Search results for: cloud computing framework

Authors: Heesuk Seo, Yongtae Kim

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Online learning started in the 1990s, the spread of the Internet has been through the era of e-learning paradigm of online education in the era of smart learning change. Reflecting the different nature of the mobile to anywhere anytime, anywhere was also allows the form of learning, it was also available through the learning content and interaction. We are developing a cloud system, 'TLINKS CLOUD' that allows you to configure the environment of the smart learning without the need for additional infrastructure. Using the big-data analysis for e-learning contents, we provide an integrated solution for e-learning tailored to individual study.

Keywords: authority tool, big data analysis, e-learning, HTML5

Procedia PDF Downloads 406

Authors: J. K. Lee, S. L. Lee

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Due to the improvement in performance of computer hardware and the development of operating systems, a multi-tasking for several programs has become one of the basic functions to computer users. It is natural for computer users to want more functional, convenient, and visual GUI functions (Graphic User Interface). In this paper, a 3D virtual desktop system was proposed to meet users’ requirements for cloud environments such as a virtual desktop function in the Windows environment. The proposed system uses the handles of the windows to hide or restore several windows. It connects the list of task spaces using the circular double linked list to manage the handles. Each handle list is registered in the corresponding task space being executed. The 3D virtual desktop is efficient and flexible in handling the numbers of task spaces and can help users to work under more comfortable environments. Acknowledgment: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIP) (NRF-2015R1D1A1A01057680).

Keywords: virtual desktop, GUI, cloud, virtualization

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Authors: Kaarthik K., Harshini S., Karthika M., Kripanandhini T.

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Water is an essential part of living organisms. Major water pollution is caused due to contamination of industrial wastewater in the river. The most important step in bringing wastewater contaminants down to levels that are safe for nature is wastewater treatment. The contamination of river water harms both humans who consume it and the aquatic life that lives there. We introduce a new cloud-based industrial IoT paradigm in this work for real-time control and monitoring of wastewater. The proposed system prevents prohibited entry of industrial wastewater into the plant by monitoring temperature, hydrogen power (pH), CO₂ and turbidity factors from the wastewater input that the wastewater treatment facility will process. Real-time sensor values are collected and uploaded to the cloud by the system using an IoT Wi-Fi Module. By doing so, we can prevent the contamination of industrial wastewater entering the river earlier, and the necessary actions will be taken by the users. The proposed system's results are 90% efficient, preventing water pollution due to industry and protecting human lives.

Keywords: sensors, pH, CO₂, temperature, turbidity

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Authors: Purnomo Yustianto, Robin Doss

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As a process of developing a service system, the term ‘service engineering’ evolves in scope and definition. To achieve an integrated understanding of the process, a general framework and an ontology are required. This paper extends a previously built service engineering framework by exploring metamodels for the framework artefacts based on a foundational ontology and a metamodel landscape. The first part of this paper presents a correlation map between the proposed framework with the ontology as a form of evaluation for the conceptual coverage of the framework. The mapping also serves to characterize the artefacts to be produced for each activity in the framework. The second part describes potential metamodels to be used, from the metamodel landscape, as alternative formats of the framework artefacts. The results suggest that the framework sufficiently covers the ontological concepts, both from general service context and software service context. The metamodel exploration enriches the suggested artefact format from the original eighteen formats to thirty metamodel alternatives.

Keywords: artefact, framework, service, metamodel

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Authors: Hazem Abd El Megeed, Mohamed El-Refaay, Norhan Magdi Osman

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Spectrum scarcity is a challenging obstacle in wireless communications systems. It hinders the introduction of innovative wireless services and technologies that require larger bandwidth comparing to legacy technologies. In addition, the current worldwide allocation of radio spectrum bands is already congested and can not afford additional squeezing or optimization to accommodate new wireless technologies. This challenge is a result of accumulative contributions from different factors that will be discussed later in this paper. One of these factors is the radio spectrum allocation policy governed by national regulatory authorities nowadays. The framework for this policy allocates specified portion of radio spectrum to a particular wireless service provider on exclusive utilization basis. This allocation is executed according to technical specification determined by the standard bodies of each Radio Access Technology (RAT). Dynamic access of spectrum is a framework for flexible utilization of radio spectrum resources. In this framework there is no exclusive allocation of radio spectrum and even the public safety agencies can share their spectrum bands according to a governing policy and service level agreements. In this paper, we explore different methods for accessing the spectrum dynamically and its associated implementation challenges.

