Search results for: abstract extraction

Authors: I. Kade Wiratama

Abstract:

As a result of the significant growth of wind turbines in size, blade load control has become the main challenge for large wind turbines. Many advanced techniques have been investigated aiming at developing control devices to ease blade loading. Amongst them, trailing edge flaps have been proven as effective devices for load alleviation. The present study aims at investigating the potential benefits of flaps in enhancing the energy capture capabilities rather than blade load alleviation. A software tool is especially developed for the aerodynamic simulation of wind turbines utilising blades equipped with flaps. As part of the aerodynamic simulation of these wind turbines, the control system must be also simulated. The simulation of the control system is carried out via solving an optimisation problem which gives the best value for the controlling parameter at each wind turbine run condition. Developing a genetic algorithm optimisation tool which is especially designed for wind turbine blades and integrating it with the aerodynamic performance evaluator, a design optimisation tool for blades equipped with flaps is constructed. The design optimisation tool is employed to carry out design case studies. The results of design case studies on wind turbine AWT 27 reveal that, as expected, the location of flap is a key parameter influencing the amount of improvement in the power extraction. The best location for placing a flap is at about 70% of the blade span from the root of the blade. The size of the flap has also significant effect on the amount of enhancement in the average power. This effect, however, reduces dramatically as the size increases. For constant speed rotors, adding flaps without re-designing the topology of the blade can improve the power extraction capability as high as of about 5%. However, with re-designing the blade pretwist the overall improvement can be reached as high as 12%.

Keywords: flaps, design blade, optimisation, simulation, genetic algorithm, WTAero

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Authors: Omuvwie R. Ewien

Abstract:

Palm oil production in Delta State, Nigeria, is a significant economic activity. However, ensuring its sustainability is crucial to mitigate environmental impacts, promote social equity, and maintain long-term economic viability. This abstract provides an overview of palm oil production sustainability in Delta State, focusing on environmental, social, and economic aspects. In terms of environmental sustainability, the impact of palm oil plantations on deforestation and biodiversity loss is explored. The adoption of sustainable land use practices and measures to reduce greenhouse gas emissions, such as conserving high conservation value areas and utilizing methane capture systems, are highlighted. Social sustainability considerations encompass the inclusion and empowerment of smallholders, ensuring fair labor practices and community engagement. Supporting small-scale farmers, promoting fair working conditions, and investing in local infrastructure and services are identified as key strategies. Economic sustainability is emphasized through yield improvement, efficiency, and value addition. Enhancing productivity and profitability for farmers, promoting downstream processing and market diversification, and building economic resilience is crucial for long-term sustainability. Government policies, including regulatory frameworks and public-private collaborations, play a pivotal role in promoting sustainable palm oil production. Enabling policies and partnerships with industry stakeholders and NGOs facilitates the adoption of sustainable practices. Challenges such as illegal activities, the need to balance economic development with environmental conservation, and leveraging technology for sustainability are discussed. The abstract concludes by emphasizing the importance of stakeholders' commitment to prioritize sustainable palm oil production in Delta State, Nigeria, for a sustainable future.

Keywords: palm oil production, environmental sustainability, community development, yield improvement, future outlook

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Authors: Misganaw Ayele Gelaw

Abstract:

This abstract presents an overview of agricultural investment in Ethiopia, with a focus on the Oromia Region. Ethiopia is a developing country that heavily relies on agriculture as a major contributor to its economic growth and employment. The Oromia Region, located in the central part of the country, is the largest region in Ethiopia and plays a significant role in the agricultural sector. The study aims to explore the current state of agricultural investment in the Oromia Region, focusing on the opportunities, challenges, and potential benefits that arise from such investments. It also highlights the key agricultural investment strategies and policies implemented by the Ethiopian government to attract domestic and foreign investors. To achieve these objectives, a comprehensive literature review and analysis of relevant reports, publications, and government policies will be conducted. The study will also incorporate qualitative and quantitative data collection methods, such as interviews, surveys, and statistical analysis, to provide a well-rounded understanding of agricultural investment dynamics in the Oromia Region. The findings of this study are expected to shed light on the impact of agricultural investments on local farmers, rural development, food security, income generation, and overall economic growth in the Oromia Region. It will also identify the key risk factors and potential mitigations associated with agricultural investment, offering recommendations to policymakers, investors, and stakeholders to improve the effectiveness and sustainability of investment efforts in the region. This abstract highlights the importance of agricultural investment in the Oromia Region and Ethiopia as a whole, as it strives to enhance productivity, increase farmers' income, and contribute to the country's long-term development goals. By understanding the challenges and opportunities associated with agricultural investment, policymakers and investors can develop targeted strategies to ensure inclusive and sustainable growth in the agricultural sector, leading to improved livelihoods and economic prosperity in the Oromia Region.

Keywords: agriculture, investment, agriculture policy, economy

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Authors: Tatjana Jurić, Bojana Blagojević, Denis Uka, Ružica Ždero Pavlović, Boris M. Popović

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Natural deep eutectic solvents (NADES) represent a class of modern systems that have been developed as a green alternative to toxic organic solvents, which are commonly used as extraction media. It has been considered that hydrogen bonding is the main interaction leading to the formation of NADES. The aim of this study was phytochemical characterization and determination of the antioxidant and antibacterial activity of Mentha piperita leaf extracts obtained by six choline chloride-based NADES. NADES were prepared by mixing choline chloride with different hydrogen bond donors in 1:1 molar ratio following the addition of 30% (w/w) water. The mixtures were then heated (60 °C) and stirred (650 rpm) until the clear homogenous liquids were obtained. The Mentha piperita extracts were prepared by mixing 75 mg of peppermint leaves with 1 mL of NADES following by the heating and stirring (60 °C, 650 rpm) within 30 min. The content of six phenolics in extracts was determined using HPLC-PDA. The dominant compounds presented in peppermint leaves - rosmarinic acid and luteolin 7-O-glucoside, were extracted by NADES at a similar level as 70% ethanol. The microdilution method was applied to test the antibacterial activity of extracts. Compared with 70% ethanol, all NADES systems showed higher antibacterial activity towards Pseudomonas aeruginosa (Gram -), Staphylococcus aureus (Gram +), Escherichia coli (Gram -), and Salmonella enterica (Gram -), especially NADES containing organic acids. The majority of NADES extracts showed a better ability to neutralize DPPH radical than conventional solvent and similar ability to reduce Fe3+ to Fe2+ ions in FRAP assay. The obtained results introduce NADES systems as the novel, sustainable, and low-cost solvents with a variety of applications.

