Rapid Mold Detection in Chinese Herbal Medicine Using Enhanced Deep Learning Technology.

Mold contamination poses a significant challenge in the processing and storage of Chinese herbal medicines (CHM), leading to quality degradation and reduced efficacy. To address this issue, we propose a rapid and accurate detection method for molds in CHM, with a specific focus on Atractylodes macrocephala, using electronic nose (e-nose) technology. The proposed method introduces an eccentric temporal convolutional network (ETCN) model, which effectively captures temporal and spatial information from the e-nose data, enabling efficient and precise mold detection in CHM. In our approach, we employ the stochastic resonance (SR) technique to eliminate noise from the raw e-nose data. By comprehensively analyzing data from eight sensors, the SR-enhanced ETCN (SR-ETCN) method achieves an impressive accuracy of 94.3%, outperforming seven other comparative models that use only the response time of 7.0 seconds before the rise phase. The experimental results showcase the ETCN model's accuracy and efficiency, providing a reliable solution for mold detection in Chinese herbal medicine. This study contributes significantly to expediting the assessment of herbal medicine quality, thereby helping to ensure the safety and efficacy of traditional medicinal practices.

Parrot optimizer: Algorithm and applications to medical problems.

Stochastic optimization methods have gained significant prominence as effective techniques in contemporary research, addressing complex optimization challenges efficiently. This paper introduces the Parrot Optimizer (PO), an efficient optimization method inspired by key behaviors observed in trained Pyrrhura Molinae parrots. The study features qualitative analysis and comprehensive experiments to showcase the distinct characteristics of the Parrot Optimizer in handling various optimization problems. Performance evaluation involves benchmarking the proposed PO on 35 functions, encompassing classical cases and problems from the IEEE CEC 2022 test sets, and comparing it with eight popular algorithms. The results vividly highlight the competitive advantages of the PO in terms of its exploratory and exploitative traits. Furthermore, parameter sensitivity experiments explore the adaptability of the proposed PO under varying configurations. The developed PO demonstrates effectiveness and superiority when applied to engineering design problems. To further extend the assessment to real-world applications, we included the application of PO to disease diagnosis and medical image segmentation problems, which are highly relevant and significant in the medical field. In conclusion, the findings substantiate that the PO is a promising and competitive algorithm, surpassing some existing algorithms in the literature. The supplementary files and open source codes of the proposed Parrot Optimizer (PO) is available at https://aliasgharheidari.com/PO.html and https://github.com/junbolian/PO.

Quantitative determination of d-psicose based on flexible copper film materials and electrochemical scanning methods.

In this paper, a three-electrode electrochemical detection system was designed. Platinum electrode was used as the counter electrode, saturated KCl electrode as reference electrode, and copper film material as working electrode, respectively. Cyclic voltammetry (CV) and chronoamperometry (i-t) methods were used for d-psicose scanning. CV results indicated that d-psicose presented the oxidation-reduction reacting procedure on the surface of copper film electrode. Testing parameters optimization was conducted using CV scanning in different scanning rates. d-psicose quantitative determination model was developed by i-t scanning results. The sensitivity was9419.1A×cm-2·mol/L, the detection limit was1.04311×10-8mol/L. The proposed method has some advantages including high sensitivity, low detection line, and fast response speed. Negative control testing results by using glucose, sucrose, and NaCl solutions demonstrated that the proposed method had good selectivity.

[Research Progress on Enzyme-free Blood Glucose Sensor].

Blood glucose monitoring is of great significance to diabetic patients, and the development of rapid, accurate and real-time glucose detection technology has become a research hotspot nowadays. This study introduces the concept and classification of the enzyme-free glucose sensor, expounds enzymefree glucose sensor electrode characterization methods and the application progress of different materials in enzyme-free blood glucose sensors. Meanwhile, some problems of enzyme-free glucose sensor existing in the current research and its future application prospects also will be discussed.

An Energy-Efficient Clustering Routing Protocol Based on a High-QoS Node Deployment with an Inter-Cluster Routing Mechanism in WSNs.

Currently, wireless sensor network (WSN) protocols are mainly used to achieve low power consumption of the network, but there are few studies on the quality of services (QoS) of these networks. Coverage can be used as a measure of the WSN's QoS, which can further reflect the quality of data information. Additionally, the coverage requirements of regional monitoring target points are different in real applications. On this basis, this paper proposes an energy-efficient clustering routing protocol based on a high-QoS node deployment with an inter-cluster routing mechanism (EECRP-HQSND-ICRM) in WSNs. First, this paper proposes formula definitions for information integrity, validity, and redundancy from the coverage rate and introduces a node deployment strategy based on twofold coverage. Then, in order to satisfy the uniformity of the distribution of cluster heads (CHs), the monitoring area is divided into four small areas centered on the base station (BS), and the CHs are selected in the respective cells. Finally, combined with the practical application of the WSN, this paper optimizes the Dijkstra algorithm, including: (1) nonessential paths neglecting considerations, and (2) a simultaneous introduction of end-to-end weights and path weights, achieving the selection of optimal information transmission paths between the CHs. The simulation results show that, compared with the general node deployment strategies, the deployment strategy of the proposed protocol has higher information integrity and validity, as well as lower redundancy. Meanwhile, compared with some classic protocols, this protocol can greatly reduce and balance network energy consumption and extend the network lifetime.

