Size distribution of wastewater COD fractions as an index for biodegradability

The study proposes direct particle size measurement by sequential filtration and ultrafiltration as a convenient method for wastewater characterization for appropriate treatment technology. It also explores the correlation between particle size distribution (PSD) and chemical oxygen demand (COD) fractionation, as an index for biological treatability. Profiles obtained through PSD-based COD fractionation serve as the fingerprints for wastewaters, and as demonstrated in this study, reflect different pictures for textile wastewater and domestic sewage. PSD-based COD fractionation profiles identify the soluble range below 2 nm as the size interval housing both the soluble inert COD initially present in the wastewater and soluble inert microbial products generated during biological treatment, as also supported by the metabolic fractionation attained through respirometric analyses. Moreover, PSD-based color profiling offers a good index for the fate of biologically resistant chemicals passing through biological treatment. Compatible results obtained from comparative evaluation of PSD-based COD and color profiles provide useful information on the biodegradability of the textile wastewater studied.     

Analysis of international debt problem using artificial neural networks and statistical methods

It is known from the scientific researches that artificial neural networks are alternatives of statistical methods such as regression analysis and classification in recent years. Since multi-layer backpropagation neural network models are nonlinear, it is expected that the neural network models should make better classifications and predictions. The studies on this subject support that idea. In this study, a macro-economic problem on rescheduling or non-rescheduling of the countries’ international debts is taken into account. Among the statistical methods, logistic and probit regression, and the different neural network backpropagation algorithms are applied and comparisons are made. Evaluations and suggestions are made depending on the results and different neural network architecture.     

Rule-based inference and decomposition for distributed in-network processing in wireless sensor networks

Wireless sensor networks are application specific and necessitate the development of specific network and information processing architectures that can meet the requirements of the applications involved. A common type of application for wireless sensor networks is the event-driven reactive application, which requires reactive actions to be taken in response to events. In such applications, the interest is in the higher-level information described by complex event patterns, not in the raw sensory data of individual nodes. Although the central processing of information produces the most accurate results, it is not an energy-efficient method because it requires a continuous flow of raw sensor readings over the network. As communication operations are the most expensive in terms of energy usage, the distributed processing of information is indispensable for viable deployments of applications in wireless sensor networks. This method not only helps in reducing the total amount of packets transmitted in the network and the total energy consumed by the sensor nodes, but also produces scalable and fault-tolerant networks. For this purpose, we present two schemes that distribute information processing to appropriate nodes in the network. These schemes use reactive rules, which express relations between event patterns and actions, in order to capture reactive behavior. We also share the results of the performance of our algorithms and the simulations based on our approach that show the success of our methods in decreasing network traffic while still realizing the desired functionality.     

A numerical scheme for two-dimensional optimal control problems with memory effect

A new formulation for multi-dimensional fractional optimal control problems is presented in this article. The fractional derivatives which are coming from the formulation of the problem are defined in the Riemann–Liouville sense. Some terminal conditions are imposed on the state and control variables whose dimensions need not be the same. A numerical scheme is described by using the Grünwald–Letnikov definition to approximate the Riemann–Liouville Fractional Derivatives. The set of fractional differential equations, which are obtained after the discretization of the time domain, are solved within the Grünwald–Letnikov approximation to obtain the state and the control variable numerically. A two-dimensional fractional optimal control problem is studied as an example to demonstrate the performance of the scheme.     

A coordinate transformation approach for efficient repeated solution of Helmholtz equation pertaining to obstacle scattering by shape deformations

A computational model is developed for efficient solutions of electromagnetic scattering from obstacles having random surface deformations or irregularities (such as roughness or randomly-positioned bump on the surface), by combining the Monte Carlo method with the principles of transformation electromagnetics in the context of finite element method. In conventional implementation of the Monte Carlo technique in such problems, a set of random rough surfaces is defined from a given probability distribution; a mesh is generated anew for each surface realization; and the problem is solved for each surface. Hence, this repeated mesh generation process places a heavy burden on CPU time. In the proposed approach, a single mesh is created assuming smooth surface, and a transformation medium is designed on the smooth surface of the object. Constitutive parameters of the medium are obtained by the coordinate transformation technique combined with the form-invariance property of Maxwell’s equations. At each surface realization, only the material parameters are modified according to the geometry of the deformed surface, thereby avoiding repeated mesh generation process. In this way, a simple, single and uniform mesh is employed; and CPU time is reduced to a great extent. The technique is demonstrated via various finite element simulations for the solution of two-dimensional, Helmholtz-type and transverse magnetic scattering problems.     

