基于自组装行为的微球复合水凝胶制备及性能

以超支化的聚乙烯亚胺和烯丙基缩水甘油醚通过环氧开环反应制备了具有温敏性的聚合物,该温敏性聚合物在水溶液中表现出相转变行为,当达到临界温度时能够自组装形成微球。以该微球为交联剂、丙烯酰胺为单体原位在室温下制备出一种微球复合水凝胶(MCH)。力学性能测试表明,制备的微球复合水凝胶具有优异的力学性能,断裂伸长率达到2800%。流变行为分析表明,该凝胶是一种黏弹性材料,其网络结构兼具物理和化学双重交联。此外,凝胶还表现出良好的形变能力,循环拉伸测试表明其结构中存在明显的能量耗散机制。

Machine Learning-Based Temperature and Wind Forecasts in the Zhangjiakou Competition Zone during the Beijing 2022 Winter Olympic Games

Weather forecasting for the Zhangjiakou competition zone of the Beijing 2022 Winter Olympic Games is a challenging task due to its complex terrain.Numerical weather prediction models generally perform poorly for cold air pools and winds over complex terrains,due to their low spatiotemporal resolution and limitations in the description of dynamics,thermodynamics,and microphysics in mountainous areas.This study proposes an ensemble-learning model,named ENSL,for surface temperature and wind forecasts at the venues of the Zhangjiakou competition zone,by integrating five individual models—linear regression,random forest,gradient boosting decision tree,support vector machine,and artificial neural network(ANN),with a ridge regression as meta model.The ENSL employs predictors from the high-resolution ECMWF model forecast(ECMWF-HRES) data and topography data,and targets from automatic weather station observations.Four categories of predictors(synoptic-pattern related fields,surface element fields,terrain,and temporal features) are fed into ENSL.The results demonstrate that ENSL achieves better performance and generalization than individual models.The root-mean-square error(RMSE) for the temperature and wind speed predictions is reduced by 48.2% and 28.5%,respectively,relative to ECMWF-HRES.For the gust speed,the performance of ENSL is consistent with ANN(best individual model) in the whole dataset,whereas ENSL outperforms on extreme gust samples(42.7% compared with 38.7% obtained by ECMWF-HRES in terms of RMSE reduction).Sensitivity analysis of predictors in the four categories shows that ENSL fits their feature importance rankings and physical explanations effectively.

Fast detection of the early decay in oranges using visible-LED structuredillumination imaging combined with spiral phase transform and feature-based classification model

The early decay of citrus can cause economic and serious food safety issues.The early decayed area has no obvious visual characteristics,making effective detection of this damage very difficult for the citrus industry.This study constructed a new detection system based on visible-light emitting diode(LED)structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform(SPT)algorithm for the early detection of decayed oranges.Each sample obtained three phase-shifting pattern images with phase shifts of−2π/3,0,and 2π/3 at a spatial frequency of 0.25 cycles/mm.Three strategies(i.e.,the conventional three-phase-shifting method,2-phase SPT,and 1-phase SPT)were used to demodulate the original patterned images to recover the direct component(DC)and amplitude component(AC)images.The partial least squares discriminant analysis(PLS-DA)and least squares support vector machine(LS-SVM)classification models were established based on the texture features of DC,AC,and RT(i.e.the ratio of AC to DC)images.Then,the random frog(RF)algorithm was used to simplify the optimal full-featured model.Finally,the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1%for all tested samples.This study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges.

Recent advances in emerging techniques for non-destructive detection of seed viability: A review

Over the past decades,imaging and spectroscopy techniques have been developed rapidly with widespread applications in non-destructive agro-food quality determination.Seeds are one of themost fundamental elements of agriculture and forestry.Seed viability is of great significance in seed quality characteristics reflecting potential seed germination,and there is a great need for a quick and effective method to determine the germination condition and viability of seeds prior to cultivate,sale and plant.Some researches based on spectra and/or image processing and analysis have been explored in terms of the external and internal quality of a variety of seeds.Many attempts have been made in image segmentation and spectra correction methods to predict seed quality using various traditional and novel methods.This review focuses on the comparative introduction,development and applications of emerging techniques in the analysis of seed viability,in particular,near infrared spectroscopy,hyperspectral and multispectral imaging,Raman spectroscopy,infrared thermography,and soft X-ray imaging methods.The basic theories,principle components,relative chemometric processing,analytical methods and prediction accuracies are reported and compared.Additionally,on the foundation of the observed applications,the technical challenges and future outlook for these emerging techniques are also discussed.

Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm

Nondestructive determination the internal quality of thick-skin fruits has always been a challenge.In order to investigate the prediction ability of full transmittance mode on the soluble solid content(SSC)in thick-skin fruits,the full transmittance spectra of citrus were collected using a visible/near infrared(Vis/NIR)portable spectrograph(550–1100 nm).Three obvious absorption peakswere found at 710,810 and 915 nmin the original spectra curve.Four spectral preprocessing methods including Smoothing,multiplicative scatter correction(MSC),standard normal variate(SNV)and first derivativewere employed to improve the quality of the original spectra.Subsequently,the effective wavelengths of SSC were selected from the original and pretreated spectra with the algorithms of successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)and genetic algorithm(GA).Finally,the prediction models of SSC were established based on the full wavelengths and effectivewavelengths.Results showed that SPA performed the best performance on eliminating the useless information variable and optimizing the number of effective variables.The optimal predictionmodel was established based on 10 characteristic variables selected from the spectra pretreated by SNV with the algorithmof SPA,with the correlation coefficient,root mean square error,and residual predictive deviation for prediction set being 0.9165,0.5684°Brix and 2.5120,respectively.Overall,the full transmittance mode was feasible to predict the internal quality of thick-skin fruits,like citrus.Additionally,the combination of spectral preprocessing with a variable selection algorithmwas effective for developing the reliable predictionmodel.The conclusions of this study also provide an alternative method for fast and real-time detection of the internal quality of thick-skin fruits using Vis/NIR spectroscopy.

Multi model forecast biases of the diurnal variations of intense rainfall in the Beijing-Tianjin-Hebei region

Forecasts of the intense rainfall events are important for the disaster prevention and reduction in the Beijing-TianjinHebei region(BTHR). What are the common biases in the forecasts of intense rainfall in the current operational numerical models? What are the possible causes of model bias? In this study, intense rainfall events in the BTHR were categorized into two types: those mainly due to strong synoptic forcings(SSF) and those with weak synoptic forcings(WSF). The results showed that,the numerical forecasts tend to overestimate the frequency of intense rainfall events but underestimate the rainfall intensity. Of these, the overestimation of precipitation frequency mainly appeared in the mountainous areas in the afternoon. Compared with global models, high-resolution mesoscale models showed a notable improvement in forecasting the afternoon intense rainfall,while they all have an obvious bias in forecasting the nighttime rainfall. For the WSF type, both global model and mesoscale model have a low forecast skill, with large biases in subdaily propagation feature. The possible causes are related to a poor performance of the model in reproducing the local thermodynamical circulations and the dynamical processes in the planetary boundary layer. So, the biases in forecasting the WSF type intense rainfall showed notable features of nonlinearity, which made it really challenging to understand their physical processes and to improve the associated forecasts.