Search results for: deep eutectic systems

Authors: Mihai-Ionut Firescu, Mark A. P. Carson

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We present a case of inferior vena cava agenesis (IVCA) associated with bilateral deep venous thrombosis (DVT) in a patient with Beckwith-Wiedemann syndrome (BWS). In adult patients with BWS presenting with bilateral lower limb oedema, specific aetiological factors should be considered. These include cardiomyopathy and intraabdominal tumours. Congenital malformations of the IVC, through causing relative venous stasis, can lead to lower limb oedema either directly or indirectly by favouring lower limb venous thromboembolism; however, they are yet to be reported as an associated feature of BWS. Given its life-threatening potential, the prompt initiation of treatment for bilateral DVT is paramount. In BWS patients, however, this can prove more complicated. Due to overgrowth, the above-average birth weight can continue throughout childhood. In this case, the patient’s weight reached 170 kg, impacting on anticoagulation choice, as direct oral anticoagulants have a limited evidence base in patients with a body mass above 120 kg. Furthermore, the presence of IVCA leads to a long-term increased venous thrombosis risk. Therefore, patients with IVCA and bilateral DVT warrant specialist consideration and may benefit from multidisciplinary team management, with hematology and vascular surgery input. Conclusion: Here, we showcased a rare cause for bilateral lower limb oedema, respectively bilateral deep venous thrombosis complicating IVCA in a patient with Beckwith-Wiedemann syndrome. The importance of this case lies in its novelty, as the association between IVC agenesis and BWS has not yet been described. Furthermore, the treatment of DVT in such situations requires special consideration, taking into account the patient’s weight and the presence of a significant, predisposing vascular abnormality.

Keywords: Beckwith-Wiedemann syndrome, bilateral deep venous thrombosis, inferior vena cava agenesis, venous thromboembolism

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 159

Authors: Kun-Peng Jin, Jin Liang, Ti-Jun Xiao

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In this work, we are concerned with the dynamic behaviors of solutions to some coupled systems with infinite memory, which consist of two partial differential equations where only one partial differential equation has damping. Such coupled systems are good mathematical models to describe the deformation and stress characteristics of some viscoelastic materials affected by temperature change, external forces, and other factors. By using the theory of operator semigroups, we give wellposedness results for the Cauchy problem for these coupled systems. Then, with the help of some auxiliary functions and lemmas, which are specially designed for overcoming difficulties in the proof, we show that the solutions of the coupled systems decay to zero in a strong way under a few basic conditions. The results in this dynamic analysis of coupled systems are generalizations of many existing results.

Keywords: dynamic analysis, coupled system, infinite memory, damping.

Procedia PDF Downloads 202

Authors: F. Noto, M. Castro, R. Garraffo, An. Mirabella, A. Rizzo, G. Cuttone

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The door fan test is a verification procedure on the tightness of a room, necessary following the installation of saturation extinguishing systems and made mandatory according to the UNI 15004-1: 2019 standard whenever a gas extinguishing system is designed and installed. The door fan test was carried out at the Portopalo di Capo Passero headquarters of the Southern National Laboratories and highlighted how the Data Processing Center is perfectly up to standard, passing the door fan test in an excellent way. The Southern National Laboratories constitute a solid research reality, well established in the international scientific panorama. The CED in the Portopalo site has been expanded, so the extinguishing system has been expanded according to a detailed design. After checking the correctness of the design to verify the absence of air leaks, we carried out the door fan test. The activities of the LNS are mainly aimed at basic research in the field of Nuclear Physics, Nuclear and Particle Astrophysics. The Portopalo site will host some of the largest submarine wired scientific research infrastructures built in Europe and in the world, such as KM3NeT and EMSO ERIC; in particular, the site research laboratory in Portopalo will host the power supply and data acquisition systems of the underwater infrastructures, and a technological backbone will be created, unique in the Mediterranean, capable of allowing the connection, at abyssal depths, of dozens of real-time surveying and research structures of the marine environment deep.

