Search results for: cloud data privacy and integrity

Authors: Mahdi Daghmhehci Firoozjaei, Minchang Kim, Dima Alhadidi

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In this paper, we present a power profiling model for smart grid consumers based on real time load data acquired smart meters. It profiles consumers’ power consumption behaviour using the dynamic time warping (DTW) clustering algorithm. Due to the invariability of signal warping of this algorithm, time-disordered load data can be profiled and consumption features be extracted. Two load types are defined and the related load patterns are extracted for classifying consumption behaviour by DTW. The classification methodology is discussed in detail. To evaluate the performance of the method, we analyze the time-series load data measured by a smart meter in a real case. The results verify the effectiveness of the proposed profiling method with 90.91% true positive rate for load type clustering in the best case.

Keywords: power profiling, user privacy, dynamic time warping, smart grid

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Authors: P. K. Purohit, Azad A. Mansoori, Parvaiz A. Khan, Purushottam Bhawre, Sharad C. Tripathi, A. M. Aslam, Malik A. Waheed, Shivangi Bhardwaj, A. K. Gwal

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The geomagnetic storm represents the most outstanding example of solar wind-magnetospheric interaction, which causes global disturbances in the geomagnetic field as well as the trigger ionospheric disturbances. We study the behaviour of ionospheric Total Electron Content (TEC) during the geomagnetic storms. For the present investigation we have selected 47 intense geomagnetic storms (Dst ≤ -100nT) that were observed during the solar cycle 23 i.e. during 1998-2006. We then categorized these storms into four categories depending upon their solar sources like Magnetic Cloud (MC), Co-rotating Interaction Region (CIR), SH+ICME and SH+MC. We then studied the behaviour of ionospheric TEC at a mid latitude station Usuda (36.13N, 138.36E), Japan during these storm events produced by four different solar sources. During our study we found that the smooth variations in TEC are replaced by rapid fluctuations and the value of TEC is strongly enhanced during the time of these storms belonging to all the four categories. However, the greatest enhancements in TEC are produced during those geomagnetic storms which are either caused by sheath driven magnetic cloud (SH+MC) or sheath driven ICME (SH+ICME). We also derived the correlation between the TEC enhancements produced during storms of each category with the minimum Dst. We found the strongest correlation exists for the SH+ICME category followed by SH+MC, MC and finally CIR. Since the most intense storms were either caused by SH+ICME or SH+MC while the least intense storms were caused by CIR, consequently the correlation was the strongest with SH+ICME and SH+MC and least with CIR.

Keywords: GPS, TEC, geomagnetic storm, sheath driven magnetic cloud

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Authors: S. R. Dehghani, G. F. Naterer, Y. S. Muzychka

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Wave-impact sea spray creates many droplets which form a spray cloud traveling over marine objects same as marine vessels and offshore structures. In cold climates such as Arctic reigns, sea spray icing, which is ice accretion on cold substrates, is strongly dependent on the wave-impact sea spray. The rate of cooling of droplets affects the process of icing that can yield to dry or wet ice accretion. Trajectories of droplets determine the potential places for ice accretion. Combining two models of trajectories and heat transfer for droplets can predict the risk of ice accretion reasonably. The majority of the cooling of droplets is because of droplet evaporations. In this study, a combined model using trajectory and heat transfer evaluate the situation of a cloud of spray from the generation to impingement. The model uses some known geometry and initial information from the previous case studies. The 3D model is solved numerically using a standard numerical scheme. Droplets are generated in various size ranges from 7 mm to 0.07 mm which is a suggested range for sea spray icing. The initial temperature of droplets is considered to be the sea water temperature. Wind velocities are assumed same as that of the field observations. Evaluations are conducted using some important heading angles and wind velocities. The characteristic of size-velocity dependence is used to establish a relation between initial sizes and velocities of droplets. Time intervals are chosen properly to maintain a stable and fast numerical solution. A statistical process is conducted to evaluate the probability of expected occurrences. The medium size droplets can reach the highest heights. Very small and very large droplets are limited to lower heights. Results show that higher initial velocities create the most expanded cloud of spray. Wind velocities affect the extent of the spray cloud. The rate of droplet cooling at the start of spray formation is higher than the rest of the process. This is because of higher relative velocities and also higher temperature differences. The amount of water delivery and overall temperature for some sample surfaces over a marine vessel are calculated. Comparing results and some field observations show that the model works accurately. This model is suggested as a primary model for ice accretion on marine vessels.

