Search results for: computer technologies

Authors: Yannick Willemin

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Carbon fibre-reinforced plastic (CFRP) offers outstanding value. However, like all materials, CFRP also has its challenges. Many forming processes are largely manual and hard to automate, making it challenging to control repeatability and reproducibility (R&R); they generate significant scrap and are too slow for high-series production; fibre costs are relatively high and subject to supply and cost fluctuations; the supply chain is fragmented; many forms of CFRP are not recyclable, and many materials have yet to be fully characterized for accurate simulation; shelf life and outlife limitations add cost; continuous-fibre forms have design limitations; many materials are brittle; and small and/or thick parts are costly to produce and difficult to automate. A majority of small structural parts are metal due to high CFRP fabrication costs for the small-size class. The fact that CFRP manufacturing processes that produce the highest performance parts also tend to be the slowest and least automated is another reason CFRP parts are generally higher in cost than comparably performing metal parts, which are easier to produce. Fortunately, business is in the midst of a major manufacturing evolution—Industry 4.0— one technology seeing rapid growth is additive manufacturing/3D printing, thanks to new processes and materials, plus an ability to harness Industry 4.0 tools. No longer limited to just prototype parts, metal-additive technologies are used to produce tooling and mold components for high-volume manufacturing, and polymer-additive technologies can incorporate fibres to produce true composites and be used to produce end-use parts with high aesthetics, unmatched complexity, mass customization opportunities, and high mechanical performance. A new hybrid manufacturing process combines the best capabilities of additive—high complexity, low energy usage and waste, 100% traceability, faster to market—and post-consolidation—tight tolerances, high R&R, established materials, and supply chains—technologies. The platform was developed by Zürich-based 9T Labs AG and is called Additive Fusion Technology (AFT). It consists of a design software offering the possibility to determine optimal fibre layup, then exports files back to check predicted performance—plus two pieces of equipment: a 3d-printer—which lays up (near)-net-shape preforms using neat thermoplastic filaments and slit, roll-formed unidirectional carbon fibre-reinforced thermoplastic tapes—and a post-consolidation module—which consolidates then shapes preforms into final parts using a compact compression press fitted with a heating unit and matched metal molds. Matrices—currently including PEKK, PEEK, PA12, and PPS, although nearly any high-quality commercial thermoplastic tapes and filaments can be used—are matched between filaments and tapes to assure excellent bonding. Since thermoplastics are used exclusively, larger assemblies can be produced by bonding or welding together smaller components, and end-of-life parts can be recycled. By combining compression molding with 3D printing, higher part quality with very-low voids and excellent surface finish on A and B sides can be produced. Tight tolerances (min. section thickness=1.5mm, min. section height=0.6mm, min. fibre radius=1.5mm) with high R&R can be cost-competitively held in production volumes of 100 to 10,000 parts/year on a single set of machines.

Keywords: additive manufacturing, composites, thermoplastic, hybrid manufacturing

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Authors: Arul Ayyaswami, Vidhya Ramalingam

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Per- and polyfluoroalkyl substances (PFAS) have emerged as a significant environmental concern due to their ubiquity and persistence in the environment. Their chemical characteristics and adverse effects on human health demands more effective and sustainable solutions in remediation of the PFAS. The work presented here encompasses an overview of treatment technologies with two case studies that utilize effective approaches in addressing PFAS contaminated media. Currently the options for treatment of PFAS compounds include Activated carbon adsorption, Ion Exchange, Membrane Filtration, Advanced oxidation processes, Electrochemical treatment, and Precipitation and Coagulation. In the first case study, a pilot study application of colloidal activated carbon (CAC) was completed to address PFAS from aqueous film-forming foam (AFFF) used to extinguish a large fire. The pilot study was used to demonstrate the effectiveness of a CAC in situ permeable reactive barrier (PRB) in effectively stopping the migration of PFOS and PFOA, moving from the source area at high concentrations. Before the CAC PRB installation, an injection test using - fluorescein dye was conducted to determine the primary fracture-induced groundwater flow pathways. A straddle packer injection delivery system was used to isolate discrete intervals and gain resolution over the 70 feet saturated zone targeted for treatment. Flow rates were adjusted, and aquifer responses were recorded for each interval. The results from the injection test were used to design the pilot test injection plan using CAC PRB. Following the CAC PRB application, the combined initial concentration 91,400 ng/L of PFOS and PFOA were reduced to approximately 70 ng/L (99.9% reduction), after only one month following the injection event. The results demonstrate the remedy's effectiveness to quickly and safely contain high concentrations of PFAS in fractured bedrock, reducing the risk to downgradient receptors. The second study involves developing a reductive defluorination treatment process using UV and electron acceptor. This experiment indicates a significant potential in treatment of PFAS contaminated waste media such as landfill leachates. The technology also shows a promising way of tacking these contaminants without the need for secondary waste disposal or any additional pre-treatments.

