Heat Kernel Bounds on Metric Measure Spaces and Some Applications

Let $(X,d,\mu)$ be a $RCD^\ast(K, N)$ space with $K\in \mathbb{R}$ and $N\in [1,\infty]$. For $N\in [1,\infty)$, we derive the upper and lower bounds of the heat kernel on $(X,d,\mu)$ by applying the parabolic Harnack inequality and the comparison principle, and then sharp bounds for its gradient, which are also sharp in time. When $N=\infty$, we also establish a sharp upper bound of the heat kernel by using the dimension free Harnack inequality. For applications, we study the large time behavior of the heat kernel, the stability of solutions to the heat equation, and show the $L^p$ boundedness of (local) Riesz transforms.Comment: 27pp,Section 6 was removed, to appear in Potential Ana

Data fusion technology for precision forestry applications

Presently precision forestry is playing an important role in realizing sustainable development and improving societal and economical efficiency for forestry applications. Based on analyzing the features of precision forestry's information requirements, the data needed for precision forestry were classified and the characteristics of the different information were summarized. Data fusion for precision forestry was studied in this paper. The architecture for precision forestry information processing, which integrated information fusion and data mining, was put forward. New and emerging technologies such as Remote Sensing (RS), Geographical Information System (GIS), Global Position System (GPS), Data Base Management System (DBMS), Data Fusion, Decision Support Systems (DSS), and Variable Rate technology (VRT) are applied in forestry production as aids in producers' and managers' decision-making process. Precision irrigation, precision fertilizing, precision pesticide application, precision harvesting, and precision deforestation can promote the realization of minimizing resource inputs, minimizing environmental impacts, and maximizing forest outputs

High-throughput phenotyping of plant leaf morphological, physiological, and biochemical traits on multiple scales using optical sensing

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, physiological, and biochemical traits of plant leaves on multiple scales. We summarize the characteristics, advantages and limitations of optical sensing and data-processing methods applied in various plant phenotyping scenarios. Finally, we discuss the future prospects of plant leaf phenotyping research. This review aims to help researchers choose appropriate optical sensors and data processing methods to acquire plant leaf phenotypes rapidly, accurately, and cost-effectively

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Background: Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion

Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results: The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion: All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum

Soil Moisture but Not Warming Dominates Nitrous Oxide Emissions During Freeze–Thaw Cycles in a Qinghai–Tibetan Plateau Alpine Meadow With Discontinuous Permafrost

Large quantities of organic matter are stored in frozen soils (permafrost) within the Qinghai–Tibetan Plateau (QTP). The most of QTP regions in particular have experienced significant warming and wetting over the past 50 years, and this warming trend is projected to intensify in the future. Such climate change will likely alter the soil freeze–thaw pattern in permafrost active layer and toward significant greenhouse gas nitrous oxide (N2O) release. However, the interaction effect of warming and altered soil moisture on N2O emission during freezing and thawing is unclear. Here, we used simulation experiments to test how changes in N2O flux relate to different thawing temperatures (T5–5°C, T10–10°C, and T20–20°C) and soil volumetric water contents (VWCs, W15–15%, W30–30%, and W45–45%) under 165 F–T cycles in topsoil (0–20 cm) of an alpine meadow with discontinuous permafrost in the QTP. First, in contrast to the prevailing view, soil moisture but not thawing temperature dominated the large N2O pulses during F–T events. The maximum emissions, 1,123.16–5,849.54 μg m–2 h–1, appeared in the range of soil VWC from 17% to 38%. However, the mean N2O fluxes had no significant difference between different thawing temperatures when soil was dry or waterlogged. Second, in medium soil moisture, low thawing temperature is more able to promote soil N2O emission than high temperature. For example, the peak value (5,849.54 μg m–2 h–1) and cumulative emissions (366.6 mg m–2) of W30T5 treatment were five times and two to four times higher than W30T10 and W30T20, respectively. Third, during long-term freeze–thaw cycles, the patterns of cumulative N2O emissions were related to soil moisture. treatments; on the contrary, the cumulative emissions of W45 treatments slowly increased until more than 80 cycles. Finally, long-term freeze–thaw cycles could improve nitrogen availability, prolong N2O release time, and increase N2O cumulative emission in permafrost active layer. Particularly, the high emission was concentrated in the first 27 and 48 cycles in W15 and W30, respectively. Overall, our study highlighted that large emissions of N2O in F–T events tend to occur in medium moisture soil at lower thawing temperature; the increased number of F–T cycles may enhance N2O emission and nitrogen mineralization in permafrost active layer

