Effects of Micro-tapping with Different Concentration of Ethylene on Yield and Physiological Features of Rubber Tree(Hevea brasiliensis) Clone PR107

In this study, the rubber tree clone PR107 was selected as test material,and its yields and physiological features under micro-tapping with different concentrations of ethylene were compared. The results showed that there was no significant correlation between rubber yield and ethylene concentration applied by microtapping on PR107. Too high level of ethylene was not conducive to improvement of rubber yield. When the ethylene concentration for micro-tapping was higher than60%, physical deficiencies appeared in rubber tree PR107, the dry rubber content was reduced, and the yield was unstable. Instead, when the ethylene concentration was lower than 40%, the physiological features of PR107 were steady, the dry rubber content was stable, and the yield was relatively high.

Dwarfi ng apple rootstock responses to elevated temperatures: A study on plant physiological features and transcription level of related genes

The aim of this study was to investigate the impact of heat stress on physiological features, together with endogenous hormones and the transcription level of related genes, to estimate the heat resistance ability and stress injury mechanism of different dwarfing apple rootstocks. Among the six rootstocks, the rootstocks of native Shao series（SH series） showed better heat stress resistance than those of Budagovski 9（B9）, Cornell-Geneva 24（CG24）, and Malling 26（M26） from abroad. Among SH series rootstocks, SH1 and SH6 showed higher heat stress resistance than SH40. M26 demonstrated the lowest adaption ability to heat stress, showing higher leaf conductivity and lower liquid water content（LWC） with the increase in temperature. Heat stress also resulted in the suppression of photosynthesis, which showed no significant restoration after 7-day recovery. It should be noted that although a higher temperature led to a lower LWC and photosynthetic efficiency（P_n） of CG24, there was no significant increase in leaf conductivity, and 7 days after the treatment, the P_n of CG24 recovered. The extremely high temperature tolerance of SH series rootstocks could be related to the greater osmotic adjustment（OA）, which was reflected by smaller reductions in leaf relative water content（RWC） and higher turgor potentials and leaf gas exchange compared with the other rootstocks. Determination of hormones indicated multivariate regulation, and it is presumed that a relatively stable expression levels of functional genes under high-temperature stress is necessary for heat stress resistance of rootstocks.

Effect of Removing the Lower Leaves on the Physiological Features and Aroma Constituents of Flue-Cured Tobacco

Yunyan 97 was selected as the raw material. The effects of removing different number （0, 2, 3 and 4） of lower leaves on root activity, chlorophyll content, physiological features and contents of neutral aroma constituents in the flue-cured tobacco were analyzed. The results showed that the removal of lower leaves could significantly increase the root activity, chlorophyll content, net photosynthetic rate （Pn） and transpiration rate （Tr）, and delay the photosynthetic functional decline. Such effects were the greatest in lower leaves, followed by middle leaves and upper leaves. Moreover, the degree of the effects increased with higher number of leaves removed. After She lower leaves were removed, the water use efficiency （WUE） of leaves in the first 10 d became higher with more leaves removed. In the later periods （24 d, 38 d）, WUE decreased with more leaves removed. For the middle and upper leaves, the removal of three leaves （T2） and two leaves （T1） resulted in the highest contents of aroma constituents, respectively. For the tobaccos cultured in soil with moderate fertility under the experimental conditions, the appropriate number of lower leaves removed should be 2-3.

Physiological-physical feature fusion for automatic voice spoofing detection

Biometric speech recognition systems are often subject to various spoofing attacks,the most common of which are speech synthesis and speech conversion attacks.These spoofing attacks can cause the biometric speech recognition system to incorrectly accept these spoofing attacks,which can compromise the security of this system.Researchers have made many efforts to address this problem,and the existing studies have used the physical features of speech to identify spoofing attacks.However,recent studies have shown that speech contains a large number of physiological features related to the human face.For example,we can determine the speaker's gender,age,mouth shape,and other information by voice.Inspired by the above researches,we propose a spoofing attack recognition method based on physiological-physical features fusion.This method involves feature extraction,a densely connected convolutional neural network with squeeze and excitation block(SE-DenseNet),and feature fusion strategies.We first extract physiological features in audio from a pretrained convolutional network.Then we use SE-DenseNet to extract physical features.Such a dense connection pattern has high parameter efficiency,and squeeze and excitation blocks can enhance the transmission of the feature.Finally,we integrate the two features into the classification network to identify the spoofing attacks.Experimental results on the ASVspoof 2019 data set show that our model is effective for voice spoofing detection.In the logical access scenario,our model improves the tandem decision cost function and equal error rate scores by 5%and 7%,respectively,compared to existing methods.

AUTHENTICATED SYMMETRIC-KEY ESTABLISHMENT FOR MEDICAL BODY SENSOR NETWORKS

This study concerns security issues of the emerging Wireless Body Sensor Network (WBSN) formed by biomedical sensors worn on or implanted in the human body for mobile healthcare appli-cations. A novel authenticated symmetric-key establishment scheme is proposed for WBSN,which fully exploits the physiological features obtained by network entities via the body channel available in WBSN but not other wireless networks. The self-defined Intrinsic Shared Secret (ISS) is used to replace the pre-deployment of secrets among network entities,which thus eliminates centralized services or au-thorities essential in existing protocols,and resolves the key transport problem in the pure symmet-ric-key cryptosystem for WBSN as well. The security properties of the proposed scheme are demon-strated in terms of its attack complexity and the types of attacks it can resist. Besides,the scheme can be implemented under a light-weight way in WBSN systems. Due to the importance of the ISS concept,the analysis on using false acceptance/false rejection method to evaluate the performance of ISS for its usage in the scheme is also demonstrated.

Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such patients suffering from OLD since,after early diagnosis,breathing exercises and medical precautions can effectively improve their health state.A secure non-invasive early diagnosis of OLD is a primordial need,and in this context,digital image processing supported by Artificial Intelligence(AI)techniques is reliable and widely used in the medical field,especially for improving early disease diagnosis.Hence,this article presents an AIbased non-invasive and secured diagnosis for OLD using physiological and iris features.This research work implements different machine-learning-based techniques which classify various subjects,which are healthy and effective patients.The iris features include gray-level run-length matrix-based features,gray-level co-occurrence matrix,and statistical features.These features are extracted from iris images.Additionally,ten different classifiers and voting techniques,including hard and soft voting,are implemented and tested,and their performances are evaluated using several parameters,which are precision,accuracy,specificity,F-score,and sensitivity.Based on the statistical analysis,it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.

Decoding pilot behavior consciousness of EEG, ECG, eye movements via an SVM machine learning model

To decode the pilot’s behavioral awareness,an experiment is designed to use an aircraft simulator obtaining the pilot’s physiological behavior data.Existing pilot behavior studies such as behavior modeling methods based on domain experts and behavior modeling methods based on knowledge discovery do not proceed from the characteristics of the pilots themselves.The experiment starts directly from the multimodal physiological characteristics to explore pilots’behavior.Electroencephalography,electrocardiogram,and eye movement were recorded simultaneously.Extracted multimodal features of ground missions,air missions,and cruise mission were trained to generate support vector machine behavior model based on supervised learning.The results showed that different behaviors affects different multiple rhythm features,which are power spectra of theθwaves of EEG,standard deviation of normal to normal,root mean square of standard deviation and average gaze duration.The different physiological characteristics of the pilots could also be distinguished using an SVM model.Therefore,the multimodal physiological data can contribute to future research on the behavior activities of pilots.The result can be used to design and improve pilot training programs and automation interfaces.