Keywords: KM3Net, fire protection, door fan test, CED

Procedia PDF Downloads 79

Authors: Tim Farrelly

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In recent years, AI-powered object detection systems have opened the doors for innovative new applications and products, especially those operating in the real world or ‘on edge’ – namely, in sport. This paper investigates the viability of an ultra-low parameter convolutional neural network specially designed for the detection of footballs on ‘on the edge’ devices. The main contribution of this paper is the exploration of integrating new design features (depth-wise separable convolutional blocks and squeezed and excitation modules) into an ultra-low parameter network and demonstrating subsequent improvements in performance. The results show that tracking the ball from Full HD images with negligibly high accu-racy is possible in real-time.

Keywords: deep learning, object detection, machine vision applications, sport, network design

Procedia PDF Downloads 126

Authors: Hasan Kalyoncu, Füsun Kılçık, Hatice Gülboy Akyıldırım, Aynur Özen, Mehmet Acar, Nur Yoluk

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For evaluation of water quality of the river systems in Antalya Basin, macrozoobenthos samples were taken from 22 determined stations by a hand net and identified at family level. Water quality of Antalya Basin was determined according to Biological Monitoring Working Party (BMWP) system, by using macrozoobenthic invertebrates and physicochemical parameters. As a result of the evaluation, while Aksu Stream was determined as the most polluted stream in Antalya Basin, Isparta Stream was determined as the most polluted tributary of Aksu Stream. Pollution level of the Isparta Stream was determined as quality class V and it is the extremely polluted part of stream. Pollution loads at the sources of the streams were determined in low levels in general. Due to some parts of the streams have passed through deep canyons and take their sources from nonresidential and non-arable regions, majority of the streams that take place in Antalya Basin are at high quality level. Waste water, which comes from agricultural and residential regions, affects the lower basins of the streams. Because of the waste water, lower parts of the stream basins exposed to the pollution under anthropogenic effects. However, in Aksu Stream, which differs by being exposed to domestic and industrial wastes of Isparta City, extreme pollution was determined, particularly in the Isparta Stream part.

Keywords: Antalya basin, biomonitoring, BMWP, water quality

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Authors: J. Ptačinová, J. Ďurica, P. Jurči, M Kusý

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Ledeburitic tool steel Vanadis 6 has been subjected to sub-zero treatment (SZT) at -140 °C and -196 °C, for different durations up to 48 h. The microstructure and hardness have been examined with reference to the same material after room temperature quenching, by using the light microscopy, scanning electron microscopy, X-ray diffraction, and Vickers hardness testing method. The microstructure of the material consists of the martensitic matrix with certain amount of retained austenite, and of several types of carbides – eutectic carbides, secondary carbides, and small globular carbides. SZT reduces the retained austenite amount – this is more effective at -196 °C than at -140 °C. Alternatively, the amount of small globular carbides increases more rapidly after SZT at -140 °C than after the treatment at -140 °C. The hardness of sub-zero treated material is higher than that of conventionally treated steel when tempered at low temperature. Compressive hydrostatic stresses are developed in the retained austenite due to the application of SZT, as a result of more complete martensitic transformation. This is also why the population density of small globular carbides is substantially increased due to the SZT. In contrast, the hardness of sub-zero treated samples decreases more rapidly compared to that of conventionally treated steel, and in addition, sub-zero treated material induces a loss the secondary hardening peak.

Keywords: microstructure, Vanadis 6 tool steel, sub-zero treatment, carbides

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Authors: M. Paraipan, V. M. Javadova, S. I. Tyutyunnikov

