Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species

Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries

S tím, jak se neustále vyvíjejí nové technologie pro energeticky náročná průmyslová odvětví, stávající zařízení postupně zaostávají v efektivitě a produktivitě. Tvrdá konkurence na trhu a legislativa v oblasti životního prostředí nutí tato tradiční zařízení k ukončení provozu a k odstavení. Zlepšování procesu a projekty modernizace jsou zásadní v udržování provozních výkonů těchto zařízení. Současné přístupy pro zlepšování procesů jsou hlavně: integrace procesů, optimalizace procesů a intenzifikace procesů. Obecně se v těchto oblastech využívá matematické optimalizace, zkušeností řešitele a provozní heuristiky. Tyto přístupy slouží jako základ pro zlepšování procesů. Avšak, jejich výkon lze dále zlepšit pomocí moderní výpočtové inteligence. Účelem této práce je tudíž aplikace pokročilých technik umělé inteligence a strojového učení za účelem zlepšování procesů v energeticky náročných průmyslových procesech. V této práci je využit přístup, který řeší tento problém simulací průmyslových systémů a přispívá následujícím: (i)Aplikace techniky strojového učení, která zahrnuje jednorázové učení a neuro-evoluci pro modelování a optimalizaci jednotlivých jednotek na základě dat. (ii) Aplikace redukce dimenze (např. Analýza hlavních komponent, autoendkodér) pro vícekriteriální optimalizaci procesu s více jednotkami. (iii) Návrh nového nástroje pro analýzu problematických částí systému za účelem jejich odstranění (bottleneck tree analysis – BOTA). Bylo také navrženo rozšíření nástroje, které umožňuje řešit vícerozměrné problémy pomocí přístupu založeného na datech. (iv) Prokázání účinnosti simulací Monte-Carlo, neuronové sítě a rozhodovacích stromů pro rozhodování při integraci nové technologie procesu do stávajících procesů. (v) Porovnání techniky HTM (Hierarchical Temporal Memory) a duální optimalizace s několika prediktivními nástroji pro podporu managementu provozu v reálném čase. (vi) Implementace umělé neuronové sítě v rámci rozhraní pro konvenční procesní graf (P-graf). (vii) Zdůraznění budoucnosti umělé inteligence a procesního inženýrství v biosystémech prostřednictvím komerčně založeného paradigmatu multi-omics.Zlepšení průmyslových procesů, Model založený na datech, Optimalizace procesu, Strojové učení, Průmyslové systémy, Energeticky náročná průmyslová odvětví, Umělá inteligence.

Bioinformatics Applications Based On Machine Learning

The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Cloud computing research: a review of research themes, frameworks, methods and future research directions

This paper presents a meta-analysis of cloud computing research in information systems with the aim of taking stock of literature and their associated research frameworks, research methodology, geographical distribution, level of analysis as well as trends of these studies over the period of 7 years. A total of 285 articles from 67 peer review journals from year 2009 to 2015 were used in the analysis. The findings indicate that extant cloud computing literature tends to skew towards the technological dimension to the detriment of other under researched dimensions such as business, conceptualization and application domain. Whilst there has been a constant increase in cloud computing studies over the last seven years, a significant number of these studies have not been underpinned by theoretical frameworks and models. Also, majority of cloud computing studies utilized experiment and simulation as methods of enquiry as compared to the qualitative, quantitative, and mixed methodologies. This study contributes to cloud computing research by providing holistic insights into trends on themes, methodology, research framework, geographical focus and future research directions

Advanced machine learning algorithms for Canadian wetland mapping using polarimetric synthetic aperture radar (PolSAR) and optical imagery

Wetlands are complex land cover ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remote sensing data are greatly beneficial, as they capture a synoptic and multi-temporal view of landscapes. The ability to extract useful information from satellite imagery greatly affects the accuracy and reliability of the final products. This is of particular concern for mapping complex land cover ecosystems, such as wetlands, where complex, heterogeneous, and fragmented landscape results in similar backscatter/spectral signatures of land cover classes in satellite images. Accordingly, the overarching purpose of this thesis is to contribute to existing methodologies of wetland classification by proposing and developing several new techniques based on advanced remote sensing tools and optical and Synthetic Aperture Radar (SAR) imagery. Specifically, the importance of employing an efficient speckle reduction method for polarimetric SAR (PolSAR) image processing is discussed and a new speckle reduction technique is proposed. Two novel techniques are also introduced for improving the accuracy of wetland classification. In particular, a new hierarchical classification algorithm using multi-frequency SAR data is proposed that discriminates wetland classes in three steps depending on their complexity and similarity. The experimental results reveal that the proposed method is advantageous for mapping complex land cover ecosystems compared to single stream classification approaches, which have been extensively used in the literature. Furthermore, a new feature weighting approach is proposed based on the statistical and physical characteristics of PolSAR data to improve the discrimination capability of input features prior to incorporating them into the classification scheme. This study also demonstrates the transferability of existing classification algorithms, which have been developed based on RADARSAT-2 imagery, to compact polarimetry SAR data that will be collected by the upcoming RADARSAT Constellation Mission (RCM). The capability of several well-known deep Convolutional Neural Network (CNN) architectures currently employed in computer vision is first introduced in this thesis for classification of wetland complexes using multispectral remote sensing data. Finally, this research results in the first provincial-scale wetland inventory maps of Newfoundland and Labrador using the Google Earth Engine (GEE) cloud computing resources and open access Earth Observation (EO) collected by the Copernicus Sentinel missions. Overall, the methodologies proposed in this thesis address fundamental limitations/challenges of wetland mapping using remote sensing data, which have been ignored in the literature. These challenges include the backscattering/spectrally similar signature of wetland classes, insufficient classification accuracy of wetland classes, and limitations of wetland mapping on large scales. In addition to the capabilities of the proposed methods for mapping wetland complexes, the use of these developed techniques for classifying other complex land cover types beyond wetlands, such as sea ice and crop ecosystems, offers a potential avenue for further research

