Digital Scholarship and Disciplinary Culture: An Investigation of Sultan Qaboos University, Oman

The emergence of networked information and communication has transformed the accessibility and delivery of scholarly information and fundamentally impacted on the processes of research and scholarly communication. The purpose of this study is to investigate disciplinary differences in the use of networked information for research and scholarly communication at Sultan Qaboos University, Oman. This study has produced quantitative data about how and why academics within different disciplines utilize networked information that is made available either internally through the university library, or externally through networked services accessed by the Internet. The results indicate some significant differences between the attitudes and practice of academics in the science disciplines when compared to those from the social sciences and humanities. While respondents from science disciplines show overall longer and more frequent use of networked information, respondents from humanities and social sciences indicated more positive attitudes and a greater degree of satisfaction toward library networked services.

The Culture of Interethnic Concord in Kazakhstan: Peculiarities of Formation and Development

This paper describes the historical development of interethnic concord in the Republic of Kazakhstan, and emphasizes the role of tolerance mentality of the Kazakh people in ethno-political policy of the country. Moreover, pointing out interethnic concord as a powerful stabilizing factor, it analyses the specifics of interethnic policy in multinational Kazakh society. It summarizes that the culture of interethnic concord can be a model of ethno- political policy of Kazakhstan.

Implementation of Feed-in Tariffs into Multi-Energy Systems

This paper considers the influence of promotion instruments for renewable energy sources (RES) on a multi-energy modeling framework. In Europe, so called Feed-in Tariffs are successfully used as incentive structures to increase the amount of energy produced by RES. Because of the stochastic nature of large scale integration of distributed generation, many problems have occurred regarding the quality and stability of supply. Hence, a macroscopic model was developed in order to optimize the power supply of the local energy infrastructure, which includes electricity, natural gas, fuel oil and district heating as energy carriers. Unique features of the model are the integration of RES and the adoption of Feed-in Tariffs into one optimization stage. Sensitivity studies are carried out to examine the system behavior under changing profits for the feed-in of RES. With a setup of three energy exchanging regions and a multi-period optimization, the impact of costs and profits are determined.

An H1-Galerkin Mixed Method for the Coupled Burgers Equation

In this paper, an H1-Galerkin mixed finite element method is discussed for the coupled Burgers equations. The optimal error estimates of the semi-discrete and fully discrete schemes of the coupled Burgers equation are derived.

EAAC: Energy-Aware Admission Control Scheme for Ad Hoc Networks

The decisions made by admission control algorithms are based on the availability of network resources viz. bandwidth, energy, memory buffers, etc., without degrading the Quality-of-Service (QoS) requirement of applications that are admitted. In this paper, we present an energy-aware admission control (EAAC) scheme which provides admission control for flows in an ad hoc network based on the knowledge of the present and future residual energy of the intermediate nodes along the routing path. The aim of EAAC is to quantify the energy that the new flow will consume so that it can be decided whether the future residual energy of the nodes along the routing path can satisfy the energy requirement. In other words, this energy-aware routing admits a new flow iff any node in the routing path does not run out of its energy during the transmission of packets. The future residual energy of a node is predicted using the Multi-layer Neural Network (MNN) model. Simulation results shows that the proposed scheme increases the network lifetime. Also the performance of the MNN model is presented.

Recent Trends in Nonlinear Methods of HRV Analysis: A Review

The linear methods of heart rate variability analysis such as non-parametric (e.g. fast Fourier transform analysis) and parametric methods (e.g. autoregressive modeling) has become an established non-invasive tool for marking the cardiac health, but their sensitivity and specificity were found to be lower than expected with positive predictive value

A Novel Pilot Scheme for Frequency Offset and Channel Estimation in 2x2 MIMO-OFDM

The Carrier Frequency Offset (CFO) due to timevarying fading channel is the main cause of the loss of orthogonality among OFDM subcarriers which is linked to inter-carrier interference (ICI). Hence, it is necessary to precisely estimate and compensate the CFO. Especially for mobile broadband communications, CFO and channel gain also have to be estimated and tracked to maintain the system performance. Thus, synchronization pilots are embedded in every OFDM symbol to track the variations. In this paper, we present the pilot scheme for both channel and CFO estimation where channel estimation process can be carried out with only one OFDM symbol. Additional, the proposed pilot scheme also provides better performance in CFO estimation comparing with the conventional orthogonal pilot scheme due to the increasing of signal-tointerference ratio.