Keywords: licensed shared access, cognitive radio, spectrum sharing, spectrum congestion, dynamic spectrum access, spectrum database, spectrum trading, reconfigurable radio systems, opportunistic spectrum allocation (OSA)

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Authors: M. Pepe, S. Ackermann, L. Fregonese, C. Achille

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3D models obtained with advanced survey techniques such as close-range photogrammetry and laser scanner are nowadays particularly appreciated in Cultural Heritage and Archaeology fields. In order to produce high quality models representing archaeological evidences and anthropological artifacts, the appearance of the model (i.e. color) beyond the geometric accuracy, is not a negligible aspect. The integration of the close-range photogrammetry survey techniques with the laser scanner is still a topic of study and research. By combining point cloud data sets of the same object generated with both technologies, or with the same technology but registered in different moment and/or natural light condition, could construct a final point cloud with accentuated color dissimilarities. In this paper, a methodology to uniform the different data sets, to improve the chromatic quality and to highlight further details by balancing the point color will be presented.

Keywords: color models, cultural heritage, laser scanner, photogrammetry

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Authors: Adeyinka Adesanya, Isaac Bamgboye

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There is a growing need to develop the processes to produce renewable fuels and chemicals due to the economic, political, and environmental concerns associated with fossil fuels. Lignocellulosic biomass is an excellent renewable feedstock because it is both abundant and inexpensive. This project aims at producing bioethanol from lignocellulosic plants (Sorghum Arundinacieum and Heteropogon Contortus) by biochemical means, computing the energy audit of the process and determining the fuel properties of the produced ethanol. Acid pretreatment (0.5% H2SO4 solution) and enzymatic hydrolysis (using malted barley as enzyme source) were employed. The ethanol yield of wild sorghum was found to be 20% while that of spear grass was 15%. The fuel properties of the bioethanol from wild sorghum are 1.227 centipoise for viscosity, 1.10 g/cm3 for density, 0.90 for specific gravity, 78 °C for boiling point and the cloud point was found to be below -30 °C. That of spear grass was 1.206 centipoise for viscosity, 0.93 g/cm3 for density 1.08 specific gravity, 78 °C for boiling point and the cloud point was also found to be below -30 °C. The energy audit shows that about 64 % of the total energy was used up during pretreatment, while product recovery which was done manually demanded about 31 % of the total energy. Enzymatic hydrolysis, fermentation, and distillation total energy input were 1.95 %, 1.49 % and 1.04 % respectively, the alcoholometric strength of bioethanol from wild sorghum was found to be 47 % and the alcoholometric strength of bioethanol from spear grass was 72 %. Also, the energy efficiency of the bioethanol production for both grasses was 3.85 %.

Keywords: lignocellulosic biomass, wild sorghum, spear grass, biochemical conversion

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Authors: Igor Kontorovich, Rina Zazkis

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We present a framework of researcher knowledge ‎development in conducting a study in mathematics education. The key ‎components of the framework are: knowledge germane to conducting a ‎particular study, processes of knowledge accumulation, and catalyzing ‎filters that influence a researcher decision making. The components of ‎the framework originated from a confluence between constructs and ‎theories in Mathematics Education, Higher Education and Sociology. ‎Drawing on a self-reflective interview with a leading researcher in ‎mathematics education, professor Michèle Artigue, we illustrate how ‎the framework can be utilized in data analysis. Criteria for framework ‎evaluation are discussed. ‎

Keywords: community of practice, knowledge development, mathematics education research, researcher knowledge

