Clarification of Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique

Apples are known for their nutrition and economic value. Accurate and rapid diagnosis of water status in apple seedlings on an individual rootstock basis is a prerequisite for precision water management. This study presents a rapid and non-destructive approach for estimating water content in apple seedlings at leaf levels. A PIKA L system collects hyperspectral images(400-1000nm) of apple leaves. To the author's knowledge, no prior work was conducted using the spectral-texture approach in plant water stress. Our research extracts spatial information, gray-level co-occurrence matrix (GLCM), from feature wavelength images of hypercubes. Machine learning methods are applied to these spatial feature matrixs to identify apple leaves under different water stresses. In addition, differences in spectral responses were analysed using machine learning techniques for sorting apple seedlings with varying water treatments (dry, normal, and overwatering). Also, we measure chlorophyll to determine the relationship between hyperspectral characteristics and physiological changes. The achievements of the research indicate that the fusion of texture and hyperspectral imaging coupled with machine learning techniques is promising and presents a powerful potential to determine the water stress in the leaves of apple seedlings

Highlighting Water Stress in Apple Seedlings Using HSI Texture with Machine Learning Technique

Apples are known for their nutrition and economic value. Accurate and rapid diagnosis of water status in apple seedlings on an individual rootstock basis is a prerequisite for precision water management. This study presents a rapid and non-destructive approach for estimating water content in apple seedlings at leaf levels. A PIKA L system collects hyperspectral images (400-1000nm) of apple leaves. Our research extracts spatial information, gray-level co-occurrence matrix (GLCM), from feature wavelength images of hypercubes. Machine learning methods are applied to these spatial feature matrixs to identify apple leaves under different water stresses. In addition, differences in spectral responses were analysed using machine learning techniques for sorting apple seedlings with varying water treatments (dry, normal, and overwatering). Also, we measure chlorophyll to determine the relationship between hyperspectral characteristics and physiological changes. The achievements of the research indicate that the fusion of texture and hyperspectral imaging coupled with machine learning techniques is promising and presents a powerful potential to determine the water stress in the leaves of apple seedlings

The Operating Efficiency Evaluation of the Highway Network Under Accident Conditions

AbstractIn order to improve the running safety of highway, to minimize traffic delay, and to avoid the secondary traffic accident, it is essential to evaluate the operating efficiency of the highway network under accident conditions. This article selects the time reliability as evaluation index and compares the index value of the highway network under normal, accident conditions and after emergency traffic organizations. Differences are used to perform an analysis on the impact on traffic of the accident, the result of the emergency traffic organization and the recovery degree of the transport. The paper provides some basis for the traffic organization plan optimization of the road network under the accident conditions

Variación espacial y estacional de Cianobacterias y sus tasas de fijación de nitrógeno en la Bahía de Sanya en el Sur del Mar de China

