Search results for: artificial membranes

Authors: Wei-Song Hung

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We utilized pressure, vacuum, and evaporation-assisted self-assembly techniques through which graphene oxide (GO) was deposited on modified polyacrylonitrile (mPAN). The fabricated composite GO/mPAN membranes were applied to dehydrate 1-butanol mixtures by pervaporation. Varying driving forces in the self-assembly techniques induced different GO assembly layer microstructures. XRD results indicated that the GO layer d-spacing varied from 8.3 Å to 11.5 Å. The self-assembly technique with evaporation resulted in a heterogeneous GO layer with loop structures; this layer was shown to be hydrophobic, in contrast to the hydrophilic layer formed from the other two techniques. From the pressure-assisted technique, the composite membrane exhibited exceptional pervaporation performance at 30 C: concentration of water at the permeate side = 99.6 wt% and permeation flux = 2.54 kg m-2 h-1. Moreover, the membrane sustained its operating stability at a high temperature of 70 C: a high water concentration of 99.5 wt% was maintained, and a permeation flux as high as 4.34 kg m-2 h-1 was attained. This excellent separation performance stemmed from the dense, highly ordered laminate structure of GO.

Keywords: graphene oxide, self-assembly, alcohol dehydration, polyacrylonitrile (mPAN)

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Authors: Vibhuti Amarnath Madhu Agrawal

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Artificial Intelligence (AI) and Intellectual Property are two emerging concepts that are growing at a fast pace and have the potential of having a huge impact on the economy in the coming times. In simple words, AI is nothing but work done by a machine without any human intervention. It is a coded software embedded in a machine, which over a period of time, develops its own intelligence and begins to take its own decisions and judgments by studying various patterns of how people think, react to situations and perform tasks, among others. Intellectual Property, especially Copyright Law, on the other hand, protects the rights of individuals and Companies in content creation that primarily deals with application of intellect, originality and expression of the same in some tangible form. According to some of the reports shared by the media lately, ChatGPT, an AI powered Chatbot, has been involved in the creation of a wide variety of original content, including but not limited to essays, emails, plays and poetry. Besides, there have been instances wherein AI technology has given creative inputs for background, lights and costumes, among others, for films. Copyright Law offers protection to all of these different kinds of content and much more. Considering the two key parameters of Copyright – application of intellect and originality, the question, therefore, arises that will awarding Copyright protection to a person who has not directly invested his / her intellect in the creation of that content go against the basic spirit of Copyright laws? This study aims to analyze the current scenario and provide answers to the following questions: a. If the content generated by AI technology satisfies the basic criteria of originality and expression in a tangible form, why should such content be denied protection in the name of its creator, i.e., the specific AI tool / technology? B. Considering the increasing role and development of AI technology in our lives, should it be given the status of a ‘Legal Person’ in law? C. If yes, what should be the modalities of awarding protection to works of such Legal Person and management of the same? Considering the current trends and the pace at which AI is advancing, it is not very far when AI will start functioning autonomously in the creation of new works. Current data and opinions on this issue globally reflect that they are divided and lack uniformity. In order to fill in the existing gaps, data obtained from Copyright offices from the top economies of the world have been analyzed. The role and functioning of various Copyright Societies in these countries has been studied in detail. This paper provides a roadmap that can be adopted to satisfy various objectives, constraints and dynamic conditions related AI technology and its protection under Copyright Law.

Keywords: artificial intelligence technology, copyright law, copyright societies, intellectual property

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Authors: Elisa Coraggio, Dawei Han, Weiru Liu, Theo Tryfonas

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Water is one of the most important resources for human society. The world is currently undergoing a wave of urban growth, and pollution problems are of a great impact. Monitoring water quality is a key task for the future of the environment and human species. In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for environmental monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the artificial intelligence algorithm. This study derives the methodology and demonstrates its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.In recent times, researchers, using Smart Cities technologies are trying to mitigate the problems generated by the population growth in urban areas. The availability of huge amounts of data collected by a pervasive urban IoT can increase the transparency of decision making. Several services have already been implemented in Smart Cities, but more and more services will be involved in the future. Water quality monitoring can successfully be implemented in the urban IoT. The combination of water quality sensors, cloud computing, smart city infrastructure, and IoT technology can lead to a bright future for the environment monitoring. In the past decades, lots of effort has been put on monitoring and predicting water quality using traditional approaches based on manual collection and laboratory-based analysis, which are slow and laborious. The present study proposes a new methodology for implementing a water quality prediction model using artificial intelligence techniques and comparing the results obtained with different algorithms. Furthermore, a 3D numerical model will be created using the software D-Water Quality, and simulation results will be used as a training dataset for the Artificial Intelligence algorithm. This study derives the methodology and demonstrate its implementation based on information and data collected at the floating harbour in the city of Bristol (UK). The city of Bristol is blessed with the Bristol-Is-Open infrastructure that includes Wi-Fi network and virtual machines. It was also named the UK ’s smartest city in 2017.