High‑throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel

Hyperspectral reflectance data in the visible, near infrared and shortwave infrared range (VIS–NIR– SWIR, 400–2500 nm) are commonly used to nondestructively measure plant leaf properties. We investigated the usefulness of VIS–NIR–SWIR as a high-throughput tool to measure six leaf properties of maize plants including chlorophyll content (CHL), leaf water content (LWC), specific leaf area (SLA), nitrogen (N), phosphorus (P), and potassium (K). This assessment was performed using the lines of the maize diversity panel. Data were collected from plants grown in greenhouse condition, as well as in the field under two nitrogen application regimes. Leaf-level hyperspectral data were collected with a VIS–NIR–SWIR spectroradiometer at tasseling. Two multivariate modeling approaches, partial least squares regression (PLSR) and support vector regression (SVR), were employed to estimate the leaf properties from hyperspectral data. Several common vegetation indices (VIs: GNDVI, RENDVI, and NDWI), which were calculated from hyperspectral data, were also assessed to estimate these leaf properties

Identification of Tea Storage Times by Linear Discrimination Analysis and Back-Propagation Neural Network Techniques Based on the Eigenvalues of Principal Components Analysis of E-Nose Sensor Signals

An electronic nose (E-nose) was employed to detect the aroma of green tea after different storage times. Longjing green tea dry leaves, beverages and residues were detected with an E-nose, respectively. In order to decrease the data dimensionality and optimize the feature vector, the E-nose sensor response data were analyzed by principal components analysis (PCA) and the five main principal components values were extracted as the input for the discrimination analysis. The storage time (0, 60, 120, 180 and 240 days) was better discriminated by linear discrimination analysis (LDA) and was predicted by the back-propagation neural network (BPNN) method. The results showed that the discrimination and testing results based on the tea leaves were better than those based on tea beverages and tea residues. The mean errors of the tea leaf data were 9, 2.73, 3.93, 6.33 and 6.8 days, respectively

New naphthalene whole-cell bioreporter for measuring and assessing naphthalene in polycyclic aromatic hydrocarbons contaminated site

A new naphthalene bioreporter was designed and constructed in this work. A new vector, pWH1274_Nah, was constructed by the Gibson isothermal assembly fused with a 9 kb naphthalene-degrading gene nahAD (nahAa nahAb nahAc nahAd nahB nahF nahC nahQ nahE nahD) and cloned into Acinetobacter ADPWH_lux as the host, capable of responding to salicylate (the central metabolite of naphthalene). The ADPWH_Nah bioreporter could effectively metabolize naphthalene and evaluate the naphthalene in natural water and soil samples. This whole-cell bioreporter did not respond to other polycyclic aromatic hydrocarbons (PAHs; pyrene, anthracene, and phenanthrene) and demonstrated a positive response in the presence of 0.01 μM naphthalene, showing high specificity and sensitivity. The bioluminescent response was quantitatively measured after a 4 h exposure to naphthalene, and the model simulation further proved the naphthalene metabolism dynamics and the salicylate-activation mechanisms. The ADPWH_Nah bioreporter also achieved a rapid evaluation of the naphthalene in the PAH-contaminated site after chemical spill accidents, showing high consistency with chemical analysis. The engineered Acinetobacter variant had significant advantages in rapid naphthalene detection in the laboratory and potential in situ detection. The state-of-the-art concept of cloning PAHs-degrading pathway in salicylate bioreporter hosts led to the construction and assembly of high-throughput PAH bioreporter array, capable of crude oil contamination assessment and risk management