Simultaneous determination of six constituents in the fruit of <i>Acanthopanax sessiliflorus</i> (Rupr. et Maxim.) Seem. by HPLC–UV

<div><p>A simple and accurate liquid chromatographic method was developed for the simultaneous determination of six constituents in the fruit of <i>Acanthopanax sessiliflorus</i>. The conditions of sample extraction were optimised by using orthogonal design. The method provided good accuracy with recoveries in the range of 95.6–101.6% and good precision with RSD values less than 3.0%, which has been successfully applied to the quantitative determination of the six compounds in the fruit of <i>A</i>.<i> sessiliflorus</i> from two maturation periods.</p></div

Identification of Alfalfa Leaf Diseases Using Image Recognition Technology

<div><p>Common leaf spot (caused by <i>Pseudopeziza medicaginis</i>), rust (caused by <i>Uromyces striatus</i>), Leptosphaerulina leaf spot (caused by <i>Leptosphaerulina briosiana</i>) and Cercospora leaf spot (caused by <i>Cercospora medicaginis</i>) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including <i>K</i>_means clustering, fuzzy <i>C</i>-means clustering and <i>K</i>_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the <i>K</i>_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and <i>K</i>-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.</p></div

New Reaction Pathway Induced by Plasmon for Selective Benzyl Alcohol Oxidation on BiOCl Possessing Oxygen Vacancies

Selective organic transformation under mild conditions constitutes a challenge in green chemistry, especially for alcohol oxidation, which typically requires environmentally unfriendly oxidants. Here, we report a new plasmonic catalyst of Au supported on BiOCl containing oxygen vacancies. It photocatalyzes selective benzyl alcohol oxidation with O₂ under visible light through synergistic action of plasmonic hot electrons and holes. Oxygen vacancies on BiOCl facilitate the trapping and transfer of plasmonic hot electrons to adsorbed O₂, producing •O₂^– radicals, while plasmonic hot holes remaining on the Au surface mildly oxidize benzyl alcohol to corresponding carbon-centered radicals. The hypothesized concerted ring addition between these two radical species on the BiOCl surface highly favors the production of benzaldehyde along with an unexpected oxygen atom transfer from O₂ to the product. The results and understanding acquired in this study, based on the full utilization of hot charge carriers in a plasmonic metal deposited on a rationally designed support, will contribute to the development of more active and/or selective plasmonic catalysts for a wide variety of organic transformations

Why are some latrines cleaner than others? Determining the factors of habitual cleaning behaviour and latrine cleanliness in rural Burundi

Access to improved sanitation is fundamental for the prevention of diarrhoea and other diseases. However, for a sanitation facility to be safe, its cleanliness must be assured. The aim of the present study was, first, to assess how cleaning behaviour, household characteristics and infrastructural factors influenced latrine cleanliness and, second, to assess which psychological factors influenced cleaning behaviour. In a study in rural Burundi, 762 standardised household interviews with the primary household caregiver were carried out to assess habitual cleaning behaviour and psychological factors according to behaviour change models. In addition, the characteristics and cleanliness of the latrine were observed, and two multiple linear regressions were performed to analyse predictors of latrine cleanliness and of cleaning behaviour. Latrine cleanliness was determined by cleaning behaviour, the possibility of locking the door, the height of the superstructure, the material of the superstructure and the availability of an even slab. The number of households or people sharing the latrine was not influential. Commitment to cleaning, satisfaction with the cleanliness of the latrine and self-efficacy determined habitual cleaning behaviour. Interventions focussing on commitment, self-efficacy and satisfaction with a clean latrine like public commitment or guided practice interventions are recommended to promote cleaning behaviour

Recognition results for four alfalfa leaf diseases using SVM models based on selected features using the ReliefF method, the 1R method and the CFS method.

<p>Recognition results for four alfalfa leaf diseases using SVM models based on selected features using the ReliefF method, the 1R method and the CFS method.</p

Work flow diagram of main steps for lesion image segmentation.

<p>Work flow diagram of main steps for lesion image segmentation.</p

Performance evaluations of the twelve segmentation methods based on the sub-images of four alfalfa leaf diseases.

<p>Performance evaluations of the twelve segmentation methods based on the sub-images of four alfalfa leaf diseases.</p

Exploiting the Yb³⁺ and Er³⁺ Codoped β‑NaYF₄ Nanoparticles as Luminescent Thermometers for White-LED-Free Thermal Sensing at the Nanoscale

Luminescent ratiometric technology has been regarded as one of the most promising methods for temperature measurement, as it enables noncontact thermal sensing with minimal disturbance to the object of interest. Particularly, it, as expected, is free from the influences of many surrounding factors. However, we demonstrate here that, in some cases, this technology is under the influence of white LED that are commonly used in our daily life and industrial processes, considering the fact that these involved emitting lines used for thermal sensing are overlapped severely with the emitting spectrum of white LED. It is found that using the traditional green upconversion (UC) luminescence emanating from Er³⁺, namely, the ²H_11/2/⁴S_3/2–⁴I_15/2 transitions, for thermal sensing leads to a very large temperature error, up to 17 at 303 K in the case where there is the influence of white LED. While the two violet UC luminescence bands, separately originating from the ⁴G_11/2/²H_9/2–⁴I_15/2 transitions of Er³⁺ embedded in the β-NaYF₄:40% Yb³⁺, 2% Er³⁺ nanoparticles, are capable of enabling white-LED-free thermal sensing in that these two emission bands are absent from the emitting spectrum of white LED. Our work is likely to provide a new perspective for the study of luminescent ratiometric technology for thermal sensing. Most importantly, it presents a strategy for white-LED-free thermal sensing when the influence of white LED cannot be ignored in practical applications

Names of image features extracted and results of feature selection using the ReliefF method, the 1R method and the CFS method.

<p>Names of image features extracted and results of feature selection using the ReliefF method, the 1R method and the CFS method.</p

Recognition results of each alfalfa leaf disease using the optimal model (Model 4).

<p>Recognition results of each alfalfa leaf disease using the optimal model (Model 4).</p