Keywords: artificial intelligence, hydroinformatics, numerical modelling, smart cities, water quality

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Authors: Baker Ssekitto, Ambrose Mbogo

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The study reveals that indeed agriculture employs over 70% of Uganda’s population, of which majority are youth and women. The study further reveals that over 70% of the farmers are smallholder farmers based in rural areas, whose operations are greatly affected by; climate change, weak digital skills, limited access to productivity knowledge along value chains, limited access to quality farm inputs, weak logistics systems, limited access to quality extension services, weak business intelligence, limited access to quality markets among others. It finds that the emerging 4th industrial revolution powered by artificial intelligence, 5G and data science will provide possibilities of addressing some of these challenges. Furthermore, the study finds that despite rapid development of ICT4Agric Innovation, their uptake is constrained by a number of factors including; limited awareness of these innovations, low internet and smart phone penetration especially in rural areas, lack of appropriate digital skills, inappropriate programmes implementation models which are project and donor driven, limited articulation of value addition to various stakeholders among others. Majority of farmers and other value chain actors lacked knowledge and skills to harness the power of ICTs, especially their application of ICTs in monitoring and evaluation on quality of service in the extension system and farm level processes.

Keywords: artificial intelligence, productivity, ICT4agriculture, value chain, logistics

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Authors: Natalia Hordynska, Magdalena Szechynska-Hebda, Miroslaw Sobczak, Elzbieta Rozanska, Joanna Troczynska, Zofia Banaszak, Maria Wedzony

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Albinism is a common problem encountered in wheat and triticale breeding programs, which require in vitro culture steps e.g. generation of doubled haploids via androgenesis process. Genetic factor is a major determinant of albinism, however, environmental conditions such as temperature and media composition influence the frequency of albino plant formation. Cold incubation of wheat and triticale spikes induced a switch from gametophytic to sporophytic development. Further, androgenic structures formed from anthers of the genotypes susceptible to androgenesis or treated with cold stress, had a pool of structurally primitive plastids, with small starch granules or swollen thylakoids. High temperature was a factor inducing andro-genesis of wheat and triticale, but at the same time, it was a factor favoring the formation of albino plants. In genotypes susceptible to albinism or after heat stress conditions, cells formed from anthers were vacuolated, and plastids were eliminated. Partial or complete loss of chlorophyll pigments and incomplete differentiation of chloroplast membranes result in formation of tissues or whole plant unable to perform photosynthesis. Indeed, susceptibility to the andro-genesis process was associated with an increase of total concentration of photosynthetic pigments in anthers, spikes and regenerated plants. The proper balance of the synthesis of various pigments, was the starting point for their proper incorporation into photosynthetic membranes. In contrast, genotypes resistant to the androgenesis process and those treated with heat, contained 100 times lower content of photosynthetic pigments. In particular, the synthesis of violaxanthin, zeaxanthin, lutein and chlorophyll b was limited. Furthermore, deregulation of starch and lipids synthesis, which led to the formation of very complex starch granules and an increased number of oleosomes, respectively, correlated with the reduction of the efficiency of androgenesis. The content of other sugars varied depending on the genotype and the type of stress. The highest content of various sugars was found for genotypes susceptible to andro-genesis, and highly reduced for genotypes resistant to androgenesis. The most important sugars seem to be glucose and fructose. They are involved in sugar sensing and signaling pathways, which affect the expression of various genes and regulate plant development. Sucrose, on the other hand, seems to have minor effect at each stage of the androgenesis. The sugar metabolism was related to metabolic activity of microspores. The genotypes susceptible to androgenesis process had much faster mitochondrium- and chloroplast-dependent energy conversion and higher heat production by tissues. Thus, the effectiveness of metabolic processes, their balance and the flexibility under the stress was a factor determining the direction of microspore development, and in the later stages of the androgenesis process, a factor supporting the induction of androgenic structures, chloroplast formation and the regeneration of green plants. The work was financed by Ministry of Agriculture and Rural Development within Program: ‘Biological Progress in Plant Production’, project no HOR.hn.802.15.2018.

Keywords: androgenesis, chloroplast, metabolism, temperature stress

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Authors: Eugenio Ferrer Santiago

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The growing complexities of the modern world on high-end gadgets, software applications, scams, identity theft, and Artificial Intelligence (AI) make the “uninformed” the weak and vulnerable to be victims of cybercrimes. Artificial Intelligence is not a new thing in our daily lives; the principles of database management, logical programming, and garbage in and garbage out are all connected to AI. The Philippines had in place legal safeguards against the abuse of cyberspace, but self-regulation of key industry players and self-protection by individuals are primordial to attain the success of these initiatives. Data protection, Data Privacy, and Research Ethics must work hand in hand during the policy process in the course of urban planning practice in different environments. This paper focuses on the interconnection of data protection, data privacy, and research ethics in coming up with clear-cut policies against perpetrators in the urban planning professional practice relevant in sustainable communities and smart cities. This paper shall use expository methodology under qualitative research using secondary data from related literature, interviews/blogs, and the World Wide Web resources. The claims and recommendations of this paper will help policymakers and implementers in the policy cycle. This paper shall contribute to the body of knowledge as a simple treatise and communication channel to the reading community and future researchers to validate the claims and start an intellectual discourse for better knowledge generation for the good of all in the near future.