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Authors: Yangzhi Li

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Network transfer of information and performance customization is now a viable method of digital industrial production in the era of Industry 4.0. Robot platforms and network platforms have grown more important in digital design and construction. The pressing need for novel building techniques is driven by the growing labor scarcity problem and increased awareness of construction safety. Robotic approaches in construction research are regarded as an extension of operational and production tools. Several technological theories related to robot autonomous recognition, which include high-performance computing, physical system modeling, extensive sensor coordination, and dataset deep learning, have not been explored using intelligent construction. Relevant transdisciplinary theory and practice research still has specific gaps. Optimizing high-performance computing and autonomous recognition visual guidance technologies improves the robot's grasp of the scene and capacity for autonomous operation. Intelligent vision guidance technology for industrial robots has a serious issue with camera calibration, and the use of intelligent visual guiding and identification technologies for industrial robots in industrial production has strict accuracy requirements. It can be considered that visual recognition systems have challenges with precision issues. In such a situation, it will directly impact the effectiveness and standard of industrial production, necessitating a strengthening of the visual guiding study on positioning precision in recognition technology. To best facilitate the handling of complicated components, an approach for the visual recognition of parts utilizing machine learning algorithms is proposed. This study will identify the position of target components by detecting the information at the boundary and corner of a dense point cloud and determining the aspect ratio in accordance with the guidelines for the modularization of building components. To collect and use components, operational processing systems assign them to the same coordinate system based on their locations and postures. The RGB image's inclination detection and the depth image's verification will be used to determine the component's present posture. Finally, a virtual environment model for the robot's obstacle-avoidance route will be constructed using the point cloud information.

Keywords: robotic construction, robotic assembly, visual guidance, machine learning

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Authors: Mariem Kchaou, Wiem Khlif, Faiez Gargouri

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Modeling business processes is an essential task when evaluating, improving, or documenting existing business processes. To be efficient in such tasks, a business process model (BPM) must have high structural quality and high performance. Evidently, evaluating the performance of a business process model is a necessary step to reduce time, cost, while assessing the structural quality aims to improve the understandability and the modifiability of the BPMN model. To achieve these objectives, a set of structural and performance measures have been proposed. Since the diversity of measures, we propose a framework that integrates both structural and performance aspects for classifying them. Our measure classification is based on business process model perspectives (e.g., informational, functional, organizational, behavioral, and temporal), and the elements (activity, event, actor, etc.) involved in computing the measures. Then, we implement this framework in a tool assisting the structural quality and the performance of a business process. The tool helps the designers to select an appropriate subset of measures associated with the corresponding perspective and to calculate and interpret their values in order to improve the structural quality and the performance of the model.

Keywords: performance, structural quality, perspectives, tool, classification framework, measures

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Authors: Seonaid Hume, Gordon Dobie, Graeme West

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Detecting and localizing radioactive sources is a necessity for safe and secure decommissioning of nuclear facilities. An important aspect for the management of the sort-and-segregation process is establishing the spatial distributions and quantities of the waste radionuclides, their type, corresponding activity, and ultimately classification for disposal. The data received from surveys directly informs decommissioning plans, on-site incident management strategies, the approach needed for a new cell, as well as protecting the workforce and the public. Manual classification of nuclear waste from a nuclear cell is time-consuming, expensive, and requires significant expertise to make the classification judgment call. Also, in-cell decommissioning is still in its relative infancy, and few techniques are well-developed. As with any repetitive and routine tasks, there is the opportunity to improve the task of classifying nuclear waste using autonomous systems. Hence, this paper proposes a new framework for the automatic classification of nuclear waste. This framework consists of five main stages; 3D spatial mapping and object detection, object classification, radiological mapping, source localisation based on gathered evidence and finally, waste classification. The first stage of the framework, 3D visual mapping, involves object detection from point cloud data. A review of related applications in other industries is provided, and recommendations for approaches for waste classification are made. Object detection focusses initially on cylindrical objects since pipework is significant in nuclear cells and indeed any industrial site. The approach can be extended to other commonly occurring primitives such as spheres and cubes. This is in preparation of stage two, characterizing the point cloud data and estimating the dimensions, material, degradation, and mass of the objects detected in order to feature match them to an inventory of possible items found in that nuclear cell. Many items in nuclear cells are one-offs, have limited or poor drawings available, or have been modified since installation, and have complex interiors, which often and inadvertently pose difficulties when accessing certain zones and identifying waste remotely. Hence, this may require expert input to feature match objects. The third stage, radiological mapping, is similar in order to facilitate the characterization of the nuclear cell in terms of radiation fields, including the type of radiation, activity, and location within the nuclear cell. The fourth stage of the framework takes the visual map for stage 1, the object characterization from stage 2, and radiation map from stage 3 and fuses them together, providing a more detailed scene of the nuclear cell by identifying the location of radioactive materials in three dimensions. The last stage involves combining the evidence from the fused data sets to reveal the classification of the waste in Bq/kg, thus enabling better decision making and monitoring for in-cell decommissioning. The presentation of the framework is supported by representative case study data drawn from an application in decommissioning from a UK nuclear facility. This framework utilises recent advancements of the detection and mapping capabilities of complex radiation fields in three dimensions to make the process of classifying nuclear waste faster, more reliable, cost-effective and safer.

