Traffic congestion and the lifetime of networks with moving nodes

For many power-limited networks, such as wireless sensor networks and mobile ad hoc networks, maximizing the network lifetime is the first concern in the related designing and maintaining activities. We study the network lifetime from the perspective of network science. In our model, nodes are initially assigned a fixed amount of energy moving in a square area and consume the energy when delivering packets. We obtain four different traffic regimes: no, slow, fast, and absolute congestion regimes, which are basically dependent on the packet generation rate. We derive the network lifetime by considering the specific regime of the traffic flow. We find that traffic congestion inversely affects network lifetime in the sense that high traffic congestion results in short network lifetime. We also discuss the impacts of factors such as communication radius, node moving speed, routing strategy, etc., on network lifetime and traffic congestion.

Does Economic Crisis Force to Consumption Changes Regarding Fruits and Vegetables?

This study focuses on consumers' behaviour towards Fruits and Vegetables (FVs) under economic crisis. The implementation of both factor analysis and logistic regression reveals discrete consumer groups, affected and not affected by the ongoing economic crisis. Interviewees were selected randomly. In total, 250 questionnaires were completed and 238 of them were used for computations. There are two consumer groups, one affected by the crisis and one which did not. For the former, the price criterion prevails, while for the latter parameters like locality of production and heath concerns lead them to purchasing decisions. The economic crisis has reduced the quantities of FVs being consumed, and the retail chain stores fail to meet the criteria of locality and secure traceability procedures about the origin of the products. Nevertheless, educated consumers with higher incomes prefer to visit super markets, while elderly people with low incomes prefer grocery stores and open markets.

Space pruning monotonic search for the non-unique probe selection problem

Identification of targets, generally viruses or bacteria, in a biological sample is a relevant problem in medicine. Biologists can use hybridisation experiments to determine whether a specific DNA fragment, that represents the virus, is presented in a DNA solution. A probe is a segment of DNA or RNA, labelled with a radioactive isotope, dye or enzyme, used to find a specific target sequence on a DNA molecule by hybridisation. Selecting unique probes through hybridisation experiments is a difficult task, especially when targets have a high degree of similarity, for instance in a case of closely related viruses. After preliminary experiments, performed by a canonical Monte Carlo method with Heuristic Reduction (MCHR), a new combinatorial optimisation approach, the Space Pruning Monotonic Search (SPMS) method, is introduced. The experiments show that SPMS provides high quality solutions and outperforms the current state-of-the-art algorithms.

Data mining in psychiatric research

Mathematical sciences and computational methods have found new applications in fields like medicine over the last few decades. Modern data acquisition and data analysis protocols have been of great assistance to medical researchers and clinical scientists. Especially in psychiatry, technology and science have made new computational methods available to assist the development of predictive modeling and to identify diseases more accurately. Data mining (or knowledge discovery) aims to extract information from large datasets and solve challenging tasks, like patient assessment, early mental disease diagnosis, and drug efficacy assessment. Accurate and fast data analysis methods are very important, especially when dealing with severe psychiatric diseases like schizophrenia. In this paper, we focus on computational methods related to data analysis and more specifically to data mining. Then, we discuss some related research in the field of psychiatry.

Towards a Model-Centric Approach for Developing Enterprise Information Systems

This chapter aims to present a new modeling paradigm that promises to significantly increase the efficiency of developing enterprise information systems. Currently, the software industry faces considerable challenges as it tries to build larger, more complex, software systems with fewer resources. Although modern programming languages such as C++ and Java have in general improved the software development process, they have failed to significantly increase developer’s productivity. Thus, developers are considering other paths to address this issue. One of the potential paths is designing, developing and deploying enterprise information systems using the Model Driven Architecture (MDA). MDA is a model-centric approach that allows for modeling the overall business of an enterprise and capturing requirements to developing, deploying, integrating, and managing different kinds of software components without considering any particular implementation technology. At the center of this approach are models; the software development process is driven by constructing models representing the software under development. Code that expresses the implementation of the model in a certain underlying technology is obtained as a result of model transformation. Thus, the intellectual investment spent in developing the business model of an enterprise is not jeopardized by the continuous changes of the implementation technologies. Currently there are two main approaches trying to implement MDA-based tools. One of the approaches is based on the Object Constraint Language and the other on Action Language. An example of designing, developing and deploying an application using this new modeling paradigm is presented. The MDA approach to software development is considered as the biggest shift since the move from Assembler to the first high level languages.

A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model

Animal Models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis: a pilot study

PURPOSE

Distinguishing nonconvulsive status epilepticus (NCSE) from some nonepileptic encephalopathies is a challenging problem. In many situations, NCSE and nonepileptic encephalopathies are indistinguishable by clinical symptoms and can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or delay to diagnosis of NCSE may increase the rate of morbidity and mortality.

METHODS

We developed a fast-differentiating algorithm using quantitative EEG analysis to distinguish NCSE patients from patients with toxic/metabolic encephalopathy (TME). EEG recordings were collected from 11 patients, including 6 with NCSE and 5 with TME. Three nonlinear dynamic measures were used in the proposed algorithm: the maximum short-term Lyapunov exponent (STLmax), phase of attractor (phase/angular frequency), and approximate entropy (ApEn). A further refined metric derived from STLmax and phase of attractor (the mean distance to EEG epoch samples from their centroid in the feature space) was also utilized as a criterion. Paired t tests were carried out to further clarify the separation between the EEG patterns of NCSE and TME.

RESULTS

Computational results showed that the performance of the proposed algorithm was sufficient to distinguish NCSE from TME. The results were consistent in all subjects in our study.

CONCLUSIONS

The study presents evidence that the maximum short-term Lyapunov exponents (STLmax) and phase of attractors (phase/angular frequency) can be useful in assisting clinical diagnosis of NCSE. Findings presented in this article provide a promising indication that the proposed algorithm may correctly distinguish NCSE from TME. Although the exact mechanism of this association remains unknown, the authors suggest that epileptic activity is highly associated with and can be modeled by dynamic systems.

Antiepileptic drug intervention decouples electroencephalogram (EEG) signals: a case study in Unverricht-Lundborg Disease

Change in severity of myoclonus as an outcome measure of antiepileptic drug (AED) treatment in patients with Unverricht-Lundborg Disease (ULD) has been estimated by utilizing the Unified Myoclonus Rating Scale (UMRS). In this study, we measure treatment effects through EEG analysis using mutual information approach to quantify interdependence/coupling strength among different electrode sites. Mutual information is known to have the ability to capture linear and non-linear dependencies between EEG time series with superior performance over the traditional linear measures. One subject with ULD participated in this study and 1-hour EEG recordings were acquired before and after treatment of AED. Our results indicate that the mutual information is significantly lower after taking the add-on AED for four weeks at least. This finding could lead to a new insight for developing a new outcome measure for patient with ULD, when UMRS could potentially fail to detect a significant difference.

An investigation of EEG dynamics in an animal model of temporal lobe epilepsy using the maximum Lyapunov exponent

Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STL(max)), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STL(max) values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STL(max) values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STL(max) and T-index (a measure of convergence/divergence of STL(max) between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STL(max) was lower (p<0.05) than other periods, STL(max) values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p<0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG's dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.