Keywords: data privacy, data protection, urban planning, research ethics

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Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Anamarija Mandic, Katarina Penov Gasi, Marija Sakac, Aleksandar Okljesa, Andrea Nikolic

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The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology).

Keywords: artificial neural networks, liquid chromatography, molecular descriptors, steroids, sum of ranking differences

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Authors: Sadegh Sadeghi, Negar Shabani

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From a thermal point of view, zeotropic mixtures are likely to be more efficient than azeotropic fluids in low-temperature thermodynamic cycles due to their suitable boiling characteristics. In this study, performance of a low-temperature Kalina cycle with R717/water working fluid used in different existing power plants is mathematically investigated. To analyze the behavior of the cycle, mass conservation, energy conservation, and exergy balance equations are presented. With regard to the similarity in molar mass of R717 (17.03 gr/mol) and water (18.01 gr/mol), there is no need to alter the size of Kalina system components such as turbine and pump. To optimize the cycle energy and exergy efficiencies simultaneously, a constrained multi-objective optimization is carried out applying an Artificial Bee Colony algorithm. The main motivation behind using this algorithm lies on its robustness, reliability, remarkable precision and high–speed convergence rate in dealing with complicated constrained multi-objective problems. Convergence rates of the algorithm for calculating the optimal energy and exergy efficiencies are presented. Subsequently, due to the importance of exergy concept in Kalina cycles, exergy destructions occurring in the components are computed. Finally, the impacts of pressure, temperature, mass fraction and mass flow rate on the energy and exergy efficiencies are elaborately studied.

Keywords: artificial bee colony algorithm, binary zeotropic mixture, constrained multi-objective optimization, energy efficiency, exergy efficiency, Kalina cycle

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Authors: Omil Nady Mahrous Maximous

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The construction industry is currently experiencing a shift towards digitisation. This transformation is driven by adopting technologies like Building Information Modelling (BIM), drones, and augmented reality (AR). These advancements are revolutionizing the process of designing, constructing, and operating projects. BIM, for instance, is a new way of communicating and exploiting technology such as software and machinery. It enables the creation of a replica or virtual model of buildings or infrastructure projects. It facilitates simulating construction procedures, identifying issues beforehand, and optimizing designs accordingly. Drones are another tool in this revolution, as they can be utilized for site surveys, inspections, and even deliveries. Moreover, AR technology provides real-time information to workers involved in the project. Implementing these technologies in the construction industry has brought about improvements in efficiency, safety measures, and sustainable practices. BIM helps minimize rework and waste materials, while drones contribute to safety by reducing workers' exposure to areas. Additionally, AR plays a role in worker safety by delivering instructions and guidance during operations. Although the digital transformation within the construction industry is still in its early stages, it holds the potential to reshape project delivery methods entirely. By embracing these technologies, construction companies can boost their profitability while simultaneously reducing their environmental impact and ensuring safer practices.

Keywords: architectural education, construction industry, digital learning environments, immersive learning BIM, digital construction, construction technologies, digital transformation artificial intelligence, collaboration, digital architecture, digital design theory, material selection, space construction

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Authors: Yingjeng James Li, Chih Chi Hsu, Chiao-Chih Hu

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Membrane electrode assemblies MEAs for proton exchange membrane PEM water electrolyzers were prepared by employing 175um perfluorosulfonic acid PFSA membranes as the PEM, onto which iridium oxide catalyst was coated on one side as the anode and platinum catalyst was coated on the other side as the cathode. The cathode catalyst ink was prepared so that the weight ratio of the catalyst powder to ionomer was 75:25, 70:30, 65:35, 60:40, and 55:45, respectively. Whereas, the ratio of catalyst powder to ionomer of the anode catalyst ink keeps constant at 50:50. All the MEAs have a catalyst coated area of 5cm*5cm. The test cell employs a platinum plated titanium grid as anode gas diffusion media; whereas, carbon paper was employed as the cathode gas diffusion media. The measurements of the MEA gases production rate were carried out by holding the cell voltage ranging from 1.6 to 2.8 volts at room temperature. It was found that the MEA with cathode catalyst to ionomer ratio of 65:35 gives the largest hydrogen production rate which is 2.8mL/cm2*min.