Keywords: per- and polyfluoroalkyl substances (PFAS), colloidal activated carbon (CAC), destructive PFAS treatment technology, aqueous film-forming foam (AFFF)

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Authors: Anirban Dey, Sukanta Kumar Dash, Bishnupada Mandal

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Carbondioxide (CO2) is a major greenhouse gas responsible for global warming and fossil fuel power plants are the main emitting sources. Therefore the capture of CO2 is essential to maintain the emission levels according to the standards. Carbon capture and storage (CCS) is considered as an important option for stabilization of atmospheric greenhouse gases and minimizing global warming effects. There are three approaches towards CCS: Pre combustion capture where carbon is removed from the fuel prior to combustion, Oxy-fuel combustion, where coal is combusted with oxygen instead of air and Post combustion capture where the fossil fuel is combusted to produce energy and CO2 is removed from the flue gases left after the combustion process. Post combustion technology offers some advantage as existing combustion technologies can still be used without adopting major changes on them. A number of separation processes could be utilized part of post –combustion capture technology. These include (a) Physical absorption (b) Chemical absorption (c) Membrane separation (d) Adsorption. Chemical absorption is one of the most extensively used technologies for large scale CO2 capture systems. The industrially important solvents used are primary amines like Monoethanolamine (MEA) and Diglycolamine (DGA), secondary amines like diethanolamine (DEA) and Diisopropanolamine (DIPA) and tertiary amines like methyldiethanolamine (MDEA) and Triethanolamine (TEA). Primary and secondary amines react fast and directly with CO2 to form stable carbamates while Tertiary amines do not react directly with CO2 as in aqueous solution they catalyzes the hydrolysis of CO2 to form a bicarbonate ion and a protonated amine. Concentrated Piperazine (PZ) has been proposed as a better solvent as well as activator for CO2 capture from flue gas with a 10 % energy benefit compared to conventional amines such as MEA. However, the application of concentrated PZ is limited due to its low solubility in water at low temperature and lean CO2 loading. So following the performance of PZ its derivative 2-Aminoethyl piperazine (AEP) which is a cyclic amine can be explored as an activator towards the absorption of CO2. Vapour liquid equilibrium (VLE) in CO2 capture systems is an important factor for the design of separation equipment and gas treating processes. For proper thermodynamic modeling accurate equilibrium data for the solvent system over a wide range of temperatures, pressure and composition is essential. The present work focuses on the determination of VLE data for (AEP + H2O) system at 40 °C for various composition range.

Keywords: absorption, aminoethyl piperazine, carbondioxide, vapour liquid equilibrium

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Authors: Chukwuma Mgboji

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Since the advent of Artificial Intelligence, robots have become a major stay in developing societies. Robots are deployed in Education, Health, Food and in other spheres of life. Nigeria a country in West Africa has a very low profile in the advancement of Artificial Intelligence especially in the grass roots. The benefits of Artificial intelligence are not fully maximised and harnessed. Advances in artificial intelligence are perceived as impossible or observed as irrelevant. This study seeks to ascertain the needed skills for the development of artificialintelligence amongst secondary schools in Nigeria. The study focused on South East Nigeria with Five states namely Imo, Abia, Ebonyi, Anambra and Enugu. The sample size is 1000 students drawn from Five Government owned Universities offering Computer Science, Computer Education, Electronics Engineering across the Five South East states. Survey method was used to solicit responses from respondents. The findings from the study identified mathematical skills, analytical skills, problem solving skills, computing skills, programming skills, algorithm skills amongst others. The result of this study to the best of the author’s knowledge will be highly beneficial to all stakeholders involved in the advancements and development of artificial intelligence.

