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Article
Publication date: 19 September 2016

Qianqian Zheng, Liangliang Chen, Luyao Lu and Xuesong Ye

Olfaction plays a very important role in daily life. The olfactory system has the ability to recognize, discriminate and identify thousands of odorant compounds with…

Abstract

Purpose

Olfaction plays a very important role in daily life. The olfactory system has the ability to recognize, discriminate and identify thousands of odorant compounds with extremely high sensitivity and specificity. The research on olfactory system has very important values in exploring the mechanisms of information processing in the other sensory nervous systems and brain. Recently, with the development of molecular biological and microelectronics technology research, the study of olfactory cell-based sensors has made great progress. The purpose of this paper is to provide details of recent developments in olfactory cell-based sensors.

Design/methodology/approach

Following an introduction, this paper first discusses some olfactory cell-based biosensors, which focus on the light-addressable potentiometric sensors and the microelectrode arrays. Second, surface modification, microfabrication and microfluidic technology which can improve the efficiency of cell immobilization will be summarized. The research trends of olfactory cell-based sensor in future will be proposed.

Findings

This paper shows that the biosensors’ performance is expected to be greatly improved due to the fast development of nanotechnology, optical technology and microelectronics. More and more emerging intelligent olfactory sensors will have a promising prospect in many application fields, including food quality and safety assessment, environmental monitor and human diseases detection.

Originality/value

This paper provides a detailed and timely review of the rapidly growing research in the olfactory cell-based sensors.

Details

Sensor Review, vol. 36 no. 4
Type: Research Article
ISSN: 0260-2288

Keywords

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Article
Publication date: 17 June 2020

Seyed Foad Mousavi, Seyed Hassan Hashemabadi and Jalil Jamali

The purpose of this study is to numerically simulate the Lamb wave propagation through a clamp-on ultrasonic gas flowmeter (UGF) in contact mode, using a new semi…

Abstract

Purpose

The purpose of this study is to numerically simulate the Lamb wave propagation through a clamp-on ultrasonic gas flowmeter (UGF) in contact mode, using a new semi three-dimensional approach. Moreover, experimental and analytical modeling results for transit time difference method have been used to confirm the simulation results at different gas flow velocities from 0.3 to 2.4 m/s.

Design/methodology/approach

The new semi three-dimensional approach involves the simulation of the flow field of the gas in a three-dimensional model and subsequently the simulation of wave generation, propagation and reception in a two-dimensional (2D) model. Moreover, the analytical model assumes that the wave transitions occur in a 2D mode.

Findings

The new approach is a semi three-dimensional approach used in this work, has better accuracy than a complete 2D simulation while maintaining the computing time and costs approximately constant. It is faster and less expensive than a complete 3D simulation and more accurate than a complete 2D simulation. It was concluded that the new approach could be extended to simulate all types of ultrasonic gas and non-gas flowmeters, even under harsh conditions.

Originality/value

In this work, a new approach for the numerical simulation of all types of ultrasonic flowmeters is introduced. It was used for simulation of a Lamb wave ultrasonic flow meter in contact mode.

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Article
Publication date: 11 February 2021

Xiaoyue Zhu, Yaoguo Dang and Song Ding

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal…

Abstract

Purpose

Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.

Design/methodology/approach

This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the GM(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal GM(1,1) model (SAGM(1,1)), the self-adaptive seasonal discrete grey model (SADGM(1,1)) and the self-adaptive seasonal fractional-order grey model (SAFGM(1,1)), are put forward for prognosticating the air quality of all provinces in China .

Findings

The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the SARIMA model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.

Research limitations/implications

Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.

Practical implications

Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.

Originality/value

The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the SARIMA model.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

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