Keywords: electrolyzer, membrane electrode assembly, proton exchange membrane, ionomer, hydrogen

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Authors: Van-Thanh Ho, Hyoungsoon Lee, Jaiyoung Ryu

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Efficient pressure distribution within soft robotic systems, specifically to the pneumatic artificial muscle (PAM) regions, is essential to minimize energy consumption. This optimization involves adjusting reservoir pressure, pipe diameter, and branching network layout to reduce flow speed and pressure drop while enhancing flow efficiency. The outcome of this optimization is a lightweight power source and reduced mechanical impedance, enabling extended wear and movement. To achieve this, a branching network system was created by combining pipe components and intricate cross-sectional area variations, employing the principle of minimal work based on a complete virtual human exosuit. The results indicate that modifying the cross-sectional area of the branching network, gradually decreasing it, reduces velocity and enhances momentum compensation, preventing flow disturbances at separation regions. These optimized designs achieve uniform velocity distribution (uniformity index > 94%) prior to entering the connection pipe, with a pressure drop of less than 5%. The design must also consider the length-to-diameter ratio for fluid dynamic performance and production cost. This approach can be utilized to create a comprehensive PAM system, integrating well-designed tube networks and complex pneumatic models.

Keywords: pneumatic artificial muscles, pipe networks, pressure drop, compressible turbulent flow, uniformity flow, murray's law

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Authors: Masataka Inada, Masanao Kinoshita, Nobuaki Matsumori

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It has been demonstrated that lipid molecules in biological membranes are responsible for the functionalization and structuration of membrane proteins. However, it is still unclear how the interaction of lipid molecules with membrane proteins is correlated with the function of the membrane proteins. Here we first developed an evaluation method for the interaction between membrane proteins and lipid molecules via surface plasmon resonance (SPR) analysis. Bacteriorhodopsin (bR), which was obtained by the culture of halobacteria, was used as a membrane protein. We prepared SPR sensor chips covered with self-assembled monolayer containing mercaptocarboxylic acids, and immobilized bR onto them. Then, we evaluated the interactions with various lipids that have different structures. As a result, the halobacterium-specific glycolipid S-TGA-1 was found to have much higher affinity with bRs than other lipids. This is probably due to not only hydrophobic and electrostatic interactions but also hydrogen bonds with sugar moieties in the glycolipid. Next, we analyzed the roles of the lipid in the structuration and functionalization of bR. CD analysis showed that S-TGA-1 could promote trimerization of bR monomers more efficiently than any other lipids. Flash photolysis further indicated that bR trimers formed by S-TGA-1 reproduced the photocyclic activity of bR in purple membrane, halobacterium-membrane. These results suggest that S-TGA-1 promotes trimerization of bR through strong interactions and consequently fulfills the bR’s function efficiently.

Keywords: membrane protein, lipid, interaction, bacteriorhodopsin, glycolipid

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Authors: Mehrdad Ebrahimi, Oliver Schmitz, Axel Schmidt, Peter Czermak

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“Produced water” (PW) is any fossil water that is brought to the surface along with crude oil or natural gas. By far, PW is the largest waste stream by volume associated with oil and gas production operations. Due to the increasing volume of waste all over the world in the current decade, the outcome and effect of discharging PW on the environment has lately become a significant issue of environmental concerns. Therefore, there is a need for new technologies for PW treatment due to increase focus on water conservation and environmental regulation. The use of membrane processes for treatment of PW has several advantages over many of the traditional separation techniques. In oilfield produced water treatment with ceramic membranes, process efficiency is characterized by the specific permeate flux and by the oil separation performance. Apart from the membrane properties, the permeate flux during filtration of oily wastewaters is known to be strongly dependent on the constituents of the feed solution, as well as on process conditions, e.g. trans-membrane pressure (TMP) and cross-flow velocity (CFV). The research project presented in these report describes the application of different ceramic membrane filtration technologies for the efficient treatment of oil-field produced water and different model oily solutions.

Keywords: ceramic membrane, membrane fouling, oil rejection, produced water treatment

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Authors: Mohammed Hakim Jafferali, Balaje Vijayaraghavan, Ricardo A. Figueroa, Ellinor Crafoord, Veronica J. Larsson, Einar Hallberg, Santhosh Gudise

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The nuclear envelope which surrounds the chromatin of eukaryotic cells contains more than a hundred transmembrane proteins. Mutations in some genes encoding nuclear envelope proteins give rise to human diseases including neurological disorders. The function of many nuclear envelope proteins is not well established. This is partly because nuclear envelope proteins and their interactions are difficult to study due to the inherent resistance to extraction of nuclear envelope proteins. We have developed a novel method called MCLIP, to identify interacting partners of nuclear envelope proteins in live cells. Using MCLIP, we found three new binding partners of the inner nuclear membrane protein Samp1: the intermediate filament protein Lamin B1, the LINC complex protein Sun1 and the G-protein Ran. Furthermore, using in vitro studies, we show that Samp1 binds both Emerin and Ran directly. We have also studied the interaction between Samp1 and Ran in detail. The results show that the Samp1 binds stronger to RanGTP than RanGDP. Samp1 is the first transmembrane protein known to bind Ran and it is tempting to speculate that Samp1 may provide local binding sites for RanGTP at membranes.

