Emergency decision support modeling for COVID-19 based on spherical fuzzy information

Significant emergency measures should be taken until an emergency event occurs. It is understood that the emergency is characterized by limited time and information, harmfulness and uncertainty, and decision-makers are always critically bound by uncertainty and risk. This paper introduces many novel approaches to addressing the emergency situation of COVID-19 under spherical fuzzy environment. Fundamentally, the paper includes six main sections to achieve appropriate and accurate measures to address the situation of emergency decision-making. As the spherical fuzzy set (FS) is a generalized framework of fuzzy structure to handle more uncertainty and ambiguity in decision-making problems (DMPs). First, we discuss basic algebraic operational laws (AOLs) under spherical FS. In addition, elaborate on the deficiency of existing AOLs and present three cases to address the validity of the proposed novel AOLs under spherical fuzzy settings. Second, we present a list of Einstein aggregation operators (AgOp) based on the Einstein norm to aggregate uncertain information in DMPs. Thirdly, we are introducing two techniques to demonstrate the unknown weight of the criteria. Fourthly, we develop extended TOPSIS and Gray relational analysis approaches based on AgOp with unknown weight information of the criteria. In fifth, we design three algorithms to address the uncertainty and ambiguity information in emergency DMPs. Finally, the numerical case study of the novel carnivorous (COVID-19) situation is provided as an application for emergency decision-making based on the proposed three algorithms. Results explore the effectiveness of our proposed methodologies and provide accurate emergency measures to address the global uncertainty of COVID-19.     

A Dual-Band Printed Antenna Design Based on Annular Koch Snowflake Slot Structure

Wireless Personal Communications - The design of a fractal based slot antenna, to serve the dual-band communication applications, is proposed in this paper. The structure of the proposed antenna is...     

Silicon Mitigates Drought Stress in Wheat (Triticum aestivum L.) Through Improving Photosynthetic Pigments,Biochemical and Yield Characters

Silicon - Feeling prone to stress differs with plant production stage, water scarcity near commencement of grain filling phase has a significant reduced grain yield through fewer endosperm and sink...     

Comparison of γ-aminobutyric acid accumulation capability in different mung bean (Vigna radiata L.) varieties under heat and relative humidity treatment,and its correlation with endogenous amino acids and polyamines

In this study, the accumulation of GABA and its inherent factors across different varieties of mung bean (Vigna radiata L.) in response to heat and relative humidity (HRH) were investigated. Results showed the average GABA content in mung bean varieties was increased 7.52 times following HRH treatment, and the black mung bean variety (A8) exhibited the highest GABA accumulation capability (1.76–84.57 mg per 100 g DW). From the perspective of GABA shunt metabolites, the free glutamic acid content of mung beans significantly decreased (P < 0.05) after HRH treatment and presented a significant correlation (P < 0.05) with GABA content. In polyamine degradation pathway, although the average levels of spermine and spermidine of mung bean varieties significantly decreased (P < 0.05) after HRH treatment, no significant correlation with GABA content was identified. Hence, the GABA accumulation was predominantly attributed to GABA shunt. Besides, free amino acids including glutamic acid, serine, ornithine, arginine and glycine in mung beans showed a significant positive correlation (P < 0.05) with GABA content and increment following HRH treatment, which suggested that mung beans enriched in these free amino acids might accumulate higher amounts of GABA after HRH treatment and be useful for industrial applications.     

Proposed network structures and combined adaptive algorithms for electrocardiogram signal denoising

An electrocardiogram (ECG) signal is a record of the electrical activities of heart muscle and is used clinically to diagnose heart diseases. An ECG signal should be presented as clear as possible to support accurate decisions made by doctors. This article proposes different combinations of combined adaptive algorithms to derive different noise-cancelling structures to remove (denoise) different kinds of noise from ECG signals. The algorithms are applied to the following types of noise: power line interference, baseline wander, electrode motion artifact, and muscle artifacts. Moreover, the results of the suggested models and algorithms are compared with those of conventional denoising tools such as the discrete wavelet transform, an adaptive filter, and a multilayer neural network (NN) to ensure the superiority of the proposed combined structures and algorithms. Furthermore, the hybrid concept is based on dual, triple, and quadruple combinations of well-known algorithms that derive adaptive filters, such as the least mean squares, normalized least mean squares and recursive least squares algorithms. The combinations are formulated based on partial update, variable step-size (VSS), and second iterative VSS algorithms, which are considered in different combinations. In addition, biased NN and unbiased linear neural network (ULNN) structures are considered. The performance of the different structures and related algorithms are evaluated by measuring the post-signal-to-noise ratio, mean square error, and percentage root mean square difference.     

Group Decision Making by Using Incomplete Fuzzy Preference Relations Based on T‐Consistency and the Order Consistency

The existing group decision making techniques may not satisfy the order consistency for aggregation in some cases. The algorithm proposed in this paper overcomes the weaknesses of the existing techniques. The method determines the unknown preferences for group decision making in such a manner that the resulting matrix is T‐consistent and order consistent simultaneously.     

Improving outdoor air quality based on building morphology: Numerical investigation

Due to rapid urbanization around the world, high concentrations of vehicular pollutants have deteriorated the outdoor air quality, which can affect the physical and psychological well-being of humans. Numerous strategies have been proposed to overcome these harmful impacts by improving the dispersion of air pollutants. Consequently, a question arises regarding the potential effects of building morphology on the dispersion of pollutants. Subsequently, transient three-dimensional Computational Fluid Dynamics (CFD) simulations are performed to examine the effect of building morphology on PM₁₀ dispersion. Eleven cases with various prototypes and morphological methods are compared with a simple building form to identify the patterns of PM₁₀ dispersion within a given time sequence under a prevailing inflow condition. The results indicate that the different designs of building morphology with varying Relative compactness (R_C) indicator highlight the importance of considering morphological factors to improve outdoor air quality. In addition, the proposed prototypes can reduce PM₁₀ concentrations by approximately 30%–90% at specific points in the studied time sequence. In particular, the vertical, horizontal, and grid folded prototypes can be considered more effective as an approximate decrease between 70% and 90% in PM₁₀ concentrations is observed, which reflects the influence of building morphology on improving outdoor air quality.