Ahmet Ugur

Altered heart rhythm dynamics in very low birth weight infants with impending intraventricular hemorrhage.

OBJECTIVE. Intraventricular hemorrhage remains an important problem among very low birth weight infants and may result in long-term neurodevelopmental disabilities. Neonatologists have been unable to accurately predict impending intraventricular hemorrhage. Because alterations in the autonomic nervous systems control of heart rhythm have been associated with intraventricular hemorrhage after its development, we sought to determine if early subtle alterations of heart rhythm could be predictive of impending intraventricular hemorrhage in very low birth weight infants. METHODS. This case-control study included 10 newborn very low birth weight infants with intraventricular hemorrhage (5 grade IV, 4 grade III, and 1 grade II) and 14 control infants without intraventricular hemorrhage. Heart rhythm data from the first day of life before the development of intraventricular hemorrhage were evaluated. Detrended fluctuation analysis, a nonlinear fractal heart rate variability method, was used to assess the fractal dynamics of the heart rhythm. Fractal scaling exponents were calculated by using this analysis. RESULTS. Twenty-four infants (mean ± SD, birth weight: 845 ± 213g: gestational age: 26.1 ± 1.9 weeks) participated in the study. The short-term scaling exponent was significantly larger in infants who later developed intraventricular hemorrhage compared with those who did not (0.60 ± 0.1 vs 0.45 ± 0.1). A value of 0.52 resulted in 70% sensitivity and positive predictive value and 79% specificity and negative predictive value. The short-term scaling exponent was the only significant predictor of intraventricular hemorrhage. CONCLUSIONS. Fractal dynamics of the heart rhythm is significantly altered in very low birth weight infants before developing intraventricular hemorrhage and may be predictive of impending intraventricular hemorrhage.

Teaching computing and programming fundamentals via App Inventor for Android

In this age of growing importance for interdisciplinary studies, the field of computing, and its indispensable component, programming, have become increasingly important not only for STEM areas but also for many other fields. Computational chemistry, bio-informatics, computational linguistics, computational toxicology, etc. are just a few examples of the crossover disciplines that benefit significantly from the developments in the computing and Information Technology (IT). Instructors are facing more challenges today than ever in trying to come up with new, fresh and appealing methodologies to attract and retain students in delivering computing and IT related topics to a much broader audience. Computing courses and topics both for majors and non-majors need new approaches that motivate students to feel comfortable with the lifelong learning of computing concepts and tools. The goal of this paper is to summarize our teaching experience in and the great potential of App Inventor for Android (AIA) in broadening the appeal and diffusion of fundamental computing and programming concepts. With a pedagogical foundation stemming from constructionist learning and contextualized computing education, we present our motivation and the details of courses that can greatly benefit from AIA.

Particle swarm optimization for bitmap join indexes selection problem in data warehouses

Data warehouses are very large databases usually designed using the star schema. Queries defined on data warehouses are generally complex due to join operations involved. The performance of star schema queries in data warehouses is highly critical and its optimization is hard in general. Several query performance optimization methods exist, such as indexes and table partitioning. In this paper, we propose a new approach based on binary particle swarm optimization for solving the bitmap join index selection problem in data warehouses. This approach selects the optimal set of bitmap join indexes based on a mathematical cost model. Several experiments are performed to demonstrate the effectiveness of the proposed method on the bitmap join index selection problem. Further testing of the method is performed using a database environment specific cost function. The binary particle swarm optimization is found to be more effective than both the genetic algorithm and data mining based approaches.

The p-sized partitioning algorithm for fast computation of factorials of numbers

Computing products of large numbers has always been a challenging task in the field of computing. One such example would be the factorial function. Several methods have been implemented to compute this function including naive product, recursive product, Boiten split, and prime factorization, and linear difference. The method presented here is unique in the sense that it exploits finite order differences to reduce the number of multiplications necessary to compute the factorial. The differences generated are regrouped into a new sequence of numbers, which have at most half as many elements of the original sequence. When the terms of this new sequence are multiplied together, the factorial value is obtained. The cardinality of the new sequence can further be reduced by partitioning. The sequence is computed by using several difference tables that assist in establishing the pattern that determines the sequence. An analysis of the algorithm is presented. The analysis shows that the execution time can be reduced significantly by the algorithm presented.