Keywords: MCLIP, nuclear envelope, ran, Samp1

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Authors: Erva Akin

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– The use of copyrighted material for machine learning purposes is a challenging issue in the field of artificial intelligence (AI). While machine learning algorithms require large amounts of data to train and improve their accuracy and creativity, the use of copyrighted material without permission from the authors may infringe on their intellectual property rights. In order to overcome copyright legal hurdle against the data sharing, access and re-use of data, the use of copyrighted material for machine learning purposes may be considered permissible under certain circumstances. For example, if the copyright holder has given permission to use the data through a licensing agreement, then the use for machine learning purposes may be lawful. It is also argued that copying for non-expressive purposes that do not involve conveying expressive elements to the public, such as automated data extraction, should not be seen as infringing. The focus of such ‘copy-reliant technologies’ is on understanding language rules, styles, and syntax and no creative ideas are being used. However, the non-expressive use defense is within the framework of the fair use doctrine, which allows the use of copyrighted material for research or educational purposes. The questions arise because the fair use doctrine is not available in EU law, instead, the InfoSoc Directive provides for a rigid system of exclusive rights with a list of exceptions and limitations. One could only argue that non-expressive uses of copyrighted material for machine learning purposes do not constitute a ‘reproduction’ in the first place. Nevertheless, the use of machine learning with copyrighted material is difficult because EU copyright law applies to the mere use of the works. Two solutions can be proposed to address the problem of copyright clearance for AI training data. The first is to introduce a broad exception for text and data mining, either mandatorily or for commercial and scientific purposes, or to permit the reproduction of works for non-expressive purposes. The second is that copyright laws should permit the reproduction of works for non-expressive purposes, which opens the door to discussions regarding the transposition of the fair use principle from the US into EU law. Both solutions aim to provide more space for AI developers to operate and encourage greater freedom, which could lead to more rapid innovation in the field. The Data Governance Act presents a significant opportunity to advance these debates. Finally, issues concerning the balance of general public interests and legitimate private interests in machine learning training data must be addressed. In my opinion, it is crucial that robot-creation output should fall into the public domain. Machines depend on human creativity, innovation, and expression. To encourage technological advancement and innovation, freedom of expression and business operation must be prioritised.

Keywords: artificial intelligence, copyright, data governance, machine learning

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Authors: Anastasios Papathanasiou, George Liontos, Athanasios Katsouras, Vasiliki Liagkou, Euripides Glavas

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Email remains a crucial communication tool due to its efficiency, accessibility and cost-effectiveness, enabling rapid information exchange across global networks. However, the global adoption of email has also made it a prime target for cyber threats, including phishing, malware and Business Email Compromise (BEC) attacks, which exploit its integral role in personal and professional realms in order to perform fraud and data breaches. To combat these threats, this research advocates for a multi-layered defense strategy incorporating advanced technological tools such as anti-spam and anti-malware software, machine learning algorithms and authentication protocols. Moreover, we developed an artificial intelligence model specifically designed to analyze email headers and assess their security status. This AI-driven model examines various components of email headers, such as "From" addresses, ‘Received’ paths and the integrity of SPF, DKIM and DMARC records. Upon analysis, it generates comprehensive reports that indicate whether an email is likely to be malicious or benign. This capability empowers users to identify potentially dangerous emails promptly, enhancing their ability to avoid phishing attacks, malware infections and other cyber threats.

Keywords: email security, artificial intelligence, header analysis, threat detection, phishing, DMARC, DKIM, SPF, ai model

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Authors: Garry Gorman, Nigel McKelvey, James Connolly

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This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Federico Garrido, Mostafa El Hayani, Ahmed Shams

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The exploration of the potential of artificial intelligence (AI) in architecture is still embryonic, however, its latent capacity to change design disciplines is significant. 'Synthetic Classism' is a research project that questions the underlying aspects of classically organized architecture not just in aesthetic terms but also from a geometrical and morphological point of view, intending to generate new architectural information using historical examples as source material. The main aim of this paper is to explore the uses of artificial intelligence and machine learning algorithms in architectural design while creating a coherent narrative to be contained within a design process. The purpose is twofold: on one hand, to develop and train machine learning algorithms to produce architectural information of small pavilions and on the other, to synthesize new information from previous architectural drawings. These algorithms intend to 'interpret' graphical information from each pavilion and then generate new information from it. The procedure, once these algorithms are trained, is the following: parting from a line profile, a synthetic 'front view' of a pavilion is generated, then using it as a source material, an isometric view is created from it, and finally, a top view is produced. Thanks to GAN algorithms, it is also possible to generate Front and Isometric views without any graphical input as well. The final intention of the research is to produce isometric views out of historical information, such as the pavilions from Sebastiano Serlio, James Gibbs, or John Soane. The idea is to create and interpret new information not just in terms of historical reconstruction but also to explore AI as a novel tool in the narrative of a creative design process. This research also challenges the idea of the role of algorithmic design associated with efficiency or fitness while embracing the possibility of a creative collaboration between artificial intelligence and a human designer. Hence the double feature of this research, both analytical and creative, first by synthesizing images based on a given dataset and then by generating new architectural information from historical references. We find that the possibility of creatively understand and manipulate historic (and synthetic) information will be a key feature in future innovative design processes. Finally, the main question that we propose is whether an AI could be used not just to create an original and innovative group of simple buildings but also to explore the possibility of fostering a novel architectural sensibility grounded on the specificities on the architectural dataset, either historic, human-made or synthetic.

