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The objective of this study is to develop a monitoring algorithm that measures the displacement of concrete structures using light detection and ranging (LiDAR). The suggested method is based on non-contact measurements providing 3D point clouds of the scanning area with high resolution. This overcomes the limitation of traditional contact-type and point-based measurement methods such as linear variable differential transformer (LVDT) and strain gauge. The developed algorithm enables one to track the boundaries of a concrete specimen and measures the vertical or lateral displacement. To demonstrate that displacement in the horizontal and vertical direction can be measured irrespective of the field of view (FOV), two different concrete specimens were constructed where gradually increasing vertical or lateral loads were applied. Then, the displacements were monitored using the set of LVDT and LiDAR for the correlation analysis. The results demonstrated a high accuracy of 98~99% correlation in comparison between LVDT and LiDAR.

This paper reports a database, namely, the Automated Identification and Quantification Database System (AIQS-DB), which consists of three components, including retention times, mass data, and calibration curves, without the requirement to analyze standard substances. The AIQS-DB that are pre-registered in the database are used as the replacement for the process of measuring chemical standards. Both the target and unknown substances in the real samples were determined by the same conditions of GC-MS as those used for the initial database register in the AIQS-DB system. The article provides a comprehensive overview of the wide-ranging applications of AIQS-DB in various fields and highlights its usefulness as a tool for the simultaneous analysis of organic compounds in different matrixes such as water, soil, sediment and air, etc. It could be considered as the basis in further applications of the AIQS-DB method in determining organic compounds in other fields, specifically biology, food, agriculture, medicine, etc., allowing assessment and reflection on the quality and status of the studied products quickly and cost-effectively.

Taking inspiration from the brain, spiking neural networks (SNNs) have been proposed to understand and diminish the gap between machine learning and neuromorphic computing. Supervised learning is the most commonly used learning algorithm in traditional ANNs. However, directly training SNNs with backpropagation-based supervised learning methods is challenging due to the discontinuous and non-differentiable nature of the spiking neuron. To overcome these problems, this paper proposes a novel metaheuristic-based supervised learning method for SNNs by adapting the temporal error function. We investigated seven well-known metaheuristic algorithms called Harmony Search (HS), Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Grammatical Evolution (GE) as search methods for carrying out network training. Relative target firing times were used instead of fixed and predetermined ones, making the computation of the error function simpler. The performance of our proposed approach was evaluated using five benchmark databases collected in the UCI Machine Learning Repository. The experimental results showed that the proposed algorithm had a competitive advantage in solving the four classification benchmark datasets compared to the other experimental algorithms, with accuracy levels of 0.9858, 0.9768, 0.7752, and 0.6871 for iris, cancer, diabetes, and liver datasets, respectively. Among the seven metaheuristic algorithms, CS reported the best performance.

Recently, in the solar energy society, several key technologies have been reported to meet a grid parity, such as cost-efficient materials, simple processes, and designs. Among them, the assistive plasmonic of metal nanoparticles (MNPs) integrating with the downshifting on luminescent materials attracts much attention. Hereby, Si-based Schottky junction solar cells are fabricated and examined to enhance the performance. CdSe/ZnS quantum dots (QDs) with different gold nanoparticles (Au NPs) sizes were incorporated on a Si light absorbing layer. Due to the light scattering effect from plasmonic resonance, the sole Au NPs layer results in the overall enhancement of Si solar cell’s efficiency in the visible spectrum. However, the back-scattering and high reflectance of Au NPs lead to efficiency loss in the UV region. Therefore, the QDs layer acting as a luminescent downshifter is deployed for further efficiency enhancement. The QDs layer absorbs high-energy photons and re-emits lower energy photons in 528 nm of wavelength. Such a downshift layer can enhance the overall efficiency of Si solar cells due to poor intrinsic spectral response in the UV region. The optical properties of Au NPs and CdSe QDs, along with the electrical properties of solar cells in combination with Au/QD layers, are studied in depth. Moreover, the influence of Au NPs size on the solar cell performance has been investigated. Upon decreasing the diameters of Au NPs, the blueshift of absorbance has been observed, cooperating with QDs, which leads to the improvement of the quantum efficiency in the broadband of the solar spectrum.