Determining Subarctic Peatland Vegetation Using an Unmanned Aerial System (UAS)

A single artificial neural network (ANN) model is inadequate for predicting phytoplankton biomass in a large lake due to its high spatial heterogeneity. In this study, ANN was combined with a clustering technique to simulate phytoplankton biomass in a large lake (Lake Poyang) using a 7-year dataset. Two ANN models (named ANN_Downstream and ANN_Upstream) were developed for the downstream and upstream areas based on the k-means clustering results of 17 sampling sites at Lake Poyang, China. They performed better than ANN_Poyang (an ANN model for the whole lake), indicating the success of the clustering technique in improving ANN models for predicting phytoplankton biomass in different sub-regions of the large lake. A sensitivity analysis based on ANN_Downstream and ANN_Upstream showed that phytoplankton dynamics responded differently to environmental variables in different sub-regions of Lake Poyang. This case study demonstrated the good performance of ANN models in describing phytoplankton dynamics, and the potential of coupling ANN with a clustering technique to describe the spatial heterogeneity of natural ecosystems.

Historical data from oil and gas exploration in the delta of the Mackenzie River, Northwest Territories, in the 1970s provided an opportunity to estimate decadal-scale impacts of exploratory oil and gas drilling on native plant communities in low Arctic tundra. We assessed changes in vegetation composition and associated environmental gradients across seven drilling mud sumps in the Kendall Island Bird Sanctuary, Mackenzie Delta. Three decades after disturbance, drilling sumps had developed vegetation coverage equivalent to that in undisturbed areas, although bare soil persisted in ponded areas and where a salt crust was present. Vegetation on sumps was composed of communities dominated by forbs, grasses, and tall shrubs that were distinct from adjacent, undisturbed sedge and low shrub communities. The area of altered vegetation around a sump was generally larger in upland or saline environments than in lowland areas. Pooled water observed around many sumps was likely associated with thaw subsidence that occurred following construction, which was subsequently compounded by snow drifting and increased soil temperatures along the margins of the sump mound. Changes in drainage, active-layer depth, and surface salt concentrations appear to be key environmental factors that have helped shape plant communities established on drilling sumps in the three decades after disturbance.

Elemental composition and optical properties reveal changes in dissolved organic matter along a permafrost thaw chronosequence in a subarctic peatland

Early crop yield estimation aids growers of cotton and other crops in making in-season management decisions and estimating the crop’s market value. However, predicting yield early in the growing season is challenging for various reasons including environmental factors and soil variability. Several techniques have been used to estimate cotton yield, and in the last decade machine learning (especially artificial neural networks or ANNs) have been widely adopted. In a standard ANN model, all the input data are collated without considering the temporal characteristics of the data, such as when the data were collected relative to the growth stage of the plants. A modular network called plus artificial neural network (ANN+) was devised to independently receive crop data that has been collected at different stages of crop growth. This study evaluated the potential of adopting ANN+ for early estimation of cotton yield. For this purpose, a field experiment was conducted in central Texas in e2020 and 2021. The study site consisted of three different treatments: variable nitrogen rate, variable fertilizer and irrigation × variety. An unmanned aerial vehicle equipped (UAV) with a five-band multispectral sensor was flown at various cotton growth stages to collect remote sensing data multiple times within 100 days after planting. The UAV was flown at 30 m above ground level, producing a spatial resolution of approximately 0.02 m. The multispectral imagery was used to extract crop spectral, textural and structural information. Along with this information, weather information in term of growing degree days, solar insolation and precipitation were collected for yield estimation. The custom ANN+ model achieved an R2 of 0.90 and mean absolute percentage error of 12.29% for cotton yield estimation. Seasonal temperature data contributed the most information to the model but crop structural and textural metrics from the image data also contributed strongly to the model, suggesting that autonomous aerial systems can be an important part of providing cotton growers early predictions of yield.

Fault is defined as degradation between 100% performance and complete failure. The authors demonstrate how an artificial neural network can detect and diagnose faults from online process data. A wide range of input patterns can be learned by artificial neural networks in the presence of noise by changing the interconnections of the nodes, their thresholds for activation, and their individual weights. Artificial neural networks are able to take inputs from the processes without knowing the process model, allowing representation of complex engineering systems which are difficult to model either with traditional model-based engineering or knowledge-based expert systems. A description is given of some of the characters of a neural network that are useful for fault discrimination in a chemical plant. It is shown that even when using noisy sensor data, the misclassification rate is nil