Keywords: architecture, central pavilions, classicism, machine learning

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Authors: Abdul Rehman, Bo Liu

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Axial flow turbines are commonly designed with high loads that generate strong secondary flows and result in high secondary losses. These losses contribute to almost 30% to 50% of the total losses. Non-axisymmetric endwall profiling is one of the passive control technique to reduce the secondary flow loss. In this paper, the non-axisymmetric endwall profile construction and optimization for the stator endwalls are presented to improve the efficiency of a high pressure turbine. The commercial code NUMECA Fine/ Design3D coupled with Fine/Turbo was used for the numerical investigation, design of experiments and the optimization. All the flow simulations were conducted by using steady RANS and Spalart-Allmaras as a turbulence model. The non-axisymmetric endwalls of stator hub and shroud were created by using the perturbation law based on Bezier Curves. Each cut having multiple control points was supposed to be created along the virtual streamlines in the blade channel. For the design of experiments, each sample was arbitrarily generated based on values automatically chosen for the control points defined during parameterization. The Optimization was achieved by using two algorithms i.e. the stochastic algorithm and gradient-based algorithm. For the stochastic algorithm, a genetic algorithm based on the artificial neural network was used as an optimization method in order to achieve the global optimum. The evaluation of the successive design iterations was performed using artificial neural network prior to the flow solver. For the second case, the conjugate gradient algorithm with a three dimensional CFD flow solver was used to systematically vary a free-form parameterization of the endwall. This method is efficient and less time to consume as it requires derivative information of the objective function. The objective function was to maximize the isentropic efficiency of the turbine by keeping the mass flow rate as constant. The performance was quantified by using a multi-objective function. Other than these two classifications of the optimization methods, there were four optimizations cases i.e. the hub only, the shroud only, and the combination of hub and shroud. For the fourth case, the shroud endwall was optimized by using the optimized hub endwall geometry. The hub optimization resulted in an increase in the efficiency due to more homogenous inlet conditions for the rotor. The adverse pressure gradient was reduced but the total pressure loss in the vicinity of the hub was increased. The shroud optimization resulted in an increase in efficiency, total pressure loss and entropy were reduced. The combination of hub and shroud did not show overwhelming results which were achieved for the individual cases of the hub and the shroud. This may be caused by fact that there were too many control variables. The fourth case of optimization showed the best result because optimized hub was used as an initial geometry to optimize the shroud. The efficiency was increased more than the individual cases of optimization with a mass flow rate equal to the baseline design of the turbine. The results of artificial neural network and conjugate gradient method were compared.

Keywords: artificial neural network, axial turbine, conjugate gradient method, non-axisymmetric endwall, optimization

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Authors: Adriana Martínez-Arias, John Josephraj Selvaraj, Luis Octavio González-Salcedo

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In the Colombian Pacific, Dorado fish (Coryphaena hippurus L.) fisheries is of great commercial interest. However, its habitat and fisheries may be affected by climatic change especially by the actual increase in sea surface temperature. Hence, it is of interest to study the dynamics of these species fishing zones. In this study, we developed Artificial Neural Networks (ANN) models to predict Catch per Unit Effort (CPUE) as an indicator of species abundance. The model was based on four oceanographic variables (Chlorophyll a, Sea Surface Temperature, Sea Level Anomaly and Bathymetry) derived from satellite data. CPUE datasets for model training and cross-validation were obtained from logbooks of commercial fishing vessel. Sea surface Temperature for Colombian Pacific were projected under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 using Coupled Model Intercomparison Project Phase 5 (CMIP5) and CPUE maps were created. Our results indicated that an increase in sea surface temperature reduces the potential fishing zones of this species in the Colombian Pacific. We conclude that ANN is a reliable tool for simulation of climate change effects on the potential fishing zones. This research opens a future agenda for other species that have been affected by climate change.

Keywords: climatic change, artificial neural networks, dorado fish, CPUE

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Authors: Yibeltal Chanie Manie, Zebider Asire Munyelet

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5G and 6G technology offers enhanced quality of service with high data transmission rates, which necessitates the implementation of the Internet of Things (IoT) in 5G/6G architecture. In this paper, we proposed the integration of free space optical communication (FSO) with fiber sensor networks for IoT applications. Recently, free-space optical communications (FSO) are gaining popularity as an effective alternative technology to the limited availability of radio frequency (RF) spectrum. FSO is gaining popularity due to flexibility, high achievable optical bandwidth, and low power consumption in several applications of communications, such as disaster recovery, last-mile connectivity, drones, surveillance, backhaul, and satellite communications. Hence, high-speed FSO is an optimal choice for wireless networks to satisfy the full potential of 5G/6G technology, offering 100 Gbit/s or more speed in IoT applications. Moreover, machine learning must be integrated into the design, planning, and optimization of future optical wireless communication networks in order to actualize this vision of intelligent processing and operation. In addition, fiber sensors are important to achieve real-time, accurate, and smart monitoring in IoT applications. Moreover, we proposed deep learning techniques to estimate the strain changes and peak wavelength of multiple Fiber Bragg grating (FBG) sensors using only the spectrum of FBGs obtained from the real experiment.