Keywords: nuclear decommissioning, radiation detection, object detection, waste classification

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Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

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Authors: Walter Bernhofer, Peter Haber, Tobias Mayer, Manfred Mayr, Markus Ziegler

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Current preventive maintenance measures are cost intensive and not efficient. With the available sensor data of state of the art internet of things devices new possibilities of automated data processing emerge. Current advances in data science and in machine learning enable new, so called predictive maintenance technologies, which empower data scientists to forecast possible system failures. The goal of this approach is to cut expenses in preventive maintenance by automating the detection of possible failures and to improve efficiency and quality of maintenance measures. Additionally, a centralization of the sensor data monitoring can be achieved by using this approach. This paper describes the approach of three students to define a reference architecture for a predictive maintenance solution in the internet of things domain with a connected smartphone app for service technicians. The reference architecture is validated by a case study. The case study is implemented with current Microsoft Azure cloud technologies. The results of the case study show that the reference architecture is valid and can be used to achieve a system for predictive maintenance execution with the cloud components of Microsoft Azure. The used concepts are technology platform agnostic and can be reused in many different cloud platforms. The reference architecture is valid and can be used in many use cases, like gas station maintenance, elevator maintenance and many more.

Keywords: case study, internet of things, predictive maintenance, reference architecture

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Authors: Abdal-Hafeez Alhussein

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Artificial Intelligence (AI) has become a transformative force in the field of information technologies, reshaping how data is processed, analyzed, and utilized across various domains. This paper explores the multifaceted applications of AI within information technology, focusing on three key areas: automation, scalability, and data-driven decision-making. We delve into how AI-powered automation is optimizing operational efficiency in IT infrastructures, from automated network management to self-healing systems that reduce downtime and enhance performance. Scalability, another critical aspect, is addressed through AI’s role in cloud computing and distributed systems, enabling the seamless handling of increasing data loads and user demands. Additionally, the paper highlights the use of AI in cybersecurity, where real-time threat detection and adaptive response mechanisms significantly improve resilience against sophisticated cyberattacks. In the realm of data analytics, AI models—especially machine learning and natural language processing—are driving innovation by enabling more precise predictions, automated insights extraction, and enhanced user experiences. The paper concludes with a discussion on the ethical implications of AI in information technologies, underscoring the importance of transparency, fairness, and responsible AI use. It also offers insights into future trends, emphasizing the potential of AI to further revolutionize the IT landscape by integrating with emerging technologies like quantum computing and IoT.