In a 100-km2 area surveyed for beaver activity, one-third of the active colonies were in bogs and two-thirds were in lakes and rivers. From 1979 to 1981, the percent of colonies in bogs increased from 29% to 36%. Of 481 bogs in the study area, 200 (42%) had current or previous history of beaver activity (colony sites and work areas). At 101 bogs (21 %), dam construction in seepage zones created flowages. Most impoundments were built at minerotrophic sites. Moats were used at 99 bogs (21 %), where beavers preferred sedge-moss cover and avoided tall shrub and wooded cover. The results show that bogs are suitable habitat for beavers. INTRODUCTION The suitability of lake and stream habitat for beavers (Castor canadensis Kuhl.) has been well-studied (Retzer et al., 1956; Northcott, 1964, Tufts, 1967). Although beavers frequently colonize lakes and streams that are adjacent to bog areas, few researchers have mentioned that beavers are capable of colonizing bogs that lack open water. Northcott (1964) reported a single instance of a colony in a closed bog, but reference by others to similar phenomena has been anecdotal (Schwintzer and Williams, 1974; Buell and Buell, 1975; Worley, 1981). Marshall and Miguelle (1978) in their review of wildlife on Minnesota peatlands mentioned only that beavers colonize bogs that have existing open water. Although there are few accounts of beavers colonizing bogs, colonies in bogs are locally common in N-central Minnesota, and in some areas they are more prevalent than in lakes or streams. Beavers are well-adapted to aquatic habitats, but their ability to create open water in closed bogs is an unusual situation that may involve specialized behavior. The purpose of this paper is to document the extent of bog use by beavers in a section of Cass Co., Minnesota, and to determine what biological and physical features of bogs are associated with use by beavers. METHODS AND PROCEDURE The 100-km2 study area, located E of Backus in Cass Co., included most of Deerfield Township (T139N R31W) and a few adjacent sections of Hiram, Bull Moose, Powers and Birch Lake townships. The topography is characterized by a rugged terminal moraine belt with ca. 250 small lakes and 500 bogs in depressions between hills. The upland forest is predominantly paper birch (Betula papyrifera) and trembling aspen (Populus trernuloides). I used aerial photographs to detect the 512 bogs larger than 0.12 ha in the study area. Bog areas adjacent to lakes and streams were not included unless the connection was minimal and the bog occupied a separate basin. In the field, 481 bogs were evaluated for the presence of beaver activity and classified according to predominant cover type as sedge-moss, tall shrub or wooded bog. Sedge-moss bogs were characterized by floating mats of sedges (Carex) and/or Spagnum moss and ericaceous shrubs covering most of the basin. Tall shrub bogs were dominated by alder (Alnus rugosa) (the brushy fen of Boelter and Verry, 1977), or by Betula pumila, Larix laricina and Picea mariana -the early seral stages of bog forest on ombrotrophic sites. Wooded bogs were dominated by L. laricina, P mariana and occasionally by trees from later stages. Most bogs in the study area were less than 3 ha and had either monotypic vegetation or predominant cover types that were easily recognizable. Aerial and ground surveys were used to detect both colony sites and work areas at 'Present address: Department of Botany, Louisiana State University, Baton Rouge 70803.

We aimed to validate the Multinational Association for Supportive Care in Cancer (MASCC) risk index, and compare it with the Talcott model and artificial neural network (ANN) in predicting the outcome of febrile neutropenia in a Chinese population. We prospectively enrolled adult cancer patients who developed febrile neutropenia after chemotherapy and risk classified them according to MASCC score and Talcott model. ANN models were constructed and temporally validated in prospectively collected cohorts. From October 2005 to February 2008, 227 consecutive patients were enrolled. Serious medical complications occurred in 22% of patients and 4% died. The positive predictive value of low risk prediction was 86% (95% CI = 81-90%) for MASCC score ≥ 21, 84% (79-89%) for Talcott model, and 85% (78-93%) for the best ANN model. The sensitivity, specificity, negative predictive value, and misclassification rate were 81%, 60%, 52%, and 24%, respectively, for MASCC score ≥ 21; and 50%, 72%, 33%, and 44%, respectively, for Talcott model; and 84%, 60%, 58%, and 22%, respectively, for ANN model. The area under the receiver-operating characteristic curve was 0.808 (95% CI = 0.717-0.899) for MASCC, 0.573 (0.455-0.691) for Talcott, and 0.737 (0.633-0.841) for ANN model. In the low risk group identified by MASCC score ≥ 21 (70% of all patients), 12.5% developed complications and 1.9% died, compared with 43.3%, and 9.0%, respectively, in the high risk group (p < 0.0001). The MASCC risk index is prospectively validated in a Chinese population. It demonstrates a better overall performance than the Talcott model and is equivalent to ANN model.