Keywords: evaporation, sea spray, marine icing, numerical solution, trajectory

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Authors: AlHanoof A. Alharbi, Fatima M. Alamoudi, Razan A. Albrahim, Sarah F. Alharbi, Abdullah M Almuhaideb, Norah A. Almubairik, Abdulrahman Alharby, Naya M. Nagy

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Recently, drones received a rapid interest in different industries worldwide due to its powerful impact. However, limitations still exist in this emerging technology, especially privacy violation. These aircrafts consistently threaten the security of entities by entering restricted areas accidentally or deliberately. Therefore, this research project aims to develop drone detection and prevention mechanism to protect the restricted area. Until now, none of the solutions have met the optimal requirements of detection which are cost-effectiveness, high accuracy, long range, convenience, unaffected by noise and generalization. In terms of prevention, the existing methods are focusing on impractical solutions such as catching a drone by a larger drone, training an eagle or a gun. In addition, the practical solutions have limitations, such as the No-Fly Zone and PITBULL jammers. According to our study and analysis of previous related works, none of the solutions includes detection and prevention at the same time. The proposed solution is a combination of detection and prevention methods. To implement the detection system, a passive radar will be used to properly identify the drone against any possible flying objects. As for the prevention, jamming signals and forceful safe landing of the drone integrated together to stop the drone’s operation. We believe that applying this mechanism will limit the drone’s invasion of privacy incidents against highly restricted properties. Consequently, it effectively accelerates drones‘ usages at personal and governmental levels.

Keywords: detection, drone, jamming, prevention, privacy, RF, radar, UAV

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Authors: Farid Fahami

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This article explores the transformative role of technology in the finance, banking, and insurance sectors. It examines key technological trends such as AI, blockchain, data analytics, and digital platforms and their impact on operations, customer experiences, and business models. The article highlights the benefits of technology adoption, including improved efficiency, cost reduction, enhanced customer experiences, and expanded financial inclusion. It also addresses challenges like cybersecurity, data privacy, and the need for upskilling. Real-world case studies demonstrate successful technology integration, and recommendations for stakeholders emphasize embracing innovation and collaboration. The article concludes by emphasizing the importance of technology in shaping the future of these sectors.

Keywords: banking, finance, insurance, technology

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Authors: Christiana Ebobo

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Cybercrime consists of numerous crimes that are perpetrated on the internet on daily basis. The forms include but not limited to Identity theft, Pretentious dating, Desktop counterfeiting, Internet chat room, Cyber harassment, Fraudulent electronic mails, Automated Teller Machine Spoofing, Pornography, Piracy, Hacking, Credit card frauds, Phishing and Spamming. The general term used among the youths for this type of crime in Nigeria is ‘Yahoo Yahoo’. Cybercrime is on the increase among the youths at all levels as such this study aims at examining the impact of cybercrime on youth development in Nigeria. The study examines the impact of cybercrime on youths’ academic performance, integrity, employment and religious practices. The study is a survey which made use of questionnaire and focus group discussion among 150 randomly selected youths in Gwagwalada LCDA, Federal Capital Territory, Nigeria. The study adopts the systems theory as its theoretical framework. The study also adopts the simple frequency table and percentage for its data analysis. The study reveals that cybercrime has eaten deep into the minds of some youths and some of them are practicing diabolic means to succeed in it. It is also reveals that majority (68%) of the respondents believe that cybercrime impacts negatively on youths’ academic performance in Nigeria. The major recommendation of this study is that cybercrime offenders should be treated like armed robbers in order to discourage other youths from getting involved in it.

Keywords: armed robber, cybercrime, integrity, youth

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Authors: Anurag Niranjan, Michael Fitzpatrick, Yin Jin Janin, Jazeel Chukkan, Niall Smyth

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Fillet welds are used in joining longitudinal stiffeners in ship structures. Welding residual stresses in fillet welds are generally distributed in a non-uniform manner, as shown in previous research the residual stress redistribution occurs under the cyclic loading that is experienced by such joints during service, and the combination of the initial residual stress, local constraints, and loading can alter the stress field in ways that are extremely difficult to predict. As the residual stress influences the crack propagation originating from the toe of the fillet welds, full understanding of the residual stress field and how it evolves is very important for structural integrity calculations. Knowledge of the residual stress redistribution in the presence of a flaw is therefore required for better fatigue life prediction. Moreover, defect assessment procedures such as BS7910 offer very limited guidance for flaw acceptance and the associated residual stress redistribution in the assessment of fillet welds. Therefore the objective of this work is to study a surface-breaking flaw at the weld toe region in a fillet weld under cyclic load, in conjunction with residual stress measurement at pre-defined crack depths. This work will provide details of residual stress redistribution under cyclic load in the presence of a crack. The outcome of this project will inform integrity assessment with respect to the treatment of residual stress in fillet welds. Knowledge of the residual stress evolution for this weld geometry will be greatly beneficial for flaw tolerance assessments (BS 7910, API 591).