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The concept of energy amplifier or accelerator driven system (ADS) involves the use of a particle accelerator coupled with a nuclear reactor. The accelerated particle beam generates a supplementary source of neutrons, which allows the subcritical functioning of the reactor, and consequently a safe exploitation. The harder neutron spectrum realized ensures a better incineration of the actinides. The almost generalized opinion is that the optimal beam for ADS is represented by protons with energy around 1 GeV (gigaelectronvolt). In the present work, a systematic analysis of the energy gain for proton beams with energy from 0.5 to 3 GeV and ion beams from deuteron to neon with energies between 0.25 and 2 AGeV is performed. The target is an assembly of metallic U-Pu-Zr fuel rods in a bath of lead-bismuth eutectic coolant. The rods length is 150 cm. A beryllium converter with length 110 cm is used in order to maximize the energy released in the target. The case of a linear accelerator is considered, with a beam intensity of 1.25‧10¹⁶ p/s, and a total accelerator efficiency of 0.18 for proton beam. These values are planned to be achieved in the European Spallation Source project. The energy gain G is calculated as the ratio between the energy released in the target to the energy spent to accelerate the beam. The energy released is obtained through simulation with the code Geant4. The energy spent is calculating by scaling from the data about the accelerator efficiency for the reference particle (proton). The analysis concerns the G values, the net power produce, the accelerator length, and the period between refueling. The optimal energy for proton is 1.5 GeV. At this energy, G reaches a plateau around a value of 8 and a net power production of 120 MW (megawatt). Starting with alpha, ion beams have a higher G than 1.5 GeV protons. A beam of 0.25 AGeV(gigaelectronvolt per nucleon) ⁷Li realizes the same net power production as 1.5 GeV protons, has a G of 15, and needs an accelerator length 2.6 times lower than for protons, representing the best solution for ADS. Beams of ¹⁶O or ²⁰Ne with energy 0.75 AGeV, accelerated in an accelerator with the same length as 1.5 GeV protons produce approximately 900 MW net power, with a gain of 23-25. The study of the evolution of the isotopes composition during irradiation shows that the increase in power production diminishes the period between refueling. For a net power produced of 120 MW, the target can be irradiated approximately 5000 days without refueling, but only 600 days when the net power reaches 1 GW (gigawatt).

Keywords: accelerator driven system, ion beam, electrical power, energy gain

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Authors: Ali Hamza, Bertug Akintug

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Having an appropriate plan of sanitation systems is one of the critical issues for global urban slums. Poor sanitation systems in urban slums outcomes an enhanced vulnerability of severe diseases, low hygiene and environmental risks within our environment. Mentioning human excreta being one of the most highly risked pollutants among all the other major contributors of sanitation pollutants is increasing public health risks and amounts of pollution loads within the slum environment. Higher population growth, urge of urbanization and illegal status of urban slums makes it impossible to increase the level of performance of sanitation systems in urban slums. According to Sustainable Sanitation Alliance, design parameters for sanitation systems were set up to ensure sustainable environment. This paper reviews the characteristics of human excreta at present, treatment technologies, and procedures of processes that can be adopted feasibly in the urban slums. Keeping these factors as our significant concern of study, assessment of sustainable sanitation systems is done using sanitation chain concept in accordance to the pre-determined sustainability indicators and criteria which reflect the potential and feasible application of waterless sanitation systems bringing sustainable sanitation systems in urban slums.

Keywords: human excreta, sanitation chain, sustainable sanitation systems, urban slums

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Authors: Abdulwakeel B. Raji

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This paper introduces a concept of an intelligent virtual learning environment that involves communication between learners and an artificially intelligent teaching agent in an attempt to replicate classroom learning interactions. The benefits of this technology over current e-learning practices is that it creates a virtual classroom where real time adaptive learning interactions are made possible. This is a move away from the static learning practices currently being adopted by e-learning systems. Over the years, artificial intelligence has been applied to various fields, including and not limited to medicine, military applications, psychology, marketing etc. The purpose of e-learning applications is to ensure users are able to learn outside of the classroom, but a major limitation has been the inability to fully replicate classroom interactions between teacher and students. This study used comparative surveys to gain information and understanding of the current learning practices in Nigerian universities and how they compare to these practices compare to the use of a developed e-learning system. The study was conducted by attending several lectures and noting the interactions between lecturers and tutors and as an aftermath, a software has been developed that deploys the use of an artificial intelligent teaching-agent alongside an e-learning system to enhance user learning experience and attempt to create the similar learning interactions to those found in classroom and lecture hall settings. Dialogflow has been used to implement a teaching-agent, which has been developed using JSON, which serves as a virtual teacher. Course content has been created using HTML, CSS, PHP and JAVASCRIPT as a web-based application. This technology can run on handheld devices and Google based home technologies to give learners an access to the teaching agent at any time. This technology also implements the use of definite clause grammars and natural language processing to match user inputs and requests with defined rules to replicate learning interactions. This technology developed covers familiar classroom scenarios such as answering users’ questions, asking ‘do you understand’ at regular intervals and answering subsequent requests, taking advanced user queries to give feedbacks at other periods. This software technology uses deep learning techniques to learn user interactions and patterns to subsequently enhance user learning experience. A system testing has been undergone by undergraduate students in the UK and Nigeria on the course ‘Introduction to Database Development’. Test results and feedback from users shows that this study and developed software is a significant improvement on existing e-learning systems. Further experiments are to be run using the software with different students and more course contents.