Keywords: artificial intelligence, secondary school, robotics, skills

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Authors: Clement Yeboah, Eva Laryea

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A pretest-posttest within subjects experimental design was employed to examine the effects of a computerized basic statistics learning game on achievement and statistics-related anxiety of students enrolled in introductory graduate statistics course. Participants (N = 34) were graduate students in a variety of programs at state-funded research university in the Southeast United States. We analyzed pre-test posttest differences using paired samples t-tests for achievement and for statistics anxiety. The results of the t-test for knowledge in statistics were found to be statistically significant, indicating significant mean gains for statistical knowledge as a function of the game-based intervention. Likewise, the results of the t-test for statistics-related anxiety were also statistically significant, indicating a decrease in anxiety from pretest to posttest. The implications of the present study are significant for both teachers and students. For teachers, using computer games developed by the researchers can help to create a more dynamic and engaging classroom environment, as well as improve student learning outcomes. For students, playing these educational games can help to develop important skills such as problem solving, critical thinking, and collaboration. Students can develop an interest in the subject matter and spend quality time to learn the course as they play the game without knowing that they are even learning the presupposed hard course. The future directions of the present study are promising as technology continues to advance and become more widely available. Some potential future developments include the integration of virtual and augmented reality into educational games, the use of machine learning and artificial intelligence to create personalized learning experiences, and the development of new and innovative game-based assessment tools. It is also important to consider the ethical implications of computer game-based pedagogy, such as the potential for games to perpetuate harmful stereotypes and biases. As the field continues to evolve, it will be crucial to address these issues and work towards creating inclusive and equitable learning experiences for all students. This study has the potential to revolutionize the way basic statistics graduate students learn and offers exciting opportunities for future development and research. It is an important area of inquiry for educators, researchers, and policymakers and will continue to be a dynamic and rapidly evolving field for years to come.

Keywords: pretest-posttest within subjects, computer game-based learning, statistics achievement, statistics anxiety

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Authors: Simiao Ren, En Wei

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Drowning is a significant safety issue worldwide, and a robust computer vision-based alert system can easily prevent such tragedies in swimming pools. However, due to domain shift caused by the visual gap (potentially due to lighting, indoor scene change, pool floor color etc.) between the training swimming pool and the test swimming pool, the robustness of such algorithms has been questionable. The annotation cost for labeling each new swimming pool is too expensive for mass adoption of such a technique. To address this issue, we propose a domain-aware data augmentation pipeline based on Gaussian Poisson Generative Adversarial Network (GP-GAN). Combined with YOLOv8, we demonstrate that such a domain adaptation technique can significantly improve the model performance (from 0.24 mAP to 0.82 mAP) on new test scenes. As the augmentation method only require background imagery from the new domain (no annotation needed), we believe this is a promising, practical route for preventing swimming pool drowning.

Keywords: computer vision, deep learning, YOLOv8, detection, swimming pool, drowning, domain adaptation, generative adversarial network, GAN, GP-GAN

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Maha Mourad, Fady Todros

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The objective of this research is to find an explanatory model to define the most important variables and factors that affect the acceptance of Mobile Internet in the Egyptian market. A qualitative exploratory research was conducted to support the conceptual framework followed with a quantitative research in the form of a survey distributed among 411 respondents. It was clear that relative advantage, complexity, compatibility, perceived price level and perceived playfulness have a dominant role in influencing consumers to adopt mobile internet, while observability is correlated to the adoption but when measured with the other factors it lost its value. The perceived price level has a negative relationship with the adoption as well the compatibility.

