Effect of Anionic and Non-ionic Surfactants on Activated Sludge Oxygen Uptake Rate and Nitrification

A local wastewater treatment plant (WWTP) experiencing poor nitrification tracked down high level of surfactants in the plant-s influent and effluent. The aims of this project were to assess the potential inhibitory effect of surfactants on activated sludge processes. The effect of the presence of TergitolNP-9, TrigetolNP-7, Trigetol15-S-9, dodecylbenzene sulphonate (SDBS) and sodium dodecyl sulfate (SDS) on activated sludge oxygen uptake rate (OUR) and nitrification were assessed. The average concentration of non-ionic and anionic surfactants in the influent to the local WWTP were 7 and 8.7 mg/L, respectively. Removal of 67% to 90% of the non-ionic and 93-99% of the anionic surfactants tested were measured. All surfactants tested showed inhibitory effects both on OUR and nitrification. SDS incurred the lowest inhibition whereas SDBS and NP-9 caused severe inhibition to OUR and Nitrification. Activated sludge flocs sizes slightly decreased after 3 hours contact with the surfactant present in the test. The results obtained indicated that high concentrations of surfactants are likely to have an adverse effect on the performance of WWTPs utilizing activated sludge processes.

Ethics, Identity and Organizational Learning –Challenges for South African Managers

As a result of the ever-changing environment and the demands of rganisations- customers, it is important to recognise the importance of some important managerial challenges. It is the sincere belief that failure to meet these challenges, will ultimately contribute to inevitable problems for organisations. This recognition requires from managers and by implication organisations to be engaged in ethical behaviour, identity awareness and learning organisational behaviour. All these aspects actually reflect on the importance of intellectual capital as the competitive weapons for organisations in the future.

Mechanical Design and Theoretical Analysis of a Skip-Cycle Mechanism for an Internal Combustion Engine

Skip cycle is a working strategy for spark ignition engines, which allows changing the effective stroke of an engine through skipping some of the four stroke cycles. This study proposes a new mechanism to achieve the desired skip-cycle strategy for internal combustion engines. The air and fuel leakage, which occurs through the gas exchange, negatively affects the efficiency of the engine at high speeds and loads. An absolute sealing is assured by direct use of poppet valves, which are kept in fully closed position during the skipped mode. All the components of the mechanism were designed according to the real dimensions of the Anadolu Motor's gasoline engine and modeled in 3D by means of CAD software. As the mechanism operates in two modes, two dynamically equivalent models are established to obtain the force and strength analysis for critical components.

Visualizing Transit Through a Web Based Geographic Information System

Currently in many major cities, public transit schedules are disseminated through lists of routes, grids of stop times and static maps. This paper describes a web based geographic information system which disseminates the same schedule information through intuitive GIS techniques. Using data from Calgary, Canada, an map based interface has been created to allow users to see routes, stops and moving buses all at once. Zoom and pan controls as well as satellite imagery allows users to apply their personal knowledge about the local geography to achieve faster, and more pertinent transit results. Using asynchronous requests to web services, users are immersed in an application where buses and stops can be added and removed interactively, without the need to wait for responses to HTTP requests.

Vector Control of Multimotor Drive

Three-phase induction machines are today a standard for industrial electrical drives. Cost, reliability, robustness and maintenance free operation are among the reasons these machines are replacing dc drive systems. The development of power electronics and signal processing systems has eliminated one of the greatest disadvantages of such ac systems, which is the issue of control. With modern techniques of field oriented vector control, the task of variable speed control of induction machines is no longer a disadvantage. The need to increase system performance, particularly when facing limits on the power ratings of power supplies and semiconductors, motivates the use of phase number other than three, In this paper a novel scheme of connecting two, three phase induction motors in parallel fed by two inverters; viz. VSI and CSI and their vector control is presented.

Fuzzy Trust for Peer-to-Peer Based Systems

Trust management is one of the drawbacks in Peer-to-Peer (P2P) system. Lack of centralized control makes it difficult to control the behavior of the peers. Reputation system is one approach to provide trust assessment in P2P system. In this paper, we use fuzzy logic to model trust in a P2P environment. Our trust model combines first-hand (direct experience) and second-hand (reputation)information to allow peers to represent and reason with uncertainty regarding other peers' trustworthiness. Fuzzy logic can help in handling the imprecise nature and uncertainty of trust. Linguistic labels are used to enable peers assign a trust level intuitively. Our fuzzy trust model is flexible such that inference rules are used to weight first-hand and second-hand accordingly.