Keywords: fillet weld, fatigue, residual stress, structure integrity

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Authors: Al-Naqdi Ibtehal Abdulmonem

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Kindergarten buildings are essential for early childhood education, providing a secure environment for children's development. However, they are susceptible to seismic forces, which can endanger occupants during earthquakes. This article emphasizes the importance of conducting thorough seismic analysis and implementing proper structural design to protect the well-being of children, staff, and visitors. By prioritizing structural integrity and considering functional requirements, engineers can mitigate risks associated with seismic events. The use of specialized software like ETABS is crucial for designing earthquake-resistant kindergartens. An analysis using ETABS software compared the structural performance of two single-story kindergartens in Iraq's Ministry of Education, designed with and without seismic considerations. The analysis aimed to assess the impact of seismic design on structural integrity and safety. The kindergarten was designed with seismic considerations, including moment frames. In contrast, the same kindergarten was analyzed without seismic effects, revealing a lack of structural elements to resist lateral forces, rendering it vulnerable to structural failure during an earthquake. Maximum major shear increased over 4 times and over 5 times for bending moment in both kindergartens designed with seismic considerations induced by lateral loads and seismic forces. This component of shear force is vital for designing elements to resist lateral loads and ensure structural stability.

Keywords: seismic analysis, structural design, lateral loads, earthquake resistance, major shear, ETABS

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Authors: Igor Kuzminov

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Till now, the nature of lightning is not established. A hypothesis of the origin of lightning is proposed. The lightning charge is formed by electromagnetic induction. The role of the conductor is performed by the air mass of the cloud. This conductor moves in the Earth's magnetic field. The upper and lower edges of the cloud are the plates of the capacitor. Lightning is a special case of electromagnetic processes in an atmosphere. The category of lightning occurs in the process of accumulation of a charge. The process of accumulation goes constantly, but the charge is not fixed. Naturally, the hypothesis demands the carrying out of additional experiments and official acknowledgement. As the proof of a hypothesis can serve that the maximal lighting activity in an equatorial zone where cosφ it is close to 1. An experiment conducted privately showed that there is a potential difference in the atmosphere at different levels. The probability of applied value development of power installation is great.

Keywords: electromagnetic induction, Earth's magnetic field, plates of the capacitors, charge accumulation

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Authors: Janusz Bedkowski, Grzegorz Kisala, Michal Wlasiuk, Piotr Pokorski

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Ground-truth data is essential for VO (Visual Odometry) and SLAM (Simultaneous Localization and Mapping) quantitative evaluation using e.g. ATE (Absolute Trajectory Error) and RPE (Relative Pose Error). Many open-access data sets provide raw and ground-truth data for benchmark purposes. The issue appears when one would like to validate Visual Odometry and/or SLAM approaches on data captured using the device for which the algorithm is targeted for example mobile phone and disseminate data for other researchers. For this reason, we propose an open source, open hardware groundtruth system that provides an accurate and precise trajectory with a 3D point cloud. It is based on LiDAR Livox Mid-360 with a non-repetitive scanning pattern, on-board Raspberry Pi 4B computer, battery and software for off-line calculations (camera to LiDAR calibration, LiDAR odometry, SLAM, georeferencing). We show how this system can be used for the evaluation of various the state of the art algorithms (Stella SLAM, ORB SLAM3, DSO) in typical indoor monocular VO/SLAM.

Keywords: SLAM, ground truth, navigation, LiDAR, visual odometry, mapping

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Authors: Lucy W. Mburu, Richard Karanja, Simon N. Mwendia

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Over recent years, learners have experienced a constant need to access on demand knowledge that is fully aligned with the paradigm of cloud computing. As motivated by the global sustainable development goal to ensure inclusive and equitable learning opportunities, this research has developed a framework hinged on the knowledge-as-a-service architecture that utilizes knowledge from ambient learning systems. Through statistical analysis and decision tree modeling, the study discovers influential variables for ambient learning among university students. The main aim is to generate a platform for disseminating and exploiting the available knowledge to aid the learning process and, thus, to improve educational support on the ambient learning system. The research further explores how collaborative effort can be used to form a knowledge network that allows access to heterogeneous sources of knowledge, which benefits knowledge consumers, such as the developers of ambient learning systems.

