2,700 research outputs found

    Evaluation of mixed microalgae species biorefinery of Desmodesmus sp. And Scenedesmus sp. For bioproducts synthesis

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    Microalgae is known to produce numerous bioactive compounds for instance proteins, fatty acid, polysaccharides, enzymes, sterols, and antioxidants. Due to their valuable biochemical composition, microalgae are regarded as a very intriguing source to produce novel food products and can be utilised to improve the nutritional content of traditional foods. Additionally, microalgae are used as animal feed and additives in the cosmetics, pharmaceutical as well as nutraceutical industries. As compared to other terrestrial plants and other microorganisms, microalgae possess few advantages: (1) rapid growth rate; (2) able to grow in non-arable land and harsh cultivation conditions; (3) low nutritional requirements; (4) high productivity; and (5) reduce emission of carbon dioxide. Despite the large number of microalgae species found in nature, only a few species are identified and commercialized such as Chlorella sp., Spirulina sp. Haematococcus pluvialis, Nannochloropsis sp. and Chlamydomonas reinhardtii, which is one of the major obstacles preventing the full utilisation of microalgae-based technology. This thesis provides information on the overall composition of mixed microalgae species, Desmodesmus sp. and Scenedesmus sp., for instance protein, carbohydrate, lipid, antioxidants, and pigment. This thesis firstly introduces the application of triphasic partitioning (TPP) in the extraction and partitioning of the biomolecules from the microalgae. The latest advancement of technology has evolved from a liquid biphasic flotation (LBF) to TPP. T-butanol and ammonium sulphate are used in TPP to precipitate desired biomolecules from the aqueous solutions with the formation of three layer. TPP is a simple, time- and cost- efficient, as well as scalable process that does not require toxic organic solvents. Lipase is abundantly produced by microbes, bacteria, fungi, yeast, mammals, and plants. Lipase is widely used in the oleochemical, detergent, dairy, leather, cosmetics, paper, cosmetics, and nutraceutical industries. Therefore, this thesis also discusses the possibility of identifying and extracting enzyme lipase from the microalgae using LBF. Several parameters (volume and concentration of solvents, weight of biomass, flotation kinetics and solvent types, etc.) have been investigated to optimize the lipase extraction from LBF. Chlorophyll is the main pigment present in the microalgae. Thus, this work proposes the digital imaging approach to determine the chlorophyll concentration in the microalgae rapidly because the chlorophyll content has a significant impact on microalgae physiological health status as well as identifies the chlorophyll concentration in the production of by-products. Lastly, microalgae oil can be used as the feedstock for biodiesel as well as nutraceutical, pharmaceutical, and health-care products. The challenge in the lipid extraction is the co-extraction of chlorophyll into the oil, which can have serious consequences for downstream processing. Therefore, the removal of the chlorophyll from the microalgae using activated clay or sodium chlorite in the pre-treatment procedure are examined. The research achievements in these works and future opportunities are highlighted in the last chapter of the thesis

    Image-based Decision Support Systems: Technical Concepts, Design Knowledge, and Applications for Sustainability

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    Unstructured data accounts for 80-90% of all data generated, with image data contributing its largest portion. In recent years, the field of computer vision, fueled by deep learning techniques, has made significant advances in exploiting this data to generate value. However, often computer vision models are not sufficient for value creation. In these cases, image-based decision support systems (IB-DSSs), i.e., decision support systems that rely on images and computer vision, can be used to create value by combining human and artificial intelligence. Despite its potential, there is only little work on IB-DSSs so far. In this thesis, we develop technical foundations and design knowledge for IBDSSs and demonstrate the possible positive effect of IB-DSSs on environmental sustainability. The theoretical contributions of this work are based on and evaluated in a series of artifacts in practical use cases: First, we use technical experiments to demonstrate the feasibility of innovative approaches to exploit images for IBDSSs. We show the feasibility of deep-learning-based computer vision and identify future research opportunities based on one of our practical use cases. Building on this, we develop and evaluate a novel approach for combining human and artificial intelligence for value creation from image data. Second, we develop design knowledge that can serve as a blueprint for future IB-DSSs. We perform two design science research studies to formulate generalizable principles for purposeful design — one for IB-DSSs and one for the subclass of image-mining-based decision support systems (IM-DSSs). While IB-DSSs can provide decision support based on single images, IM-DSSs are suitable when large amounts of image data are available and required for decision-making. Third, we demonstrate the viability of applying IBDSSs to enhance environmental sustainability by performing life cycle assessments for two practical use cases — one in which the IB-DSS enables a prolonged product lifetime and one in which the IB-DSS facilitates an improvement of manufacturing processes. We hope this thesis will contribute to expand the use and effectiveness of imagebased decision support systems in practice and will provide directions for future research