Efficient Scheduling Algorithm for QoS Support in High Speed Downlink Packet Access Networks

In this paper, we propose APO, a new packet scheduling scheme with Quality of Service (QoS) support for hybrid of real and non-real time services in HSDPA networks. The APO scheduling algorithm is based on the effective channel anticipation model. In contrast to the traditional schemes, the proposed method is implemented based on a cyclic non-work-conserving discipline. Simulation results indicated that proposed scheme has good capability to maximize the channel usage efficiency in compared to another exist scheduling methods. Simulation results demonstrate the effectiveness of the proposed algorithm.

Fabrication of Nanoporous Template of Aluminum Oxide with High Regularity Using Hard Anodization Method

Anodizing is an electrochemical process that converts the metal surface into a decorative, durable, corrosion-resistant, anodic oxide finish. Aluminum is ideally suited to anodizing, although other nonferrous metals, such as magnesium and titanium, also can be anodized. The anodic oxide structure originates from the aluminum substrate and is composed entirely of aluminum oxide. This aluminum oxide is not applied to the surface like paint or plating, but is fully integrated with the underlying aluminum substrate, so cannot chip or peel. It has a highly ordered, porous structure that allows for secondary processes such as coloring and sealing. In this experimental paper, we focus on a reliable method for fabricating nanoporous alumina with high regularity. Starting from study of nanostructure materials synthesize methods. After that, porous alumina fabricate in the laboratory by anodization of aluminum oxide. Hard anodization processes are employed to fabricate the nanoporous alumina using 0.3M oxalic acid and 90, 120 and 140 anodized voltages. The nanoporous templates were characterized by SEM and FFT. The nanoporous templates using 140 voltages have high ordered. The pore formation, influence of the experimental conditions on the pore formation, the structural characteristics of the pore and the oxide chemical reactions involved in the pore growth are discuss.

Structure-vibration Analysis of a Power Transformer(154kV/60MVA/Single Phase)

The most common cause of power transformer failures is mechanical defect brought about by excessive vibration, which is formed by the combination of multiples of a frequency of 120 Hz. In this paper, the types of mechanical exciting forces applied to the power transformer were classified, and the mechanical damage mechanism of the power transformer was identified using the vibration transfer route to the machine or structure. The general effects of 120 Hz-vibration on the enclosure, bushing, Buchholz relay, pressure release valve and tap changer of the transformer were also examined.

Using Genetic Algorithms in Closed Loop Identification of the Systems with Variable Structure Controller

This work presents a recursive identification algorithm. This algorithm relates to the identification of closed loop system with Variable Structure Controller. The approach suggested includes two stages. In the first stage a genetic algorithm is used to obtain the parameters of switching function which gives a control signal rich in commutations (i.e. a control signal whose spectral characteristics are closest possible to those of a white noise signal). The second stage consists in the identification of the system parameters by the instrumental variable method and using the optimal switching function parameters obtained with the genetic algorithm. In order to test the validity of this algorithm a simulation example is presented.

Maximizer of the Posterior Marginal Estimate for Noise Reduction of JPEG-compressed Image

We constructed a method of noise reduction for JPEG-compressed image based on Bayesian inference using the maximizer of the posterior marginal (MPM) estimate. In this method, we tried the MPM estimate using two kinds of likelihood, both of which enhance grayscale images converted into the JPEG-compressed image through the lossy JPEG image compression. One is the deterministic model of the likelihood and the other is the probabilistic one expressed by the Gaussian distribution. Then, using the Monte Carlo simulation for grayscale images, such as the 256-grayscale standard image “Lena" with 256 × 256 pixels, we examined the performance of the MPM estimate based on the performance measure using the mean square error. We clarified that the MPM estimate via the Gaussian probabilistic model of the likelihood is effective for reducing noises, such as the blocking artifacts and the mosquito noise, if we set parameters appropriately. On the other hand, we found that the MPM estimate via the deterministic model of the likelihood is not effective for noise reduction due to the low acceptance ratio of the Metropolis algorithm.