An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization.

To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4⁻0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.

Effects of chitosan combined with nisin treatment on storage quality of large yellow croaker (Pseudosciaena crocea).

Effects of chitosan combined with different concentrations of nisin on quality enhancement of large yellow croaker (Pseudosciaena crocea) stored at 4 °C were evaluated for 8 days. Changes in sensory score and volatile spoilage products, total viable counts (TVC), and physiochemical indexes including weight loss, colour, pH, total volatile basic nitrogen (TVB-N), and K-value were examined. Results demonstrated that nisin-treated samples presented better quality preservation effects than chitosan alone. 1% chitosan combined with 0.6% nisin presented optimal quality enhancement effects, such as moisture loss control, volatile spoilage inhibition, TVB-N reduction, TVC growth control, and colour and sensory acceptability maintenance. Therefore, chitosan combined with nisin is promising in large yellow croaker shelf life extension.

Sucrose quantitative and qualitative analysis from tastant mixtures based on Cu foam electrode and stochastic resonance.

Quantitative and qualitative determination of sucrose from complex tastant mixtures using Cu foam electrode was investigated in this study. Cu foam was prepared and its three-dimensional (3-D) mesh structure was characterized by scanning electron microscopy (SEM). Cu foam was utilized as working electrode in three-electrode electrochemical system. Cyclic voltammetry (CV) scanning results exhibited the oxidation procedure of sucrose on Cu foam electrode. Amperometric i-t scanning results indicated that Cu foam electrode selectively responded to sucrose from four tastant mixtures with low limit of detection (LOD) of 35.34 µM, 49.85 µM, 45.89 µM, and 26.81 µM, respectively. The existence of quinine, NaCl, citric acid (CA) and their mixtures had no effect on sucrose detection. Furthermore, mixtures containing different tastants could be discriminated by non-linear double-layered cascaded series stochastic resonance (DCSSR) output signal-to-noise ratio (SNR) eigen peak parameters of CV measurement data. The proposed method provides a promising way for sweetener analysis of commercial food.

Study on non-linear bistable dynamics model based EEG signal discrimination analysis method.

Electroencephalogram (EEG) is the recording of electrical activity along the scalp. EEG measures voltage fluctuations generating from ionic current flows within the neurons of the brain. EEG signal is looked as one of the most important factors that will be focused in the next 20 years. In this paper, EEG signal discrimination based on non-linear bistable dynamical model was proposed. EEG signals were processed by non-linear bistable dynamical model, and features of EEG signals were characterized by coherence index. Experimental results showed that the proposed method could properly extract the features of different EEG signals.

Litchi freshness rapid non-destructive evaluating method using electronic nose and non-linear dynamics stochastic resonance model.

In this paper, litchi freshness rapid non-destructive evaluating method using electronic nose (e-nose) and non-linear stochastic resonance (SR) was proposed. EN responses to litchi samples were continuously detected for 6 d Principal component analysis (PCA) and non-linear stochastic resonance (SR) methods were utilized to analyze EN detection data. PCA method could not totally discriminate litchi samples, while SR signal-to-noise ratio (SNR) eigen spectrum successfully discriminated all litchi samples. Litchi freshness predictive model developed using SNR eigen values shows high predictive accuracy with regression coefficients R(2) = 0 .99396.

E-nose based rapid prediction of early mouldy grain using probabilistic neural networks.

In this paper, early mouldy grain rapid prediction method using probabilistic neural network (PNN) and electronic nose (e-nose) was studied. E-nose responses to rice, red bean, and oat samples with different qualities were measured and recorded. E-nose data was analyzed using principal component analysis (PCA), back propagation (BP) network, and PNN, respectively. Results indicated that PCA and BP network could not clearly discriminate grain samples with different mouldy status and showed poor predicting accuracy. PNN showed satisfying discriminating abilities to grain samples with an accuracy of 93.75%. E-nose combined with PNN is effective for early mouldy grain prediction.

Winter jujube (Zizyphus jujuba Mill.) quality forecasting method based on electronic nose.