Chronic kidney disease (CKD) can be treated if it is detected early, but as the disease progresses, recovery becomes impossible. Eventually, renal replacement therapy such as transplantation or dialysis is necessary. Ultrasound is a test method with which to diagnose kidney cancer, inflammatory disease, nodular disease, chronic kidney disease, etc. It is used to determine the degree of inflammation using information such as the kidney size and internal echo characteristics. The degree of the progression of chronic kidney disease in the current clinical trial is based on the value of the glomerular filtration rate. However, changes in the degree of inflammation and disease can even be observed with ultrasound. In this study, from a total of 741 images, 251 normal kidney images, 328 mild and moderate CKD images, and 162 severe CKD images were tested. In order to diagnose CKD in clinical practice, three ROIs were set: the cortex of the kidney, the boundary between the cortex and medulla, and the medulla, which are areas examined to obtain information from ultrasound images. Parameters were extracted from each ROI using the GLCM algorithm, which is widely used in ultrasound image analysis. When each parameter was extracted from the three areas, a total of 57 GLCM parameters were extracted. Finally, a total of 58 parameters were used by adding information on the size of the kidney, which is important for the diagnosis of chronic kidney disease. The artificial neural network (ANN) was composed of 58 input parameters, 10 hidden layers, and 3 output layers (normal, mild and moderate CKD, and severe CKD). Using the ANN model, the final classification rate was 95.4%, the epoch needed for training was 38 times, and the misclassification rate was 4.6%.

Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

In this study, we reviewed methods, sensors and study areas in estimation of vegetation biophysical (VBph) and biochemical (VBch) parameters using satellite optical remote sensing, in terms of (1) their performance and (2) extent of their use. Performance of parametric regression methods is dependent on the type of sensor, vegetation parameter, vegetation cover and biome of study area. Artificial neural network, partial least-squares regression, random forest, support vector machine and Gaussian process regression (GPR) had acceptable performances in the estimations. Performance of physically based methods depends on selecting appropriate radiative transfer model, inversion method and cost function, optimal solution (e.g. mean of some of the best lookup-table solutions) and adding appropriate Gaussian noise. In recent years, the use of active learning techniques (especially Euclidean distance-based diversity), to optimize training datasets against field datasets, with machine learning regression algorithms (MLRAs) (especially GPR) has led to acceptable performances by hybrid methods in estimation of VBph and VBch parameters using real and/or simulated (Re/Si) satellite hyperspectral (and multispectral) data with dimensionality reduction techniques in the spectral domain (especially principal component analysis with 20 principal components). In overall, Re/Si satellite optical sensors data containing red-edge (mostly for VBch parameters) and/or short-wave infrared (mostly for VBph parameters) spectral band(s) performed well. Emerging trend observed in recent years in the estimations by using Re/Si medium spatial resolution sensors data with relatively good spectral resolutions (e.g. precursore iperspettrale della missione applicativa, environmental mapping and analysis program and Sentinel-2 multispectral instrument) and non-parametric regression (especially MLRAs) and hybrid methods.

In a traditional system, ECG leads are connected to the patient's chest to detect the electrical performance of the heart which create long term discomfort for the patient. As ballistocardiogram (BCG) and valvular diseases are both mechanical phenomena, we conjectured that valvular disease could be diagnosed from non-contact BCG measurement. In this paper, we proposed a non-contact way to determine the valvular diseases of the heart which is favourable for long term observation of the patient. We classified the data using artificial neural network (ANN) and support vector machine (SVM). We collected data from normal persons and persons affected by mitral and pulmonary valve stenosis. We compared the result using overall accuracy, misclassification rate and fitness. We got the highest test accuracy of 79.12% for SVM technique for decomposition level 1. As this technique is completely new and advantageous, it can lead to a new research area of valvular disease detection.

The flow regimes of large arid river catchments are amongst the most variable in the world. Plant communities which inhabit arid floodplains typically exhibit high spatial heterogeneity and are temporally dynamic in response to changing flow conditions. It has been suggested that arid floodplain ecosystems, adapted as they are to variability, will be relatively resilient to anthropogenic alterations to flow. This paper argues that floodplain plant communities in arid catchments, as in temperate and tropical regions, are primarily structured by flow regimes despite their inherent unpredictability. Consequently, changes to flood pulses through water extraction can be expected to result in changes in vegetation composition and structure which in turn may have a dramatic effect on wider ecosystem functioning. Results are presented from an ongoing study of the Cooper Creek floodplain in central Australia which illustrate the relationships between plant community dynamics and variable flood pulses. These results indicate that alterations to flow may result in a shift in community structure and an eventual loss of biodiversity. It is essential, therefore, that water resource managers in arid regions consider the requirements of floodplain plant communities when allocating environmental flows. Copyright © 2003 John Wiley &amp; Sons, Ltd.