Development of Web-based Teams Management System in Construction

Construction project control attempts to obtain real-time information and effectively enhance dynamic control and management via information sharing and analysis among project participants to eliminate construction conflicts and project delays. However, survey results for Taiwan indicate that construction commercial project management software is not widely accepted for subcontractors and suppliers. To solve the project communications problems among participants, this study presents a novel system called the Construction Dynamic Teams Communication Management (Con-DTCM) system for small-to-medium sized subcontractors and suppliers in Taiwanese Construction industry, and demonstrates that the Con-DTCM system responds to the most recent project information efficiently and enhances management of project teams (general contractor, suppliers and subcontractors) through web-based environment. Web-based technology effectively enhances information sharing during construction project management, and generates cost savings via the Internet. The main unique characteristic of the proposed Con-DTCM system is extremely user friendly and easily design compared with current commercial project management applications. The Con-DTCM system is applied to a case study of construction of a building project in Taiwan to confirm the proposed methodology and demonstrate the effectiveness of information sharing during the construction phase. The advantages of the Con-DTCM system are in improving project control and management efficiency for general contractors, and in providing dynamic project tracking and management, which enables subcontractors and suppliers to acquire the most recent project-related information. Furthermore, this study presents and implements a generic system architecture.

Optimized Data Fusion in an Intelligent Integrated GPS/INS System Using Genetic Algorithm

Most integrated inertial navigation systems (INS) and global positioning systems (GPS) have been implemented using the Kalman filtering technique with its drawbacks related to the need for predefined INS error model and observability of at least four satellites. Most recently, a method using a hybrid-adaptive network based fuzzy inference system (ANFIS) has been proposed which is trained during the availability of GPS signal to map the error between the GPS and the INS. Then it will be used to predict the error of the INS position components during GPS signal blockage. This paper introduces a genetic optimization algorithm that is used to update the ANFIS parameters with respect to the INS/GPS error function used as the objective function to be minimized. The results demonstrate the advantages of the genetically optimized ANFIS for INS/GPS integration in comparison with conventional ANFIS specially in the cases of satellites- outages. Coping with this problem plays an important role in assessment of the fusion approach in land navigation.

Discovery of Quantified Hierarchical Production Rules from Large Set of Discovered Rules

Automated discovery of Rule is, due to its applicability, one of the most fundamental and important method in KDD. It has been an active research area in the recent past. Hierarchical representation allows us to easily manage the complexity of knowledge, to view the knowledge at different levels of details, and to focus our attention on the interesting aspects only. One of such efficient and easy to understand systems is Hierarchical Production rule (HPRs) system. A HPR, a standard production rule augmented with generality and specificity information, is of the following form: Decision If < condition> Generality Specificity . HPRs systems are capable of handling taxonomical structures inherent in the knowledge about the real world. This paper focuses on the issue of mining Quantified rules with crisp hierarchical structure using Genetic Programming (GP) approach to knowledge discovery. The post-processing scheme presented in this work uses Quantified production rules as initial individuals of GP and discovers hierarchical structure. In proposed approach rules are quantified by using Dempster Shafer theory. Suitable genetic operators are proposed for the suggested encoding. Based on the Subsumption Matrix(SM), an appropriate fitness function is suggested. Finally, Quantified Hierarchical Production Rules (HPRs) are generated from the discovered hierarchy, using Dempster Shafer theory. Experimental results are presented to demonstrate the performance of the proposed algorithm.

Optimal Space Vector Control for Permanent Magnet Synchronous Motor based on Nonrecursive Riccati Equation

In this paper the optimal control strategy for Permanent Magnet Synchronous Motor (PMSM) based drive system is presented. The designed full optimal control is available for speed operating range up to base speed. The optimal voltage space-vector assures input energy reduction and stator loss minimization, maintaining the output energy in the same limits with the conventional PMSM electrical drive. The optimal control with three components is based on the energetically criteria and it is applicable in numerical version, being a nonrecursive solution. The simulation results confirm the increased efficiency of the optimal PMSM drive. The properties of the optimal voltage space vector are shown.