Keywords: artificial intelligence, information technology, automation, scalability

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Authors: Jianning Tang, Trevor Hocksun Kwan, Xiaofeng Wu

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With the increasing demand for space use in multiple sectors (navigation, telecommunication, imagery, etc.), the deployment and maintenance demand of satellites are growing. Considering the high launching cost and the restrictions on weight and size of the payload when using launch vehicle, the technique of on-orbit manufacturing has obtained more attention because of its significant potential to support future space missions. 3D printing is the most promising manufacturing technology that could be applied in space. However, due to the lack of autonomous quality assessment, the operation of conventional 3D printers still relies on human presence to supervise the printing process. This paper is proposed to develop an automatic 3D reconstruction system aiming at detecting failures on the 3D printed objects through application of point cloud technology. Based on the data obtained from the point cloud, the 3D printer could locate the failure and repair the failure. The system will increase automation and provide 3D printing with more feasibilities for space use without human interference.

Keywords: 3D printing, quality assessment, point cloud, on-orbit manufacturing

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Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

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Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

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Authors: Bilal Wajid, Erchin Serpedin

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The health industry is aiming towards personalized medicine. If the patient’s genome needs to be sequenced it is important that the entire analysis be completed quickly. This paper explores use of parallel computing to analyze very large sequences. Two cases have been considered. In the first case, the sequence is kept constant and the effect of increasing the number of MPI-based processes is evaluated in terms of execution time, speed and efficiency. In the second case the number of MPI-based processes have been kept constant whereas, the length of the sequence was increased.

Keywords: parallel computing, alignment, genome assembly, alignment

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Authors: Radul Shishkov, Orlin Davchev

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The digital era has revolutionized architectural practices, with building information modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research introduces a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data -a collection of data points in space, typically produced by 3D scanners- into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. Our methodology has been tested on several real-world case studies, demonstrating its capability to handle diverse architectural styles and complexities. The results showcase a substantial reduction in time and resources required for BIM model generation while maintaining high levels of accuracy and detail. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historic preservation.

Keywords: BIM, 3D point cloud, algorithmic modeling, computational design, architectural reconstruction

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Authors: Norbayah Mohd Suki, Norazah Mohd Suki

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This study aims to examine relationships between attitude, self-efficacy, and subjective norm with students’ behavioural intention to use public computing facilities at a library. Data was collected from 200 undergraduate students enrolled at a higher learning institution in the Federal Territory of Labuan, Malaysia via a structured questionnaire comprising closed-ended questions. Data was analyzed using multiple regression analysis. The results show that students’ behavioural intention to use public computing facilities at the library is widely affected by subjective norm factor i.e. influence of the support of family members, friends and neighbours. The findings of this study provide a better understanding of factors likely to influence students’ behavioural intention to use public computing facilities at a library. It also offers valuable insights into factors which university librarians need to focus on to improve students’ behavioural intention to actively use public computing facilities at a library for quality information retrieval. Direction for future research is also presented.

Keywords: attitude, self-efficacy, subjective norm, behavioural intention

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Authors: V. K. Banga

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Robots are now playing a very promising role in industries. Robots are commonly used in applications in repeated operations or where operation by human is either risky or not feasible. In most of the industrial applications, robotic arm manipulators are widely used. Robotic arm manipulator with two link or three link structures is commonly used due to their low degrees-of-freedom (DOF) movement. As the DOF of robotic arm increased, complexity increases. Instrumentation involved with robotics plays very important role in order to interact with outer environment. In this work, optimal control for movement of various DOFs of robotic arm using various soft computing techniques has been presented. We have discussed about different robotic structures having various DOF robotics arm movement. Further stress is on kinematics of the arm structures i.e. forward kinematics and inverse kinematics. Trajectory planning of robotic arms using soft computing techniques is demonstrating the flexibility of this technique. The performance is optimized for all possible input values and results in optimized movement as resultant output. In conclusion, soft computing has been playing very important role for achieving optimized movement of robotic arm. It also requires very limited knowledge of the system to implement soft computing techniques.

