Fractional Order Junctions

Gottfried Leibniz generalized the derivation and integration, extending the operators from integer up to real, or even complex, orders. It is presently recognized that the resulting models capture long term memory effects difficult to describe by classical tools. Leon Chua generalized the set of lumped electrical elements that provide the building blocks in mathematical models. His proposal of the memristor and of higher order elements broadened the scope of variables and relationships embedded in the development of models. This paper follows the two directions and proposes a new logical step, by generalizing the concept of junction. Classical junctions interconnect system elements using simple algebraic restrictions. Nevertheless, this simplistic approach may be misleading in the presence of unexpected dynamical phenomena and requires including additional “parasitic” elements. The novel γ-junction includes, as special cases, the standard series and parallel connections and allows a new degree of freedom when building models. The proposal motivates the search for experimental and real world manifestations of the abstract conjectures.

Dynamical Analysis and Visualization of Tornadoes Time Series

In this paper we analyze the behavior of tornado time-series in the U.S. from the perspective of dynamical systems. A tornado is a violently rotating column of air extending from a cumulonimbus cloud down to the ground. Such phenomena reveal features that are well described by power law functions and unveil characteristics found in systems with long range memory effects. Tornado time series are viewed as the output of a complex system and are interpreted as a manifestation of its dynamics. Tornadoes are modeled as sequences of Dirac impulses with amplitude proportional to the events size. First, a collection of time series involving 64 years is analyzed in the frequency domain by means of the Fourier transform. The amplitude spectra are approximated by power law functions and their parameters are read as an underlying signature of the system dynamics. Second, it is adopted the concept of circular time and the collective behavior of tornadoes analyzed. Clustering techniques are then adopted to identify and visualize the emerging patterns.

Integer and Fractional Order Entropy Analysis of Earthquake Data-series

This paper studies the statistical distributions of worldwide earthquakes from year 1963 up to year 2012. A Cartesian grid, dividing Earth into geographic regions, is considered. Entropy and the Jensen–Shannon divergence are used to analyze and compare real-world data. Hierarchical clustering and multi-dimensional scaling techniques are adopted for data visualization. Entropy-based indices have the advantage of leading to a single parameter expressing the relationships between the seismic data. Classical and generalized (fractional) entropy and Jensen–Shannon divergence are tested. The generalized measures lead to a clear identification of patterns embedded in the data and contribute to better understand earthquake distributions.

Hand Involvement in Ollier Disease and Maffucci Syndrome: a Case Series

Ollier Disease and Maffucci Syndrome are two rare diseases that can cause tumors in several organs, having a special predilection for the hand. However, there have been very few reports in the literature focusing on hand manifestations of these diseases. We report the cases of three female patients: one with Ollier Disease, and two other with Maffucci Syndrome. All patients had hand involvement as their initial primary complaint. The Ollier Disease patient developed chondrosarcomas of two digits and had to have these fingers amputated. One of the Maffucci patients died one year after presentation from a brain glioblastoma. These cases emphasize the importance of early diagnosis of Ollier Disease and Maffucci Syndrome, as these two conditions are associated not only to crippling hand deformity, but also to a significant risk of chondrosarcoma, and other malignant tumors.

Entropy Analysis of Industrial Accident Data Series

Complex industrial plants exhibit multiple interactions among smaller parts and with human operators. Failure in one part can propagate across subsystem boundaries causing a serious disaster. This paper analyzes the industrial accident data series in the perspective of dynamical systems. First, we process real world data and show that the statistics of the number of fatalities reveal features that are well described by power law (PL) distributions. For early years, the data reveal double PL behavior, while, for more recent time periods, a single PL fits better into the experimental data. Second, we analyze the entropy of the data series statistics over time. Third, we use the Kullback–Leibler divergence to compare the empirical data and multidimensional scaling (MDS) techniques for data analysis and visualization. Entropy-based analysis is adopted to assess complexity, having the advantage of yielding a single parameter to express relationships between the data. The classical and the generalized (fractional) entropy and Kullback–Leibler divergence are used. The generalized measures allow a clear identification of patterns embedded in the data.

Allocation of Parallel Real-Time Tasks in Distributed Multi-core Architectures supported by an FTT-SE Network

Distributed real-time systems such as automotive applications are becoming larger and more complex, thus, requiring the use of more powerful hardware and software architectures. Furthermore, those distributed applications commonly have stringent real-time constraints. This implies that such applications would gain in flexibility if they were parallelized and distributed over the system. In this paper, we consider the problem of allocating fixed-priority fork-join Parallel/Distributed real-time tasks onto distributed multi-core nodes connected through a Flexible Time Triggered Switched Ethernet network. We analyze the system requirements and present a set of formulations based on a constraint programming approach. Constraint programming allows us to express the relations between variables in the form of constraints. Our approach is guaranteed to find a feasible solution, if one exists, in contrast to other approaches based on heuristics. Furthermore, approaches based on constraint programming have shown to obtain solutions for these type of formulations in reasonable time.