Keywords: actionable knowledge, ambient learning, cloud computing, decision trees, knowledge as a service

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Authors: Takashi Yamaguchi, Ichio Asanuma, Jong G. Park, Kenneth J. Mackin, John Mittleman

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In this paper, vessel detection using the artificial neural network is proposed in order to automatically construct the vessel detection model from the satellite imagery of day/night band (DNB) in visible infrared in the products of Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (Suomi-NPP).The goal of our research is the establishment of vessel detection method using the satellite imagery of DNB in order to monitor the change of vessel activity over the wide region. The temporal vessel monitoring is very important to detect the events and understand the circumstances within the maritime environment. For the vessel locating and detection techniques, Automatic Identification System (AIS) and remote sensing using Synthetic aperture radar (SAR) imagery have been researched. However, each data has some lack of information due to uncertain operation or limitation of continuous observation. Therefore, the fusion of effective data and methods is important to monitor the maritime environment for the future. DNB is one of the effective data to detect the small vessels such as fishery ships that is difficult to observe in AIS. DNB is the satellite sensor data of VIIRS on Suomi-NPP. In contrast to SAR images, DNB images are moderate resolution and gave influence to the cloud but can observe the same regions in each day. DNB sensor can observe the lights produced from various artifact such as vehicles and buildings in the night and can detect the small vessels from the fishing light on the open water. However, the modeling of vessel detection using DNB is very difficult since complex atmosphere and lunar condition should be considered due to the strong influence of lunar reflection from cloud on DNB. Therefore, artificial neural network was applied to learn the vessel detection model. For the feature of vessel detection, Brightness Temperature at the 3.7 μm (BT3.7) was additionally used because BT3.7 can be used for the parameter of atmospheric conditions.

Keywords: artificial neural network, day/night band, remote sensing, Suomi National Polar-orbiting Partnership, vessel detection, Visible Infrared Imaging Radiometer Suite

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Authors: Amir Hadi Ziaie, Mostafa Hashemi, Shahram Jalalzadeh

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It is now known that the end state of the collapse process of a dense star under its own gravity is the formation of a spacetime singularity. This is the spacetime event where the energy density and spacetime curvature diverge, and the classical general relativity breaks down. As we know, a realistic star is composed of fermions so that their spin effects could alter the final fate of the collapse scenario. The underlying theory within which the inclusion of spin effects can be worked out is the Einstein-Cartan theory. In this theory, the spacetime torsion which is defined as a geometrical quantity, is related to an intrinsic angular momentum of fermions (spin). In this work, we study the collapse process of a homogeneous spin fluid in such a framework and show that taking into account the spin effects of the collapsing cloud could prevent the formation of spacetime singularity.

Keywords: gravitational collapse, einstein-cartan theory, spacetime singularity, black hole physics

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Authors: Marat Kozhomberdiev

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The research will investigate the modern corporate finance management technologies currently used in the era of digitalization of the global economy and the degree to which financial institutions are utilizing these new technologies in the field of corporate finance management in Kyrgyzstan. The main purpose of the research is to reveal the role of financial management technologies as joint service centers, intercompany banks, specialized payment centers in the third-world country. Particularly, the analysis of the implacability of automated corporate finance management systems such as enterprise resource planning system (ERP) and treasury management system (TMS) will be carried out. Moreover, the research will investigate the role of cloud accounting systems in corporate finance management in Kyrgyz banks and whether it has any impact on the field of improving corporate finance management. The study will utilize a data collection process via surveying 3 banks in Kyrgyzstan, namely Mol-Bulak, RSK, and KICB. The banks were chosen based on their ownerships, such as state banks, private banks with local authorized capital, and private bank with international capital. The regression analysis will be utilized to reveal the correlation between the ownership of the bank and the use of new financial management technologies. The research will provide policy recommendations to both private and state banks on developing strategies for switching and utilizing modern corporate finance management technologies in their daily operations.

Keywords: digital economy, corporate finance, digital environment, digital technologies, cloud technologies, financial management

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Authors: Prageet Aeron, Shilpi Jain

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The present work explores the trust building mechanisms in the context of e-commerce vendors and reconciles trust as a cognitive as well as a knowledge-based mechanism in the framework which is developed. The paper conducts an empirical examination of the variables integrity, benevolence, and ability with trust as the dependent variable and propensity to trust as the mediating variable. Authors establish ability and integrity as primary antecedents as well as establish the central role of trust propensity in the online context for Indian buyers. Authors further identify that benevolence in the context of Indian buyers online behaviour seems insignificant, and this seems counter-intutive given the role of discounts in the Indian market. Lastly, authors conclude that the role of media and social influencers in building a perception of trust seems of little consequence.