The nitrogen fixation rates of planktonic and intertidal benthic cyanobacteria were investigated in Sanya Bay from 2003 to 2005. Trichodesmium thiebautii was the dominant species of planktonic cyanobacteria during our study. Significant seasonal and spatial variations in Trichodesmium spp. abundance were observed (P<0.01). The highest Trichodesmium concentrations occurred during intermonsoon periods and in the outer region of Sanya Bay (Outer Bay stations). At fixed station S03 the abundance of T. thiebautii ranged from 1.14×103 to 2060×103 trichomes m–2, with an annual mean of 273×103 trichomes m–2. The average nitrogen fixation rate per colony of T. thiebautii was 0.27 nmol N h-1 colony-1 and it did not show any obvious seasonal variations. Nitrogen fixation by planktonic cyanobacteria was highest in the Outer Bay stations, where the estimated amount of new nitrogen introduced by Trichodesmium contributed 0.03 to 1.63% of the total primary production and up to 11.64% of the new production. Statistical results showed that significant seasonal and spatial variations of nitrogen fixation rates were found among the intertidal communities. The main benthic nitrogen-fixing cyanobacteria were identified as members of the genera Anabaena, Calothrix, Lyngbya, Nostoc and Oscillatoria. The highest nitrogen fixation rate was found in microbial mats and the lowest in reefs and rocky sediments. All the benthic communities studied presented their highest nitrogen fixation activity in summer, with an average nitrogen fixation rate of 33.31 µmol N h-1 m-2, whereas the lowest nitrogen activity was detected in winter, with an average nitrogen fixation rate of 5.66 µmol N h-1 m-2. A Pearson correlation analysis indicated that the nitrogen fixation rate of three types of intertidal communities was significantly positively correlated to seawater temperature (P<0.05), whereas only the nitrogen fixation rate of the reefs and rock communities was significantly negatively correlated to seawater salinity (P<0.05).Las tasas de fijación de nitrógeno de cianobacterias intermareales y bentónicas fueron investigadas en la Bahía de Sanya, desde 2003 a 2005. Trichodesmium thiebautii era la especie dominante de las cianobacterias planctónicas durante nuestra investigación. Se observaron variaciones espaciales y estacionales significativas (P<0.01) en la abundancia de Trichodesmium spp. La concentración más elevada de Trichodesmium se observó durante los períodos de intermonzón y en la región exterior de la Bahía de Sanya (estaciones fuera de la Bahía). En la estación fija S03 la abundancia de T. thiebautii variaba desde 1.14×103 a 2060×103 tricomas m–2, con una media anual de 273×103 tricomas m–2. El promedio de la tasa de fijación de nitrógeno por colonia de T. thiebautii era de 0.27 nmol N h-1 colonia y no mostraba una clara variación estacional. La fijación de nitrógeno por las cianobacterias planctónicas era superior en las estaciones de fuera de la Bahía, donde la cantidad estimada de nitrógeno nuevo introducido por Trichodesmium contribuía del 0.03 al 1.63% del total de la producción primaria y hasta el 11.64% de la producción nueva. Estadísticamente los resultados mostraban que las variaciones espaciales y estacionales significativas de las tasas de fijación de nitrógeno fueron encontradas entre las comunidades intermareales. Las principales cianobacterias bentónicas fijadoras de nitrógeno fueron identificadas como miembros de los géneros Anabaena, Calothrix, Lyngbya, Nostoc y Oscillatoria. La tasa de fijación de nitrógeno más alta fue encontrada en los tapetes microbianos y las más bajas en los arrecifes y sedimentos rocosos. Todas las comunidades bentónicas estudiadas presentaban la mayor actividad de fijación de nitrógeno en verano, con un promedio de tasas de fijación de 33.31 ?mol N h-1 m-2, mientras que la menor actividad de fijación de nitrógeno fue detectada en invierno, con un promedio de 5.66 ?m N h-1 m-2. Análisis de correlación (Pearson) indicaban que las tasas de fijación de nitrógeno en los tres tipos de comunidades intermareales estaban significativamente correlacionados con la temperatura del agua (P<0.05). Mientras que la tasa de fijación de nitrógeno de las comunidades de los arrecifes y sedimentos rocosos estaban correlacionadas significativamente con la salinidad del agua de mar (P<0.05)

Characteristic classes of vector bundles over CP(j)timesHP(k) CP(j)times HP(k) and involutions fixing CP(2m+1)timesHP(k) CP(2m+1)times HP(k)

In this paper, we determine the total Stiefel-Whitney classes of vector bundles over the product of the complex projective space $CP(j)$ with the quaternionic projective space $HP(k)$. Moreover, we show that every involution fixing $CP(2m+1)times HP(k)$ bounds

Distribución del fitoplancton y su relación con variables ambientales en Sanya Bay, mar del Sur de la China