Keywords: artificial intelligence, kinematics, robotic arm, neural networks, fuzzy logic

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Authors: Anirudh Lahiri

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Neuromorphic computing, inspired by the intricate operations of biological neural networks, offers a revolutionary approach to overcoming the limitations of traditional computing architectures. This research proposes the integration of spintronics with neuromorphic systems, aiming to enhance computational performance, scalability, and energy efficiency. Traditional computing systems, based on the Von Neumann architecture, struggle with scalability and efficiency due to the segregation of memory and processing functions. In contrast, the human brain exemplifies high efficiency and adaptability, processing vast amounts of information with minimal energy consumption. This project explores the use of spintronics, which utilizes the electron's spin rather than its charge, to create more energy-efficient computing systems. Spintronic devices, such as magnetic tunnel junctions (MTJs) manipulated through spin-transfer torque (STT) and spin-orbit torque (SOT), offer a promising pathway to reducing power consumption and enhancing the speed of data processing. The integration of these devices within a neuromorphic framework aims to replicate the efficiency and adaptability of biological systems. The research is structured into three phases: an exhaustive literature review to build a theoretical foundation, laboratory experiments to test and optimize the theoretical models, and iterative refinements based on experimental results to finalize the system. The initial phase focuses on understanding the current state of neuromorphic and spintronic technologies. The second phase involves practical experimentation with spintronic devices and the development of neuromorphic systems that mimic synaptic plasticity and other biological processes. The final phase focuses on refining the systems based on feedback from the testing phase and preparing the findings for publication. The expected contributions of this research are twofold. Firstly, it aims to significantly reduce the energy consumption of computational systems while maintaining or increasing processing speed, addressing a critical need in the field of computing. Secondly, it seeks to enhance the learning capabilities of neuromorphic systems, allowing them to adapt more dynamically to changing environmental inputs, thus better mimicking the human brain's functionality. The integration of spintronics with neuromorphic computing could revolutionize how computational systems are designed, making them more efficient, faster, and more adaptable. This research aligns with the ongoing pursuit of energy-efficient and scalable computing solutions, marking a significant step forward in the field of computational technology.

Keywords: material science, biological engineering, mechanical engineering, neuromorphic computing, spintronics, energy efficiency, computational scalability, synaptic plasticity.

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Authors: Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Hassan Ouajji, Mohamed Ouadi Bensalah

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In this paper, we propose a new distributed environment for High Performance Computing (HPC) based on mobile agents. It allows us to perform parallel programs execution as distributed one over a flexible grid constituted by a cooperative mobile agent team works. The distributed program to be performed is encapsulated on team leader agent which deploys its team workers as Agent Virtual Processing Unit (AVPU). Each AVPU is asked to perform its assigned tasks and provides the computational results which make the data and team works tasks management difficult for the team leader agent and that influence the performance computing. In this work we focused on the implementation of the Mobile Provider Agent (MPA) in order to manage the distribution of data and instructions and to ensure a load balancing model. It grants also some interesting mechanisms to manage the others computing challenges thanks to the mobile agents several skills.

Keywords: image processing, distributed environment, mobile agents, parallel and distributed computing

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Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

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Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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Authors: Songwu Shen, Garrett Drysdale, Veerendranath Mannepalli, Qihua Dai, Yuan Wang, Yuli Chen, David Qian, Utkarsh Kakaiya

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Hardware accelerator has been a promising solution to reduce the cost of cloud data centers. This paper investigates the QoS enhancement of the acceleration of an important datacenter workload: the webserver (or proxy) that faces high computational consumption originated from secure sockets layer (SSL) or transport layer security (TLS) procession in the cloud environment. Our study reveals that for the accelerator maintenance cases—need to upgrade driver/firmware or hardware reset due to hardware hang; we still can provide cryptography services by switching to software during maintenance phase and then switching back to accelerator after maintenance. The switching is seamless to server application such as Nginx that runs inside a VM on top of the server. To achieve this high availability goal, we propose a comprehensive fallback solution based on Intel® QuickAssist Technology (QAT). This approach introduces an architecture that involves the collaboration between physical function (PF) and virtual function (VF), and collaboration among VF, OpenSSL, and web application Nginx. The evaluation shows that our solution could provide high reliability, availability, and scalability (RAS) of hardware cryptography service in a 7x24x365 manner in the cloud environment.