Winter jujube (Zizyphus jujuba Mill.) quality forecasting method utilising electronic nose (EN) and double-layered cascaded series stochastic resonance (DCSSR) was investigated. EN responses to jujubes stored at room temperature were continuously measured for 8 days. Jujubes' physical/chemical indexes, such as firmness, colour, total soluble solids (TSS), and ascorbic acid (AA), were synchronously examined. Examination results indicated that jujubes were getting ripe during storage. EN measurement data was processed by stochastic resonance (SR) and DCSSR. SR and DCSSR output signal-to-noise ratio (SNR) maximums (SNR-MAX) discriminated jujubes under different storage time successfully. Multiple variable regression (MVR) results between physical/chemical indexes and SR/DCSSR eigen values demonstrated that DCSSR eigen values were more suitable for jujube quality determination. Quality forecasting model was developed using non-linear fitting regression of DCSSR eigen values. Validating experiments demonstrated that forecasting accuracy of this model is 97.35%. This method also presented other advantages including fast response, non-destructive, etc.

Electrochemical impedance spectrum frequency optimization of bitter taste cell-based sensors.

Electrochemical impedance spectrum frequency optimization to bitter taste receptor cell-based sensors is discussed in this paper. The bitter taste receptor cells (the enteroendocrine STC-1 cells and the ICR mouse isolated taste bud cells) are cultured on carbon screen printed electrodes and used as sensing elements. The HEK-293 cells and dead isolated ICR mouse taste bud cells, without bitter taste receptor expression, are used in negative control experiments. The electrochemical impedance spectrum data is recorded and processed by bistable stochastic resonance for signal-to-noise ratio analysis. The bitter taste receptor cell-based sensor selectively responds to bitter tastants. The tastants species and concentrations can be decided by signal-to-noise ratio parameters. The signal-to-noise ratio eigen peak changes with the shift of electrochemical impedance spectrum frequencies. ICR mouse isolated taste bud cell-based sensor presents bitter tastants perception abilities. 9kHz is the optimal frequency for STC-1 cell-based sensor measurement. For isolated ICR mouse taste bud cells, 1.2kHz is the optimal frequency. Negative control experiments results indicate that cells with no taste receptor expression have no discriminating ability for tastant even if they are modulated by different frequencies. The taste cell-based sensor is of great practical value.

Sweet and bitter tastants specific detection by the taste cell-based sensor.

Sweet and bitter tastants specific detection by cell-based sensor is investigated in this paper. Human enteroendocrine NCI-H716 cells, expressing G protein-coupled receptors and sweet receptors (type 1, member 2/type 1, member 3), and human enteroendocrine STC-1 cells, expressing G protein-coupled receptors and bitter receptors (type 2 members) are used as sensing devices. The HEK-293 cells, without taste receptor expression, are used as negative control. The electrochemical impedance spectrum data is recorded and processed by bistable stochastic resonance for signal-to-noise ratio calculation. NCI-H716 cell-based sensor selectively responds to sweeteners and sweet tastant mixtures. STC-1 cell-based sensor selectively responds to bitter tastants and bitter tastant mixtures. The tastants species and concentrations can be decided by signal-to-noise ratio parameters. HEK-293 cell-based sensor lacks the tastants discriminating ability. The taste cell-based sensor is easy to prepare and operate. This work offers a useful way in gustatory mechanism research.

A novel sweet taste cell-based sensor.

In this paper, a novel sweet taste cell-based sensor is proposed for tastants detection. The human colorectal carcinoma NCI-H716 cell lines, which express α-gustducin and sweet taste receptor T1R1/T1R3, are cultured on the carbon screen-printed electrode with the pre-coated poly-L-ornithine and lamimin for adhesion in extracellular matrix. The electrode is placed into a fluidic and environment regulation system. When stimulated by an electric field at a fixed frequency of 1 kHz and 10 µA, the electrochemical impedance spectrum data was recorded by EG&G 273A and high performance dual phase analog lock-in amplifier 5210 and processed by bistable stochastic resonance method. Four basic tastants and sucrose solutions in seven concentrations can be decided by maximums of the signal-to-noise ratio and relevant noise intensity. A negative control experiment utilizing COLO-205 cell lines demonstrate that carbon screen-printed electrode with mammalian cell line without the expression of gust TIR2 and TIR3 lacked the tastant detecting ability. The sensor system presents good stability and repeatability. The proposed sensor is promising for practical applications, and provides a novel way for taste mechanism investigation.

[Progress in research on LiquiChip technology in biomedical engineering].

As a novel BioChip technique, LiquiChip technique uses fluorescent polystyrene beads as the carrriers of various probes, thus the related reactions of bio-molecules are in liquid system. LiquiChip technique is of use in the methods to detect bio-macromolecules, e. g. DNA detection, immunoassay, cytokine assay, hormone assay, environmental survey and analysis. In comparison to conventional biochip, LiquiChip presents the advantages of high-throughput, high sensitivity, high accuracy, fine repeatability, wide linear range, etc. Now LiquiChip technique is widely used in biomedical engineering field.