Keywords: antibacterial activity, antioxidant activity, green extraction, natural deep eutectic solvents, polyphenols

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Authors: Kebron Asnake

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Water security and transboundary issues are critical concerns for countries, particularly in regions where shared water resources are significant. This Research focuses on exploring the challenges and opportunities related to water security and transboundary issues in Ethiopia, using the case of the Nile River. Ethiopia, as a riparian country of the Nile River, faces complex water security issues due to its dependence on this transboundary water resource. This abstract aims to analyze the various factors that affect water security in Ethiopia, including population growth, climate change, and competing water demands. The Study examines the challenges linked to transboundary water management of the Nile River. It delves into the complexities of negotiating water allocations and addressing potential conflicts among the downstream riparian countries. The paper also discusses the role of international agreements and cooperation in promoting sustainable water resource management. Additionally, the paper highlights the opportunities for collaboration and sustainable development that arise from transboundary water management. It explores the potential for joint investments in water infrastructure, hydropower generation, and irrigation systems that can contribute to regional economic growth and water security. Furthermore, the study emphasizes the need for integrated water management approaches in Ethiopia to ensure the equitable and sustainable use of the Nile River's waters. It highlights the importance of involving stakeholders from diverse sectors, including agriculture, energy, and environmental conservation, in decision-making processes. By presenting the case of the Nile River in Ethiopia, this Abstract contributes to the understanding of water security and transboundary issues. It underscores the significance of regional cooperation and informed policy-making to address the challenges and opportunities presented by transboundary water resources. The paper serves as a foundation for further research and policy in water management in Ethiopia and other regions facing similar challenges.

Keywords: water, health, agriculture, medicine

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Olga Shuvaeva, Tamara Romanova, Sergey Volynkin, Valentina Podolinnaya

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Green chemistry concept is focused on the prevention of environmental pollution caused by human activity. However, there are a lot of contaminated areas in the world which pose a serious threat to ecosystems in terms of their conservation. Therefore in accordance with the principles of green chemistry, it should not be forgotten about the need to clean these areas. Furthermore, the waste material often contains the valuable components, the extraction of which by traditional wet chemical technologies is inefficient both from the economic and environmental protection standpoint. Wherein, the plants may be successfully used to ‘scavenge’ a range of metals from polluted land sites in an approach allowing to carry out both of these processes – phytoremediation and phytomining in conjunction. The goal of the present work was to study bioaccumulation ability of floating macrophytes such as water hyacinth and pondweed toward Hg, Ba, Cd, Mo and Pb as pollutants in aquatic medium and terrestrial plants (birch, reed, and cane) towards gold and silver as valuable components. The peculiarity of ongoing research was that the plants grew under extreme conditions (pH of drainage and pore waters was about 2.5). The study was conducted at the territory of Ursk tailings (Southwestern Siberia, Russia) formed as a result of primary polymetallic ores cyanidation. The waste material is mainly presented (~80%) by pyrite (FeS₂) and barite (BaSO₄), the raw minerals included FeAsS, HgS, PbS, Ag₂S as minor ones. It has been shown that water hyacinth demonstrates high ability to accumulate different metals, and what is especially important – to remove mercury from polluted waters with BCF value more than 1000. As for the gold, its concentrations in reed and cane growing near the waste material were estimated as 500 and 900 μg∙kg⁻¹ respectively. It was also found that the plants can survive under extreme conditions of acidic environment and hence we can assume that there is a principal opportunity to use them for the valuable substances extraction from an area of the mining waste dumps burial.

Keywords: bioaccumulation, gold, heavy metals, mine tailing

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Authors: Claudia Schier, Arvid Biallas, Bernd Friedrich

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The demand for Li-ion-batteries owing to their benefits, such as; fast charging time, high energy density, low weight, large temperature range, and a long service life performance is increasing compared to other battery systems. These characteristics are substantial not only for battery-operated portable devices but also in the growing field of electromobility where high-performance energy storage systems in the form of batteries are highly requested. Due to the sharp rising production, there is a tremendous interest to recycle spent Li-Ion batteries in a closed-loop manner owed to the high content of valuable metals such as cobalt, manganese, and lithium as well as regarding the increasing demand for those scarce applied metals. Currently, there are just a few industrial processes using hydrometallurgical methods to recover valuable metals from Li-ion-battery waste. In this study, the extraction of valuable metals from spent Li-ion-batteries is investigated by pretreated and subsequently leached battery wastes using different precipitation methods in a comparative manner. For the extraction of lithium, cobalt, and other valuable metals, pelletized battery wastes with an initial Li content of 2.24 wt. % and cobalt of 22 wt. % is used. Hydrochloric acid with 4 mol/L is applied with 1:50 solid to liquid (s/l) ratio to generate pregnant leach solution for subsequent precipitation steps. In order to obtain pure precipitates, two different pathways (pathway 1 and pathway 2) are investigated, which differ from each other with regard to the precipitation steps carried out. While lithium carbonate recovery is the final process step in pathway 1, pathway 2 requires a preliminary removal of lithium from the process. The aim is to evaluate both processes in terms of purity and yield of the products obtained. ICP-OES is used to determine the chemical content of leach liquor as well as of the solid residue.