Keywords: innovation, Egypt, communication technologies, diffusion, innovation adoption, emerging market

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Authors: A. de Folly d’Auris, R. Bagatin, P. Filtri

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This paper discusses the importance of having a good initial characterization of soil samples when thermal desorption has to be applied to polluted soils for the removal of contaminants. Particular attention has to be devoted on the desorption kinetics of the samples to identify the gases evolved during the heating, and contaminant degradation pathways. In this study, two samples coming from different points of the same contaminated site were considered. The samples are much different from each other. Moreover, the presence of high initial quantity of heavy hydrocarbons strongly affected the performance of thermal desorption, resulting in formation of dangerous intermediates. Analytical techniques such TGA (Thermogravimetric Analysis), DSC (Differential Scanning Calorimetry) and GC-MS (Gas Chromatography-Mass) provided a good support to give correct indication for field application.

Keywords: desorption kinetics, hydrocarbons, thermal desorption, thermogravimetric measurements

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Authors: Er-Yuan Chuang

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Bio-mimetic matters have biological functionalities. This might be valuable in the development of versatile biomaterials. At biological fields, protein-based materials might be components to form a 3D network of extracellular biomolecules, containing growth factors. Also, the protein-based biomaterial provides biochemical and structural assistance of adjacent cells. In this study, we try to prepare protein based biomaterial, which was harvested from living animal. We analyzed it’s chemical, physical and biological property in vitro. Besides, in vivo bio-interaction of the prepared biomimetic matrix was tested in an animal model. The protein-based biomaterial has degradability and biocompatibility. This development could be used for tissue regenerations and be served as platform technologies.

Keywords: protein based, in vitro study, in vivo study, biomaterials

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Authors: Daniel Gonzales, Zev Winkelman, Trung Tran, Ricardo Sanchez, Dulani Woods, John Hollywood

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We have developed a distributed computing capability, Digital Forensics Compute Cluster (DFORC2) to speed up the ingestion and processing of digital evidence that is resident on computer hard drives. DFORC2 parallelizes evidence ingestion and file processing steps. It can be run on a standalone computer cluster or in the Amazon Web Services (AWS) cloud. When running in a virtualized computing environment, its cluster resources can be dynamically scaled up or down using Kubernetes. DFORC2 is an open source project that uses Autopsy, Apache Spark and Kafka, and other open source software packages. It extends the proven open source digital forensics capabilities of Autopsy to compute clusters and cloud architectures, so digital forensics tasks can be accomplished efficiently by a scalable array of cluster compute nodes. In this paper, we describe DFORC2 and compare it with a standalone version of Autopsy when both are used to process evidence from hard drives of different sizes.

Keywords: digital forensics, cloud computing, cyber security, spark, Kubernetes, Kafka

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Authors: Cornelius Femi Popoola

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This study investigated logistics information systems in the distribution of flour in Nigeria. A case study design was used and 50 staff of Honeywell Flour Mill was sampled for the study. Data generated through a questionnaire were analysed using correlation and regression analysis. The findings of the study revealed that logistic information systems such as e-commerce, interactive telephone systems and electronic data interchange positively correlated with the distribution of flour in Honeywell Flour Mill. Finding also deduced that e-commerce, interactive telephone systems and electronic data interchange jointly and positively contribute to the distribution of flour in Honeywell Flour Mill in Nigeria (R = .935; Adj. R2 = .642; F (3,47) = 14.739; p < .05). The study therefore recommended that Honeywell Flour Mill should upgrade their logistic information systems to computer-to-computer communication of business transactions and documents, as well adopt new technology such as, tracking-and-tracing systems (barcode scanning for packages and palettes), tracking vehicles with Global Positioning System (GPS), measuring vehicle performance with ‘black boxes’ (containing logistic data), and Automatic Equipment Identification (AEI) into their systems.