Phytoplankton quantification was conducted in Sanya Bay from January 2005 to February 2006. A submersible in situ spectrofluorometer, which permits the differentiation of four algal groups (green algae, diatoms and dinoflagellates, cryptophytes and cyanobacteria) was used. Seasonal variation of total chlorophyll a concentration showed that high value appeared in summer and low concentration occurred in spring. Diatoms and dinoflagellates group was the predominant phytoplankton all year in the Bay. The stable stratification of phytoplankton vertical distribution came into being in July. During the stratification event, the total chlorophyll a concentration of deep layer was much higher than the surface; cyanobacteria and cryptophyta groups decreased and almost disappeared, however, the concentration of green algae and diatoms and dinoflagellates groups increased. In deep layer, the concentration of diatoms and dinoflagellates group increased sharply, which was about eight times more than that in the surface layer. The vertical profiles character of phytoplankton showed that from inshore stations to outer bay the stratification of phytoplankton vertical distribution gradually strengthened. Dissolved inorganic nutrient especially phosphate and inorganic nitrogen and cold-water upwelling were the main regulating factor for phytoplankton distribution.Desde enero 2005 a febrero 2006, en Sanya Bay se llevó a cabo la cuantificación del fitoplancton. Para ello se usó un espectrofotómetro sumergible in situ que permitía la diferenciación de cuatro grupos de algas (algas verdes, diatomeas y dinoflagelados, criptofitas y cianobacterias). La variación estacional de la concentración de clorofila a, mostraba que los valores altos aparecían en verano y los bajos en invierno. Durante todo el año el grupo de fitoplancton predominante era el de dinoflagelados y diatomeas. La estratificación estable de la distribución vertical del fitoplancton aparecía en julio. Durante la estratificación la concentración total de la clorofila a de la capa profunda era más alta que en la superficie; los grupos de crisófitas y cianobacterias decrecían hasta casi desaparecer, sin embargo la concentración de algas verdes y diatomeas se incrementaba. En la capa profunda la concentración del grupo formado por diatomeas y dinoflagelados se incrementaba considerablemente con concentraciones ocho veces más elevadas que en la superficie. El carácter de los perfiles verticales de fitoplancton desde las estaciones costeras hasta fuera de la Bahía aparecía gradualmente más definido. Los nutrientes inorgánicos correspondientes a fósforo y nitrógeno junto con la subida de agua fría eran los factores principales que regulaban la distribución del fitoplancton

Enhanced stability of Zr-doped Ba(CeTb)O3−δ-Ni cermet membrane for hydrogen separation

A mixed protonic and electronic conductor material BaCe0.85Tb0.05Zr0.1O3−δ (BCTZ) is prepared and a Ni-BCTZ cermet membrane is synthesized for hydrogen separation. Stable hydrogen permeation fluxes can be obtained for over 100 h through the Ni-BCTZ membrane in both dry and humid conditions, which exhibits an excellent stability compared with Ni-BaCe0.95Tb0.05O3−δ membrane due to the Zr doping.Sino-German center for Science Promotion/GZ 911National Science Foundation of China/21225625National Science Foundation of China/2117608

AMD-DBSCAN: An Adaptive Multi-density DBSCAN for datasets of extremely variable density

DBSCAN has been widely used in density-based clustering algorithms. However, with the increasing demand for Multi-density clustering, previous traditional DSBCAN can not have good clustering results on Multi-density datasets. In order to address this problem, an adaptive Multi-density DBSCAN algorithm (AMD-DBSCAN) is proposed in this paper. An improved parameter adaptation method is proposed in AMD-DBSCAN to search for multiple parameter pairs (i.e., Eps and MinPts), which are the key parameters to determine the clustering results and performance, therefore allowing the model to be applied to Multi-density datasets. Moreover, only one hyperparameter is required for AMD-DBSCAN to avoid the complicated repetitive initialization operations. Furthermore, the variance of the number of neighbors (VNN) is proposed to measure the difference in density between each cluster. The experimental results show that our AMD-DBSCAN reduces execution time by an average of 75% due to lower algorithm complexity compared with the traditional adaptive algorithm. In addition, AMD-DBSCAN improves accuracy by 24.7% on average over the state-of-the-art design on Multi-density datasets of extremely variable density, while having no performance loss in Single-density scenarios. Our code and datasets are available at https://github.com/AlexandreWANG915/AMD-DBSCAN.Comment: Accepted at DSAA202