Keywords: e-commerce, trust, e-retailers, propensity to trust

Procedia PDF Downloads 341

Authors: Sozon, Pok Wei Fong, Sia Bee Chuan, Omar Hamdan Mohammad

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Owing to the widespread phenomenon of plagiarism and cheating in higher education institutions (HEIs), it is now difficult to ensure academic integrity and quality education. Moreover, the COVID-19 pandemic has intensified the issue by shifting educational institutions into virtual teaching and assessment mode. Thus, there is a need to carry out an extensive and holistic systematic review of the literature to highlight plagiarism and cheating in both prevalence and form among HEIs. This paper systematically reviews the literature concerning academic plagiarism and cheating in HEIs to determine the most common forms and suggest strategies for resolution and boosting the academic integrity of students. The review included 45 articles and publications for the period from February 12, 2018, to September 12, 2022, in the Scopus database aligned with the Systematic Review and Meta-Analysis (PRISMA) guidelines in the selection, filtering, and reporting of the papers for review from which a conclusion can be drawn. Based on the results, out of the studies reviewed, 48% of the quantitative results of students were plagiarized and obtained through cheating, with 84% coming from the fields of Humanities. Moreover, Psychology and Social Sciences studies accumulated 9% and 7% articles respectively. Based on the results, individual factors, institutional factors, and social and cultural factors have contributed to plagiarism and cheating cases in HEIs. The resolution of this issue can be the establishment of ethical and moral development initiatives and modern academic policies and guidelines supported by technological strategies of testing.

Keywords: plagiarism, cheating, systematic review, academic integrity

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Authors: Malika Yaici, Anis Oussayah, Mohamed Ahmed Takerrabet

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Security of context-aware ubiquitous systems is paramount, and authentication plays an important aspect in cloud computing and ubiquitous computing. Trust management has been identified as vital component for establishing and maintaining successful relational exchanges between trading partners in cloud and ubiquitous systems. Establishing trust is the way to build good relationship with both client and provider which positive activates will increase trust level, otherwise destroy trust immediately. We propose a new context-aware authentication system using a trust management system between client and server, and between servers, a trust which induces partnership, thus to a close cooperation between these servers. We defined the rules (algorithms), as well as the formulas to manage and calculate the trusting degrees depending on context, in order to uniquely authenticate a user, thus a single sign-on, and to provide him better services.

Keywords: ubiquitous computing, authentication, context-awareness, trust management

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Authors: Vladimir Kulyukin, Tanwir Zaman, Sarat Kiran Andhavarapu

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Research on nutrition label use identifies four factors that impede comprehension and retention of nutrition information by consumers: label’s location on the package, presentation of information within the label, label’s surface size, and surrounding visual clutter. In this paper, a system is presented that makes nutrition label use more effective for nutrition information comprehension and retention. The system’s front end is a smartphone application. The system’s back end is a four node Linux cluster for image recognition and data storage. Image frames captured on the smartphone are sent to the back end for skewed or aligned barcode recognition. When barcodes are recognized, corresponding nutrition labels are retrieved from a cloud database and presented to the user on the smartphone’s touchscreen. Each displayed nutrition label is positioned centrally on the touchscreen with no surrounding visual clutter. Wikipedia links to important nutrition terms are embedded to improve comprehension and retention of nutrition information. Standard touch gestures (e.g., zoom in/out) available on mainstream smartphones are used to manipulate the label’s surface size. The nutrition label database currently includes 200,000 nutrition labels compiled from public web sites by a custom crawler. Stress test experiments with the node cluster are presented. Implications for proactive nutrition management and food policy are discussed.

Keywords: mobile computing, cloud computing, nutrition label use, nutrition management, barcode scanning

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in various domains has created a pressing need for effective methods to transform this data into meaningful knowledge. In this era of big data, mathematical and statistical innovations play a crucial role in unlocking insights and facilitating informed decision-making in data analytics. This abstract aims to explore the transformative potential of these innovations and their impact on converting raw data into actionable knowledge. Drawing upon a comprehensive review of existing literature, this research investigates the cutting-edge mathematical and statistical techniques that enable the conversion of data into knowledge. By evaluating their underlying principles, strengths, and limitations, we aim to identify the most promising innovations in data analytics. To demonstrate the practical applications of these innovations, real-world datasets will be utilized through case studies or simulations. This empirical approach will showcase how mathematical and statistical innovations can extract patterns, trends, and insights from complex data, enabling evidence-based decision-making across diverse domains. Furthermore, a comparative analysis will be conducted to assess the performance, scalability, interpretability, and adaptability of different innovations. By benchmarking against established techniques, we aim to validate the effectiveness and superiority of the proposed mathematical and statistical innovations in data analytics. Ethical considerations surrounding data analytics, such as privacy, security, bias, and fairness, will be addressed throughout the research. Guidelines and best practices will be developed to ensure the responsible and ethical use of mathematical and statistical innovations in data analytics. The expected contributions of this research include advancements in mathematical and statistical sciences, improved data analysis techniques, enhanced decision-making processes, and practical implications for industries and policymakers. The outcomes will guide the adoption and implementation of mathematical and statistical innovations, empowering stakeholders to transform data into actionable knowledge and drive meaningful outcomes.

