical and physical parameters of this material, they remain incapable of fully understanding of the morphological disorders. In this study, x-ray photoelectron spectroscopy (XPS) with an external voltage bias across the sample is used for the characterization of morphological defects in large area of a few layers graphene in a chemically specific fashion. For the XPS measurements, an external +6 V bias applied between the two electrodes and areal analysis for three different elements, C1s, O1s, and Au4f, were performed. By monitoring the variations of the binding energy, the authors extract the voltage variations in the graphene layer which reveal information about the structural defects, cracks, impurities, and oxidation levels in graphene layer which are created purposely or not. Raman spectroscopy was also utilized to confirm some of the findings. This methodology the authors offer is simple but provides promising chemically specific electrical and morphological information. � 2016 American Vacuum Society

Electrical properties from photoinduced charging on Cd-doped (100) surfaces of CuInSe2 epitaxial thin films

The photoresponse of Cd-doped CuInSe2 (CIS) epitaxial thin films on GaAs(100) was studied using x-ray photoelectron spectroscopy under illumination from a 532 nm laser between sample temperatures of 28-260 °C. The initial, air-exposed surface shows little to no photoresponse in the photoelectron binding energies, the Auger electron kinetic energies or peak shapes. Heating between 50 and 130 °C in the analysis chamber results in enhanced n-type doping at the surface and an increased light-induced binding energy shift, the magnitude of which persists when the samples are cooled to room temperature from 130 °C but which disappears when cooling from 260 °C. Extra negative charge trapped on the Cu and Se atoms indicates deep trap states that dissociate after cooling from 260 °C. Analysis of the Cd modified Auger parameter under illumination gives experimental verification of electron charging on Cd atoms thought to be shallow donors in CIS. The electron charging under illumination disappears at 130 °C but occurs again when the sample is cooled to room temperature. © 2016 American Vacuum Society

Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract

Shams et al. report that glioma patients' motor status is predicted accurately by diffusion MRI metrics along the corticospinal tract based on support vector machine method, reaching an overall accuracy of 77%. They show that these metrics are more effective than demographic and clinical variables. Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 +/- 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts' profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model's performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.Peer reviewe