Keywords: virtual learning, natural language processing, definite clause grammars, deep learning, artificial intelligence

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Authors: Sakshi V. Tantak, Vishap K. Malik, Neelanjney Pilarisetty

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A patent is an exclusive right granted for an invention. It can be a product or a process that provides an innovative method of doing something, or offers a new technical perspective or solution to a problem. A patent can be obtained by making the technical information and details about the invention publicly available. The patent owner has exclusive rights to prevent or stop anyone from using the patented invention for commercial uses. Any commercial usage, distribution, import or export of a patented invention or product requires the patent owner’s consent. It has been observed that the central and important parts of patents are scripted in idiosyncratic and complex linguistic structures that can be difficult to read, comprehend or interpret for the masses. The abstracts of these patents tend to obfuscate the precise nature of the patent instead of clarifying it via direct and simple linguistic constructs. This makes it necessary to have an efficient access to this knowledge via concise and transparent summaries. However, as mentioned above, due to complex and repetitive linguistic constructs and extremely long sentences, common extraction-oriented automatic text summarization methods should not be expected to show a remarkable performance when applied to patent documents. Other, more content-oriented or abstractive summarization techniques are able to perform much better and generate more concise summaries. This paper proposes an efficient summarization system for patents using artificial intelligence, natural language processing and deep learning techniques to condense the knowledge and essential information from a patent document into a single summary that is easier to understand without any redundant formatting and difficult jargon.

Keywords: abstractive summarization, deep learning, natural language Processing, patent document

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Authors: John Eric C. Bargas, Ma. Cecilia M. Marcos

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One of the most common ground improvement techniques is Deep Soil Mixing (DSM). As the technique progresses, there is still lack in the development when it comes to depth control. This was the issue experienced during the installation of DSM in one of the National projects in the Philippines. This study assesses the feasibility of using equipment drilling parameters such as hydraulic pressure, drilling speed and rotational speed in determining the Standard Penetration Test N-value of a specific soil. Hydraulic pressure and drilling speed with a constant rotational speed of 30 rpm have a positive correlation with SPT N-value for cohesive soil and sand. A linear trend was observed for cohesive soil. The correlation of SPT N-value and hydraulic pressure yielded a R²=0.5377 while the correlation of SPT N-value and drilling speed has a R²=0.6355. While the best fitted model for sand is polynomial trend. The correlation of SPT N-value and hydraulic pressure yielded a R²=0.7088 while the correlation of SPT N-value and drilling speed has a R²=0.4354. The low correlation may be attributed to the behavior of sand when the auger penetrates. Sand tends to follow the rotation of the auger rather than resisting which was observed for very loose to medium dense sand. Specific Energy and the product of hydraulic pressure and drilling speed yielded same R² with a positive correlation. Linear trend was observed for cohesive soil while polynomial trend for sand. Cohesive soil yielded a R²=0.7320 which has a strong relationship. Sand also yielded a strong relationship having a coefficient of determination, R²=0.7203. It is feasible to use hydraulic pressure and drilling speed to estimate the SPT N-value of the soil. Also, the product of hydraulic pressure and drilling speed can be a substitute to specific energy when estimating the SPT N-value of a soil. However, additional considerations are necessary to account for other influencing factors like ground water and physical and mechanical properties of soil.

