Synthesis of heterocyclic metal complexes and their evaluation as marine antifoulants

4-Amino-3-thio-1,24-triazolidine (L₁) and 4-amino-5-thio-1,3,4-thiadiazole (L_II) as well as their metal complexes of the general formulae ML_I·2H₂O and ML_IICl (M; Pb(II), Cd(II) and Zn(II)) were prepared. With Sn(II), we obtained Sn(L_I)₂·2H₂O and SnL_IICl, respectively. The structures of the compounds were identified through elemental analysis, and IR and UV spectra measurements, in addition to thermal analysis in case of the metal complexes. The antifouling properties of the compounds were tested by their incorporation into paint formulations which were applied to PVC substrates and tested in water from Alexandria western harbour. When the compounds were added at 17·5% by volume, the coated panels were fouled after 3 months of immersion. The addition of 6·7% by volume of tributyltin oxide to 15·8% of the prepared compounds in one formulation elevated the paint efficiency and prevented fouling for 11 months. Paint containing solely tributyltin oxide at the same concentration was inactive against algae.     

Towards an Online Continuous Adaptation Mechanism (OCAM) for Enhanced Engagement: An EEG Study

ABSTRACT

Individual preferences for learning environments can be linked to a specific behavior. The tendency of such behavior can somehow be associated with an individual’s ability to cognitively engage in the learning process without being distracted by other stimuli. An online continuous adaptation mechanism (OCAM) of learning contents was developed in order to regulate the presentation of learning contents based on changes in the learner’s aptitude level. This was claimed to stimulate a better cognitive and emotional response among learners, thus stimulating their engagement. A total of 41 students (36 male and 5 female; age 20–25 years) participated in this study. The results revealed that learners’ levels of concentration and cognitive load were positively influenced by the OCAM, which significantly increased their engagement. Our findings can be used to inform designers and developers of online learning systems about the importance of regulating the presentation of learning contents according to the aptitude level of individual learners. The proposed OCAM can improve learners’ ability to process specific information meaningfully and make the inferences necessary for understanding the learning content.     

An assessment of k-ε turbulence models for gas distribution analysis

This paper presents the gas distribution analysis by injecting air fountain into the containment and simulations with the HYDRAGON code. Turbulence models of standard k-ε(SKE), re-normalization group k-ε(RNG) and a realizable k-ε(RLZ) are used to assess the effects on the gas distribution analysis during a severe accident in a nuclear power plant. By comparing with experimental data,the simulation results of the RNG and SKE turbulence models agree well with the experimental data on the prediction of dimensionless density distributions. The results illustrate that the turbulence model choice had a small effect on the simulation results, particularly the region near to the air fountain source.     

Synthesis and evaluation of 99m Tc-NIDA and 99m Tc-DMIDA complexes for hepatobiliary imaging

1-Naphthylcarbamoylmethyliminodiacetic acid (NIDA) and diphenylmethylcarbamoylmethyliminodiacetic acid (DMIDA) were synthesized, characterized, and labeled with ^99m Tc using SnCl₂ as reducing agent. The parameters affecting the yield of ^99m Tc-NIDA and ^99m Tc-DMIDA were studied in detail. The optimum conditions ensuring high yield of ^99m Tc-NIDA (94.2 ± 2%) and ^99m Tc-DMIDA (93.1 ± 2%) are as follows: 30 mg of NIDA or DMIDA, 0.3 mg of SnCl₂·H₂O, pH 6, 15 min. The biodistribution in mice injected with ^99m Tc-NIDA and ^99m Tc-DMIDA showed high liver uptake at 10 min post injection, with fast biliary excretion. Accumulation of the activity in kidneys was negligible, especially after a long time post injection. Both ^99m Tc-NIDA and ^99m Tc-DMIDA can be applied as hepatobiliary imaging agents for the evaluation of the functional status of the hepatocytes and the patency of the biliary duct.     

Subspace-based technique for speech encryption

The use of signal processing techniques in cryptographic field is an attractive approach in recent years. As an example, the intractability of the under-determined blind source separation (BSS) problem has been used for the proposal of BSS-based speech encryption. However, some weaknesses of this proposal from a cryptographic point of view have been recently published. In this paper, we propose a new encryption method that bypass these weaknesses. The proposed approach is based on the subspace concept together with the use of nonlinear functions and key signals. An interesting feature of the proposed technique is that only a part of the secret key parameters used during encryption is necessary for decryption. Furthermore, if no plain-text is fed to the encryption algorithm, the latter will provide no contents. Analysis results show that the proposed method significantly enhances the security level of existing BSS-based speech encryption methods.     

The polynomial discrete Radon transform

This paper presents a new approach called polynomial discrete Radon transform (PDRT), regarded as a generalization of the classical finite discrete Radon transform. Specifically, the PDRT transforms an image into Radon space by summing the pixels according to polynomial curves. The PDRT can be applied on square \(p \times p\) images where \(p\) is assumed to be a prime number. It is based on a simple arithmetic operations and requires no data interpolation. An interesting property of the PDRT is its exact inversion. This means that an image can be transformed and then perfectly reconstructed. Through this study, we show that the new approach can be applied for some pattern recognition applications.     

Analysis and design of ribbon cables for high‐speed digital applications

Ribbon cables have been widely used as subsystem interconnections in a large number of digital systems, because they can convey numerous bits of a digital signal simultaneously. In this article, finite difference and finite difference time domain (FDTD) methods are used to analyze and optimize the electrostatic analysis design of ribbon cables, and measurements are used to verify the numerical results. © 2002 Wiley Periodicals, Inc. Int J RF and Microwave CAE 12: 148–158, 2002.     

GIS and Multi-Criteria Evaluation: Robust Tools for Integrated Water Resources Management

Abstract

Integrated water resources planning and management are considered very complex issues. These issues are usually addressed through the multi-sectoral, interdisciplinary and hierarchal decomposition approaches. In general, integrated resource management indicates the consideration of water, social, socio-economic, economic and environmental issues. The current study aims at merging the GIS and Multi-Criteria Evaluation (MCE) techniques for the integrated water resource management of a cropped area. An area of about 120,000 Hectares located in the Northern Nile River Delta area with a coastal zone on the Mediterranean was selected and GIS was applied to represent the area's different environment, social, economic, and water factors. Different randomization cropping pattern distribution scenarios were proposed. Through the merging of GIS and MCE approaches, three scenarios were run and evaluated at three different levels: farm, canal catchment area and whole area. This merging resulted in a very powerful tool for the evaluation of different plans. The merging of GIS and MCE really facilitated the decision making process for these types of integrated water management problems.     

Comparative analysis of requirements change prediction models: manual,linguistic, and neural network

Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input.