Disruptive Technologies in Agricultural Operations: A Systematic Review of AI-driven AgriTech Research

YesThe evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations

Investigation of Microbiota in Health and Disease of Poultry

The microbiotas play vital roles in health and diseases of both humans and animals. 16S rRNA genes sequence analysis is one of the most popular and commonly used methods in the analysis of microbiotas associated with hosts. In this dissertation, the microbiotas of chickens (broilers, breeders, and layers) and turkeys were evaluated by 16S rRNA gene sequencing. Characterization of the culturable subpopulations of Lactobacillus in the chicken gut can serve as a valuable resource for probiotic development. In Chapter 2, Lactobacillus subpopulations recovered on MRS from chicken gut were defined comprehensively for the first time using 16S rRNA gene profiling, where they varied with different regions (cecum vs. ileum) and locations (lumen vs. mucosa) with in the same region. In Chapter 3, we investigated the effect of cell densities as determined by varying levels of sample dilution on the culture-enriched microbiota profiles using MRS agar medium as a model system. The dilution levels of original samples was found to alter the resulting culture-enriched microbiota profiles via unknown density-dependent mechanisms. In chapter 4, Bacillus isolates (B. subtilis and B. amyloliquefaciens) were used to evaluate their therapeutic and prophylactic effects against Salmonella Enteritidis, and found their potentialities to reduce S. Enteritidis colonization and improve the intestinal health in broiler chickens possibly through altering the composition and functions of gut microbiota. In chapter 5, we investigated the cecal microbiota and egg production in two strains of Hy-Line (Brown and W-36) housed in conventional cages (CC) and enriched colony cages (EC), and noticed differences in egg production and cecal microbiota between strains and housing types. In chapter 6, we performed a comprehensive survey of the litter microbiotas using booty swab samples in the 5 commercial turkey farms of the Northwest Arkansas. The litter microbiotas were found to differ between farms, and flocks which were further affected by the ages of turkeys. In Chapter 7, we developed and evaluated the nested TaqMan probe based qPCR assay for the quantitative detection of Clostridium septicum that targets the alpha toxin gene (csa)

Supraglacial systems biology of dynamic Arctic microbial ecosystems

Arctic glacier surfaces are a biologically active region of the cryosphere, supporting \ud several cosmopolitan microbial taxa. Bacterial communities across the different ice \ud surfaces are spatially variable and significantly influenced by biogeography and \ud biogeochemistry. In summer, the supraglacial surface reveals extensive cryoconite \ud hole coverage that is correlated to surface albedo, melt rate, mass balance and \ud biological activity. However, the relative importance of temporal changes on \ud bacterial community composition and activity in these supraglacial niches have yet \ud to be determined. To enhance this knowledge, community dynamics of bacteria in \ud cryoconite, snow and meltwater streams were investigated synthetically and on \ud Foxfonna ice cap, Foxfonna valley glacier and the Greenland Ice Sheet. By means of \ud microscopy, metabolomics and high throughput sequencing of 16S rRNA genes and \ud cDNA from 16S rRNA, the summer bacterial community was evaluated to determine \ud the relative importance of taxa on supraglacial surfaces. This aided in unravelling the \ud complex interactions that are prevalent in a simple microbial niche exposed to unique \ud environmental conditions, nutritional deficits and geological constraints. Overall, the \ud bacteria on Foxfonna and Greenland supraglacial surfaces display distinct seaso\ud naltransient behaviour. Taxa appear selective to their physical environment and \ud biogeochemical state in the cryosphere, characterized by integral associations with \ud the photoautotrophic Cyanobacteria, Phormidesmis priestleyi, that mediates formation of a robust microhabitat conglomerated with humics, extracellular polymeric substances and minerals that are essential to the diverse and productive cryoconite community. The rare biosphere provides a source for heterotrophi c bacterial recruitment in cryoconite, snow and stream habitats, the latter of which exhibit high abundances of proteobacterial subclasses only minimally dissimilar from cryoconite during the boreal summer. \ud Network analysis predicts that these taxa may be responsible for the observed seasonal shifts of activity in favourable conditions, while generating the essential nutrient reserves required during winter dormancy periods