Keywords: hydrochloric acid, hydrometallurgy, Li-ion-batteries, metal recovery

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Authors: Dharell B. Siano, Wendy C. Mateo, Victorino T. Taylan, Francisco D. Cuaresma

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Biofuel is one of the renewable energy sources adapted by the Philippine government in order to lessen the dependency on foreign fuel and to reduce carbon dioxide emissions. Rain tree pods were seen to be a promising source of bioethanol since it contains significant amount of fermentable sugars. The study was conducted to establish the complete procedure in processing rain tree pods for village level hydrous bioethanol production. Production processes were done for village level hydrous bioethanol production from collection, drying, storage, shredding, dilution, extraction, fermentation, and distillation. The feedstock was sundried, and moisture content was determined at a range of 20% to 26% prior to storage. Dilution ratio was 1:1.25 (1 kg of pods = 1.25 L of water) and after extraction process yielded a sugar concentration of 22 ⁰Bx to 24 ⁰Bx. The dilution period was three hours. After three hours of diluting the samples, the juice was extracted using extractor with a capacity of 64.10 L/hour. 150 L of rain tree pods juice was extracted and subjected to fermentation process using a village level anaerobic bioreactor. Fermentation with yeast (Saccharomyces cerevisiae) can fasten up the process, thus producing more ethanol at a shorter period of time; however, without yeast fermentation, it also produces ethanol at lower volume with slower fermentation process. Distillation of 150 L of fermented broth was done for six hours at 85 °C to 95 °C temperature (feedstock) and 74 °C to 95 °C temperature of the column head (vapor state of ethanol). The highest volume of ethanol recovered was established at with yeast fermentation at five-day duration with a value of 14.89 L and lowest actual ethanol content was found at without yeast fermentation at three-day duration having a value of 11.63 L. In general, the results suggested that rain tree pods had a very good potential as feedstock for bioethanol production. Fermentation of rain tree pods juice can be done with yeast and without yeast.

Keywords: fermentation, hydrous bioethanol, fermentation, rain tree pods, village level

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Rajesh S. Mony, Vaidyaratnam Oushadhasala

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An attempt is made in this study to evolve a drug development modality based on classical Ayurvedic knowledge base as well as on modern scientific methodology. The present study involves (a) identification of a specific ailment condition, (b) the selection of a polyherbal formulation, (c) deciding suitable extraction procedure, (d) confirming the efficacy of the combination by in-vitro trials and (e) fixing up the recommended dose. The ailment segment selected is arthritic condition. The selected herbal combination is Kunturushka, Vibhitaki, Guggulu, Haridra, Maricha and Nirgundi. They were selected as per Classical Ayurvedic references, Authentified as per API (Ayurvedic Pharmacopeia of India), Extraction of each drug was done by different ratios of Hydroalcoholic menstrums, Invitro assessment of each extract after removing residual solvent for anti-Inflammatory, anti-arthritic activities (by UV-Vis. Spectrophotometer with positive control), Invitro assessment of each extract for COX enzyme inhibition (by UV-Vis. Spectrophotometer with positive control), Selection of the extracts was made having good in-vitro activity, Performed the QC testing of each selected extract including HPTLC, that is the in process QC specifications, h. Decision of the single dose with mixtures of selected extracts was made as per the level of in-vitro activity and available toxicology data, Quantification of major groups like Phenolics, Flavonoids, Alkaloids and Bitters was done with both standard Spectrophotometric and Gravimetric methods, Method for Marker assay was developed and validated by HPTLC and a good resolved HPTLC finger print was developed for the single dosage API (Active Pharmaceutical Ingredient mixture of extracts), Three batches was prepared to fix the in process and API (Active Pharmaceutical Ingredient) QC specifications.

Keywords: drug development, antiinflammatory, quality stardardisation, planar chromatography

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Authors: Mindaugas Liaudanskas, Darius Kviklys, Kristina Zymonė, Raimondas Raudonis, Jonas Viškelis, Norbertas Uselis, Pranas Viškelis, Valdimaras Janulis

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Apples are among the most commonly consumed fruits in the world. Based on data from the year 2014, approximately 84.63 million tons of apples are grown per annum. Apples are widely used in food industry to produce various products and drinks (juice, wine, and cider); they are also used unprocessed. Apples in human diet are an important source of different groups of biological active compounds that can positively contribute to the prevention of various diseases. They are a source of various biologically active substances – especially vitamins, organic acids, micro- and macro-elements, pectins, and phenolic, triterpenic, and other compounds. Triterpenic compounds, which are characterized by versatile biological activity, are the biologically active compounds found in apples that are among the most promising and most significant for human health. A specific analytical procedure including sample preparation and High Performance Liquid Chromatography (HPLC) analysis was developed, optimized, and validated for the detection of triterpenic compounds in the samples of different apples, their peels, and flesh from widespread apple cultivars 'Aldas', 'Auksis', 'Connel Red', 'Ligol', 'Lodel', and 'Rajka' grown in Lithuanian climatic conditions. The conditions for triterpenic compound extraction were optimized: the solvent of the extraction was 100% (v/v) acetone, and the extraction was performed in an ultrasound bath for 10 min. Isocratic elution (the eluents ratio being 88% (solvent A) and 12% (solvent B)) for a rapid separation of triterpenic compounds was performed. The validation of the methodology was performed on the basis of the ICH recommendations. The following characteristics of validation were evaluated: the selectivity of the method (specificity), precision, the detection and quantitation limits of the analytes, and linearity. The obtained parameters values confirm suitability of methodology to perform analysis of triterpenic compounds. Using the optimised and validated HPLC technique, four triterpenic compounds were separated and identified, and their specificity was confirmed. These compounds were corosolic acid, betulinic acid, oleanolic acid, and ursolic acid. Ursolic acid was the dominant compound in all the tested apple samples. The detected amount of betulinic acid was the lowest of all the identified triterpenic compounds. The greatest amounts of triterpenic compounds were detected in whole apple and apple peel samples of the 'Lodel' cultivar, and thus apples and apple extracts of this cultivar are potentially valuable for use in medical practice, for the prevention of various diseases, for adjunct therapy, for the isolation of individual compounds with a specific biological effect, and for the development and production of dietary supplements and functional food enriched in biologically active compounds. Acknowledgements. This work was supported by a grant from the Research Council of Lithuania, project No. MIP-17-8.