Keywords: ground improvement, equipment drilling parameters, standard penetration test, deep soil mixing

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Authors: Yuanchao Sang, Zhihao Gong, Longsheng Chen, Long Chen

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Clothing fashion represents a human’s aesthetic appreciation towards everyday outfits and appetite for fashion, and it reflects the development of status in society, humanity, and economics. However, modelling fashion by machine is extremely challenging because fashion is too abstract to be efficiently described by machines. Even human beings can hardly reach a consensus about fashion. In this paper, we are dedicated to answering a fundamental fashion-related problem: what image feature best describes clothing fashion? To address this issue, we have designed and evaluated various image features, ranging from traditional low-level hand-crafted features to mid-level style awareness features to various current popular deep neural network-based features, which have shown state-of-the-art performance in various vision tasks. In summary, we tested the following 9 feature representations: color, texture, shape, style, convolutional neural networks (CNNs), CNNs with distance metric learning (CNNs&DML), AutoEncoder, CNNs with multiple layer combination (CNNs&MLC) and CNNs with dynamic feature clustering (CNNs&DFC). Finally, we validated the performance of these features on two publicly available datasets. Quantitative and qualitative experimental results on both intra-domain and inter-domain fashion clothing image retrieval showed that deep learning based feature representations far outweigh traditional hand-crafted feature representation. Additionally, among all deep learning based methods, CNNs with explicit feature clustering performs best, which shows feature clustering is essential for discriminative fashion feature representation.

Keywords: convolutional neural network, feature representation, image processing, machine modelling

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Authors: Bharatendra Rai

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The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Ankur Jain, W. Wilfred Godfrey

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Embedded systems are usually microcontroller-based systems that represent a class of reliable and dependable dedicated computer systems designed for specific purposes. Micro-controllers are used in most electronic devices in an endless variety of ways. Some micro-controller-based embedded systems are required to respond to external events in the shortest possible time and such systems are known as real-time embedded systems. So in multitasking system there is a need of task Scheduling,there are various scheduling algorithms like Fixed priority Scheduling(FPS),Earliest deadline first(EDF), Rate Monotonic(RM), Deadline Monotonic(DM),etc have been researched. In this Report various conventional algorithms have been reviewed and analyzed, these algorithms consists of single shape task, A new Multishape scheduling algorithms has been proposed and implemented and analyzed.

Keywords: dm, edf, embedded systems, fixed priority, microcontroller, rtos, rm, scheduling algorithms

Procedia PDF Downloads 384

Authors: A. Zecchin, E. Vallicella, I. Alberi, A. Dalle Carbonare, A. Festi, F. Galeone, S. Garzon, R. Raffaelli, P. Pomini, M. Franchi

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Introduction: Severe endometriosis is a multiorgan disease, that involves bowel in 31% of cases. Disabling symptoms and deep infiltration can lead to bowel obstruction: surgical bowel treatment may be needed. In these cases, colorectal segment resection is usually performed by inferior mesenteric artery ligature, as radically as for oncological surgery. This study was made on surgery based on intestinal vascular axis’ preservation. It was assessed postoperative complications risks (mainly rate of dehiscence of intestinal anastomoses), and results were compared with the ones found in literature about classical colorectal resection. Materials and methods: This was a retrospective study based on 62 patients with deep infiltrating endometriosis of the bowel, which undergo segmental resection with intestinal vascular axis preservation, between 2013 and 2016. It was assessed complications related to the intervention both during hospitalization and 30-60 days after resection. Particular attention was paid to the presence of anastomotic dehiscence. 52 patients were finally telephonically interviewed in order to investigate the presence or absence of intestinal constipation. Results and Conclusion: Segmental intestinal resection performed in this study ensured a more conservative vascular approach, with lower rate of anastomotic dehiscence (1.6%) compared to classical literature data (10.0% to 11.4% ). No complications were observed regarding spontaneous recovery of intestinal motility and bladder emptying. Constipation in some patients, even after years of intervention, is not assessable in the absence of a preoperative constipation state assessment.

