Comparative analysis of volatile constituents between herbal pair flos lonicerae-caulis lonicerae and its single herbs

Gas chromatography-mass spectrometry(GC-MS) and the chemometric resolution method(alternative moving window factor analysis,AMWFA) were used for comparative analysis of volatile constituents in herbal pair(HP) flos lonicerae-caulis lonicerae(FL-CL) and its single herbs.The temperature-programmed retention index(PTRI) was also employed for the identification of compounds.In total,44,39,and 50 volatile chemical components in volatile oil of FL,CL and HP FL-CL were separately determined qualitatively and quantitatively,accounting for 87.22%,94.54% and 90.08% total contents of volatile oil of FL,CL and HP FL-CL,respectively.The results show that there are 32 common volatile constituents between HP FL-CL and single herb FL,33 common volatile constituents between HP FL-CL and single herb CL,and 10 new constituents in the volatile oil of HP FL-CL.

The Influence of Window Length Analysis on the Time and Frequency Domain of Mechanomyographic and Electromyographic Signals of Submaximal Fatiguing Contractions

Mechanomyography (MMG) acquires the oscillatory waves of contracting muscles. Electromyography (EMG) is a tool for monitoring muscle overall electrical activity. During muscle contractions, both techniques can investigate the changes that occur in the muscle properties. EMG and MMG parameters have been used for detecting muscle fatigue with diverse test protocols, sensors and filtering. Depending on the analysis window length (WLA), monitoring physiological events could be compromised due to imprecision in the determination of parameters. Therefore, this study investigated the influence of WLA variation on different MMG and EMG parameters during submaximal isometric contractions monitoring MMG and EMG parameters. Ten male volunteers performed isometric contractions of elbow joint. Triaxial accelerometer-based MMG sensor and EMG electrodes were positioned on the biceps brachii muscle belly. Torque was monitored with a load cell. Volunteers remained seated with hip and elbow joint at angles of 110° and 90°, respectively. The protocol consisted in maintaining torque at 70% of maximum voluntary contraction as long as they could. Parameter data of EMG and the modulus of MMG were determined for four segments of the signal. Statistical analysis consisted of analyses of variance and Fisher’s least square differences post-hoc test. Also, Pearson’s correlation was calculated to determine whether parameters that monitor similar physiological events would have strong correlation. The modulus of MMG mean power frequency (MPF) and the number of crossings in the baseline could detect changes between fresh and fatigued muscle with 1.0 s WLA. MPF and the skewness of the spectrum (μ3), parameters related to the compression of the spectrum, behaved differently when monitored with a triaxial MMG sensor. The EMG results show that for the 1.0 s and 2.0 s WLAs have normalized RMS difference with fatigued muscle and that there was strong correlation between parameters of different domains.

Assessment of forest geospatial patterns over the three giant forest areas of China

Geospatial patterns of forest fragmentation over the three traditional giant forested areas of China （Northeastern, southwestern and Southern China） were analyzed comparatively and reported based on a 250-m resolution land cover dataset. Specifically, the spatial patterns of forest fragmentation were characterized by combining geospatial metrics and forest fragmentation models. The driving forces resulting in the differences of the forest spatial patterns were also investigated. Results suggested that forests in southwest China had the highest severity of forest fragmentation, followed by south region and northeast region. The driving forces of forest fragmentation in China were primarily the giant population and improper exploitation of forests. In conclusion, the generated information in the study provided valuable insights and implications as to the fragmentation patterns and the conservation of hiodiversity or genes, and the use of the chosen geospatial metrics and forest fragmentation models was quite useful for depicting forest fragmentation patterns.

Telecoupled land-use changes in distant countries

International food trade has become a key driving force of agricultural land-use changes in trading countries, which has influenced food production and the global environment. Researchers have studied agricultural land-use changes and related environmental issues across multi-trading countries together, but most studies rely on statistic data without spatial attributes. However, agricultural land-use changes are spatially heterogeneous. Uncovering spatial attributes can reveal more critical information that is of scientific significance and has policy implications for enhancing food security and protecting the environment. Based on an integrated framework of telecoupling （socioeconomic and environmental interactions over distances）, we studied spatial attributes of soybean land changes within and among trading countries at the same time. Three distant countries -- Brazil, China, and the United States -- constitute an excellent example of telecoupled systems through the process of soybean trade. Our results presented the spatial distribution of soybean land changes-- highlighting the hotspots of soybean gain and soybean loss, and indicated these changes were spatially clustered, different across multi-spatial scales, and varied among the trading countries. Assisted by the results, global challenges like food security and biodiversity loss within and among trading countries can be targeted and managed efficiently. Our work provides simul- taneously spatial information for understanding agricultural land-use changes caused by international food trade globally, highlights the needs of coordination among trading countries, and promotes global sustainability.

Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometrics methods

Near infrared spectroscopy(NIRS),coupled with principal component analysis and wavelength selection techniques,has been sed to develop a robust and reliable reduced-spectrum classifi-cation model for determining the geographical origins of Nanfeng mandarins.The application of the changeable size moving window principal component analysis(CSMWPCA)provided a notably improved lassification model,with correct classification rates of 92.00%,100.00%,90.00%,100.00%,100.00%,100.00%and 100.00%for Fujian,Guangxi,Hunan,Baishe,Baofeng,Qiawan,Sanxi samples,respectively,as well as,a total dassification rate of 97.52%in the wavelength range from 1007 to 1296 nm.To test and apply the proposed method,the procedure was applied to the analysis of 59 samples in an independent test set.Good identification results(correct rate of 96.61%)were also received.The improvement achieved by the application of CSMWPCA method was particularly remarkable when taking the low complexities of the final model(290 variables)into account.The results of the study showed the great potential of NIRS as a fast,nondestructive and environmentally acceptable method for the rapid and reliable determination for geographical classifcation of Nanfeng mandarins.

Rolling Gaussian Process Regression with Application to Regime Shifts

Gaussian Process Regression (GPR) can be applied to the problem of estimating a spatially-varying field on a regular grid, based on noisy observations made at irregular positions. In cases where the field has a weak time dependence, one may desire to estimate the present-time value of the field using a time window of data that rolls forward as new data become available, leading to a sequence of solution updates. We introduce “rolling GPR” (or moving window GPR) and present a procedure for implementing that is more computationally efficient than solving the full GPR problem at each update. Furthermore, regime shifts (sudden large changes in the field) can be detected by monitoring the change in posterior covariance of the predicted data during the updates, and their detrimental effect is mitigated by shortening the time window as the variance rises, and then decreasing it as it falls (but within prior bounds). A set of numerical experiments is provided that demonstrates the viability of the procedure.

A primary-secondary background model with sliding window PCA algorithm

Rain and snow seriously degrade outdoor video quality.In this work,a primary-secondary background model for removal of rain and snow is built.First,we analyze video noise and use a sliding window sequence principal component analysis de-nosing algorithm to reduce white noise in the video.Next,we apply the Gaussian mixture model(GMM)to model the video and segment all foreground objects primarily.After that,we calculate von Mises distribution of the velocity vectors and ratio of the overlapped region with referring to the result of the primary segmentation and extract the interesting object.Finally,rain and snow streaks are inpainted using the background to improve the quality of the video data.Experiments show that the proposed method can effectively suppress noise and extract interesting targets.