Keywords: apples, HPLC, triterpenic compounds, validation

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Authors: M. Gomaa Abdoelatef, Robert M. Field, Lee, Yong-Kwan

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Growing human populations have placed increased demands on water supplies and a heightened interest in desalination infrastructure. Key elements of the economics of desalination projects are thermal and electrical inputs. With growing concerns over the use of fossil fuels to (indirectly) supply these inputs, coupling of desalination with nuclear power production represents a significant opportunity. Individually, nuclear and desalination technologies have a long history and are relatively mature. For desalination, Reverse Osmosis (RO) has the lowest energy inputs. However, the economically driven output quality of the water produced using RO, which uses only electrical inputs, is lower than the output water quality from thermal desalination plants. Therefore, modern desalination projects consider that RO should be coupled with thermal desalination technologies (MSF, MED, or MED-TVC) with attendant steam inputs to permit blending to produce various qualities of water. A large nuclear facility is well positioned to dispatch large quantities of both electrical and thermal power. This paper considers the supply of thermal energy to a large desalination facility to examine heat balance impact on the nuclear steam cycle. The APR1400 nuclear plant is selected as prototypical from both a capacity and turbine cycle heat balance perspective to examine steam supply and the impact on electrical output. Extraction points and quantities of steam are considered parametrically along with various types of thermal desalination technologies to form the basis for further evaluations of economically optimal approaches to the interface of nuclear power production with desalination projects. In our study, the thermodynamic evaluation will be executed by DE-TOP which is the IAEA desalination program, it is approved to be capable of analyzing power generation systems coupled to desalination systems through various steam extraction positions, taking into consideration the isolation loop between the APR-1400 and the thermal desalination plant for safety concern.

Keywords: APR-1400, desalination, DE-TOP, IAEA, MSF, MED, MED-TVC, RO

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Authors: Getaneh Berie Tarekegn

Abstract:

A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 39 cm, and more than 90% of the errors are less than 82 cm. That is, numerical results proved that, in comparison to traditional methods, the proposed SRCLoc method can significantly improve positioning performance and reduce radio map construction costs.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Afshin Shahbazi, Hadi Shadi Naghadeh, Ahmad Khodadadi Darban

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In the Magnetically Assisted Chemical Separation (MACS) process, tiny ferromagnetic particles coated with solvent extractant are used to selectively separate radionuclides and hazardous metals from aqueous waste streams. The contaminant-loaded particles are then recovered from the waste solutions using a magnetic field. In the present study, Cyanex272 or C272 (bis (2,4,4-trimethylpentyl) phosphinic acid) coated magnetic particles are being evaluated for the possible application in the extraction of Thorium (IV) from nuclear waste streams. The uptake behaviour of Th(IV) from nitric acid solutions was investigated by batch studies. Adsorption of Thorium (IV) from aqueous solution onto adsorbent was investigated in a batch system. Adsorption isotherm and adsorption kinetic studies of Thorium (IV) onto nanoparticles coated Cyanex272 were carried out in a batch system. The factors influencing Thorium (IV) adsorption were investigated and described in detail, as a function of the parameters such as initial pH value, contact time, adsorbent mass, and initial Thorium (IV) concentration. Magnetically Assisted Chemical Separation (MACS) process adsorbent showed best results for the fast adsorption of Th (IV) from aqueous solution at aqueous phase acidity value of 0.5 molar. In addition, more than 80% of Th (IV) was removed within the first 2 hours, and the time required to achieve the adsorption equilibrium was only 140 minutes. Langmuir and Frendlich adsorption models were used for the mathematical description of the adsorption equilibrium. Equilibrium data agreed very well with the Langmuir model, with a maximum adsorption capacity of 48 mg.g-1. Adsorption kinetics data were tested using pseudo-first-order, pseudo-second-order and intra-particle diffusion models. Kinetic studies showed that the adsorption followed a pseudo-second-order kinetic model, indicating that the chemical adsorption was the rate-limiting step.

Keywords: Thorium (IV) adsorption, MACS process, magnetic nanoparticles, Cyanex272

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Authors: P. Raajeswari, S. M. Devatha, R. Pragatheeswari

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The edible packaging is a new trendsetter in the era of modern packaging. The researchers and food scientist recognise edible packaging as a useful alternative or addition to conventional packaging to reduce waste and to create novel applications for improving product stability. Starch was extracted from different sources that contains abundantly like potato, tapioca, rice, wheat, and corn. The starch based edible films and cutleries are developed as an alternative for conventional packages providing the nutritional benefit when consumed along with the food. The development of starch based edible films by the extraction of starch from various raw ingredients at lab scale level. The films are developed by the employment of plasticiser at different concentrations of 1.5ml and 2ml. The films developed using glycerol as a plasticiser in filmogenic solution to increase the flexibility and plasticity of film. It reduces intra and intermolecular forces in starch, and it increases the mobility of starch based edible films. The films developed are tested for its functional properties such as thickness, tensile strength, elongation at break, moisture permeability, moisture content, and puncture strength. The cutleries like spoons and cups are prepared by making dough and rolling the starch along with water. The overall results showed that starch based edible films absorbed less moisture, and they also contributed to the low moisture permeability with high tensile strength. Food colorants extracted from red onion peel, pumpkin, and red amaranth adds on the nutritive value, colour, and attraction when incorporated in edible cutleries, and it doesn’t influence the functional properties. Addition of a low quantity of glycerol in edible films and colour extraction from onion peel, pumpkin, and red amaranth enhances biodegradability and provides a good quantity of nutrients when consumed. Therefore, due to its multiple advantages, food starch can serve as the best response for eco-friendly industrial products aimed to replace single use plastics at low cost.

Keywords: edible films, edible cutleries, plasticizer, glycerol, starch, functional property

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Authors: Sahil Kumar, Rajaram Ghadge, Ramesh Bhujade

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Historically, lipid extraction from dried algal biomass remained a focus area of the algal research. It has been realized over the past few years that the lipid-centric approach and conversion technologies that require dry algal biomass have several challenges. Algal culture in cultivation systems contains more than 99% water, with algal concentrations of just a few hundred milligrams per liter ( < 0.05 wt%), which makes harvesting and drying energy intensive. Drying the algal biomass followed by extraction also entails the loss of water and nutrients. In view of these challenges, focus has shifted toward developing processes that will enable oil production from wet algal biomass without drying. Hydrothermal liquefaction (HTL), an emerging technology, is a thermo-chemical conversion process that converts wet biomass to oil and gas using water as a solvent at high temperature and high pressure. HTL processes wet algal slurry containing more than 80% water and significantly reduces the adverse cost impact owing to drying the algal biomass. HTL, being inherently feedstock agnostic, i.e., can convert carbohydrates and proteins also to fuels and recovers water and nutrients. It is most effective with low-lipid (10--30%) algal biomass, and bio-crude yield is two to four times higher than the lipid content in the feedstock. In the early 2010s, research remained focused on increasing the oil yield by optimizing the process conditions of HTL. However, various techno-economic studies showed that simply converting algal biomass to only oil does not make economic sense, particularly in view of low crude oil prices. Making the best use of every component of algae is a key for economic viability of algal to oil process. On investigation of HTL reactions at the molecular level, it has been observed that sequential HTL has the potential to recover value-added products along with biocrude and improve the overall economics of the process. This potential of sequential HTL makes it a most promising technology for converting wet waste to wealth. In this presentation, we will share our experience on the techno-economic and engineering aspects of sequential HTL for conversion of algal biomass to algal bio-oil and co-products.

