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.     

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.     

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.     

An investigation on wear behavior of UHMWPE/carbide composites at elevated temperatures

Ultra-high molecular weight polyethylene (UHMWPE) is extensively used in frictional applications due to its advanced wear resistance. This advanced polymer is reinforced with hard particulate fillers for further developments against wear conditions. Since elevated temperatures prevail in the service conditions, wear behavior of UHMWPE composites is an important issue for the engineering applications. In the present work, UHMWPE-based composites including silicon carbide (SiC) fillers were fabricated in a compression molding chamber. In the specimen preparation stage, molding pressure, filler amount, and filler particle size were varied to investigate the influence of these variables. Upon deciding the optimum parameters from the wear tests conducted at room temperature, the wear experiments were repeated for the optimum specimen at elevated temperatures, such as 40 and 60°C. According to the results, the wear behavior of the SiC/UHMWPE composites is heavily changed by the effect of elevated temperature. Adhesive effect is pronounced at elevated temperatures while the wear characteristics possess the abrasive effect in the sliding path. In addition, the composites exhibit an accelerated material loss as temperature increases during the frictional system.     

Bicriteria robotic operation allocation in a flexible manufacturing cell

Consider a manufacturing cell of two identical CNC machines and a material handling robot. Identical parts requesting the completion of a number of operations are to be produced in a cyclic scheduling environment through a flow shop type setting. The existing studies in the literature overlook the flexibility of the CNC machines by assuming that both the allocation of the operations to the machines as well as their respective processing times are fixed. Consequently, the provided results may be either suboptimal or valid under unnecessarily limiting assumptions for a flexible manufacturing cell. The allocations of the operations to the two machines and the processing time of an operation on a machine can be changed by altering the machining conditions of that machine such as the speed and the feed rate in a CNC turning machine. Such flexibilities constitute the point of origin of the current study. The allocation of the operations to the machines and the machining conditions of the machines affect the processing times which, in turn, affect the cycle time. On the other hand, the machining conditions also affect the manufacturing cost. This study is the first to consider a bicriteria model which determines the allocation of the operations to the machines, the processing times of the operations on the machines, and the robot move sequence that jointly minimize the cycle time and the total manufacturing cost. We provide algorithms for the two 1-unit cycles and test their efficiency in terms of the solution quality and the computation time by a wide range of experiments on varying design parameters.     

Trend Assessment by the Innovative-Şen Method

Hydro-meteorological time series may include trend components mostly due to climate change since about three to four decades. Trend detection and identification in a better and refined manner are among the major current research topics in water resources domain. Even though different methodologies can be found for trend detection in literature, two well-known procedures are the Mann-Kendall (MK) trend test and recently innovative-?en trend method, which provides different aspects of the trend. The theoretical basis and application of these two methods are completely different. The MK test gives a holistic monotonic trend without any categorization of the time series into a set of clusters, but the innovative-?en method is based on cluster and provides categorical trend behavior in a given time series. The main purpose of this paper is to provide important differences between these two approaches and their possible similarities. The applications of the two approaches are given for hydro-meteorological records including relative humidity, temperature, precipitation and runoff from Ergene drainage basin in the north-western part of Turkey. It is observed that although MK trend test does not show significant trend almost in all the cases, the innovative-?en approach yields trend categorizations as “very low”, “low”, “medium” “high” and “very high”, which should be taken into consideration in future flood (“very high”) and drought (“very low”) studies.