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A primary aim of RILEM TC 267-TRM: “Tests for Reactivity of Supplementary Cementitious Materials (SCMs)” is to compare and evaluate the performance of conventional and novel SCM reactivity test methods across a wide range of SCMs. To this purpose, a round robin campaign was organized to investigate 10 different tests for reactivity and 11 SCMs covering the main classes of materials in use, such as granulated blast furnace slag, fly ash, natural pozzolan and calcined clays. The methods were evaluated based on the correlation to the 28 days relative compressive strength of standard mortar bars containing 30% of SCM as cement replacement and the interlaboratory reproducibility of the test results. It was found that only a few test methods showed acceptable correlation to the 28 days relative strength over the whole range of SCMs. The methods that showed the best reproducibility and gave good correlations used the R3 model system of the SCM and Ca(OH)2, supplemented with alkali sulfate/carbonate. The use of this simplified model system isolates the reaction of the SCM and the reactivity can be easily quantified from the heat release or bound water content. Later age (90 days) strength results also correlated well with the results of the IS 1727 (Indian standard) reactivity test, an accelerated strength test using an SCM/Ca(OH)2-based model system. The current standardized tests did not show acceptable correlations across all SCMs, although they performed better when latently hydraulic materials (blast furnace slag) were excluded. However, the Frattini test, Chapelle and modified Chapelle test showed poor interlaboratory reproducibility, demonstrating experimental difficulties. The TC 267-TRM will pursue the development of test protocols based on the R3 model systems. Acceleration and improvement of the reproducibility of the IS 1727 test will be attempted as well.

Particle swarm optimizer is a well-known efficient population and control parameter-based algorithm for global optimization of different problems. This paper focuses on a new and primary sample for PSO, which is named phasor particle swarm optimization (PPSO) and is based on modeling the particle control parameters with a phase angle ( θ ), inspired from phasor theory in the mathematics. This phase angle ( θ ) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions. The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature. The phasor model can be used to expand different types of PSO and other algorithms. The source codes of the PPSO algorithms are publicly available at https://github.com/ebrahimakbary/PPSO .

A recent metaheuristic algorithm, such as Whale optimization algorithm (WOA), was proposed. The idea of proposing this algorithm belongs to the hunting behavior of the humpback whale. However, WOA suffers from poor performance in the exploitation phase and stagnates in the local best solution. Grey wolf optimization (GWO) is a very competitive algorithm comparing to other common metaheuristic algorithms as it has a super performance in the exploitation phase, while it is tested on unimodal benchmark functions. Therefore, the aim of this paper is to hybridize GWO with WOA to overcome the problems. GWO can perform well in exploiting optimal solutions. In this paper, a hybridized WOA with GWO which is called WOAGWO is presented. The proposed hybridized model consists of two steps. Firstly, the hunting mechanism of GWO is embedded into the WOA exploitation phase with a new condition which is related to GWO. Secondly, a new technique is added to the exploration phase to improve the solution after each iteration. Experimentations are tested on three different standard test functions which are called benchmark functions: 23 common functions, 25 CEC2005 functions, and 10 CEC2019 functions. The proposed WOAGWO is also evaluated against original WOA, GWO, and three other commonly used algorithms. Results show that WOAGWO outperforms other algorithms depending on the Wilcoxon rank-sum test. Finally, WOAGWO is likewise applied to solve an engineering problem such as pressure vessel design. Then the results prove that WOAGWO achieves optimum solution which is better than WOA and fitness-dependent optimizer (FDO).

As a novel algorithm, the sparrow search algorithm has better optimization performance than other intelligent optimization algorithms. However, in complex problems, there is still the possibility of falling into a local optimum and relying on the initial population stage. In response to these shortcomings, a multi-strategy improved sparrow search algorithm (KLSSA) is proposed. First, in the initial population stage, K-means clustering method is used to cluster and differentiate the individual positions of sparrows, which speeds up the work efficiency of the population and gets rid of the influence of randomness. Then, the levy flight mechanism and adaptive local search strategy are respectively introduced in the calculation of the location update of the discoverer and the follower, so that the discoverer can conduct a wide range of searches more flexibly, and the follower has a more detailed search method. Through the 10 standard test functions, it can be seen that the multi-strategy improved sparrow search algorithm has stronger optimization ability and better optimization speed.

The present study reports the use of one-dimensional carbon wrapped VO 2 (M) nanofiber (VO 2 (M)/C) as a cost-effective counter electrode for dye-sensitized solar cells (DSSCs); where M denotes monoclinic crystal system. Uniform short length nanofiber was synthesised by a sol-gel based simple and versatile electrospinning and post carbonization technique. The investigation of nanostructure and morphological analysis were performed by X-ray diffraction (XRD), field emission scanning electron microscope (FE-SEM), and transmission electron microscope (TEM) with EDAX. The electrochemical response was comprehensively characterized by cyclic voltammetry, electrochemical impedance spectroscopy and Tafel polarization. The electrochemical analysis of the VO 2 (M)/C nanofiber counter electrode exhibits significant electrocatalytic activity towards the reduction of triiodide and low charge transfer resistance at the electrode-electrolyte interface. The DSSCs fabricated with carbon-wrapped VO 2 (M) nanofiber CE showed high power conversion efficiency of 6.53% under standard test condition of simulated 1SUN illumination at AM1.5 G, which was comparable to the 7.39% observed for conventional thermally decomposed Pt CE based DSSC under same test conditions. This result encourages the next step of modification and use of low-cost VO 2 (M) as an alternate counter electrode for DSSCs to achieve a substantial efficiency for future energy demand.