Keywords: accelerator, cryptography service, RAS, secure sockets layer/transport layer security, SSL/TLS, virtualization fallback architecture

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Authors: Zhen Peng, Baifeng Wu

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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability

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Authors: Hebberly Ahatlan

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The process of digitizing energy systems involves transforming traditional energy infrastructure into interconnected, data-driven systems that enhance efficiency, sustainability, and responsiveness. As smart grids become increasingly integral to the efficient distribution and management of electricity from both fossil and renewable energy sources, the energy industry faces strategic challenges associated with digitalization and interoperability — particularly in the context of modern energy business models, such as virtual power plants (VPPs). The critical challenge in modern smart grids is to seamlessly integrate diverse technologies and systems, including virtualization, grid computing and service-oriented architecture (SOA), across the entire energy ecosystem. Achieving this requires addressing issues like semantic interoperability, IT/OT convergence, and digital asset scalability, all while ensuring security and risk management. This paper proposes a four-layer digitalization framework to tackle these challenges, encompassing persistent data protection, trusted key management, secure messaging, and authentication of IoT resources. Data assets generated through this framework enable AI systems to derive insights for improving smart grid operations, security, and revenue generation. Furthermore, this paper also proposes a Trusted Energy Interoperability Alliance as a universal guiding standard in the development of this digitalization framework to support more dynamic and interoperable energy markets.

Keywords: digitalization, IT/OT convergence, semantic interoperability, VPP, energy blockchain

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Authors: Nadine Ayasha

Abstract:

Hail happened in Sukabumi (August 23, 2020), Sekadau (August 22, 2020), and Bogor (September 23, 2020), where this extreme weather phenomenon occurred in the dry season. This study uses the ERA5 reanalysis model data, it aims to examine the vertical velocity impact on the hail occurrence in the dry season, as well as its relation to other weather parameters such as relative humidity, streamline, and wind velocity. Moreover, HCAI product satellite data is used as supporting data for the convective cloud development analysis. Based on the results of graphs, contours, and Hovmoller vertical cut from ERA5 modeling, the vertical velocity values in the 925 Mb-300 Mb layer in Sukabumi, Sekadau, and Bogor before the hail event ranged between -1.2-(-0.2), -1.5-(-0.2), -1-0 Pa/s. A negative value indicates that there is an upward motion from the air mass that trigger the convective cloud growth, which produces hail. It is evidenced by the presence of Cumulonimbus cloud on HCAI product when the hail falls. Therefore, the vertical velocity has significant effect on the hail event. In addition, the relative humidity in the 850-700 Mb layer is quite wet, which ranges from 80-90%. Meanwhile, the streamline and wind velocity in the three regions show the convergence with slowing wind velocity ranging from 2-4 knots. These results show that the upward motion of the vertical velocity is enough to form the wet atmospheric humidity and form a convergence for the growth of the convective cloud, which produce hail in the dry season.

Keywords: hail, extreme weather, vertical velocity, relative humidity, streamline

Procedia PDF Downloads 159

Authors: Samaneh Poormohammadi, Farid Golkar, Vahideh Khatibi Sarabi

Abstract:

Climate change and consecutive droughts have increased the importance of using rainwater harvesting methods. One of the methods of rainwater harvesting and, in other words, the management of atmospheric water resources is the use of weather modification technologies. Weather modification (also known as weather control) is the act of intentionally manipulating or altering the weather. The most common form of weather modification is cloud seeding, which increases rain or snow, usually for the purpose of increasing the local water supply. Cloud seeding operations in Iran have been married since 1999 in central Iran with the aim of harvesting rainwater and reducing the effects of drought. In this research, we analyze the results of cloud seeding operations in the Simindashtplain in northern Iran. Rainwater harvesting with the help of cloud seeding technology has been evaluated through its effects on surface water and underground water. For this purpose, two different methods have been used to estimate runoff. The first method is the US Soil Conservation Service (SCS) curve number method. Another method, known as the reasoning method, has also been used. In order to determine the infiltration rate of underground water, the balance reports of the comprehensive water plan of the country have been used. In this regard, the study areas located in the target area of each province have been extracted by drawing maps of the influence coefficients of each area in the GIS software. It should be mentioned that the infiltration coefficients were taken from the balance sheet reports of the country's comprehensive water plan. Then, based on the area of each study area, the weighted average of the infiltration coefficient of the study areas located in the target area of each province is considered as the infiltration coefficient of that province. Results show that the amount of water extracted from the rain with the help of cloud seeding projects in Simindasht is as follows: an increase in runoff 63.9 million cubic meters (with SCS equation) or 51.2 million cubic meters (with logical equation) and an increase in ground water resources: 40.5 million cubic meters.

