J, Volume 2, Issue 1 (March 2019) – 7 articles

This study examined the accuracy of the various forecasting methods of the 2016 US Presidential Elections. The findings revealed a high accuracy in predicting the popular vote. However, this is most suitable in an electoral system which is not divided into constituencies. Instead, due to the Electoral College method used in the US elections, forecasting should focus on predicting the winner in every state separately. Nevertheless, miss-predicted results in only a few states led to false forecasting of the elected president in 2016. The current methods proved less accurate in predicting the vote in states that are less urbanized and with less diverse society regarding race, ethnicity, and religion. The most challenging was predicting the vote of people who are White, Protestant Christians, and highly religious. In order to improve pre-election polls, this study suggests a few changes to the current methods, mainly to adopt the “Cleavage Sampling” method that can better predict the expected turnout of specific social groups, thus leading to higher accuracy of pre-election polling. Full article

Rainfall prediction is a fundamental process in providing inputs for climate impact studies and hydrological process assessments. Rainfall events are, however, a complicated phenomenon and continues to be a challenge in forecasting. This paper introduces novel hybrid models for monthly rainfall prediction in which we combined two pre-processing methods (Seasonal Decomposition and Discrete Wavelet Transform) and two feed-forward neural networks (Artificial Neural Network and Seasonal Artificial Neural Network). In detail, observed monthly rainfall time series at the Ca Mau hydrological station in Vietnam were decomposed by using the two pre-processing data methods applied to five sub-signals at four levels by wavelet analysis, and three sub-sets by seasonal decomposition. After that, the processed data were used to feed the feed-forward Neural Network (ANN) and Seasonal Artificial Neural Network (SANN) rainfall prediction models. For model evaluations, the anticipated models were compared with the traditional Genetic Algorithm and Simulated Annealing algorithm (GA-SA) supported by Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA). Results showed both the wavelet transform and seasonal decomposition methods combined with the SANN model could satisfactorily simulate non-stationary and non-linear time series-related problems such as rainfall prediction, but wavelet transform along with SANN provided the most accurately predicted monthly rainfall. Full article

The consultation of medical images, 2D or 3D, has a crucial role for planned or ongoing surgical operations. During an intervention, this consultation induces a sterility loss for the surgeon due to the fact that the classical interaction devices are non-sterile. A solution to this problem would be to replace conventional devices by touchless interaction technologies, thereby enabling sterile interventions. In this paper, we present the conceptual development of an intuitive “gesture vocabulary” allowing the implementation of an effective touchless interactive system that is well adapted to the specificities of the surgical context. Our methodology and its implementation as well as our results are detailed. The suggested methodology and its implementation were both shown to be a valid approach to integrating this mean of interaction in the operating room. Full article

Computer-based testing (CBT) is gaining importance for studies addressing the diagnosis of competencies, because it is possible to simulate authentic action situations and may reduce the effort of analyzing the data. This benefit is most important for the phase of item design. In this phase of assessment construction, the pattern of answers of a sample is used to draw conclusions on the functionality of the items. Currently, there are no standards for the encodement of items which consider the specifications of CBT-instruments. These specifications are, for example, the a posteriori non-variability of the coding, a lack of information when using conventional test scores and the need of standardization of different formats of items. Taking these specifications into consideration, this paper proposes and discusses a two-stage coding systematization for CBT-items. For this, a distinction between item-coding and answer-coding was done. The coding is discussed for single-section and multi-section formats as well as dichotomous and polytomous answer modes. Therefore, this paper is for users of CBT-instruments who want to achieve the optimal information value of their test results with efficient coding. Full article

Most neurodegenerative diseases (NDD) are a result of changes in the chemical composition of neurons. For example, Alzheimer’s disease (AD) is the product of Aβ peptide deposition which results in changes in the ion concentration. These changes in ion concentration affect the responses of the neuron to stimuli and often result in inducing excessive excitation or inhibition. This paper investigates the dynamics of a single neuron as ion changes occur. These changes are incorporated using the Nernst equation. Within the central and peripheral nervous system, signals and hence rhythms, are propagated through the coupling of the neurons. It was found that under certain conditions the coupling strength between two neurons could mitigate changes in ion concentration. By defining the state of perfect synchrony, it was shown that the effect of ion imbalance in coupled neurons was reduced while in uncoupled neurons these changes had a more significant impact on the neuronal behavior. Full article

Aβ accumulation has been discovered to form large, relatively cation-permeable channels in the plasma membrane of a neuron. These channel formations in the membranes of a neuron could cause cell depolarisation, sodium and potassium dysregulation, depletion of neural energy stores and other types of cellular dysfunction. This study shows that the build-up of amyloid beta (Aβ) depositions during the onset of Alzheimer’s disease has profound effects on the activity of the local community of neurons in the central nervous system. These effects can include enhanced neural activity, spontaneous epileptiform activity and incidence of epileptic seizures. From the results in this area, it can be seen that the neurodegeneration observed in Alzheimer’s disease has been associated with the increase of toxicity of Aβ depositions. In this research paper, we examined this hypothesis in light of a computational model of a neuron. Full article