Keywords: e-commerce, electronic data interchange, flour distribution, information system, interactive telephone systems

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Authors: Rebeca Ramirez Acosta, Sebastian Lenhoff

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Decision makers worldwide need to determine if the implementation of a new market mechanism will contribute to the sustainability and resilience of the power system. Due to smart grid technologies, new products in the distribution and transmission system can be traded; however, the impact of changing a market rule will differ between several regions. To test systematically those impacts, a market categorization has been compiled and organized in a smart grid market testing toolbox. This toolbox maps all actual energy products and sets the basis for running a co-simulation test with the new rule to be implemented. It will help to measure the impact of the new rule, based on the sustainable and resilience indicators.

Keywords: co-simulation, electricity market, smart grid market, market testing

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Authors: Sung-Jun Yoo, Kazuhide Ito

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In this study, a comprehensive computer simulated person (CSP) that integrates computational human model (virtual manikin) and respiratory tract model (virtual airway), was applied for estimation of indoor environmental quality. Moreover, an inclusive prediction method was established by integrating computational fluid dynamics (CFD) analysis with advanced CSP which is combined with physiologically-based pharmacokinetic (PBPK) model, unsteady thermoregulation model for analysis targeting micro-climate around human body and respiratory area with high accuracy. This comprehensive method can estimate not only the contaminant inhalation but also constant interaction in the contaminant transfer between indoor spaces, i.e., a target area for indoor air quality (IAQ) assessment, and respiratory zone for health risk assessment. This study focused on the usage of the CSP as an air/thermal quality sensor in indoors, which means the application of comprehensive model for assessment of IAQ and thermal environmental quality. Demonstrative analysis was performed in order to examine the applicability of the comprehensive model to the heating, ventilation, air conditioning (HVAC) control scheme. CSP was located at the center of the simple model room which has dimension of 3m×3m×3m. Formaldehyde which is generated from floor material was assumed as a target contaminant, and flow field, sensible/latent heat and contaminant transfer analysis in indoor space were conducted by using CFD simulation coupled with CSP. In this analysis, thermal comfort was evaluated by thermoregulatory analysis, and respiratory exposure risks represented by adsorption flux/concentration at airway wall surface were estimated by PBPK-CFD hybrid analysis. These Analysis results concerning IAQ and thermal comfort will be fed back to the HVAC control and could be used to find a suitable ventilation rate and energy requirement for air conditioning system.

Keywords: CFD simulation, computer simulated person, HVAC control, indoor environmental quality

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Authors: Berker Bayazit, Gulgun Kayakutlu

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The World Energy Outlook shows that energy markets will substantially change within a few forthcoming decades. First, determined action plans according to COP21 and aim of CO₂ emission reduction have already impact on policies of countries. Secondly, swiftly changed technological developments in the field of renewable energy will be influential upon medium and long-term energy generation and consumption behaviors of countries. Furthermore, share of electricity on global energy consumption is to be expected as high as 40 percent in 2040. Electrical vehicles, heat pumps, new electronical devices and digital improvements will be outstanding technologies and innovations will be the testimony of the market modifications. In order to meet highly increasing electricity demand caused by technologies, countries have to make new investments in the field of electricity production, transmission and distribution. Specifically, electricity generation mix becomes vital for both prevention of CO₂ emission and reduction of power prices. Majority of the research and development investments are made in the field of electricity generation. Hence, the prime source diversity and source planning of electricity generation are crucial for improving the wealth of citizen life. Approaches considering the CO₂ emission and total cost of generation, are necessary but not sufficient to evaluate and construct the product mix. On the other hand, employment and positive contribution to macroeconomic values are important factors that have to be taken into consideration. This study aims to constitute new investments in renewable energies (solar, wind, geothermal, biogas and hydropower) between 2018-2023 under 4 different goals. Therefore, a multi-objective programming model is proposed to optimize the goals of minimizing the CO₂ emission, investment amount and electricity sales price while maximizing the total employment and positive contribution to current deficit. In order to avoid the user preference among the goals, Dinkelbach’s algorithm and Guzel’s approach have been combined. The achievements are discussed with comparison to the current policies. Our study shows that new policies like huge capacity allotment might be discussible although obligation for local production is positive. The improvements in grid infrastructure and re-design support for the biogas and geothermal can be recommended.