Using data mining with time series data in short-term stocks prediction

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on short- time stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

P-SOCRATES: A parallel software framework for time-critical many-core systems

Article in Press, Corrected Proof

Towards the Combination of Work-Stealing and Semi-Partitioned Scheduling for Parallel Tasks

Paper/Poster presented in Work in Progress Session, 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.

Towards the Combination of Work-Stealing and Semi-Partitioned Scheduling for Parallel Tasks

Poster presented in Work in Progress Session, 28th GI/ITG International Conference on Architecture of Computing Systems (ARCS 2015). 24 to 26, Mar, 2015. Porto, Portugal.

Real-time Parallel Applications on Many-core Architectures

Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Portugal.

Parallel Software framework for time-critical many-core systems

The recent technological advancements and market trends are causing an interesting phenomenon towards the convergence of High-Performance Computing (HPC) and Embedded Computing (EC) domains. On one side, new kinds of HPC applications are being required by markets needing huge amounts of information to be processed within a bounded amount of time. On the other side, EC systems are increasingly concerned with providing higher performance in real-time, challenging the performance capabilities of current architectures. The advent of next-generation many-core embedded platforms has the chance of intercepting this converging need for predictable high-performance, allowing HPC and EC applications to be executed on efficient and powerful heterogeneous architectures integrating general-purpose processors with many-core computing fabrics. To this end, it is of paramount importance to develop new techniques for exploiting the massively parallel computation capabilities of such platforms in a predictable way. P-SOCRATES will tackle this important challenge by merging leading research groups from the HPC and EC communities. The time-criticality and parallelisation challenges common to both areas will be addressed by proposing an integrated framework for executing workload-intensive applications with real-time requirements on top of next-generation commercial-off-the-shelf (COTS) platforms based on many-core accelerated architectures. The project will investigate new HPC techniques that fulfil real-time requirements. The main sources of indeterminism will be identified, proposing efficient mapping and scheduling algorithms, along with the associated timing and schedulability analysis, to guarantee the real-time and performance requirements of the applications.

Clinical and histological characteristics of HIV and hepatitis C virus-co-infected patients in Brazil: a case series study

Hepatitis C virus (HCV) is an important factor contributing to morbidity and mortality in patients co-infected with HIV and HCV. In addition, liver biopsy is an important tool in the clinical management of these patients. Although liver biopsy is controversial, it is recommended for all patients. Data regarding the clinical and histological characteristics of these patients are scarce not only in Brazil but in Latin America as a whole. With the goal of better understanding these characteristics and the benefit of liver biopsy indications in this disease setting, data collected from 234 patients followed from 1996 to 2004 at Casa da AIDS, São Paulo, were analyzed. The following variables were extracted from the patients' medical files at the time of liver biopsy: sex, age, hepatitis C infection risk factors, hepatitis C infection duration, ALT levels, CD4+ T cell counts, history of alcohol abuse, history of antiretroviral therapy, HCV genotype, and liver histological alterations. CONCLUSIONS: 1 - Hepatitis C virus 1 and 3 were the most frequently identified genotypes and were diagnosed in 72% and 25.5% of cases respectively; 2 - Structural liver alterations were found to be mild or absent in 48.2% (113/234) of the analyzed patients; 3 - Fifty-three patients (23%) had normal ALT levels and 4 - Significant liver architectural changes (F2-F3) were evident in 22.5% of the patients with normal ALT levels.

Fractional State Space Analysis of Temperature Time Series

Atmospheric temperatures characterize Earth as a slow dynamics spatiotemporal system, revealing long-memory and complex behavior. Temperature time series of 54 worldwide geographic locations are considered as representative of the Earth weather dynamics. These data are then interpreted as the time evolution of a set of state space variables describing a complex system. The data are analyzed by means of multidimensional scaling (MDS), and the fractional state space portrait (fSSP). A centennial perspective covering the period from 1910 to 2012 allows MDS to identify similarities among different Earth’s locations. The multivariate mutual information is proposed to determine the “optimal” order of the time derivative for the fSSP representation. The fSSP emerges as a valuable alternative for visualizing system dynamics.

Lymph scrotum: an unusual urological presentation of lymphatic filariasis. A case series study

Lymphatic filariasis (LF) causes a wide range of clinical signs and symptoms, including urogenital manifestations. Transmission control and disability/morbidity management/control are the two pillars of the overall elimination strategy for LF. Lymph scrotum is an unusual urological clinical presentation of LF with important medical, psychological, social and economic repercussions. A retrospective case series study was conducted on outpatients attended at the National Reference Service for Filariasis, in an endemic area for filariasis (Recife, Brazil), between 2000 and 2007. Over this period, 6,361 patients were attended and seven cases with lymph scrotum were identified. Mean patient age was 45 years (range, 26 to 64 years). Mean disease duration was 8.5 years (range, two to 15 years). All patients had evidence of filarial infection from at least one laboratory test (parasitological, antigen investigation or "filarial dance sign" on ultrasound). Six patients presented histories of urological surgery. The authors highlight the importance of the association between filarial infection and the inadequate surgical and clinical management of hydrocele in an endemic area, as risk factors for lymph scrotum. Thus, filarial infection should be routinely investigated in all individuals presenting urological morbidity within endemic areas, in order to identify likely links in the transmission chain.