Technical Paper Session II - Universal Law for the Transition from Chaos to Periodicity in Nonlinear Physical Systems

This paper investigates the Chaos phenomena in nonlinear psychical systems described by differential equations. A prototypical system is the Duffing oscillator, described by the nonlinear second order differential equation which presents a mathematical model of the motion performed by a plane pendulum under a periodic external force. By using numerical and phase space analysis, the transition from periodic to chaotic behavior (and vice versa) is analyzed

Kleene Algebras and Semimodules for Energy Problems

With the purpose of unifying a number of approaches to energy problems found in the literature, we introduce generalized energy automata. These are finite automata whose edges are labeled with energy functions that define how energy levels evolve during transitions. Uncovering a close connection between energy problems and reachability and B\"uchi acceptance for semiring-weighted automata, we show that these generalized energy problems are decidable. We also provide complexity results for important special cases

Regularity of Infinity for Elliptic Equations with Measurable Coefficients and Its Consequences

This paper introduces a notion of regularity (or irregularity) of the point at infinity for the unbounded open subset of \rr^{N} concerning second order uniformly elliptic equations with bounded and measurable coefficients, according as whether the A-harmonic measure of the point at infinity is zero (or positive). A necessary and sufficient condition for the existence of a unique bounded solution to the Dirichlet problem in an arbitrary open set of \rr^{N}, N\ge 3 is established in terms of the Wiener test for the regularity of the point at infinity. It coincides with the Wiener test for the regularity of the point at infinity in the case of Laplace equation. From the topological point of view, the Wiener test at infinity presents thinness criteria of sets near infinity in fine topology. Precisely, the open set is a deleted neigborhood of the point at infinity in fine topology if and only if infinity is irregular.Comment: 20 page

Towards a Notion of Distributed Time for Petri Nets

We set the ground for research on a timed extension of Petri nets where time parameters are associated with tokens and arcs carry constraints that qualify the age of tokens required for enabling. The novelty is that, rather than a single global clock, we use a set of unrelated clocks --- possibly one per place --- allowing a local timing as well as distributed time synchronisation. We give a formal definition of the model and investigate properties of local versus global timing, including decidability issues and notions of processes of the respective models

Simulations of Weighted Tree Automata

Simulations of weighted tree automata (wta) are considered. It is shown how such simulations can be decomposed into simpler functional and dual functional simulations also called forward and backward simulations. In addition, it is shown in several cases (fields, commutative rings, Noetherian semirings, semiring of natural numbers) that all equivalent wta M and N can be joined by a finite chain of simulations. More precisely, in all mentioned cases there exists a single wta that simulates both M and N. Those results immediately yield decidability of equivalence provided that the semiring is finitely (and effectively) presented.Comment: 17 pages, 2 figure

Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist the specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We conclude by suggesting future approaches to detecting ASDs using AI techniques and MRI neuroimaging

The impact of Loa loa microfilaraemia on research subject retention during a whole sporozoite malaria vaccine trial in Equatorial Guinea

Loa loa microfilariae were found on thick blood smears (TBSs) from 8 of 300 (2.7%) residents of Bioko Island, Equatorial Guinea, during a Plasmodium falciparum sporozoite malaria vaccine clinical trial. Only one subject was found to have microfilaraemia on his first exam; parasites were not discovered in the other seven until subsequent TBSs were performed, at times many weeks into the study. All infected individuals were asymptomatic, and were offered treatment with diethylcarbamazine, per national guidelines. L. loa microfilaraemia complicated the enrolment or continued participation of these eight trial subjects, and only one was able to complete all study procedures. If ruling out loiasis is deemed to be important during clinical trials, tests that are more sensitive than TBSs should be performed

Blooming Artifact Reduction in Coronary Artery Calcification by A New De-blooming Algorithm: Initial Study

The aim of this study was to investigate the use of de-blooming algorithm in coronary CT angiography (CCTA) for optimal evaluation of calcified plaques. Calcified plaques were simulated on a coronary vessel phantom and a cardiac motion phantom. Two convolution kernels, standard (STND) and high-definition standard (HD STND), were used for imaging reconstruction. A dedicated de-blooming algorithm was used for imaging processing. We found a smaller bias towards measurement of stenosis using the deblooming algorithm (STND: bias 24.6% vs 15.0%, range 10.2% to 39.0% vs 4.0% to 25.9%; HD STND: bias 17.9% vs 11.0%, range 8.9% to 30.6% vs 0.5% to 21.5%). With use of de-blooming algorithm, specificity for diagnosing significant stenosis increased from 45.8% to 75.0% (STND), from 62.5% to 83.3% (HD STND); while positive predictive value (PPV) increased from 69.8% to 83.3% (STND), from 76.9% to 88.2% (HD STND). In the patient group, reduction in calcification volume was 48.1 ± 10.3%, reduction in coronary diameter stenosis over calcified plaque was 52.4 ± 24.2%. Our results suggest that the novel de-blooming algorithm could effectively decrease the blooming artifacts caused by coronary calcified plaques, and consequently improve diagnostic accuracy of CCTA in assessing coronary stenosis