Keywords: data analytics, mathematical innovations, knowledge extraction, decision-making

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Authors: Merad Boudia Omar Rafik, Feham Mohammed

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Data aggregation in wireless sensor networks (WSNs) provides a great reduction of energy consumption. The limited resources of sensor nodes make the choice of an encryption algorithm very important for providing security for data aggregation. Asymmetric cryptography involves large ciphertexts and heavy computations but solves, on the other hand, the problem of key distribution of symmetric one. The latter provides smaller ciphertexts and speed computations. Also, the recent researches have shown that achieving the end-to-end confidentiality and the end-to-end integrity at the same is a challenging task. In this paper, we propose (SA-SPKC), a novel security protocol which addresses both security services for WSNs, and where only the base station can verify the individual data and identify the malicious node. Our scheme is based on stateful public key encryption (StPKE). The latter combines the best features of both kinds of encryption along with state in order to reduce the computation overhead. Our analysis

Keywords: secure data aggregation, wireless sensor networks, elliptic curve cryptography, homomorphic encryption

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Authors: Kyung-Jung Lee, Hyun-Sik Ahn

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This paper suggests a design methodology for the hardware and software of the Electronic Control Unit (ECU) of safety-critical vehicle applications such as braking and steering. The architecture of the hardware is a high integrity system such that it incorporates a high performance 32-bit CPU and a separate Peripheral Control-Processor (PCP) together with an external watchdog CPU. Communication between the main CPU and the PCP is executed via a common area of RAM and events on either processor which are invoked by interrupts. Safety-related software is also implemented to provide a reliable, self-testing computing environment for safety critical and high integrity applications. The validity of the design approach is shown by using the Hardware-in-the-Loop Simulation (HILS) for Electric Power Steering (EPS) systems which consists of the EPS mechanism, the designed ECU, and monitoring tools.

Keywords: electronic control unit, electric power steering, functional safety, hardware-in-the-loop simulation

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Authors: Yu-Wen Chen, Szu-Wei Huang, Chung-Hsiang Mu, Kelvin Cheng

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This paper developed a data-driven based model to deal with the causality between the Continuous Emission Monitoring System (CEMS, by Environmental Protection Administration, Taiwan) in industrial factories, and the air quality around environment. Compared to the heavy burden of traditional numerical models of regional weather and air pollution simulation, the lightweight burden of the proposed model can provide forecasting hourly with current observations of weather, air pollution and emissions from factories. The observation data are included wind speed, wind direction, relative humidity, temperature and others. The observations can be collected real time from Open APIs of civil IoT Taiwan, which are sourced from 439 weather stations, 10,193 qualitative air stations, 77 national quantitative stations and 140 CEMS quantitative industrial factories. This study completed a causal inference engine and gave an air pollution forecasting for the next 12 hours related to local industrial factories. The outcomes of the pollution forecasting are produced hourly with a grid resolution of 1km*1km on IIoTC (Industrial Internet of Things Cloud) and saved in netCDF4 format. The elaborated procedures to generate forecasts comprise data recalibrating, outlier elimination, Kriging Interpolation and particle tracking and random walk techniques for the mechanisms of diffusion and advection. The solution of these equations reveals the causality between factories emission and the associated air pollution. Further, with the aid of installed real-time flue emission (Total Suspension Emission, TSP) sensors and the mentioned forecasted air pollution map, this study also disclosed the converting mechanism between the TSP and PM2.5/PM10 for different region and industrial characteristics, according to the long-term data observation and calibration. These different time-series qualitative and quantitative data which successfully achieved a causal inference engine in cloud for factory management control in practicable. Once the forecasted air quality for a region is marked as harmful, the correlated factories are notified and asked to suppress its operation and reduces emission in advance.