Keywords: optical sensor, artificial Intelligence, Internet of Things, free-space optics

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Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

Procedia PDF Downloads 325

Authors: Eric Chappel, Dimitry Dumont-Fillon, Hugo Musard, Harald van Lintel

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A microfluidic radial diffuser typically comprises a hole in a membrane, a small gap and pillar centred with the hole. The fluid is forced to flow radially in this gap between the membrane and the pillar. Such diffusers are notably used to form flow control valves, wherein several holes are machined into a flexible membrane progressively deflecting against pillars as the pressure increases. The fluidic modelling of such diffuser is made difficult by the presence of a transition region between the hole and the diffuser itself. An experimental investigation has been conducted using SOI wafers to form membranes with only one centred hole and Pyrex wafers for the substrate and pillars, both wafers being anodically bonded after alignment. A simple fluidic model accounting for the specific geometry of the diffuser is proposed and compared to experimental results. A good match is obtained, for Reynolds number in the range 0.5 to 35 using the analytical formula of a radial diffuser in the laminar regime with an effective inner radius that is 40% smaller than the real radius, in order to simulate correctly the flow constriction at the entrance of the diffuser.

Keywords: radial diffuser, flow control valve, numerical modelling, drug delivery

Procedia PDF Downloads 264

Authors: Hasmik V. Zanginyan, Gayane S. Ghazaryan, Laura M. Hovsepyan

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Experimental autoimmune encephalomyelitis (EAE) is an inflammatory demyelinating disease of the central nervous system that is induced in laboratory animals by developing an immune response against myelin epitopes. The typical clinical course is ascending palsy, which correlates with inflammation and tissue damage in the thoracolumbar spinal cord, although the optic nerves and brain (especially the subpial white matter and brainstem) are also often affected. With multiple sclerosis, there is a violation of lipid metabolism in myelin. When membrane lipids (glycosphingolipids, phospholipids) are disturbed, metabolites not only play a structural role in membranes but are also sources of secondary mediators that transmit multiple cellular signals. The purpose of this study was to investigate the effect of ganglioside as a therapeutic agent in experimental multiple sclerosis. The biological activity of a ganglioside-containing medicinal preparation (Cronassial) was evaluated in an experimental model of multiple sclerosis in laboratory animals. An experimental model of multiple sclerosis in rats was obtained by immunization with myelin basic protein (MBP), as well as homogenization of the spinal cord or brain. EAE was induced by administering a mixture of an encephalitogenic mixture (EGM) with Complete Freund’s Adjuvant. Mitochondrial fraction was isolated in a medium containing 0,25 M saccharose and 0, 01 M tris buffer, pH - 7,4, by a method of differential centrifugation on a K-24 centrifuge. Glutathione peroxidase activity was assessed by reduction reactions of hydrogen peroxide (H₂O₂) and lipid hydroperoxides (ROOH) in the presence of GSH. LPO activity was assessed by the amount of malondialdehyde (MDA) in the total homogenate and mitochondrial fraction of the spinal cord and brain of control and experimental autoimmune encephalomyelitis rats. MDA was assessed by a reaction with Thiobarbituric acid. For statistical data analysis on PNP, SPSS (Statistical Package for Social Science) package was used. The nature of the distribution of the obtained data was determined by the Kolmogorov-Smirnov criterion. The comparative analysis was performed using a nonparametric Mann-Whitney test. The differences were statistically significant when р ≤ 0,05 or р ≤ 0,01. Correlational analysis was conducted using a nonparametric Spearman test. In the work, refrigeratory centrifuge, spectrophotometer LKB Biochrom ULTROSPECII (Sweden), pH-meter PL-600 mrc (Israel), guanosine, and ATP (Sigma). The study of the process of lipid peroxidation in the total homogenate of the brain and spinal cord in experimental animals revealed an increase in the content of malonic dialdehyde. When applied, Cronassial observed normalization of lipid peroxidation processes. Reactive oxygen species, causing lipid peroxidation processes, can be toxic both for neurons and for oligodendrocytes that form myelin, causing a violation of their lipid composition. The high content of lipids in the brain and the uniqueness of their structure determines the nature of the development of LPO processes. The lipid layer of cellular and intracellular membranes performs two main functions -barrier and matrix (structural). Damage to the barrier leads to dysregulation of intracellular processes and severe disorders of cellular functions.