Keywords: anastomotic dehiscence, deep infiltrating endometriosis, colorectal resection, vascular axis preservation

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Authors: Ying Han, Yuanxiang Chen, Yongtao Huang, Jia Fu, Kaile Li, Shangjing Lin, Jianguo Yu

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In long-haul and high-speed optical transmission system, the orthogonal frequency division multiplexing (OFDM) signal suffers various linear and non-linear impairments. In recent years, researchers have proposed compensation schemes for specific impairment, and the effects are remarkable. However, different impairment compensation algorithms have caused an increase in transmission delay. With the widespread application of deep neural networks (DNN) in communication, multi-impairment compensation based on DNN will be a promising scheme. In this paper, we propose and apply DNN to compensate multi-impairment of 16-QAM coherent optical OFDM signal, thereby improving the performance of the transmission system. The trained DNN models are applied in the offline digital signal processing (DSP) module of the transmission system. The models can optimize the constellation mapping signals at the transmitter and compensate multi-impairment of the OFDM decoded signal at the receiver. Furthermore, the models reduce the peak to average power ratio (PAPR) of the transmitted OFDM signal and the bit error rate (BER) of the received signal. We verify the effectiveness of the proposed scheme for 16-QAM Coherent Optical OFDM signal and demonstrate and analyze transmission performance in different transmission scenarios. The experimental results show that the PAPR and BER of the transmission system are significantly reduced after using the trained DNN. It shows that the DNN with specific loss function and network structure can optimize the transmitted signal and learn the channel feature and compensate for multi-impairment in fiber transmission effectively.

Keywords: coherent optical OFDM, deep neural network, multi-impairment compensation, optical transmission

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Authors: Shadi Motamedighazvini, Roohollah Taherkhani, Mahdi Mahdikhani, Najme Hashempour

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Nowadays, green building rating systems are an inevitable necessity for managing environmental considerations to achieve green buildings. The aim of this paper is to deliver a detailed recognition of what has been the focus of green building policymakers around the world; It is important to conduct this study in a way that can provide a context for researchers who intend to establish or upgrade existing rating systems. In this paper, fifteen rating systems including four worldwide well-known plus eleven local rating systems which have been selected based on the answers to the questionnaires were examined. Their similarities and differences in mandatory and prerequisite clauses, highest and lowest scores for each criterion, the most frequent criteria, and most frequent sub-criteria are determined. The research findings indicated that although the criteria of energy, water, indoor quality (except Homestar), site and materials (except GRIHA) were common core criteria for all rating systems, their sub-criteria were different. This research, as a roadmap, eliminates the lack of a comprehensive reference that encompasses the key criteria of different rating systems. It shows the local systems need to be revised to be more comprehensive and adaptable to their own country’s conditions such as climate.

Keywords: environmental assessment, green buildings, green building criteria, green building rating systems, sustainability, rating tools

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Authors: C. S. Sona, Makrand A. Khanwale, Channamallikarjun S. Mathpati

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New generation nuclear power plants are currently being developed to be highly economical, to be passive safe, to produce hydrogen. An important feature of these reactors will be the use of coolants at temperature higher than that being used in current nuclear reactors. The molten fluoride salt with a eutectic composition of 46.5% LiF - 11.5% NaF - 42% KF (mol %) commonly known as FLiNaK is a leading candidate for heat transfer coolant for these nuclear reactors. CFD simulations were carried out using large eddy simulations to investigate the flow characteristics of molten FLiNaK at 850°C at a Reynolds number of 10,500 in a cylindrical pipe. Simulation results have been validated with the help of mean velocity profile using direct numerical simulation data. Transient velocity information was used to identify and characterise turbulent structures which are important for transfer of heat across solid-fluid interface. A wavelet transform based methodology called wavelet transform modulus maxima was used to identify and characterise the singularities. This analysis was also used for flow visualisation, and also to calculate the heat transfer coefficient using small eddy model. The predicted Nusselt number showed good agreement with the available experimental data.

