Dual effect synergistically triggered Ce:(Y,Tb)3(Al,Mn)5O12 transparent ceramics enabling a high color-rendering index and excellent thermal stability for white LEDs

Ce:Y₃Al₅O₁₂ transparent ceramics (TCs) with appropriate emission light proportion and high thermal stability are significant to construct white light emitting diode devices with excellent chromaticity parameters. In this work, strategies of controlling crystal-field splitting around Ce³⁺ ion and doping orange-red emitting ion, were adopted to fabricate Ce:(Y,Tb)₃(Al,Mn)₅O₁₂ TCs via vacuum sintering technique. Notably, 85.4 % of the room-temperature luminescence intensity of the TC was retained at 150 °C, and the color rendering index was as high as 79.8. Furthermore, a 12 nm red shift and a 16.2 % increase of full width at half maximum were achieved owing to the synergistic effects of Tb³⁺ and Mn²⁺ ions. By combining TCs with a 460 nm blue chip, a warm white light with a low correlated color temperature of 4155 K was acquired. Meanwhile, the action mechanism of Tb³⁺ ion and the energy transfer between Ce³⁺ and Mn²⁺ ions were verified in prepared TCs.     

Improvement of Concrete Properties and Reinforcing Steel Inhibition Using a Natural Product Admixture

The use of a natural white juice, taken from magrabe banana stem, as concrete admixture to improve mechanical and physicrvchemical properties of concrete has been studied. The compressive strength, bulk density the free lime liberated during hydration and the combined water content were determined. The results indicate that the admixture acts as a retarder in most cases and as accelerator in some ones. Also, the admixture effect on the corrosion resistance of the reinforcing steel against surrounding aggressive media has been investigated using galvanostatic polarization technique. The addition of 0.2% admixture leads to the more inhibition of the steel     

Phase equilibria of crude oils using the continuous thermodynamics approach

This paper deals with the application of continuous thermodynamics to light and heavy oil systems using the Peng- Robinson equation of state. The composition of the high molar mass components in a reservoir oil is described by a continuous distribution function of some characterizing quantity, e.g. molar mass or boiling temperature. Numerical Gaussian quadrature methods are used to generate pseudo-components and their mole fractions from distribution data. Several examples are presented for phase equilibria of solvent/crude oil systems. The results showed that semi-infinite distribution functions, such as the Γ function, cannot be applied to all cases, e.g. dew point calculations. The Gauss- Legendre quadrature method coupled with spline fitting worked best for heavy oil systems.     

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.     

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

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.     

Thermal decomposition and pore structure of pure and doped stannic oxide gel

The surface properties of a stannic oxide gel and its thermal dehydration products obtained both in vacuo and in the presence of air in the temperature range 100–600°C have been examined by N₂ adsorption. Phase and structural changes have been followed by differential thermal analysis and X-ray diffractometry. Complete pore structure analysis showed that samples dehydrated at or below 250°C were microporous. Above 250°C the pores were found to widen with increase of temperature, the widening occurring concurrently with the crystallisation process. Doping with cations of lower valency (Li⁺ and Al³⁺) than the host cation (Sn⁴⁺) had little effect on the pore structure and specific surface area for the low temperature samples (≤250°), whereas at higher temperatures, e.g. 600°C, it increased the specific area remarkably. The dope ions produce oxygen vacancies and hinder or retard sintering in SnO₂.     

Cellular automaton-based algorithms for the dispersion of mobile wireless sensor networks

Due to the advent of sensor technology and its applications, mobile wireless sensor networks (MWSNs) have gained a significant amount of research interest. In a typical MWSN, sensors can move within the network. We develop a set of probabilistic and deterministic cellular automaton (CA)-based algorithms for motion planning problems in MWSNs. First, we consider a scenario where a group of sensors are deployed and they need to disperse in order to maximise the area covered by the network. In this variant of the problem we do not explicitly consider that the sensors should maintain the connectivity of the network while they move. Second, we consider a scenario where the sensors are initially randomly distributed and they need to disperse autonomously to both maximise the coverage of the network and maintain its connectivity. We carry out extensive simulations of both deterministic and randomised variants of the algorithms. For the first variant of the problem we compare our algorithms with one previous algorithm and find that our algorithm yields better network coverage than the earlier algorithm. We also find that probabilistic algorithms have better overall performance for the second variant. CA algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.     

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%.