Analyzing Disclosure Practice of Religious Nonprofit Organizations using Partial Disclosure Index

This study examines the relevance of disclosure practices in improving the accountability and transparency of religious nonprofit organizations (RNPOs). The assessment of disclosure is based on the annual returns of RNPOs for the financial year 2010. In order to quantify the information disclosed in the annual returns, partial disclosure indexes of basic information (BI) disclosure index, financial information (FI) disclosure index and governance information (GI) disclosure index have been built which takes into account the content of information items in the annual returns. The empirical evidence obtained revealed low disclosure practices among RNPOs in the sample. The multiple regression results showed that the organizational attribute of the board size appeared to be the most significant predictor for both partial index on the extent of BI disclosure index, and FI disclosure index. On the other hand, the extent of financial information disclosure is related to the amount of donation received by RNPOs. On GI disclosure index, the existence of an external audit appeared to be significant variable. This study has contributed to the academic literature in providing empirical evidence of the disclosure practices among RNPOs.

Sensitivity Analysis for Determining Priority of Factors Controlling SOC Content in Semiarid Condition of West of Iran

Soil organic carbon (SOC) plays a key role in soil fertility, hydrology, contaminants control and acts as a sink or source of terrestrial carbon content that can affect the concentration of atmospheric CO2. SOC supports the sustainability and quality of ecosystems, especially in semi-arid region. This study was conducted to determine relative importance of 13 different exploratory climatic, soil and geometric factors on the SOC contents in one of the semiarid watershed zones in Iran. Two methods canonical discriminate analysis (CDA) and feed-forward back propagation neural networks were used to predict SOC. Stepwise regression and sensitivity analysis were performed to identify relative importance of exploratory variables. Results from sensitivity analysis showed that 7-2-1 neural networks and 5 inputs in CDA models output have highest predictive ability that explains %70 and %65 of SOC variability. Since neural network models outperformed CDA model, it should be preferred for estimating SOC.

Introducing Sequence-Order Constraint into Prediction of Protein Binding Sites with Automatically Extracted Templates

Search for a tertiary substructure that geometrically matches the 3D pattern of the binding site of a well-studied protein provides a solution to predict protein functions. In our previous work, a web server has been built to predict protein-ligand binding sites based on automatically extracted templates. However, a drawback of such templates is that the web server was prone to resulting in many false positive matches. In this study, we present a sequence-order constraint to reduce the false positive matches of using automatically extracted templates to predict protein-ligand binding sites. The binding site predictor comprises i) an automatically constructed template library and ii) a local structure alignment algorithm for querying the library. The sequence-order constraint is employed to identify the inconsistency between the local regions of the query protein and the templates. Experimental results reveal that the sequence-order constraint can largely reduce the false positive matches and is effective for template-based binding site prediction.

An Intelligent System Framework for Generating Activity List of a Project Using WBS Mind map and Semantic Network

Work Breakdown Structure (WBS) is one of the most vital planning processes of the project management since it is considered to be the fundamental of other processes like scheduling, controlling, assigning responsibilities, etc. In fact WBS or activity list is the heart of a project and omission of a simple task can lead to an irrecoverable result. There are some tools in order to generate a project WBS. One of the most powerful tools is mind mapping which is the basis of this article. Mind map is a method for thinking together and helps a project manager to stimulate the mind of project team members to generate project WBS. Here we try to generate a WBS of a sample project involving with the building construction using the aid of mind map and the artificial intelligence (AI) programming language. Since mind map structure can not represent data in a computerized way, we convert it to a semantic network which can be used by the computer and then extract the final WBS from the semantic network by the prolog programming language. This method will result a comprehensive WBS and decrease the probability of omitting project tasks.

Experimental Study of Submersible Jet on Flow Hydraulic Parameters

Behavior of turbulent jet is relying on jet parameters, environmental and geometric parameters. In this research, it has attempt to Study effect of jet parameters of internal angle on maximum effective length and velocity on centerline from nozzle experimentally. Toward this end, four internal angles 30, 45, 60 and 90-degree are considered for this study in a flume with 600cm as long, 100cm as high and 150cm in width. Various discharges were used to evaluate effective length for a wide range of densimetric Froude numbers F0, from 17.9 to 39.4 that is defined at the nozzle. As a result, It is revealed that both velocity on centerline and effective length decreases when nozzle angle decreased from 90° to 30°. The results show that, for all range of Fr0 the Um/U0 ratio for nozzle with α=90° on centerline increases 20% - 27% than nozzle with α=30° that has lowest velocity on centerline than other nozzle.

