SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data.

Quality control in quantitative proteomics is a persistent challenge, particularly in identifying and managing outliers. Unsupervised learning models, which rely on data structure rather than predefined labels, offer potential solutions. However, without clear labels, their effectiveness might be compromised. Single models are susceptible to the randomness of parameters and initialization, which can result in a high rate of false positives. Ensemble models, on the other hand, have shown capabilities in effectively mitigating the impacts of such randomness and assisting in accurately detecting true outliers. Therefore, we introduced SEAOP, a Python toolbox that utilizes an ensemble mechanism by integrating multi-round data management and a statistics-based decision pipeline with multiple models. Specifically, SEAOP uses multi-round resampling to create diverse sub-data spaces and employs outlier detection methods to identify candidate outliers in each space. Candidates are then aggregated as confirmed outliers via a chi-square test, adhering to a 95% confidence level, to ensure the precision of the unsupervised approaches. Additionally, SEAOP introduces a visualization strategy, specifically designed to intuitively and effectively display the distribution of both outlier and non-outlier samples. Optimal hyperparameter models of SEAOP for outlier detection were identified by using a gradient-simulated standard dataset and Mann-Kendall trend test. The performance of the SEAOP toolbox was evaluated using three experimental datasets, confirming its reliability and accuracy in handling quantitative proteomics.

Dynamic decomposition graph convolutional neural network for SSVEP-based brain-computer interface.

The SSVEP-based paradigm serves as a prevalent approach in the realm of brain-computer interface (BCI). However, the processing of multi-channel electroencephalogram (EEG) data introduces challenges due to its non-Euclidean characteristic, necessitating methodologies that account for inter-channel topological relations. In this paper, we introduce the Dynamic Decomposition Graph Convolutional Neural Network (DDGCNN) designed for the classification of SSVEP EEG signals. Our approach incorporates layerwise dynamic graphs to address the oversmoothing issue in Graph Convolutional Networks (GCNs), employing a dense connection mechanism to mitigate the gradient vanishing problem. Furthermore, we enhance the traditional linear transformation inherent in GCNs with graph dynamic fusion, thereby elevating feature extraction and adaptive aggregation capabilities. Our experimental results demonstrate the effectiveness of proposed approach in learning and extracting features from EEG topological structure. The results shown that DDGCNN outperforms other state-of-the-art (SOTA) algorithms reported on two datasets (Dataset 1: 54 subjects, 4 targets, 2 sessions; Dataset 2: 35 subjects, 40 targets). Additionally, we showcase the implementation of DDGCNN in the context of synchronized BCI robotic fish control. This work represents a significant advancement in the field of EEG signal processing for SSVEP-based BCIs. Our proposed method processes SSVEP time domain signals directly as an end-to-end system, making it easy to deploy. The code is available at https://github.com/zshubin/DDGCNN.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality.

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.

Prediction of oil content in single maize kernel based on hyperspectral imaging and attention convolution neural network.

An attention (A) based convolutional neural network regression (CNNR) model, namely ACNNR, was proposed to combine hyperspectral imaging to predict oil content in single maize kernel. During the period, a reflectance HSI system was used to collect hyperspectral images of embryo side and non-embryo side of single maize kernel, and the performances of CNNR (without attention mechanism), ACNNR and partial least squares regression (PLSR) were compared. For PLSR, a series of spectral preprocessing and dimensionality reduction methods were used to finally determine the optimal hybrid PLSR model. Whereas for CNNR and ACNNR, only raw spectra were used as their inputs. The results showed that embryo side was more suitable for developing regression models; the attentional mechanism was helpful to reduce the error of prediction, making ACNNR performed best (coefficient of determination of prediction = 0.9198). Overall, the proposed method did not require additional processing on raw spectra, and performed well.

Humidity-Based Human-Machine Interaction System for Healthcare Applications.