Keywords: energy generation policies, multi-objective linear programming, portfolio planning, renewable energy

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Authors: Kazumoto Tanaka, Takayuki Fujino

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Sports games conducted as a group are a form of therapeutic exercise for aged people with decreased strength and for people suffering from permanent damage of stroke and other conditions. However, it is difficult for patients with different athletic abilities to play a game on an equal footing. This study specifically examines a computer video game designed for therapeutic exercise, and a game system with support given depending on athletic ability. Thereby, anyone playing the game can participate equally. This video-game, to be specific, is a popular variant of balloon volleyball, in which players hit a balloon by hand before it falls to the floor. In this game system, each player plays the game watching a monitor on which the system displays tailor-made video-game images adjusted to the person’s athletic ability, providing players with player-adaptive assist support. We have developed a multiplayer game system with an image generation technique for the tailor-made video-game and conducted tests to evaluate it.

Keywords: therapeutic exercise, computer video game, disability-adaptive assist, tailor-made video-game image

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Authors: Agarana Michael C., Akinlabi Esther T., Pule Kholopane

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Innovation in manufacturing process technologies and associated product design affects the prospects for manufacturing today and in near future. In this study some theoretical methods, useful as tools in advanced manufacturing, are considered. In particular, some basic Mathematical, Operational Research, Heuristic, and Statistical techniques are discussed. These techniques/methods are very handy in many areas of advanced manufacturing processes, including process planning optimization, modelling and analysis. Generally the production rate requires the application of Mathematical methods. The Emerging Trends Processes in Advanced Manufacturing can be enhanced by using Mathematical Modelling and Optimization techniques.

Keywords: mathematical modelling, optimization, emerging trends, advanced manufacturing

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Authors: Smitha Haridasan, Zelalem Demissie, Atri Dutta, Ajita Rattani

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Aided by the wind, all it takes is one ember and a few minutes to create a wildfire. Wildfires are growing in frequency and size due to climate change. Wildfires and its consequences are one of the major environmental concerns. Every year, millions of hectares of forests are destroyed over the world, causing mass destruction and human casualties. Thus early detection of wildfire becomes a critical component to mitigate this threat. Many computer vision-based techniques have been proposed for the early detection of forest fire using video surveillance. Several computer vision-based methods have been proposed to predict and detect forest fires at various spectrums, namely, RGB, HSV, and YCbCr. The aim of this paper is to propose a multi-spectral deep learning model that combines information from different spectrums at intermediate layers for accurate fire detection. A heterogeneous dataset assembled from publicly available datasets is used for model training and evaluation in this study. The experimental results show that multi-spectral deep learning models could obtain an improvement of about 4.68 % over those based on a single spectrum for fire detection.

Keywords: deep learning, forest fire detection, multi-spectral learning, natural hazard detection

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Authors: Shield B. Lin, Ziraguen O. Williams

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In a task to assist NASA in analyzing the dynamic forces caused by operational countermeasures of an astronaut’s exercise platform impacting the spacecraft, an active proportional-integral-derivative controller commanding a linear actuator is proposed in a vibration isolation system to regulate the movement of the exercise platform. Computer simulation shows promising results that most exciter forces can be reduced or even eliminated. This paper emphasizes on parameter uncertainties, variations and exciter force variations. Drift and variations of system parameters in the vibration isolation system for astronaut’s exercise platform are analyzed. An active controlled scheme is applied with the goals to reduce the platform displacement and to minimize the force being transmitted to the spacecraft structure. The controller must be robust enough to accommodate the wide variations of system parameters and exciter forces. Computer simulation for the vibration isolation system was performed via MATLAB/Simulink and Trick. The simulation results demonstrate the achievement of force reduction with small platform displacement under wide ranges of variations in system parameters.