Techniques for enhancing neuronal evolvability

Abstract High dimensionality and interactional complexity, appropriately introduced, can enhance the evolvability of a pattern processing network. We describe a processor, referred to as the cytomatrix module, that can be used to investigate the requisite conditions for such enhancement. The processor is characterized by multiplicity of component types, graded interactions among components, separation of signal integration dynamics from the readout mechanisms that interpret these dynamics, and multiplicity of parameters open to evolution (including component connectivity). The adaptation procedure is mediated by a multiparameter variation–selection algorithm that acts on the various parameters in an alternating (i.e., phasic) manner. Experiments with both structured and unstructured learning tasks, as well as with difficult parity problems, demonstrate that opening more parameters to evolution increases the flexibility exhibited by the processor in response to evolutionary pressure, essentially by loosening the coupling between the local and global aspects of the response. The cytomatrix processor can be thought of as a highly abstracted representation of signal integration within single neurons; alternatively, it can be viewed as a collection of cells in a multicellular organization.

On testing for nonlinear dependence and chaos in financial time series data

The paper is aimed at highlighting some of the pitfalls of empirical analysis in complex dynamics. Two examples of high frequency financial time series data analysis are provided in order to investigate the characteristics of the data generating processes involved and to illustrate the difficulties encountered in numerical analyses.

EMeD-Part: An Efficient Methodology for Horizontal Partitioning in Data Warehouses

Nowadays, data warehouses store Peta-bytes of data. Queries defined on data warehouses are generally complex. Several techniques are used for optimizing queries in data warehouses such as indexes, partitioning and materialized views. Selecting the best configuration of indexes, or partitions or materialized views are all NP-hard. Here, we focus on the horizontal partitioning problem in data warehouses. Several approaches were proposed for solving horizontal partitioning problem in data warehouses including genetic algorithms using a small set of query workload in general. We present a new methodology based on data mining and particle swarm optimization for solving the horizontal partitioning problem in data warehouses using relatively large query workload. First, we compute attraction between predicates followed by a hierarchical clustering of predicates. In the second step, we use discrete particle swarm optimization for selecting the best partitioning schema. Several experiments are performed to demonstrate the effectiveness of the proposed approach and the results are compared to the best well known method so far, the genetic algorithm based approach. The proposed approach is found to be faster and more effective than the genetic algorithm based approach for solving the data warehouse horizontal partitioning.

A linear programming approach for bitmap join indexes selection in data warehouses

Abstract Data warehousing is the crucial part of business intelligence applications. The data warehouse physical design is a hard task due to a large number of possible choices involved. The bitmap join indexes selection problem is crucial in the data warehouse physical design. All proposed approaches to solve the bitmap join indexes selection problem are based on statistics such as data mining or meta-heuristics such as genetic algorithm and particle swarm optimization. In the present work, we propose a new approach based on mixed-integer linear programming for solving the bitmap join indexes selection problem. Several experiments are performed to demonstrate the effectiveness of the proposed approach and the results are compared to the well known approaches that are best so far: the data mining, the genetic algorithm and particle swarm optimization based approaches. The mixed-integer linear programming is found to be faster and more effective than the genetic algorithm, particle swarm optimization and data mining approaches for solving the bitmap join indexes selection problem.

Effect of beta-blockers on the heart rhythm complexity in children

Beta-blockers are often used in the treatment of various heart diseases and affect the heart rate variability. In this study we aimed to assess the effect of various types of beta-blockers on the heart rate complexity in children using the multiscale entropy and detrended fluctuation analysis (DFA). The study involved 21 patients who were treated with beta-blockers for supraventricular tachycardia and 15 healthy children with normal cardiac assessments. The heart rate data was extracted from 24-hour Holter recordings and the DFA and multiscale entropy analyses were performed. The entropy was significantly higher in children who were treated with beta-blockers (MANOVA, p < 0.001). However DFA did not reveal significant difference between the two groups. Higher entropy indicates an increase in the heart rate complexity. Entropy and fractal behavior can change critically in disease states and the effect of beta-blockers can potentially be very useful in predicting clinical outcome.

Predictability in heartbeat data

Predicting the behavior of chaotic dynamical systems is difficult in general. It is important to study such systems since the existence of chaos implies potential short term predictability. Several methods exist to analyze time series, including correlation dimension and the Brock-Dechert-Scheinkman-LeBaron (BDSL) test. Recently, a new tool, sample entropy (SampEn), has gained importance for data differentiation. We have applied these methods to cardiovascular time series data. Our findings suggest that correlation dimension is useful in analyzing such data, but not of sufficient power to discriminate between various data generating processes while sample entropy can be used as a supplementary tool.