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiïŹed Proportional ConïŹ‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiïŹers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Spatial frequency domain imaging towards improved detection of gastrointestinal cancers

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    Early detection and treatment of gastrointestinal cancers has been shown to drastically improve patients survival rates. However, wide population based screening for gastrointestinal cancers is not feasible due to its high cost, risk of potential complications, and time consuming nature. This thesis forms the proposal for the development of a cost-effective, minimally invasive device to return quantitative tissue information for gastrointestinal cancer detection in-vivo using spatial frequency domain imaging (SFDI). SFDI is a non-invasive imaging technique which can return close to real time maps of absorption and reduced scattering coefficients by projecting a 2D sinusoidal pattern onto a sample of interest. First a low-cost, conventional bench top system was constructed to characterise tissue mimicking phantoms. Phantoms were fabricated with specific absorption and reduced scattering coefficients, mimicking the variation in optical properties typically seen in healthy, cancerous, and pre-cancerous oesophageal tissue. The system shows accurate retrieval of absorption and reduced scattering coefficients of 19% and 11% error respectively. However, this bench top system consists of a bulky projector and is therefore not feasible for in-vivo imaging. For SFDI systems to be feasible for in-vivo imaging, they are required to be miniaturised. Many conditions must be considered when doing this such as various illumination conditions, lighting conditions and system geometries. Therefore to aid in the miniaturisation of the bench top system, an SFDI system was simulated in the open-source ray tracing software Blender, where the capability to simulate these conditions is possible. A material of tunable absorption and scattering properties was characterised such that the specific absorption and reduced scattering coefficients of the material were known. The simulated system shows capability in detecting optical properties of typical gastrointestinal conditions in an up-close, planar geometry, as well in a non-planar geometry of a tube simulating a lumen. Optical property imaging in the non-planar, tubular geometry was done with the use of a novel illumination pattern, developed for this work. Finally, using the knowledge gained from the simulation model, the bench top system was miniaturised to a 3 mm diameter prototype. The novel use of a fiber array producing the necessary interfering fringe patterns replaced the bulky projector. The system showed capability to image phantoms simulating typical gastrointestinal conditions at two wavelengths (515 and 660 nm), measuring absorption and reduced scattering coefficients with 15% and 6% accuracy in comparison to the bench top system for the fabricated phantoms. It is proposed that this system may be used for cost-effective, minimally invasive, quantitative imaging of the gastrointestinal tract in-vivo, providing enhanced contrast for difficult to detect cancers

    GREEN INVESTMENT OPTIONS IN FERRY, RO-RO AND RO-PAX INDUSTRY - From theory to practice