Keywords: continuous emission monitoring system, total suspension particulates, causal inference, air pollution forecast, IoT

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Authors: Alberto Azevedo, Ricardo Martins, André B. Fortunato, Anabela Oliveira

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Water is under severe threat today because of the rising population, increased agricultural and industrial needs, and the intensifying effects of climate change. Due to sea-level rise, erosion, and demographic pressure, the coastal regions are of significant concern to the scientific community. The Water Monitoring Sentinel Cloud platform (WORSICA) service is focused on providing new tools for monitoring water in coastal and inland areas, taking advantage of remote sensing, in situ and tidal modeling data. WORSICA is a service that can be used to determine the coastline, coastal inundation areas, and the limits of inland water bodies using remote sensing (satellite and Unmanned Aerial Vehicles - UAVs) and in situ data (from field surveys). It applies to various purposes, from determining flooded areas (from rainfall, storms, hurricanes, or tsunamis) to detecting large water leaks in major water distribution networks. This service was built on components developed in national and European projects, integrated to provide a one-stop-shop service for remote sensing information, integrating data from the Copernicus satellite and drone/unmanned aerial vehicles, validated by existing online in-situ data. Since WORSICA is operational using the European Open Science Cloud (EOSC) computational infrastructures, the service can be accessed via a web browser and is freely available to all European public research groups without additional costs. In addition, the private sector will be able to use the service, but some usage costs may be applied, depending on the type of computational resources needed by each application/user. Although the service has three main sub-services i) coastline detection; ii) inland water detection; iii) water leak detection in irrigation networks, in the present study, an application of the service to Óbidos lagoon in Portugal is shown, where the user can monitor the evolution of the lagoon inlet and estimate the topography of the intertidal areas without any additional costs. The service has several distinct methodologies implemented based on the computations of the water indexes (e.g., NDWI, MNDWI, AWEI, and AWEIsh) retrieved from the satellite image processing. In conjunction with the tidal data obtained from the FES model, the system can estimate a coastline with the corresponding level or even topography of the inter-tidal areas based on the Flood2Topo methodology. The outcomes of the WORSICA service can be helpful for several intervention areas such as i) emergency by providing fast access to inundated areas to support emergency rescue operations; ii) support of management decisions on hydraulic infrastructures operation to minimize damage downstream; iii) climate change mitigation by minimizing water losses and reduce water mains operation costs; iv) early detection of water leakages in difficult-to-access water irrigation networks, promoting their fast repair.

Keywords: remote sensing, coastline detection, water detection, satellite data, sentinel, Copernicus, EOSC

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

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Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI

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Authors: Han Wang, Zhuoxian Dai, Zhe Zhu, Hui Zhang, Zhenyu Zeng

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In traditional microscopic traffic simulation, various approaches have been suggested to implement the single-agent behaviors about lane changing and intelligent driver model. However, when it comes to very large metropolitan areas, microscopic traffic simulation requires more resources and become time-consuming, then macroscopic traffic simulation aggregate trends of interests rather than individual vehicle traces. In this paper, we describe the architecture and implementation of the actor system of microscopic traffic simulation, which exploits the distributed architecture of modern-day cloud computing. The results demonstrate that our architecture achieves high-performance and outperforms all the other traditional microscopic software in all tasks. To the best of our knowledge, this the first system that enables single-agent behavior in macroscopic traffic simulation. We thus believe it contributes to a new type of system for traffic simulation, which could provide individual vehicle behaviors in microscopic traffic simulation.

Keywords: actor system, cloud computing, distributed system, traffic simulation

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Authors: Shreya Bokare, Sanjay Pawar, Shika Nema

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Growth in cloud applications is generating the tremendous amount of data, building load on traditional storage management systems. Software Defined Storage (SDS) is a new storage management concept becoming popular to handle this large amount of data. CoprHD is one of the open source SDS controller, available for experimentation and development in the storage industry. In this paper, the storage management techniques provided by CoprHD to manage heterogeneous storage platforms are experimented and analyzed. Various storage management parameters such as time to provision, storage capacity measurement, and heterogeneity are experimentally evaluated along with the theoretical expression to prove the completeness of CoprHD controller for storage management.

Keywords: software defined storage, SDS, CoprHD, open source, SMI-S simulator, clarion, Symmetrix

Procedia PDF Downloads 301

Authors: D. Madhushanka, Y. Liu, H. C. Fernando

Abstract:

Fires are one of the foremost factors of land surface disturbance in diverse ecosystems, causing soil erosion and land-cover changes and atmospheric effects affecting people's lives and properties. Generally, the severity of the fire is calculated as the Normalized Burn Ratio (NBR) index. This is performed manually by comparing two images obtained afterward. Then by using the bitemporal difference of the preprocessed satellite images, the dNBR is calculated. The burnt area is then classified as either unburnt (dNBR<0.1) or burnt (dNBR>= 0.1). Furthermore, Wildfire Severity Assessment (WSA) classifies burnt areas and unburnt areas using classification levels proposed by USGS and comprises seven classes. This procedure generates a burn severity report for the area chosen by the user manually. This study is carried out with the objective of producing an automated tool for the above-mentioned process, namely the World Wildfire Severity Assessment Tool (WWSAT). It is implemented in Google Earth Engine (GEE), which is a free cloud-computing platform for satellite data processing, with several data catalogs at different resolutions (notably Landsat, Sentinel-2, and MODIS) and planetary-scale analysis capabilities. Sentinel-2 MSI is chosen to obtain regular processes related to burnt area severity mapping using a medium spatial resolution sensor (15m). This tool uses machine learning classification techniques to identify burnt areas using NBR and to classify their severity over the user-selected extent and period automatically. Cloud coverage is one of the biggest concerns when fire severity mapping is performed. In WWSAT based on GEE, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for both pre-fire and post-fire image compositing. The parallel processing capabilities and preloaded geospatial datasets of GEE facilitated the production of this tool. This tool consists of a Graphical User Interface (GUI) to make it user-friendly. The advantage of this tool is the ability to obtain burn area severity over a large extent and more extended temporal periods. Two case studies were carried out to demonstrate the performance of this tool. The Blue Mountain national park forest affected by the Australian fire season between 2019 and 2020 is used to describe the workflow of the WWSAT. This site detected more than 7809 km2, using Sentinel-2 data, giving an error below 6.5% when compared with the area detected on the field. Furthermore, 86.77% of the detected area was recognized as fully burnt out, of which high severity (17.29%), moderate-high severity (19.63%), moderate-low severity (22.35%), and low severity (27.51%). The Arapaho and Roosevelt National Forest Park, California, the USA, which is affected by the Cameron peak fire in 2020, is chosen for the second case study. It was found that around 983 km2 had burned out, of which high severity (2.73%), moderate-high severity (1.57%), moderate-low severity (1.18%), and low severity (5.45%). These spots also can be detected through the visual inspection made possible by cloud-free images generated by WWSAT. This tool is cost-effective in calculating the burnt area since satellite images are free and the cost of field surveys is avoided.

Keywords: burnt area, burnt severity, fires, google earth engine (GEE), sentinel-2

Procedia PDF Downloads 219

Authors: Morgane Chatard, Clémentine Puech, Nathalie Perek, Frédéric Roche

Abstract:

Several neurological disorders are linked to repeated hypoxia. The impact of such repeated hypoxic stress, on endothelial cells function of the blood-brain barrier (BBB) is little studied in the literature. Indeed, the study of hypoxic stress in cellular pathways is complex using hypoxia exposure because HIF 1α (factor induced by hypoxia) has a short half life. Our study presents an innovative induction method of repeated hypoxic stress, more reproducible, which allows us to study its impacts on an in vitro contact co-culture BBB model. Repeated hypoxic stress was induced by hydralazine (a mimetic agent of hypoxia pathway) during two hours and repeated during 24 hours. Then, BBB integrity was assessed by permeability measurements (transendothelial electrical resistance and membrane permeability), tight junction protein expressions (cell-ELISA and confocal microscopy) and by studying expression and activity of efflux transporters. First, this study showed that repeated hypoxic stress leads to a BBB’s dysfunction illustrated by a significant increase in permeability. This loss of membrane integrity was linked to a significant decrease of tight junctions’ protein expressions, facilitating a possible transfer of potential cytotoxic compounds in the brain. Secondly, we demonstrated that brain microvascular endothelial cells had set-up defence mechanism. These endothelial cells significantly increased the activity of their efflux transporters which was associated with a significant increase in their expression. In conclusion, repeated hypoxic stress lead to a loss of BBB integrity with a decrease of tight junction proteins. In contrast, endothelial cells increased the expression of their efflux transporters to fight against cytotoxic compounds brain crossing. Unfortunately, enhanced efflux activity could also lead to reducing pharmacological drugs delivering to the brain in such hypoxic conditions.

Keywords: BBB model, efflux transporters, repeated hypoxic stress, tigh junction proteins

Procedia PDF Downloads 286

Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

Abstract:

IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

Procedia PDF Downloads 47

Authors: R. Gautam, P. Sastha Kanagasabai, G. N. Rathna

Abstract:

The proposed healthcare system enables quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and monitor their vitals. The growth of Brain-Computer-Interface (BCI) has led to rapid development in 'assistive systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands over the internet to the desired mobile phone user. Other Vital Information like the heartbeat, blood pressure, ECG and body temperature are monitored and uploaded to the server. Data analytics enables physicians to scan databases for a specific illness. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Keywords: brain computer interface, Intel Edison, Emotiv EPOC, IoT analytics, electroencephalogram

Procedia PDF Downloads 180