Practical Applications and Connectivity Algorithms in Future Wireless Sensor Networks

Like any sentient organism, a smart environment relies first and foremost on sensory data captured from the real world. The sensory data come from sensor nodes of different modalities deployed on different locations forming a Wireless Sensor Network (WSN). Embedding smart sensors in humans has been a research challenge due to the limitations imposed by these sensors from computational capabilities to limited power. In this paper, we first propose a practical WSN application that will enable blind people to see what their neighboring partners can see. The challenge is that the actual mapping between the input images to brain pattern is too complex and not well understood. We also study the connectivity problem in 3D/2D wireless sensor networks and propose distributed efficient algorithms to accomplish the required connectivity of the system. We provide a new connectivity algorithm CDCA to connect disconnected parts of a network using cooperative diversity. Through simulations, we analyze the connectivity gains and energy savings provided by this novel form of cooperative diversity in WSNs.

An EEG Case Study of Arithmetical Reasoning by Four Individuals Varying in Imagery and Mathematical Ability: Implications for Mathematics Education

The main issue of interest here is whether individuals who differ in arithmetical reasoning ability and levels of imagery ability display different brain activity during the conduct of mental arithmetical reasoning tasks. This was a case study of four participants who represented four extreme combinations of Maths –Imagery abilities: ie., low-low, high-high, high-low, low-high respectively. As the Ps performed a series of 60 arithmetical reasoning tasks, 128-channel EEG recordings were taken and the pre-response interval subsequently analysed using EGI GeosourceTM software. The P who was high in both imagery and maths ability showed peak activity prior to response in BA7 (superior parietal cortex) but other Ps did not show peak activity in this region. The results are considered in terms of the diverse routes that may be employed by individuals during the conduct of arithmetical reasoning tasks and the possible implications of this for mathematics education.

Image Restoration in Non-Linear Filtering Domain using MDB approach

This paper proposes a new technique based on nonlinear Minmax Detector Based (MDB) filter for image restoration. The aim of image enhancement is to reconstruct the true image from the corrupted image. The process of image acquisition frequently leads to degradation and the quality of the digitized image becomes inferior to the original image. Image degradation can be due to the addition of different types of noise in the original image. Image noise can be modeled of many types and impulse noise is one of them. Impulse noise generates pixels with gray value not consistent with their local neighborhood. It appears as a sprinkle of both light and dark or only light spots in the image. Filtering is a technique for enhancing the image. Linear filter is the filtering in which the value of an output pixel is a linear combination of neighborhood values, which can produce blur in the image. Thus a variety of smoothing techniques have been developed that are non linear. Median filter is the one of the most popular non-linear filter. When considering a small neighborhood it is highly efficient but for large window and in case of high noise it gives rise to more blurring to image. The Centre Weighted Mean (CWM) filter has got a better average performance over the median filter. However the original pixel corrupted and noise reduction is substantial under high noise condition. Hence this technique has also blurring affect on the image. To illustrate the superiority of the proposed approach, the proposed new scheme has been simulated along with the standard ones and various restored performance measures have been compared.

Diagnosis of the Abdominal Aorta Aneurysm in Magnetic Resonance Imaging Images

This paper presents a technique for diagnosis of the abdominal aorta aneurysm in magnetic resonance imaging (MRI) images. First, our technique is designed to segment the aorta image in MRI images. This is a required step to determine the volume of aorta image which is the important step for diagnosis of the abdominal aorta aneurysm. Our proposed technique can detect the volume of aorta in MRI images using a new external energy for snakes model. The new external energy for snakes model is calculated from Law-s texture. The new external energy can increase the capture range of snakes model efficiently more than the old external energy of snakes models. Second, our technique is designed to diagnose the abdominal aorta aneurysm by Bayesian classifier which is classification models based on statistical theory. The feature for data classification of abdominal aorta aneurysm was derived from the contour of aorta images which was a result from segmenting of our snakes model, i.e., area, perimeter and compactness. We also compare the proposed technique with the traditional snakes model. In our experiment results, 30 images are trained, 20 images are tested and compared with expert opinion. The experimental results show that our technique is able to provide more accurate results than 95%.

On the Solution of the Towers of Hanoi Problem

In this paper, two versions of an iterative loopless algorithm for the classical towers of Hanoi problem with O(1) storage complexity and O(2n) time complexity are presented. Based on this algorithm the number of different moves in each of pegs with its direction is formulated.