Keywords: rainwater harvesting, ground water, atmospheric water resources, weather modification, cloud seeding

Procedia PDF Downloads 104

Authors: J.Pozuelo-Muñoz, E. Cascarosa-Salillas, C. Rodríguez-Casals, A. de Echave, E. Terrado-Sieso

Abstract:

This study focuses on the development, implementation, and evaluation of an instructional sequence aimed at 16–17-year-old students, involving the design and use of a cloud chamber—a device that allows observation of subatomic particles. The research addresses the limited presence of particle physics in Spanish secondary and high school curricula, a gap that restricts students' learning of advanced physics concepts and diminishes engagement with complex scientific topics. The primary goal of this project is to introduce particle physics in the classroom through a practical, interdisciplinary methodology that promotes autonomous learning and critical thinking. The methodology is framed within Design-Based Research (DBR), an approach that enables iterative and pragmatic development of educational resources. The research proceeded in several phases, beginning with the design of an experimental teaching sequence, followed by its implementation in high school classrooms. This sequence was evaluated, redesigned, and reimplemented with the aim of enhancing students’ understanding and skills related to designing and using particle detection instruments. The instructional sequence was divided into four stages: introduction to the activity, research and design of cloud chamber prototypes, observation of particle tracks, and analysis of collected data. In the initial stage, students were introduced to the fundamentals of the activity and provided with bibliographic resources to conduct autonomous research on cloud chamber functioning principles. During the design stage, students sourced materials and constructed their own prototypes, stimulating creativity and understanding of physics concepts like thermodynamics and material properties. The third stage focused on observing subatomic particles, where students recorded and analyzed the tracks generated in their chambers. Finally, critical reflection was encouraged regarding the instrument's operation and the nature of the particles observed. The results show that designing the cloud chamber motivates students and actively engages them in the learning process. Additionally, the use of this device introduces advanced scientific topics beyond particle physics, promoting a broader understanding of science. The study’s conclusions emphasize the need to provide students with ample time and space to thoroughly understand the role of materials and physical conditions in the functioning of their prototypes and to encourage critical analysis of the obtained data. This project not only highlights the importance of interdisciplinarity in science education but also provides a practical framework for teachers to adapt complex concepts for educational contexts where these topics are often absent.

Keywords: cloud chamber, particle physics, secondary education, instructional design, design-based research, STEM

Procedia PDF Downloads 13

Authors: Ahmed Tarek, Ahmed Alveed

Abstract:

In the theory of computing, there are a wide variety of direct and indirect proof techniques. However, mathematical induction (MI) stands out to be one of the most powerful proof techniques for proving hypotheses, theorems, and new results. There are variations of mathematical induction-based proof techniques, which are broadly classified into three categories, such as structural induction (SI), weak induction (WI), and strong induction (SI). In this expository paper, several different variants of the mathematical induction techniques are explored, and the specific scenarios are discussed where a specific induction technique stands out to be more advantageous as compared to other induction strategies. Also, the essential difference among the variants of mathematical induction are explored. The points of separation among mathematical induction, recursion, and logical deduction are precisely analyzed, and the relationship among variations of recurrence relations, and mathematical induction are being explored. In this context, the application of recurrence relations, and mathematical inductions are considered together in a single framework for codewords over a given alphabet.

Keywords: alphabet, codeword, deduction, mathematical, induction, recurrence relation, strong induction, structural induction, weak induction

Procedia PDF Downloads 163