Keywords: FLiNaK, heat transfer, molten salt, turbulent structures

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Authors: Mohamed Asaad Abdelrazek, Amir Taher El-Sheikh, M. Zayan, A.M. Elhady

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The successful realization of complex systems is dependent not only on the technology issues and the process for implementing them, but on the management issues as well. Managing the systems development lifecycle requires technical management. Systems engineering management is the technical management. Systems engineering management is accomplished by incorporating many activities. The three major activities are development phasing, systems engineering process and lifecycle integration. Systems engineering management activities are performed across the system development lifecycle. Due to the ever-increasing complexity of systems as well the difficulty of managing and tracking the development activities, new ways to achieve systems engineering management activities are required. This paper presents a systematic approach used as a design management tool applied across systems engineering management roles. In this approach, Transdisciplinary System Development Lifecycle (TSDL) Model has been modified and integrated with Quality Function Deployment. Hereinafter, the name of the systematic approach is the Transdisciplinary Quality System Development Lifecycle (TQSDL) Model. The QFD translates the voice of customers (VOC) into measurable technical characteristics. The modified TSDL model is based on Axiomatic Design developed by Suh which is applicable to all designs: products, processes, systems and organizations. The TQSDL model aims to provide a robust structure and systematic thinking to support the implementation of systems engineering management roles. This approach ensures that the customer requirements are fulfilled as well as satisfies all the systems engineering manager roles and activities.

Keywords: axiomatic design, quality function deployment, systems engineering management, system development lifecycle

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Authors: K. Liagkouras, K. Metaxiotis

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Customer relationship management (CRM) systems in business are a reality of the contemporary business world for the last decade or so. Still, there are grey areas regarding the successful implementation and operation of CRM systems in business. This paper, through the systematic study of the CRM implementation paradigm, attempts to identify the most important challenges and opportunities that the CRM systems face in a rapidly changing business world.

Keywords: customer relationship management, CRM, business, literature review

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Authors: Ming-Jen Huang, Chun-Fang Huang, Chiching Wei

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With the recent advance of the deep neural network, we observe new applications of NLP (natural language processing) and CV (computer vision) powered by deep neural networks for processing business documents. However, creating a real-world document processing system needs to integrate several NLP and CV tasks, rather than treating them separately. There is a need to have a unified approach for processing documents containing textual and graphical elements with rich formats, diverse layout arrangement, and distinct semantics. In this paper, a framework that fulfills this unified approach is presented. The framework includes a representation model definition for holding the information generated by various tasks and specifications defining the coordination between these tasks. The framework is a blueprint for building a system that can process documents with rich formats, styles, and multiple types of elements. The flexible and lightweight design of the framework can help build a system for diverse business scenarios, such as contract monitoring and reviewing.

Keywords: document processing, framework, formal definition, machine learning

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Fatima Faiza Ahmed, Syed Farrukh Hussain

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The last few decades have seen a large proportion of our population bending towards e-learning technologies, starting from learning tools used in primary and elementary schools to competency based e-learning systems specifically designed for applications like finance and marketing. The huge diversity in this crowd brings about a large number of challenges for the designers of these e-learning systems, one of which is the adaptability of such systems. This paper focuses on adaptability in the learning material in an e-learning course and how artificial intelligence and the semantic web can be used as an effective tool for this purpose. The study proved that the semantic web, still a hot topic in the area of computer science can prove to be a powerful tool in designing and implementing adaptable e-learning systems.

Keywords: adaptable e-learning, HTMLParser, information extraction, semantic web

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Authors: Hossam Abdelrahman, Sama Rostom, Reem Yassein, Yara Mohamed, Salma Salah, Nour Awny

Abstract:

In today’s environment, people are becoming increasingly interested in their appearance. However, they are afraid of their unknown appearance after a plastic surgery or treatment. Accidents, burns and genetic problems such as bowing of body parts of people have a negative impact on their mental health with their appearance and this makes them feel uncomfortable and underestimated. The approach presents a revolutionary deep learning-based image inpainting method that analyses the various picture structures and corrects damaged images. In this study, A model is proposed based on the in-painting of medical images with Stable Diffusion Inpainting method. Reconstructing missing and damaged sections of an image is known as image inpainting is a key progress facilitated by deep neural networks. The system uses the input of the user of an image to indicate a problem, the system will then modify the image and output the fixed image, facilitating for the patient to see the final result.