Keywords: algae, biomass, lipid, protein

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Authors: Anna Rinaldi, Pierfrancesco Dellino

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COVID-19 vaccine hesitancy can be observed at different rates in different countries. In June 2021, 1,068 people were surveyed in France and Italy to inquire about individual potential acceptance, focusing on time preferences in a risk-return framework: having the vaccination today, in a month, and in 3 months; perceived risks of vaccination and COVID-19; and expected benefit of the vaccine. A randomized controlled trial was conducted to understand how everyday stimuli like fact-based news about vaccines impact an audience's acceptance of vaccination. The main experiment involved two groups of participants and two different articles about vaccine-related thrombosis taken from two Italian newspapers. One article used a more abstract description and language, and the other used a more anecdotal description and concrete language; each group read only one of these articles. Two other groups were assigned categorization tasks; one was asked to complete a concrete categorization task, and the other an abstract categorization task. Individual preferences for vaccination were found to be variable and unstable over time, and individual choices of accepting, refusing, or delaying could be affected by the way news is written. In order to understand these dynamic preferences, the present work proposes a new model based on seven categories of human behaviors that were validated by a neural network. A treatment effect was observed: participants who read the articles shifted to vaccine hesitancy categories more than participants assigned to other treatments and control. Furthermore, there was a significant gender effect, showing that the type of language leading to a lower hesitancy rate for men is correlated with a higher hesitancy rate for women and vice versa. This outcome should be taken into consideration for an appropriate gender-based communication campaign aimed at achieving herd immunity. The trial was registered at ClinicalTrials.gov NCT05582564 (17/10/2022).

Keywords: vaccine hesitancy, risk elicitation, neural network, covid19

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Authors: Bhalke D. G., Bormane D. S., Kharate G. K.

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This paper presents classification of musical instrument using machine learning techniques. The classification has been carried out using temporal, spectral, cepstral and wavelet features. Detail feature analysis is carried out using separate and combined features. Further, instrument model has been developed using K-Nearest Neighbor and Support Vector Machine (SVM). Benchmarked McGill university database has been used to test the performance of the system. Experimental result shows that SVM performs better as compared to KNN classifier.

Keywords: feature extraction, SVM, KNN, musical instruments

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Authors: Jone Ibarruri, Mikel Manso, Marta Cebrián

Abstract:

A high amount of food is lost or discarded in the World every year. In addition, in the last decades, an increasing demand of new alternative and sustainable sources of proteins and other valuable compounds is being observed in the food and feeding sectors and, therefore, the use of food by-products as nutrients for these purposes sounds very interesting from the environmental and economical point of view. However, the direct use of discarded fruit and vegetables that present, in general, a low protein content is not interesting as feeding ingredient except if they are used as a source of fiber for ruminants. Especially in the case of aquaculture, several alternatives to the use of fish meal and other vegetable protein sources have been extensively explored due to the scarcity of fish stocks and the unsustainability of fishing for these purposes. Fish mortality is also of great concern in this sector as this problem highly reduces their economic feasibility. So, the development of new functional and natural ingredients that could reduce the need for vaccination is also of great interest. In this work, several fermentation tests were developed at lab scale using a selected mixture of fruit and vegetable discards from a wholesale market located in the Basque Country to increase their protein content and also to produce some bioactive extracts that could be used as additives in aquaculture. Fruit and vegetable mixtures (60/40 ww) were centrifugated for humidity reduction and crushed to 2-5 mm particle size. Samples were inoculated with a selected Rhizopus oryzae strain and fermented for 7 days in controlled conditions (humidity between 65 and 75% and 28ºC) in Petri plates (120 mm) by triplicate. Obtained results indicated that the final fermented product presented a twofold protein content (from 13 to 28% d.w). Fermented product was further processed to determine their possible functionality as a feed additive. Extraction tests were carried out to obtain an ethanolic extract (60:40 ethanol: water, v.v) and remaining biomass that also could present applications in food or feed sectors. The extract presented a polyphenol content of about 27 mg GAE/gr d.w with antioxidant activity of 8.4 mg TEAC/g d.w. Remining biomass is mainly composed of fiber (51%), protein (24%) and fat (10%). Extracts also presented antibacterial activity according to the results obtained in Agar Diffusion and to the Minimum Inhibitory Concentration (MIC) tests determined against several food and fish pathogen strains. In vitro, digestibility was also assessed to obtain preliminary information about the expected effect of extraction procedure on fermented product digestibility. First results indicated that remaining biomass after extraction doesn´t seem to improve digestibility in comparison to the initial fermented product. These preliminary results show that fermented fruit and vegetables can be a useful source of functional ingredients for aquaculture applications and a substitute of other protein sources in the feeding sector. Further validation will be also carried out through “in vivo” tests with trout and bass.