Keywords: experimental autoimmune encephalomyelitis, multiple sclerosis, neuroinflammation, therapy

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Electricity prices have sophisticated features such as high volatility, nonlinearity and high frequency that make forecasting quite difficult. Electricity price has a volatile and non-random character so that, it is possible to identify the patterns based on the historical data. Intelligent decision-making requires accurate price forecasting for market traders, retailers, and generation companies. So far, many shallow-ANN (artificial neural networks) models have been published in the literature and showed adequate forecasting results. During the last years, neural networks with many hidden layers, which are referred to as DNN (deep neural networks) have been using in the machine learning community. The goal of this study is to investigate electricity price forecasting performance of the shallow-ANN and DNN models for the Turkish day-ahead electricity market. The forecasting accuracy of the models has been evaluated with publicly available data from the Turkish day-ahead electricity market. Both shallow-ANN and DNN approach would give successful result in forecasting problems. Historical load, price and weather temperature data are used as the input variables for the models. The data set includes power consumption measurements gathered between January 2016 and December 2017 with one-hour resolution. In this regard, forecasting studies have been carried out comparatively with shallow-ANN and DNN models for Turkish electricity markets in the related time period. The main contribution of this study is the investigation of different shallow-ANN and DNN models in the field of electricity price forecast. All models are compared regarding their MAE (Mean Absolute Error) and MSE (Mean Square) results. DNN models give better forecasting performance compare to shallow-ANN. Best five MAE results for DNN models are 0.346, 0.372, 0.392, 0,402 and 0.409.

Keywords: deep learning, artificial neural networks, energy price forecasting, turkey

Procedia PDF Downloads 276

Authors: Muhammad Umair, Yuri Nikolaev, Denis Artemov, Ighor Uzhinsky

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A flexible manufacturing system is an integral part of a smart factory of industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical-system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted the production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, material handling for such automated productions is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and not suitable for highly automated production systems. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.

Keywords: artificial intelligence, cyber physics system, Industry 4.0, material handling, smart factory, flexible manufacturing system

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Authors: Ghanem Al-Ghamdi

Abstract:

African Horse Sickness causes significant challenges to horse practitioners and owners in Africa and possibly in certain locations in the Arab Pensila. The aim of this work was to observe a hot spot of epidemic in Al-Baha, Southwestern of Saudi Arabia that could be AHS. A five year-old horse farm that had eight horses with no history of clinical problems was visited in late October 2014. In August 2014, horses showed clinical signs of severe pain, congestion of mucus membranes, foam oozing of the nose, recumbency, difficult breath and ultimately death. The course of the disease averaged 2 days. The farm had no previous history of this episode. Other animals including camel, sheep reside the same farm sharing feeding and water sources however no obvious similar clinical problems were noticed among the two species. Five horses showed the clinical disease and all horses were lost. Veterinary help was not available for diagnosis or treatment. A follow up visit to the farm after one year indicated that the three remaining horses were healthy but were relocated to a different facility out the Al-Baha Region. The most likely cause of such clinical problem is African Horse Sickness, however clinical exam and sampling of other horses in the region is absolute must as well as examining arthropods.

Keywords: African horse sickness, horses, Al-Baha, Saudi Arabia

Procedia PDF Downloads 330

Authors: Bruno Cossu, Elio Carlo

Abstract:

The invention (patent pending) relates to the field of devices aimed to harness wave energy (WEC) especially for artificial upwelling, forced downwelling, production of compressed air. In its basic form, the pump consists of a hydro-pneumatic machine, driven by wave energy, characterised by the fact that it has no moving mechanical parts, and is made up of only two structural components: an hollow body, which is open at the bottom to the sea and partially immersed in sea water, and a tube, both joined together to form a single body. The shape of the hollow body is like a mushroom whose cap and stem are hollow; the stem is open at both ends and the lower part of its surface is crossed by holes; the tube is external and coaxial to the stem and is joined to it so as to form a single body. This shape of the hollow body and the type of connection to the tube allows the pump to operate simultaneously as an air compressor (OWC) on the cap side, and as an airlift on the stem side. The pump can be implemented in four versions, each of which provides different variants and methods of implementation: 1) firstly, for the artificial upwelling of cold, deep ocean water; 2) secondly, for the lifting and transfer of these waters to the place of use (above all, fish farming plants), even if kilometres away; 3) thirdly, for the forced downwelling of surface sea water; 4) fourthly, for the forced downwelling of surface water, its oxygenation, and the simultaneous production of compressed air. The transfer of the deep water or the downwelling of the raised surface water (as for pump versions indicated in points 2 and 3 above), is obtained by making the water raised by the airlift flow into the upper inlet of another pipe, internal or adjoined to the airlift; the downwelling of raised surface water, oxygenation, and the simultaneous production of compressed air (as for the pump version indicated in point 4), is obtained by installing a venturi tube on the upper end of the pipe, whose restricted section is connected to the external atmosphere, so that it also operates like a hydraulic air compressor (trompe). Furthermore, by combining one or more pumps for the upwelling of cold, deep water, with one or more pumps for the downwelling of the warm surface water, the system can be used in an Ocean Thermal Energy Conversion plant to supply the cold and the warm water required for the operation of the same, thus allowing to use, without increased costs, in addition to the mechanical energy of the waves, for the purposes indicated in points 1 to 4, the thermal one of the marine water treated in the process.

Keywords: air lifted upwelling, fish farming plant, hydraulic air compressor, wave energy converter

Procedia PDF Downloads 132