Pattern Recognition Techniques Applied to Biomedical Patterns

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Dispersed Error Control based on Error Filter Design for Improving Halftone Image Quality

The error diffusion method generates worm artifacts, and weakens the edge of the halftone image when the continuous gray scale image is reproduced by a binary image. First, to enhance the edges, we propose the edge-enhancing filter by considering the quantization error information and gradient of the neighboring pixels. Furthermore, to remove worm artifacts often appearing in a halftone image, we add adaptively random noise into the weights of an error filter.

Identifying the Kinematic Parameters of Hexapod Machine Tool

Hexapod Machine Tool (HMT) is a parallel robot mostly based on Stewart platform. Identification of kinematic parameters of HMT is an important step of calibration procedure. In this paper an algorithm is presented for identifying the kinematic parameters of HMT using inverse kinematics error model. Based on this algorithm, the calibration procedure is simulated. Measurement configurations with maximum observability are decided as the first step of this algorithm for a robust calibration. The errors occurring in various configurations are illustrated graphically. It has been shown that the boundaries of the workspace should be searched for the maximum observability of errors. The importance of using configurations with sufficient observability in calibrating hexapod machine tools is verified by trial calibration with two different groups of randomly selected configurations. One group is selected to have sufficient observability and the other is in disregard of the observability criterion. Simulation results confirm the validity of the proposed identification algorithm.

Controlling 6R Robot by Visionary System

In the visual servoing systems, the data obtained by Visionary is used for controlling robots. In this project, at first the simulator which was proposed for simulating the performance of a 6R robot before, was examined in terms of software and test, and in the proposed simulator, existing defects were obviated. In the first version of simulation, the robot was directed toward the target object only in a Position-based method using two cameras in the environment. In the new version of the software, three cameras were used simultaneously. The camera which is installed as eye-inhand on the end-effector of the robot is used for visual servoing in a Feature-based method. The target object is recognized according to its characteristics and the robot is directed toward the object in compliance with an algorithm similar to the function of human-s eyes. Then, the function and accuracy of the operation of the robot are examined through Position-based visual servoing method using two cameras installed as eye-to-hand in the environment. Finally, the obtained results are tested under ANSI-RIA R15.05-2 standard.

Inverse Sets-based Recognition of Video Clips

The paper discusses the mathematics of pattern indexing and its applications to recognition of visual patterns that are found in video clips. It is shown that (a) pattern indexes can be represented by collections of inverted patterns, (b) solutions to pattern classification problems can be found as intersections and histograms of inverted patterns and, thus, matching of original patterns avoided.

Cost-Optimized SSB Transmitter with High Frequency Stability and Selectivity

Single side band modulation is a widespread technique in communication with significant impact on communication technologies such as DSL modems and ATSC TV. Its widespread utilization is due to its bandwidth and power saving characteristics. In this paper, we present a new scheme for SSB signal generation which is cost efficient and enjoys superior characteristics in terms of frequency stability, selectivity, and robustness to noise. In the process, we develop novel Hilbert transform properties.

An Anatomically-Based Model of the Nerves in the Human Foot

Sensory nerves in the foot play an important part in the diagnosis of various neuropathydisorders, especially in diabetes mellitus.However, a detailed description of the anatomical distribution of the nerves is currently lacking. A computationalmodel of the afferent nerves inthe foot may bea useful tool for the study of diabetic neuropathy. In this study, we present the development of an anatomically-based model of various major sensory nerves of the sole and dorsal sidesof the foot. In addition, we presentan algorithm for generating synthetic somatosensory nerve networks in the big-toe region of a right foot model. The algorithm was based on a modified version of the Monte Carlo algorithm, with the capability of being able to vary the intra-epidermal nerve fiber density in differentregionsof the foot model. Preliminary results from the combinedmodel show the realistic anatomical structure of the major nerves as well as the smaller somatosensory nerves of the foot. The model may now be developed to investigate the functional outcomes of structural neuropathyindiabetic patients.

Modeling and Simulating of Gas Turbine Cooled Blades

In contrast to existing methods which do not take into account multiconnectivity in a broad sense of this term, we develop mathematical models and highly effective combination (BIEM and FDM) numerical methods of calculation of stationary and quasistationary temperature field of a profile part of a blade with convective cooling (from the point of view of realization on PC). The theoretical substantiation of these methods is proved by appropriate theorems. For it, converging quadrature processes have been developed and the estimations of errors in the terms of A.Ziqmound continuity modules have been received. For visualization of profiles are used: the method of the least squares with automatic conjecture, device spline, smooth replenishment and neural nets. Boundary conditions of heat exchange are determined from the solution of the corresponding integral equations and empirical relationships. The reliability of designed methods is proved by calculation and experimental investigations heat and hydraulic characteristics of the gas turbine first stage nozzle blade.