Keywords: fungal solid state fermentation, protein increase, functional extracts, feed ingredients

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Authors: Fazl Ullah, Rahmat Ullah

Abstract:

This paper shows a comprehensive learning focused on the optimization of gas production in shale gas reservoirs through hydraulic fracturing. Shale gas has emerged as an important unconventional vigor resource, necessitating innovative techniques to enhance its extraction. The key objective of this study is to examine the influence of fracture parameters on reservoir productivity and formulate strategies for production optimization. A sophisticated model integrating gas flow dynamics and real stress considerations is developed for hydraulic fracturing in multi-stage shale gas reservoirs. This model encompasses distinct zones: a single-porosity medium region, a dual-porosity average region, and a hydraulic fracture region. The apparent permeability of the matrix and fracture system is modeled using principles like effective stress mechanics, porous elastic medium theory, fractal dimension evolution, and fluid transport apparatuses. The developed model is then validated using field data from the Barnett and Marcellus formations, enhancing its reliability and accuracy. By solving the partial differential equation by means of COMSOL software, the research yields valuable insights into optimal fracture parameters. The findings reveal the influence of fracture length, diversion capacity, and width on gas production. For reservoirs with higher permeability, extending hydraulic fracture lengths proves beneficial, while complex fracture geometries offer potential for low-permeability reservoirs. Overall, this study contributes to a deeper understanding of hydraulic cracking dynamics in shale gas reservoirs and provides essential guidance for optimizing gas production. The research findings are instrumental for energy industry professionals, researchers, and policymakers alike, shaping the future of sustainable energy extraction from unconventional resources.

Keywords: fluid-solid coupling, apparent permeability, shale gas reservoir, fracture property, numerical simulation

Procedia PDF Downloads 49

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

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Authors: Oyenike A. Adeyemo, Elizabeth Osibote, Adeyemi Adedugba, Olatunde A. Bhadmus, Adeoluwa A. Adeoshun, Mariam O. Allison

Abstract:

Lemongrass leaves are widely used for tea and the treatment of malaria. The present study used Soxhlet extraction with aqueous ethanol (v/v). Fresh and dried leaves of selected ten lemongrasses (Cymbopogon citratus) accessions from different geographical regions in Nigeria were examined for total phenolic contents, and antioxidant activities. Aqueous methanol extraction was carried out and further partitioned into hexane, ethyl acetate, and butanol to obtain fractions according to their polarities. Fourier Transform Infrared Spectroscopy (FTIR) was carried out to identify the functional groups that may be present. Among the ten accessions, the leaf extracts at five different concentrations exhibited increasing antioxidant activities using DPPH (2,2-diphenyl- 1- picrylhydrazyl) radical scavenging test, stronger activities for dried leaves (71.15 ± 0.14 - 89.79 ± 0.16µg/ml) than fresh leaves (71.65 ± 0.45 -81.94 ± 0.84 µg/ml) at 100 µg/ml of sample extract. The total phenolic contents of dried leaf extracts revealed higher amounts in all lines ranging from 19.57±0.57 to 43.17±0.67mg gallic acid equivalent /100 g DW when compared with fresh leaf extracts, where the values ranged from 9.68 ± 2.20 to 28.5 ± 3.90 mg gallic acid equivalent /100 g fresh weight except for two lines which showed greater total phenolic contents than in the dried leaves. High total phenolic content may help contribute to the overall high antioxidant activity of the plant. FTIR identified the presence of major active functional groups including alcohol, ester, amide, alkanes, alkenes, carboxylic acid, ketones, and aldehyde in four partitioning solvents (n-hexane, ethyl acetate, butanol, and methanol) leaf extracts of lemongrass samples.Lemongrass leaves are widely used for tea and the treatment of malaria. The present study used Soxhlet extraction with aqueous ethanol (v/v). Fresh and dried leaves of selected ten lemongrasses (Cymbopogon citratus) accessions from different geographical regions in Nigeria were examined for total phenolic contents, and antioxidant activities. Aqueous methanol extraction was carried out and further partitioned into hexane, ethyl acetate, and butanol to obtain fractions according to their polarities. Fourier Transform Infrared Spectroscopy (FTIR) was carried out to identify the functional groups that may be present. Among the ten accessions, the leaf extracts at five different concentrations exhibited increasing antioxidant activities using DPPH (2,2-diphenyl- 1- picrylhydrazyl) radical scavenging test, stronger activities for dried leaves (71.15 ± 0.14 - 89.79 ± 0.16µg/ml) than fresh leaves (71.65 ± 0.45 -81.94 ± 0.84 µg/ml) at 100 µg/ml of sample extract. The total phenolic contents of dried leaf extracts revealed higher amounts in all lines ranging from 19.57±0.57 to 43.17±0.67mg gallic acid equivalent /100 g DW when compared with fresh leaf extracts, where the values ranged from 9.68 ± 2.20 to 28.5 ± 3.90 mg gallic acid equivalent /100 g fresh weight except for two lines which showed greater total phenolic contents than in the dried leaves. High total phenolic content may help contribute to the overall high antioxidant activity of the plant. FTIR identified the presence of major active functional groups including alcohol, ester, amide, alkanes, alkenes, carboxylic acid, ketones, and aldehyde in four partitioning solvents (n-hexane, ethyl acetate, butanol, and methanol) leaf extracts of lemongrass samples.

Keywords: antioxidant acivity, phenolic content, natural product, FTIR

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Authors: Leigh G. Hayden, Susan E. Shepley, Cristina Passarelli, William Tingo

Abstract:

Introduction: Sensory interventions are popular therapeutic and recreational approaches for people living with all stages of dementia. However, it is unknown which sensory interventions are used to achieve which outcomes across all subtypes of dementia. Methods: To address this gap, we conducted a scoping review of sensory interventions for people living with dementia. We conducted a search of the literature for any article published in English from 1 January 1990 to 1 June 2019, on any sensory or multisensory intervention targeted to people living with any kind of dementia, which reported on patient health outcomes. We did not include complex interventions where only a small aspect was related to sensory stimulation. We searched the databases Medline, CINHAL, and Psych Articles using our institutional discovery layer. We conducted all screening in duplicate to reduce Type 1 and Type 2 errors. The data from all included papers were extracted by one team member, and audited by another, to ensure consistency of extraction and completeness of data. Results: Our initial search captured 7654 articles, and the removal of duplicates (n=5329), those that didn’t pass title and abstract screening (n=1840) and those that didn’t pass full-text screening (n=281) resulted in 174 articles included. The countries with the highest publication in this area were the United States (n=59), the United Kingdom (n=26) and Australia (n=15). The most common type of interventions were music therapy (n=36), multisensory rooms (n=27) and multisensory therapies (n=25). Seven articles were published in the 1990’s, 55 in the 2000’s, and the remainder since 2010 (n=112). Discussion: Multisensory rooms have been present in the literature since the early 1990’s. However, more recently, nature/garden therapy, art therapy, and light therapy have emerged since 2008 in the literature, an indication of the increasingly diverse scholarship in the area. The least popular type of intervention is a traditional food intervention. Taste as a sensory intervention is generally avoided for safety reasons, however it shows potential for increasing quality of life. Agitation, behavior, and mood are common outcomes for all sensory interventions. However, light therapy commonly targets sleep. The majority (n=110) of studies have very small sample sizes (n=20 or less), an indicator of the lack of robust data in the field. Additional small-scale studies of the known sensory interventions will likely do little to advance the field. However, there is a need for multi-armed studies which directly compare sensory interventions, and more studies which investigate the use of layering sensory interventions (for example, adding an aromatherapy component to a lighting intervention). In addition, large scale studies which enroll people at early stages of dementia will help us better understand the potential of sensory and multisensory interventions to slow the progression of the disease.