Keywords: generative adversarial network, large mask inpainting, stable diffusion inpainting, plastic surgery

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Authors: Yaxin Bi

Abstract:

Accurate prediction and understanding of the evolution mechanisms of earthquakes remain challenging in the fields of geology, geophysics, and seismology. This study leverages Long Short-Term Memory (LSTM) networks and Generative Adversarial Networks (GANs), a generative model tailored to time-series data, for generating synthetic time series data based on Swarm satellite data, which will be used for detecting seismic anomalies. LSTMs demonstrated commendable predictive performance in generating synthetic data across multiple countries. In contrast, the GAN models struggled to generate synthetic data, often producing non-informative values, although they were able to capture the data distribution of the time series. These findings highlight both the promise and challenges associated with applying deep learning techniques to generate synthetic data, underscoring the potential of deep learning in generating synthetic electromagnetic satellite data.

Keywords: LSTM, GAN, earthquake, synthetic data, generative AI, seismic precursors

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Authors: Syed Kashif Ali, Usman Javaid, Abdullah Chohan

Abstract:

In large enterprises multiple departments use different sort of information systems and databases according to their needs. These systems are independent and heterogeneous in nature and sharing information/data between these systems is not an easy task. The usage of middleware technologies have made data sharing between systems very easy. However, monitoring the exchange of data/information for verification purposes between target and source systems is often complex or impossible for maintenance department due to security/access privileges on target and source systems. In this paper, we are intended to present our experience of an end to end data monitoring approach at middle ware level implemented in Oracle BPEL for data verification without any help of monitoring tool.

Keywords: service level agreement, SOA, BPEL, oracle fusion middleware, web service monitoring

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Authors: Michelle Sánchez de León Brajkovich, Nuria Martí Audi

Abstract:

The envelope is one of the most important elements when one analyzes the operation of the building in terms of sustainability. Taking this into consideration, this research focuses on setting a classification system of the envelopes opaque systems, crossing the knowledge and parameters of construction systems with requirements in terms of sustainability that they may have, to have a better understanding of how these systems work with respect to their sustainable contribution to the building. Therefore, this paper evaluates the importance of the envelope design on the building sustainability. It analyses the parameters that make the construction systems behave differently in terms of sustainability. At the same time it explains the classification process generated from this analysis that results in a classification where all opaque vertical envelope construction systems enter.

Keywords: sustainable, exterior walls, envelope, facades, construction systems, energy efficiency

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Authors: Shahab Uddin, Kazi M. Uddin, Hamamah Sadiqa

Abstract:

The paper deals with the Banghabandhu Multipurpose Bridge (BMB), the 11th longest bridge in the world was constructed in 1998 aimed at contributing to promote economic development in Bangladesh. In recent years, however, the high incidence of traffic accidents and injuries at the bridge sites looms as a great safety concern. Investigation into the derailment of nine bogies out of thirteen of Dinajpur-bound intercity train ‘Drutajan Express ’were derailed and inclined on the Banghabandhu Multipurpose Bridge on 28 April 2014. The train accident in Bridge will be deep concern for both structural safety of bridge and people than other vehicles accident. In this study we analyzed the disaster probability of the Banghabandhu Multipurpose Bridge for accidents by checking the fitness of Bridge structure. We found that train accident impact is more risky than other vehicles accidents. We also found that socio-economic impact on Bangladesh will be deep concerned.

Keywords: train accident, derailment, disaster, socio-economic

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Authors: M. J. Park, S. H. Lee, C. H. Lee, O. M. Kwon

Abstract:

This paper investigates stability problem for linear systems of neutral type with time-varying delay. By constructing various Lyapunov-Krasovskii functional, and utilizing some mathematical techniques, the sufficient stability conditions for the systems are established in terms of linear matrix inequalities (LMIs), which can be easily solved by various effective optimization algorithms. Finally, some illustrative examples are given to show the effectiveness of the proposed criterion.

Keywords: neutral systems, time-delay, stability, Lyapnov method, LMI

Procedia PDF Downloads 334