The effect of self-distancing on adaptive versus maladaptive self-reflection in children.

Although children and adolescents vary in their chronic tendencies to adaptively versus maladaptively reflect over negative feelings, the psychological mechanisms underlying these different types of self-reflection among youngsters are unknown. We addressed this issue in the present research by examining the role that self-distancing plays in distinguishing adaptive versus maladaptive self-reflection among an ethnically and socioeconomically diverse sample of fifth-grade public schoolchildren. Children were randomly assigned to analyze their feelings surrounding a recent anger-related interpersonal experience from either a self-immersed or self-distanced perspective. They then rated their negative affect and described in writing the stream of thoughts they experienced when they analyzed their feelings. Children's stream-of-thought essays were content analyzed for the presence of recounting statements, reconstruing statements, and blame attributions. Path analyses indicated that children who analyzed their feelings from a self-distanced perspective focused significantly less on recounting the “hot,” emotionally arousing features of their memory (i.e., what happened to me?) and relatively more on reconstruing their experience. This shift in thought content—less recounting and more reconstruing—led children in the self-distanced group to blame the other person involved in their recalled experience significantly less, which in turn led them to display significantly lower levels of emotional reactivity. These findings help delineate the psychological mechanisms that distinguish adaptive versus maladaptive forms of self-reflection over anger experiences in children. Their basic findings and clinical implications are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A comparative analysis of the effects of computer and paper-based personalization on student achievement

The aim of this study is to investigate whether personalization of mathematical word problems affects student achievement; and if it does, to find out whether the outcomes vary depending on computer and class environment in the teaching of mathematics. The sample of the research, conducted in the 2006–2007 fall semester, consists of 90 seventh grade in four sub-groups. During the application in the classroom, paper-based personalized and paper-based non-personalized problems were given. As to the computer environment, web-based personalized and web-based non-personalized problems were presented. After the application, an achievement test was performed as an achievement post-test parallel to the pre-test. The results show no significant difference between the scores of the groups who used personalized materials and the groups who used non-personalized materials. The results also indicate that no significant difference exists between those who studied in the computer-based environment and those in the paper-based environment.     

A cross-layer fault tolerance management module for wireless sensor networks

It is a well-established fact that wireless sensor networks (WSNs) are very power constraint networks, but besides this, they are inherently more fault-prone than any other type of wireless network and their protocol design is very application specific. Major reasons for the faults are the unpredictable wireless communication channel, battery depletion, as well as fragility and mobility of the nodes. Furthermore, as traditional protocol design methods have proved inadequate, the cross-layer design (CLD) approach, which allows for interactions between different layers, providing more flexible and energy-efficient functionality, has emerged as a viable solution for WSNs. In this study we define a fault tolerance management module suitable to the requirements, limitations, and specifics of WSNs, encompassing methods for fault detection, fault prevention, fault management, and recovery. The suggested solution is in line with the CLD approach, which is an important factor in increasing the network performance. Through simulations the functionality of the network is evaluated, based on packet loss, delay, and energy consumption, and is compared with a similar solution not including fault management. The results achieved support the idea that the introduction of a unified approach to fault management improves the network performance as a whole.     

Emergence of genomic self-similarity in location independent representations

A key property for predicting the effectiveness of stochastic search techniques, including evolutionary algorithms, is the existence of a positive correlation between the form and the quality of candidate solutions. In this paper we show that when the ordering of genomic symbols in a genetic algorithm is completely independent of the fitness function and therefore free to evolve along with the candidate solutions it encodes, the resulting genomes self-organize into self-similar structures that favor this key stochastic search property.     

A note on the total and heme iron contents of ready-to-eat doner kebabs

Total and heme iron contents of ready-to-eat beef and chicken doners collected from restaurants were determined. Total and heme iron contents of beef and chicken doners were 36.70±9.85-15.98±2.86 and 14.27±4.43-4.49±1.95 μg/g, respectively. The percent heme iron contents in beef and chicken doners were 46.06% and 34.36%, respectively.