Tundra shrub expansion is a central form of change in warming Arctic ecosystems, but the pace of shrubification varies across spatial scales, complicating efforts to understand its drivers and consequences. Here, we apply convolutional neural networks (CNNs) to very‐high resolution satellite image pairs acquired 10–15 years apart (circa 2005–2019) to identify spatio‐temporal patterns of tall shrub (&gt; ~1.5 m height) expansion and their relationships to environmental covariates and antecedent shrub cover in three upland‐dominated Siberian Arctic landscapes. We developed human‐interpreted training datasets for CNN modelling of tall shrub occurrence and change for nearly 1 million 12 × 12 m image tiles using four canopy cover classes: Tundra lacking tall shrubs; Colonization with isolated shrubs; Open Shrub with discontinuous cover of mature shrubs; and Closed Shrub with dense cover of mature shrubs. F1 scores for the canopy cover maps ranged 0.83–0.92, and classification confidence was high (&gt;0.8) in both time periods for 62% of image tiles. We evaluated canopy class occurrence with respect to landscape‐scale environmental covariates related to topography, insolation, wetness and proximity to established shrubs. We detected increases in tall shrub cover in all three landscapes, but the rate of increase varied substantially (+2.4 to 26.1% decade−1). Locally, the distribution of canopy cover classes was strongly influenced by topographically derived metrics of wetness and potential insolation. Shrub colonization was further conditioned by proximity to pre‐existing shrubs. Synthesis. We found that mature, long‐established shrubs (i.e. the Open Shrub and Closed Shrub classes) were closely linked to well‐drained landscape positions; however, closed shrub stands were more likely to occur on warmer, south‐facing slopes, while Open Shrub predominated on cooler slope aspects. Contemporary shrub colonization has occurred on flatter landscape positions and across wider gradients of insolation and moisture. In our study region, northward shrubline advance appears most closely tied to microsites with favourable drainage and edaphic conditions; subsequent seed production and ameliorating effects support canopy infilling, particularly on warmer hillslope aspects. The techniques employed here provide insights into the susceptibility of landscapes to future shrub expansion and help address geographic bias regarding Arctic shrubification and its environmental drivers.

BackgroundAccurate evaluation of glomerular filtration rates (GFRs) is of critical importance in clinical practice. A previous study showed that models based on artificial neural networks (ANNs) could achieve a better performance than traditional equations. However, large-sample cross-sectional surveys have not resolved questions about ANN performance.MethodsA total of 1,180 patients that had chronic kidney disease (CKD) were enrolled in the development data set, the internal validation data set and the external validation data set. Additional 222 patients that were admitted to two independent institutions were externally validated. Several ANNs were constructed and finally a Back Propagation network optimized by a genetic algorithm (GABP network) was chosen as a superior model, which included six input variables; i.e., serum creatinine, serum urea nitrogen, age, height, weight and gender, and estimated GFR as the one output variable. Performance was then compared with the Cockcroft-Gault equation, the MDRD equations and the CKD-EPI equation.ResultsIn the external validation data set, Bland-Altman analysis demonstrated that the precision of the six-variable GABP network was the highest among all of the estimation models; i.e., 46.7 ml/min/1.73 m2 vs. a range from 71.3 to 101.7 ml/min/1.73 m2, allowing improvement in accuracy (15% accuracy, 49.0%; 30% accuracy, 75.1%; 50% accuracy, 90.5% [P<0.001 for all]) and CKD stage classification (misclassification rate of CKD stage, 32.4% vs. a range from 47.3% to 53.3% [P<0.001 for all]). Furthermore, in the additional external validation data set, precision and accuracy were improved by the six-variable GABP network.ConclusionsA new ANN model (the six-variable GABP network) for CKD patients was developed that could provide a simple, more accurate and reliable means for the estimation of GFR and stage of CKD than traditional equations. Further validations are needed to assess the ability of the ANN model in diverse populations.

Precise spatial information of crop distribution is vital for government and research organizations to monitor agriculture activities like crop health monitoring, crop yield prediction, and food security. Mapping of crop area is challenging in smallholder farming like India, where crop parcels are smaller than two hectares. With an extension of artificial intelligence, an artificial neural network has ability to learn the spectral feature of multispectral satellite images and map them to a land cover class. However, mixed pixel is a challenging problem in pixel wise classification of coarse resolution satellite images. The linear mixture model is successfully utilized to unmix the signals of a mixed classes. The success of linear mixture model is depending on the selection of endmembers of a mixed class. Therefore, this paper presents an adaptive approach for automatic selection of endmembers of a mixed pixel in linear mixture model using spectral and spatial information. The proposed approach is capable of extracting the fraction area cover of each class by using a constrained least-squares error solution. The GPS field surveys, and drone images are employed to create reference data for the accuracy assessment of proposed algorithm. The experimentation results indicate that the solution of the proposed approach outperformed recent baseline methods in terms of efficiency and accuracy of pixelwise estimated area and overall estimated area of various land cover classes.