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.     

Determination of water activities and osmotic and activity coefficients of the system NaCl–BaCl2–H2O at 298.15 K

The water activities of the mixed aqueous electrolyte NaCl–BaCl₂(aq) are measured at total molalities from 0.25 mol kg⁻¹ to about saturation for different ionic strength fractions (y) of NaCl with y=0.33,0.50,0.67. The results allow the deduction of osmotic coefficients. The experimental results are compared with the predictions of the Zdanovskii, Stokes, and Robinson (ZSR), Kusik and Meissner (KM), Robinson and Stokes (RS), Lietzke and Stoughton (LSII), Reilly, Wood, and Robinson (RWR), and Pitzer models. From these measurements, the Pitzer mixing ionic parameters are determined and used to predict the solute activity coefficients in the mixture. The excess Gibbs energy is also determined.     

Characterization of Urine Stem Cell-Derived Extracellular Vesicles Reveals B Cell Stimulating Cargo

Elucidation of the biological functions of extracellular vesicles (EVs) and their potential roles in physiological and pathological processes is an expanding field of research. In this study, we characterized USC–derived EVs and studied their capacity to modulate the human immune response in vitro. We found that the USC–derived EVs are a heterogeneous population, ranging in size from that of micro–vesicles (150 nm–1 μm) down to that of exosomes (60–150 nm). Regarding their immunomodulatory functions, we found that upon isolation, the EVs (60–150 nm) induced B cell proliferation and IgM antibody secretion. Analysis of the EV contents unexpectedly revealed the presence of BAFF, APRIL, IL–6, and CD40L, all known to play a central role in B cell stimulation, differentiation, and humoral immunity. In regard to their effect on T cell functions, they resembled the function of mesenchymal stem cell (MSC)–derived EVs previously described, suppressing T cell response to activation. The finding that USC–derived EVs transport a potent bioactive cargo opens the door to a novel therapeutic avenue for boosting B cell responses in immunodeficiency or cancer.     

Recognizing arabic handwritten characters using deep learning and genetic algorithms

Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.

     

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.     

Adaptive three-dimensional cellular genetic algorithm for balancing exploration and exploitation processes

This paper presents a new adaptive algorithm that aims to control the exploration/exploitation trade-off dynamically. The algorithm is designed based on three-dimensional cellular genetic algorithms (3D-cGAs). In this study, our methodology is based on the change in the global selection pressure induced by dynamic tuning of the local selection rate. The parameter tuning of the local selection method is a way to define the global selection pressure. A diversity speed measure is used to guide the algorithm. Therefore, the integration of existing techniques helps in achieving our aims. A benchmark of well-known continuous test functions and real world problems was selected to investigate the effectiveness of the algorithm proposed. In addition, we provide a comparison between the proposed algorithm and other static and dynamic algorithms in order to study the different effects on the performance of the algorithms. Overall, the results show that the proposed algorithm provides the most desirable performance in terms of efficiency, efficacy, and speed for most problems considered. The results also confirm that problems of various characteristics require different selection pressures, which are difficult to be identified.     

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.