Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation.

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.

A theoretical approach for a new design of an ultrasensitive angular plasmonic chemical sensor using black phosphorus and aluminum oxide architecture.

In this study, the biosensing capabilities of conventional and hybrid multilayer structures were theoretically examined based on surface plasmon resonance (SPR). The transfer matrix method is adopted to obtain the reflectance spectra of the hybrid multilayer structure in the visible region. In this regard, the considered SPR sensor is configured as, [prism (CaF2)/Al2O3/Ag/Al2O3/2D material/Al2O3/Sensing medium]. Interestingly, many optimization steps were conducted to obtain the highest sensitivity of the new SPR biosensor from the hybrid structure. Firstly, the thickness of an Al2O3 layer with a 2D material (Blue P/WS2) is optimized to obtain an upgraded sensitivity of 360° RIU-1. Secondly, the method to find the most appropriate 2D material for the proposed design is investigated to obtain an ultra-high sensitivity. Meanwhile, the inclusion of black phosphorus (BP) increases the sensor's sensitivity to 466° RIU-1. Thus, black phosphorus (BP) was obtained as the most suitable 2D material for the proposed design. In this regard, the proposed hybrid SPR biosensing design may pave the way for further opportunities for the development of various SPR sensors to be utilized in chemical and biomedical engineering fields.

Periodic and quasi-periodic one-dimensional phononic crystal biosensor: a comprehensive study for optimum sensor design.

The resonant acoustic band gap materials have introduced an innovative generation of sensing technology. Based on the local resonant transmitted peaks, this study aims to comprehensively investigate the use of periodic and quasi-periodic one-dimension (1D) layered phononic crystals (PnCs) as a highly sensitive biosensor for the detection and monitoring of sodium iodide (NaI) solution. Meanwhile, a defect layer is introduced defect layer inside the phononic crystal designs to be filled with NaI solution. The proposed biosensor is developed based on the periodic PnCs structure and quasi-periodic PnCs structure. The numerical findings demonstrated that the quasi-periodic PnCs structure provided a wide phononic band gap and a large sensitivity compared to the periodic one. Moreover, many resonance peaks through the transmission spectra are introduced for the quasi-periodic design. The results also show that the resonant peak frequency changes effectively with varying NaI solution concentrations in the third sequence of the quasi-periodic PnCs structure. The sensor can differentiate between concentrations ranging from 0 to 35% with a 5% step, which is extremely satisfying for precise detection and can contribute to a variety of issues in medical applications. Additionally, the sensor provided excellent performance for all the concentrations of the NaI solution. For instance, the sensor has a sensitivity of 959 MHz, a quality factor of 6947, a very low damping factor of 7.19 × 10-5, and a figure of merit of 323.529.

Photonic crystal nanostructure as a photodetector for NaCl solution monitoring: theoretical approach.

In this research, we have a theoretical simple and highly sensitive sodium chloride (NaCl) sensor based on the excitation of Tamm plasmon resonance through a one-dimensional photonic crystal structure. The configuration of the proposed design was, [prism/gold (Au)/water cavity/silicon (Si)/calcium fluoride (CaF2)10/glass substrate]. The estimations are mainly investigated based on both the optical properties of the constituent materials and the transfer matrix method as well. The suggested sensor is designed for monitoring the salinity of water by detecting the concentration of NaCl solution through near-infrared (IR) wavelengths. The reflectance numerical analysis showed the Tamm plasmon resonance. As the water cavity is filled with NaCl of concentrations ranging from 0 g l-1 to 60 g l-1, Tamm resonance is shifted towards longer wavelengths. Furthermore, the suggested sensor provides a relatively high performance compared to its photonic crystal counterparts and photonic crystal fiber designs. Meanwhile, the sensitivity and detection limit of the suggested sensor could reach the values of 24 700 nm per RIU (0.576 nm (g l)-1) and 0.217 g l-1, respectively. Therefore, the suggested design could be of interest as a promising platform for sensing and monitoring NaCl concentrations and water salinity as well.

One-Dimensional Phononic Crystals: A Simplified Platform for Effective Detection of Heavy Metals in Water with High Sensitivity.

Recently, the pollution of fresh water with heavy metals due to technological and industrial breakthroughs has reached record levels. Therefore, monitoring these metals in fresh water has become essentially urgent. Meanwhile, the conventional periodic one-dimensional phononic crystals can provide a novel platform for detecting the pollution of heavy metals in fresh water with high sensitivity. A simplified design of a defective, one-dimensional phononic crystals (1D-PnC) structure is introduced in this paper. The sensor is designed from a lead-epoxy multilayer with a central defect layer filled with an aqueous solution from cadmium bromide (CdBr2). The formation of a resonant peak through the transmittance spectrum is highly expected. This study primarily aims to monitor and detect the concentration of cadmium bromide in pure water based on shifting the position of this resonant peak. Notably, any change in cadmium bromide concentration can affect the acoustic properties of cadmium bromide directly. The transfer matrix method has been used to calculate the transmission spectra of the incident acoustic wave. The numerical findings are mainly based on the optimization of the cadmium bromide layer thickness, lead layer thickness, epoxy layer thickness, and the number of periods to investigate the most optimum sensor performance. The introduced sensor in this study has provided a remarkably high sensitivity (S = 1904.25 Hz) within a concentration range of (0-10,000 ppm). The proposed sensor provides a quality factor (QF), a resolution, and a figure of merit of 1398.51752, 48,875,750 Hz, and 4.12088 × 10-5 (/ppm), respectively. Accordingly, this sensor can be a potentially robust base for a promising platform to detect small concentrations of heavy metal ions in fresh water.