Silica aerogel composites reinforced with different aramid fibres have been synthesized and compared considering their potential use in thermal protection systems of Space devices. These composites were prepared from tetraethoxysilane and vinyltrimethoxysilane and the network was strengthened with aramid fibres. The results showed that the physical and chemical properties of the fibres were relevant, leading to composites with different properties/performance. In general, the obtained values for bulk density were low, down to 150 kg m −3 . Very good thermal properties were achieved, reaching thermal conductivities bellow 30 mW m −1  K −1 , and thermal stability up to 550 °C in all cases. Short length fibres produce stiffer composites with lower thermal conductivities, while among longer fibres, meta -aramid-containing fibres lead to nanocomposites with best insulation performance. Standard tests for Space materials qualification, as thermal cycling and outgassing, were conducted to assess the compliance with Space conditions, confirming the suitability of these aerogel composites for this application. Graphical abstract

Digital holographic microscopy is an emerging, potentially low-cost alternative to conventional light microscopy for micro-object imaging on earth, underwater and in space. Immediate access to micron-scale objects however requires a well-balanced system design and sophisticated reconstruction algorithms, that are commercially available, however not accessible cost-efficiently. Here, we present an open-source implementation of a lens-less digital inline holographic microscope platform, based on off-the-shelf optical, electronic and mechanical components, costing less than $190. It employs a Blu-Ray semiconductor-laser-pickup or a light-emitting-diode, a pinhole, a 3D-printed housing consisting of 3 parts and a single-board portable computer and camera with an open-source implementation of the Fresnel-Kirchhoff routine. We demonstrate 1.55 μm spatial resolution by laser-pickup and 3.91 μm by the light-emitting-diode source. The housing and mechanical components are 3D printed. Both printer and reconstruction software source codes are open. The light-weight microscope allows to image label-free micro-spheres of 6.5 μm diameter, human red-blood-cells of about 8 μm diameter as well as fast-growing plant Nicotiana-tabacum-BY-2 suspension cells with 50 μm sizes. The imaging capability is validated by imaging-contrast quantification involving a standardized test target. The presented 3D-printable portable open-source platform represents a fully-open design, low-cost modular and versatile imaging-solution for use in high- and low-resource areas of the world.

In this paper, an improved fractal image compression (FIC) based on peer adjacent scheme and domain classification was proposed. The proposed method has low computation cost since it contains no search operations, thus becoming fast irreversible fractal scheme. Comprehensive experiments on a standard test image and several types of digital radiology images revealed that the proposed method is competitive when compared to established quadtree-based FIC techniques. The novelty of the proposed method lies in the use of this improved domain classification and mapping strategy for accurate and more precise FIC encoding. The empirical result of standard test image suggests that the proposed method is more competitive compared to the established schemes and achieves better performance in terms the peak signal-to-noise ratio (PSNR) and compression time averaging at 27.27 dB and 6.88 s, respectively. Also, the proposed method obtains an efficient compression ratio with 16.13 compared to others. Additionally, experiments involving various medical image modalities confirmed the superiority of the proposed method for practical applications of medical image compression.

Multi-level thresholding is a helpful tool for several image segmentation applications. Evaluating the optimal thresholds can be applied using a widely adopted extensive scheme called Otsu’s thresholding. In the current work, bi-level and multi-level threshold procedures are proposed based on their histogram using Otsu’s between-class variance and a novel chaotic bat algorithm (CBA). Maximization of between-class variance function in Otsu technique is used as the objective function to obtain the optimum thresholds for the considered grayscale images. The proposed procedure is applied on a standard test images set of sizes (512 × 512) and (481 × 321). Further, the proposed approach performance is compared with heuristic procedures, such as particle swarm optimization, bacterial foraging optimization, firefly algorithm and bat algorithm. The evaluation assessment between the proposed and existing algorithms is conceded using evaluation metrics, namely root-mean-square error, peak signal to noise ratio, structural similarity index, objective function, and CPU time/iteration number of the optimization-based search. The results established that the proposed CBA provided better outcome for maximum number cases compared to its alternatives. Therefore, it can be applied in complex image processing such as automatic target recognition.

In this paper, a lens learning sparrow search algorithm (LLSSA) is proposed to improve the defects of the new sparrow search algorithm, which is random and easy to fall into local optimum. The algorithm has achieved good results in function optimization and has planned a safer and less costly path to the three-dimensional UAV path planning. In the discoverer stage, the algorithm introduces the reverse learning strategy based on the lens principle to improve the search range of sparrow individuals and then proposes a variable spiral search strategy to make the follower's search more detailed and flexible. Finally, it combines the simulated annealing algorithm to judge and obtain the optimal solution. Through 15 standard test functions, it is verified that the improved algorithm has strong search ability and mining ability. At the same time, the improved algorithm is applied to the path planning of 3D complex terrain, and a clear, simple, and safe route is found, which verifies the effectiveness and practicability of the improved algorithm.