Keywords: sensory interventions, dementia, scoping review

Procedia PDF Downloads 117

Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

Abstract:

Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Denis Mustafov, Emmanouil Karteris, Maria Braoudaki

Abstract:

Glioblastoma multiform (GBM) is a heterogenous primary brain tumour that kills most affected patients. To the authors best knowledge, despite all research efforts there is no early diagnostic biomarker for GBM. MicroRNAs (miRNAs) are short non-coding RNA molecules which are deregulated in many cancers. The aim of this research was to determine miRNAs with a diagnostic impact and to potentially identify promising therapeutic targets for glioblastoma multiform. In silico analysis was performed to identify deregulated miRNAs with diagnostic relevance for glioblastoma. The expression profiles of the chosen miRNAs were then validated in vitro in the human glioblastoma cell lines A172 and U-87MG. Briefly, RNA extraction was carried out using the Trizol method, whilst miRNA extraction was performed using the mirVANA miRNA isolation kit. Quantitative Real-Time Polymerase Chain Reaction was performed to verify their expression. The presence of five target proteins within the A172 cell line was evaluated by Western blotting. The expression of the CORO1C protein within 32 GBM cases was examined via immunohistochemistry. The miRNAs identified in silico included miR-21-5p, miR-34a and miR-128a. These miRNAs were shown to target deregulated GBM genes, such as CDK6, E2F3, BMI1, JAG1, and CORO1C. miR-34a and miR-128a showed low expression profiles in comparison to a control miR-RNU-44 in both GBM cell lines suggesting tumour suppressor properties. Opposing, miR-21-5p demonstrated greater expression indicating that it could potentially function as an oncomiR. Western blotting revealed expression of all five proteins within the A172 cell line. In silico analysis also suggested that CORO1C is a target of miR-128a and miR-34a. Immunohistochemistry demonstrated that 75% of the GBM cases showed moderate to high expression of CORO1C protein. Greater understanding of the deregulated expression of miR-128a and the upregulation of CORO1C in GBM could potentially lead to the identification of a promising diagnostic biomarker signature for glioblastomas.

Keywords: non-coding RNAs, gene expression, brain tumours, immunohistochemistry

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Authors: Shuaili Ji

Abstract:

The correct use of verbs is an important element of high-quality research articles, and thus for Chinese EFL learners, it is significant to master characteristics of verbs and to precisely use verbs. However, some researches have shown that there are differences in using verbs between learners and native speakers and learners have difficulty in using English verbs. This corpus-based quantitative research can enhance learners’ knowledge of English verbs and promote the quality of research article abstracts even of the whole academic writing. The aim of this study is to find the differences between learners’ and native speakers’ use of verbs and to study the factors that contribute to those differences. To this end, the research question is as follows: What are the differences between most frequently used verbs by learners and those by native speakers? The research question is answered through a study that uses corpus-based data-driven approach to analyze the verbs used by learners in their abstract writings in terms of collocation, colligation and semantic prosody. The results show that: (1) EFL learners obviously overused ‘be, can, find, make’ and underused ‘investigate, examine, may’. As to modal verbs, learners obviously overused ‘can’ while underused ‘may’. (2) Learners obviously overused ‘we find + object clauses’ while underused ‘nouns (results, findings, data) + suggest/indicate/reveal + object clauses’ when expressing research results. (3) Learners tended to transfer the collocation, colligation and semantic prosody of shǐ and zuò to make. (4) Learners obviously overused ‘BE+V-ed’ and used BE as the main verb. They also obviously overused the basic forms of BE such as be, is, are, while obviously underused its inflections (was, were). These results manifested learners’ lack of accuracy and idiomatic property in verb usage. Due to the influence of the concept transfer of Chinese, the verbs in learners’ abstracts showed obvious transfer of mother language. In addition, learners have not fully mastered the use of verbs, avoiding using complex colligations to prevent errors. Based on these findings, the present study has implications for English teaching, seeking to have implications for English academic abstract writing in China. Further research could be undertaken to study the use of verbs in the whole dissertation to find out whether the characteristic of the verbs in abstracts can apply in the whole dissertation or not.

Keywords: academic writing abstracts, Chinese EFL learners, corpus-based, data-driven, verbs

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Authors: Valentine C. Eze, Adam P. Harvey

Abstract:

Contamination of groundwater and soil by heavy metals and metalloids through anthropogenic activities and natural occurrence poses serious environmental challenges globally. A possible solution to this problem is through phytoremediation of the contaminants using hyper-accumulating plants. Conventional phytoremediation treats the contaminated hyper-accumulator biomass as a waste stream which adds no value to the heavy metal(loid)s decontamination process. This study investigates strategies for remediation of soil contaminated with arsenic and the extractive chemical routes for recovery of arsenic and phosphorus from the hyper-accumulator biomass. Pteris cretica ferns species were investigated for their uptake of arsenic from soil containing 200 ± 3ppm of arsenic. The Pteris cretica ferns were shown to be capable of hyper-accumulation of arsenic, with maximum accumulations of about 4427 ± 79mg to 4875 ± 96mg of As per kg of the dry ferns. The arsenic in the Pteris cretica fronds was extracted into various solvents, with extraction efficiencies of 94.3 ± 2.1% for ethanol-water (1:1 v/v), 81.5 ± 3.2% for 1:1(v/v) methanol-water, and 70.8 ± 2.9% for water alone. The recovery efficiency of arsenic from the molybdic acid complex process 90.8 ± 5.3%. Phosphorus was also recovered from the molybdic acid complex process at 95.1 ± 4.6% efficiency. Quantitative precipitation of Mg₃(AsO₄)₂ and Mg₃(PO₄)₂ occurred in the treatment of the aqueous solutions of arsenic and phosphorus after stripping at pH of 8 – 10. The amounts of Mg₃(AsO₄)₂ and Mg₃(PO₄)₂ obtained were 96 ± 7.2% for arsenic and 94 ± 3.4% for phosphorus. The arsenic nanoparticles produced from the Mg₃(AsO₄)₂ recovered from the biomass have the average particles diameter of 45.5 ± 11.3nm. A two-stage reduction process – a first step pre-reduction of As(V) to As(III) with L-cysteine, followed by NaBH₄ reduction of the As(III) to As(0), was required to produced arsenic nanoparticles from the Mg₃(AsO₄)₂. The arsenic nanoparticles obtained are potentially valuable for medical applications, while the Mg₃(AsO₄)₂ could be used as an insecticide. The phosphorus contents of the Pteris cretica biomass was recovered as phosphomolybdic acid complex and converted to Mg₃(PO₄)₂, which could be useful in productions of fertilizer. Recovery of these valuable products from phytoremediation biomass would incentivize and drive commercial industries’ participation in remediation of contaminated lands.

Keywords: phytoremediation, Pteris cretica, hyper-accumulator, solvent extraction, molybdic acid process, arsenic nanoparticles

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Authors: João Pedro Zanella, Michel J. O. Fagundes

Abstract:

Introduction: Drug use has been practised by humans universally for millennia, not excluding any population from these habits, however, the rampant drug use is a global concern due to the harm that affects the health of the world population. In this sense, it is observed the reduction of lasting and effective public policies for the resolution, increasing the demand for treatment services. With this comes ibogaine, an alkaloid derived from the root of an African bush (Tabernanthe Iboga), found mostly in Gabon and used widely by the native Bwiti population in rituals, and also other social groups, which demonstrates efficacy against chemical dependence, psychic and emotional disorders, opioid withdrawal was first confirmed by a study in rats done by Michailo Dzoljic and associates in 1988 and again in 1994. Methods: A brief description of the plant, its neurohumoral potential and the effects caused by ingested doses, in a simplified and objective way, will be discussed in the course of this abstract. Results: Ibogaine is not registered or passed by Anvisa, regarding safety and efficacy, and cannot be sold in Brazil. Its illegal trade reaches R$ 5 thousand for a session with the proceeds of the root, and its effect can last up to 72 hours, attributing Iboga's psychoactive effects to the alkaloid called ibogaine. The shrub where Ibogaine is located has pink and yellow flowers, and its fruit produced does not have psychoactive substances, but its root bark contains 6 to 7% indolic alkaloids. Besides extraction from the iboga plant, ibogaine hydrochloride can be semisynthesized from voacangine, another plant alkaloid that acts as a precursor. Its potential has the ability to perform multiple interactions with the neurotransmitter system, which are closely associated with addiction, including nicotinic, opioid and serotoninergic systems. Studies carried out by Edwards found that the doses administered of Iboga should be determined by a health professional when its purpose is to treat individuals for dependence on other drugs. Its use in small doses may cause an increase in sensibility, impaired vision and motor alterations; in moderate quantities, hallucinations, motor and neurological alterations and impaired vision; in high quantities it may cause hallucinations with personal events at a deeper level lasting up to 24 hours or more, followed by motor and visual alterations. Conclusion: The product extracted from the Iboga plant is of great importance in controlling addiction, reducing the need for the use of narcotics by patients, thus gaining a space of extreme importance in the treatment of users of psychoactive substances. It is remarkable the progress of the latest’s research about the usefulness of Ibogaine, and its benefits for certain treatments, even with the restriction of its sale in Brazil. Besides this, Ibogaine has an additional benefit of helping the patient to gain self-control over their destructive behaviours.

Keywords: alkaloids, dependence, Gabon, ibogaine

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Authors: Siyabonga Aubrey Mhlongo, Linda Lunga Sibali, Phumlane Selby Mdluli, Peter Papoh Ndibewu, Kholofelo Clifford Malematja

Abstract:

This paper reports on the successful extraction and quantification of an antibacterial and antifungal agent present in some consumer products (Triclosan: C₁₂H₇Cl₃O₂)generally found in wastewater or effluents using molecularly imprinted membrane adsorbent (MIMs) followed by quantification and removal on a high-performance liquid chromatography (HPLC). Triclosan is an antibacterial and antifungal agent present in some consumer products like toothpaste, soaps, detergents, toys, and surgical cleaning treatments. The MIMs was fabricated usingpolyvinylidene fluoride (PVDF) polymer with selective micro composite particles known as molecularly imprinted polymers (MIPs)via a phase inversion by immersion precipitation technique. This resulted in an improved hydrophilicity and mechanical behaviour of the membranes. Wastewater samples were collected from the Umbogintwini Industrial Complex (UIC) (south coast of Durban, KwaZulu-Natal in South Africa). central UIC effluent treatment plant and pre-treated before analysis. Experimental parameters such as sample size, contact time, stirring speed were optimised. The resultant MIMs had an adsorption efficiency of 97% of TCS with reference to NIMs and bare membrane, which had 92%, 88%, respectively. The analytical method utilized in this review had limits of detection (LoD) and limits of quantification (LoQ) of 0.22, 0.71µgL-1 in wastewater effluent, respectively. The percentage recovery for the effluent samples was 68%. The detection of TCS was monitored for 10 consecutive days, where optimum TCS traces detected in the treated wastewater was 55.0μg/L inday 9 of the monitored days, while the lowest detected was 6.0μg/L. As the concentrations of analytefound in effluent water samples were not so diverse, this study suggested that MIMs could be the best potential adsorbent for the development and continuous progress in membrane technologyand environmental sciences, lending its capability to desalination.

Keywords: molecularly imprinted membrane, triclosan, phase inversion, wastewater

Procedia PDF Downloads 105