Human-machine interaction (HMI) systems are widely used in the healthcare field, and they play an essential role in assisting the rehabilitation of patients. Currently, a large number of HMI-related research studies focus on piezoresistive sensors, self-power sensors, visual and auditory receivers, and so forth. These sensing modalities do not possess high reliability with regard to breathing condition detection. The humidity signal conveyed by breathing provides excellent stability and a fast response; however, humidity-based HMI systems have rarely been studied. Herein, we integrate a humidity sensor and a graphene thermoacoustic device into a humidity-based HMI system (HHMIS), which is capable of monitoring respiratory signals and emitting acoustic signals. HHMIS has a practical value in healthcare to assist patients. For example, it works as a prewarning system for respiratory-related disease patients with abnormal respiratory rates, and as an artificial throat device for aphasia patients. Achieved based on a laser direct writing technology, this wearable device features low cost, high flexibility, and can be prepared on a large scale. This portable non-contact HMMIS has broad application prospects in many fields such as medical health and intelligent control.

LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network.

Dissolved oxygen (DO) is an important water quality monitoring parameter of great significance in aquaculture. Accurate prediction of dissolved oxygen can help farmers to take necessary measures in advance to ensure the healthy growth of cultured species. The characteristics of multivariate and long-term correlation of water quality time series in the traditional methods make it difficult to achieve the expected prediction accuracy. To solve this problem, we propose the combined prediction method LSTM-TCN (long short-term memory network and temporal convolutional network). After the preprocessing of time series, the LSTM extracts the features of the series in time dimension, and then combines with TCN to build the fusion prediction model. In this study, we have carried out the DO predictions of LSTM and TCN algorithms separately, followed by the analysis of DO prediction, based on CNN-LSTM and LSTM-TCN combined models. The effects of attention mechanism and window size of historical time on the prediction results were also investigated. The experimental results show that the combined method has high accuracy in dissolved oxygen prediction, and can capture better characteristics of historical data with increasing time window of the historical dissolved oxygen sequence. The LSTM-TCN method achieves better prediction performance, with evaluation index values of MAE = 0.236, MAPE = 3.10%, RMSE = 0.342, and R2 = 0.94.

Near-infrared hyperspectral imaging technology combined with deep convolutional generative adversarial network to predict oil content of single maize kernel.

Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data.

Design and analysis of a novel tendon-driven continuum robotic dolphin.

In this paper, a novel continuum robotic dolphin termed 'ConRoDolI' is proposed and developed. The biomimetic robot features dual tendon driving continuum mechanisms that are utilized to replicate the twisting and bending motions of the dolphin's caudal vertebrae and thoracic vertebrae. More importantly, a central pattern generator based kinematics is analyzed to yield stable dolphin-like swimming. In the meantime, the relationship between the backbone shape and both the tendon length as well as position and orientation are explored. Furthermore, multimodal swimming gaits are designed to pave the way for a three-dimensional (3D) swimming decoupling solution, involving forwarding swimming, multiple yaw patterns, and multiple pitch patterns. All of these endow the robotic dolphin with 3D maneuverability. Finally, extensive experiments demonstrate the feasibility of the proposed biomimetic mechatronic design and control approach. The forward swimming speed is 0.44 body lengths per second (BL/s). The steering radius of the robot is about 0.11 BL with an angular velocity of 10°/s and the diving speed is about 0.13 BL/s. The average propulsion efficiency is about 0.6 with the maximum is over 0.8. The obtained results shed light on the improvement of aquatic maneuverability associated with new-concept underwater vehicles.

Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection.

Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.

An Intelligent Optical Dissolved Oxygen Measurement Method Based on a Fluorescent Quenching Mechanism.

Dissolved oxygen (DO) is a key factor that influences the healthy growth of fishes in aquaculture. The DO content changes with the aquatic environment and should therefore be monitored online. However, traditional measurement methods, such as iodometry and other chemical analysis methods, are not suitable for online monitoring. The Clark method is not stable enough for extended periods of monitoring. To solve these problems, this paper proposes an intelligent DO measurement method based on the fluorescence quenching mechanism. The measurement system is composed of fluorescent quenching detection, signal conditioning, intelligent processing, and power supply modules. The optical probe adopts the fluorescent quenching mechanism to detect the DO content and solves the problem, whereas traditional chemical methods are easily influenced by the environment. The optical probe contains a thermistor and dual excitation sources to isolate visible parasitic light and execute a compensation strategy. The intelligent processing module adopts the IEEE 1451.2 standard and realizes intelligent compensation. Experimental results show that the optical measurement method is stable, accurate, and suitable for online DO monitoring in aquaculture applications.