Classification of Torbanite and Cannel Coal. II. Insights from Organic Geochemical and Multivariate Statistical Analysis

Petrographic and megascopic criteria have traditionally been used as the basis for the classification of torbanite and cannel coal. For this study, it was hypothesized that modern analytical organic geochemical and multivariate statistical techniques could provide an alternative approach. Towards this end, the demineralized residues of 14 torbanite (rich in Botryococcus-related alginite) and cannel (essentially, rich in organic groundmass and/or sporinite) coal samples were analyzed by pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Cluster analysis performed on the Py-GC/MS data clearly distinguished the torbanite from the cannel coal, demonstrating a consistency between the chemical properties and the petrographic composition. All the torbanite samples group into one cluster, their pyrolyzates having an overwhelming predominance of straight chain hydrocarbons, a characteristic typical of Botryococcus. The presence of the C9-C26 n-,-alkadiene series is the key feature distinguishing the torbanites from the other samples. The cannel coals exhibit more chemical diversity, reflecting their greater variability in petrographic composition. The Breckinridge cannel, dominated by a highly aliphatic lamalginitic groundmass, chemically fits the torbanite category. The bituminitic groundmass-dominated cannel coals fall into a cannel sub-cluster, their pyrolyzates having a characteristic predominance of n-alk-1-enes and n-alkanes (particularly the long-chain homologues), with no detectable alkadienes. The vitrinitic groundmass-dominated Ohio Linton cannel and the sporinite-rich Canadian Melville Island cannel are readily distinguishable from the other cannels by the relatively abundant aromatic and phenolic compounds in their pyrolyzates. The internal distribution patterns of alkylaromatic and alkylphenolic isomers are shown to be less significant in the classification of this sample set. Multivariate statistical analysis of the pyrolysis data not only successfully discriminated torbanites from cannel coals, but recognized subtler differences between the examples of these two coal types, in substantial agreement with the petrographic characterization. As such, these methods can substitute for or supplement the traditional microscope-based approach

Chemistry of Maceral and Groundmass Density Fractions of Torbanite and Cannel Coal

Microscopically, torbanite and cannel coal are composed of coarser macerals set in a fine-grained to amorphous groundmass. It is often assumed that the amorphous groundmass is genetically related to the distinct macerals. The separation of macerals and groundmass from 14 late Paleozoic torbanite, cannel, and humic coals permits the analysis of individual constituents using elemental analysis and flash pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Cluster and principal component analyses of the Py-GC/MS data further reveal the chemical similarities and differences between the various constituents. Pyrolyzates of Botryococcus-related alginites are characterized by an abundance of normal alkadienes, alkenes, and alkanes. Even their alkylbenzenes and alkylnaphthalenes exhibit a relatively higher concentration of isomers with a single, linear alkyl side-chain than do other macerals and groundmass. ln contrast, vitrinite pyrolyzates are dominated by phenolic and aromatic compounds. Sporinites are enriched in aliphatic, aromatic, and phenolic structures, especially the short chain aliphatics and alkylbenzenes. They are also characterized by a predominance of 1,2-dimethylbenzene and 1-ethyl-2-methylbenzene. The groundmass is further divided into lamalginitic, bituminitic, and vitrinitic. The chemistry of the brightly-fluorescing lamalginitic groundmass is basically similar to that of alginite, but also resembles other groundmass types in normal hydrocarbon and alkylphenol distributions. The vitrinitic groundmass can be described as an aliphatic-rich vitrinite. The pyrolyzate of the bituminitic groundmass is characterized by the predominance of long chain normal hydrocarbons. Their pyrolyzates have a chemical nature intermediate between alginite and vitrinite. The relatively higher contents of hopanoids in their pyrolyzates and elemental nitrogen suggest a bacterial role in the formation of the groundmass. Chemical analysis and subsequent multivariate statistical analysis suggest that the groundmass is likely to be a mixture of bacterially-degraded algal and humic organic matter. The proportions of the two primary components vary from sample to sample, as does the extent of degradation. Bacterially-produced hopanoids are also incorporated

Wheat stripe rust grading by deep learning with attention mechanism and images from mobile devices

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions

Classification of Torbanite and Cannel Coal. I. Insights from Petrographic Analysis of Density Fractions.

Torbanite and cannel coal are considered to be coals because of their low mineral content and overall physical morphology. However, the texture and composition of the organic matter in torbanite and cannel coal are similar to the kerogen occurring in oil shales and lacustrine source rocks. Therefore, understanding the nature and origin of organic components in torbanite and cannel coal is of significance in the study of kerogen and petroleum formation. In this research, a set of torbanites and cannel coals from different locations throughout the world were petrographically characterized and processed using a density gradient centrifugation (DGC) technique. Microscopically, the torbanite and cannel coal are composed of coarser maceral particles set in a fine-grained to amorphous groundmass. The groundmass is a mixture of more than one type of substance and accounts for 10 to 80% (by volume) of the torbanites and cannel coals. Botryococcus-related alginite is the most characteristic component of the torbanite. While sporinite typically is the main phytoclast in the cannel coals, in most cases the groundmass is volumetrically the dominant component, determining the overall character of the sample. This observation calls into question the traditional practice of classifying such coals using the alginite to sporinite ratio. Variations in composition, texture and fluorescence permits the recognition of three different types of groundmass: lamalginitic, bituminitic and vitrinitic. High purity alginite, sporinite, vitrinite and varieties of groundmass were separated using the DGC technique. The distribution of density fractions closely reflects the petrographic composition of the various torbanites and cannel coals. Distinct peaks on the density profiles represent the major organic components and peak magnitudes are functions of the percentage of the components, demonstrating that the density gradient profiles can be used to distinguish the different types of torbanite and cannel coal. The separation data also indicate a gradual shift towards higher density from lamalginitic to bituminitic to vitrinitic groundmass

A facile approach to fabricate highly sensitive, flexible strain sensor based on elastomeric/graphene platelet composite film

This work developed a facile approach to fabricate highly sensitive and flexible polyurethane/graphene platelets composite film for wearable strain sensor. The composite film was fabricated via layer-by-layer laminating method which is simple and cost-effective; it exhibited outstanding electrical conductivity of 1430 ± 50 S/cm and high sensitivity to strain (the gauge factor is up to 150). In the sensor application test, the flexible strain sensor achieves real-time monitoring accurately for five bio-signals such as pulse movement, finger movement, and cheek movement giving a great potential as wearable-sensing device. In addition, the developed strain sensor shows response to pressure and temperature in a certain region. A multifaceted comparison between reported flexible strain sensors and our strain sensor was made highlighting the advantages of the current work in terms of (1) high sensitivity (gauge factor) and flexibility, (2) facile approach of fabrication, and (3) accurate monitoring for body motions

mixiTUI:A Tangible Sequencer for Electronic Live Performances

With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms (CAP) have appeared. In the mean time, CAP-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However, CAP urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types, thus requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including Task Resolution and Assignment Framework (TRAF), Integrated Resource Management (IRM), and Task Result quality Optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further reveals TRO brings enormous improvement in efficiency and a reduction in energy consumption