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    An increased focus on sustainability issues and climate change from customers as well as the whole society, jointly with more restrictive regulatory requirements, are triggering shipping lines to progressively adopt green strategies, aiming at reducing emissions and pursuing higher (energy) efficiency standards (Pallis and Vaggelas, 2019). In the shipping sector, the Ferry segment expresses global fleet of about 15,400 ships with a total gross tonnage (GT) exceeding 31 million, and globally transports about 4.27 billion passengers per year and 373 million vehicles; furthermore, it has a contribution on world GDP of 60 billion dollars and allows the employment of more than 1 million people (Interferry, 2019). Because of the importance of the traffic volumes handled both in terms of passengers and cargoes, as well as due to the related impacts originating from business operations, the Ferry, Ro-Ro and Ro-Pax industry is challenged to implement green strategies (Chang and Danao, 2007). Corporate Social Responsibility (CSR) paradigms and related theoretical constructs are expected to reshape future goals, strategies and implantation trajectories of major Ferry, Ro-Ro and Ro-Pax companies and improve firm performance (Lun et al., 2015). This PhD thesis scrutinizes green strategies and related investment options which are expected to allow Ferry, Ro-Ro and Ro-Pax group owners, operating European-flagged vessels, to reach sustainability goals without negatively affecting their economic/financial performance. The document proposes a conceptual framework that addresses the most promising green investment options which Ferry, Ro-Ro and Ro-Pax companies can exploit for making the business more sustainable, reducing environmental impacts and improving energy and material efficiency during day-by-day operations. Using insights from a systematic literature review conducted in the first sections of this PhD thesis, the most relevant green solutions widely investigated at academic level are examined and discussed for the purpose of this PhD thesis. After an exhaustive description of the state of the art and the state of the play regarding green investment options in Ferry, Ro-Ro and Ro-Pax industry, this PhD thesis proposes an analysis of the most effective green strategies and practices concretely developed and planned in the industry. Empirical evidence shows that the most promising investment options relate to ship propulsion systems, such as the adoption of hydrogen and fuel cells (Trillos et al., 2021), alternative fuels (Very Low Sulphur Fuel Oil, Ultra Low Sulphur Fuel Oil and Liquefied Natural Gas), efficiency systems for auxiliary engines and ship design solutions. As confirmed by outcomes gathered in our final empirical dataset, i.e., the “green” sample, among the different solutions analysed, the most adopted green investment options by European companies operating in Ferry, Ro-Ro and Ro-Pax industry are: a) the refitting of bulbous bow, aimed at the implementation of the current design of the hull to increase the overall hydrodynamics of the ship; b) the installation of onboard batteries as a source of energy for ship propulsion; c) the use of Liquefied Natural Gas (LNG) as alternative fuel for the ship propulsion, that contributes to a 100% reduction in sulphur oxides emissions, 80-85% of nitrogen oxides emissions, 95% of particular matter emissions and 20-30% of carbon dioxide emissions compared to HFO/MDO (Burel et al., 2013). The empirical analysis above mentioned is performed on 1,680 Ferry, Ro-Ro and Ro-Pax ships with flag from European Countries, with data gathered through IHS Maritime & Trade that holds the largest maritime ships database in the world, further integrated with data and information collected from several technical sources (e.g., news press, sustainability report and press release of European companies). Variables investigates within IHS Maritime & Trade regard the type of main and auxiliary engines and fuel type, hull materials, bulbous bow and other technologies and equipment aimed at mitigating the pollution and reducing harmful emissions. The outcomes shed light on the viability and feasibility of some green investments options by shipping companies operating in Ferry, Ro-Ro and Ro-Pax industry at European level. To deal with this limited result, due to the lack of comprehensive dataset capable to scrutinize the diverse green solutions implemented on board European-flagged vessels, it was decided to develop an original and detailed dataset, capable to provide additional information concerning the approaches, decisions and strategies adopted by each group owners and related future trends about the adoption of green strategies in the sector. Through the examination of different sources (e.g., news press, specialised journals, communications by the companies on their corporate website, academic papers, etc.), information related to innovative technical solutions and cutting-edge technologies for mitigating the pollution and reducing harmful emissions have been extracted for each ship of the database, i.e., the “overall” or “extended” sample. In this way, it has been possible to provide an exhaustive overview of the heterogeneous green solutions, equipment and components installed on board of ships that are part of the “green” sample and the related source, to allow the evaluation and examination from the point of view of the institutionalism of the source. In this way it was possible to focus on top ten best performer group owners of the “green” sample, to analyse the managerial and governance behaviours and attitudes implemented by them and to deepen in brief case histories the main strategies adopted by them
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