Keywords: control, counterweight, isolation, vibration

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Authors: Omer Cahana, Maya Herman, Ofer Levi

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Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Varsha Prasad, S. Sandya

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Cosmic Ray effects in microelectronics such as single event effect (SET) and total dose ionization (TID) have been of major concern in space electronics since 1970. Advanced CMOS technologies have demonstrated reduced sensitivity to TID effect. However, charge pump Phase Locked Loop is very much vulnerable to single event transient effect. This paper presents an SET analysis model, where the SET is modeled as a double exponential pulse. The time domain analysis reveals that the settling time of the voltage controlled oscillator (VCO) depends on the SET pulse strength, setting the time constant and the damping factor. The analysis of the proposed SET analysis model is confirmed by the simulation results.

Keywords: charge pump, phase locked loop, SET, VCO

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Authors: R. Zhankina, U. Zhanbyrbekuly, A. Tamadon, M. Askarov, R. Sherkhanov, D. Akhmetov, D. Saipiyeva, N. Keulimzhaev

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Male infertility affects about 15% of couples of reproductive age. Approximately 10–15% have azoospermia who have previously been diagnosed with male infertility. Azoospermia is regarded as the absence of spermatozoa in the ejaculate and is found in 10-15% of infertile men. Non-obstructive azoospermia is considered a cause of male infertility that is not amenable to drug therapy. Patients with non-obstructive azoospermia are unable to have their "own" children and have only options for adoption or use of donor sperm. Advances in assisted reproductive technologies such as intracytoplasmic sperm injection in vitro fertilization have significantly changed the management of patients with non-obstructive azoospermia. Advances in biotechnology have increased the options for treating patients with non-obstructive azoospermia. Mesenchymal stem cell therapy has been recognized as a new option for infertility treatment. Material and methods of the study: After obtaining informed consent, 5 patients diagnosed with non-obstructive azoospermia were included in an open, non-randomized study. The age of the patients ranged from 24 to 35 years. The examination was carried out before the start of treatment, which included biochemical blood tests, hormonal profile levels (luteinizing hormone, follicle-stimulating hormone, testosterone, prolactin, inhibin B); tests for tumor markers; genetic research. All studies were carried out in compliance with the requirements of Protocol No. 8 dated 06/09/20, approved by the Local Ethical Commission of NJSC "Astana Medical University". The control examination of patients was carried out after 6 months, by re-taking the program and hormonal profile (testosterone, luteinizing hormone, follicle-stimulating hormone, prolactin, inhibin B). Before micro-TESE of the testis, all 5 patients underwent myeloexfusion in the operating room. During the micro-TESE, autotransplantation of mesenchymal stem cells into the testicular network, previously cultured in a cell technology laboratory for 2 weeks, was performed. Results of the study: in all patients, the levels of total testosterone increased, the level of follicle-stimulating hormone decreased, the levels of luteinizing hormone returned to normal, the level of inhibin B increased. IVF with a positive result; another patient (20%) had spermatogenesis cells. Non-obstructive azoospermia and mesenchymal stem cells Conclusions: The positive results of this work serve as the basis for the application of a new cellular therapeutic approach for the treatment of non-obstructive azoospermia using mesenchymal stem cells.

Keywords: cell therapy, regenerative medicine, male infertility, mesenchymal stem cells

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Authors: Jessica Ho

Abstract:

AI has helped government, businesses and industries transform the way they do things. AI is used in automating tasks to improve decision-making and efficiency. AI is embedded in sensors and used in automation to help save time and eliminate human errors in repetitive tasks. Today, we saw the growth in AI using the collection of vast amounts of data to forecast with greater accuracy, inform decision-making, adapt to changing market conditions and offer more personalised service based on consumer habits and preferences. Government around the world share the opportunity to leverage these disruptive technologies to improve productivity while reducing costs. In addition, these intelligent solutions can also help streamline government processes to deliver more seamless and intuitive user experiences for employees and citizens. This is a critical challenge for NSW Government as we are unable to determine the risk that is brought by the unprecedented pace of adoption of AI solutions in government. Government agencies must ensure that their use of AI complies with relevant laws and regulatory requirements, including those related to data privacy and security. Furthermore, there will always be ethical concerns surrounding the use of AI, such as the potential for bias, intellectual property rights and its impact on job security. Within NSW’s public sector, agencies are already testing AI for crowd control, infrastructure management, fraud compliance, public safety, transport, and police surveillance. Citizens are also attracted to the ease of use and accessibility of AI solutions without requiring specialised technical skills. This increased accessibility also comes with balancing a higher risk and exposure to the health and safety of citizens. On the other side, public agencies struggle with keeping up with this pace while minimising risks, but the low entry cost and open-source nature of generative AI led to a rapid increase in the development of AI powered apps organically – “There is an AI for That” in Government. Other challenges include the fact that there appeared to be no legislative provisions that expressly authorise the NSW Government to use an AI to make decision. On the global stage, there were too many actors in the regulatory space, and a sovereign response is needed to minimise multiplicity and regulatory burden. Therefore, traditional corporate risk and governance framework and regulation and legislation frameworks will need to be evaluated for AI unique challenges due to their rapidly evolving nature, ethical considerations, and heightened regulatory scrutiny impacting the safety of consumers and increased risks for Government. Creating an effective, efficient NSW Government’s governance regime, adapted to the range of different approaches to the applications of AI, is not a mere matter of overcoming technical challenges. Technologies have a wide range of social effects on our surroundings and behaviours. There is compelling evidence to show that Australia's sustained social and economic advancement depends on AI's ability to spur economic growth, boost productivity, and address a wide range of societal and political issues. AI may also inflict significant damage. If such harm is not addressed, the public's confidence in this kind of innovation will be weakened. This paper suggests several AI regulatory approaches for consideration that is forward-looking and agile while simultaneously fostering innovation and human rights. The anticipated outcome is to ensure that NSW Government matches the rising levels of innovation in AI technologies with the appropriate and balanced innovation in AI governance.

Keywords: artificial inteligence, machine learning, rules, governance, government

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Authors: Mustafa Elfituri, Jonathan Cook

Abstract:

Recently, graph-based computations have become more important in large-scale scientific computing as they can provide a methodology to model many types of relations between independent objects. They are being actively used in fields as varied as biology, social networks, cybersecurity, and computer networks. At the same time, graph problems have some properties such as irregularity and poor locality that make their performance different than regular applications performance. Therefore, parallelizing graph algorithms is a hard and challenging task. Initial evidence is that standard computer architectures do not perform very well on graph algorithms. Little is known exactly what causes this. The Graph500 benchmark is a representative application for parallel graph-based computations, which have highly irregular data access and are driven more by traversing connected data than by computation. In this paper, we present results from analyzing the performance of various example implementations of Graph500, including a shared memory (OpenMP) version, a distributed (MPI) version, and a hybrid version. We measured and analyzed all the factors that affect its performance in order to identify possible changes that would improve its performance. Results are discussed in relation to what factors contribute to performance degradation.

Keywords: graph computation, graph500 benchmark, parallel architectures, parallel programming, workload characterization.

Procedia PDF Downloads 129

Authors: Naina S. Deshmukh, Manik P. Deosarkar

Abstract:

With the wide production, consumption, and disposal of pesticides in the world, the concerns over their human and environmental health impacts are rapidly growing. Among developing treatment technologies, ultrasonication, as an emerging and promising technology for the removal of pesticides in the aqueous environment, has attracted the attention of many researchers in recent years. The degradation of acephate in aqueous solutions was investigated under the influence of ultrasound irradiation (20 kHz) in the presence of heterogeneous catalysts titanium dioxide (TiO2) and Zinc oxide (ZnO). The influence of various factors such as amount of catalyst (0.25, 0.5, 0.75, 1.0, 1.25 g/l), initial acephate concentration (100, 200, 300, 400 mg/l), and pH (3, 5, 7, 9, 11) were studied. The optimum catalyst dose was found to be 1 g/l of TiO2 and 1.25 g/l of ZnO for acephate at 100 mg/l, respectively. The maximum percentage degradation of acephate was observed at pH 11 for catalyst TiO2 and ZnO, respectively.

Keywords: ultrasonic degradation, acephate, TiO2, ZnO, heterogeneous catalyst

Procedia PDF Downloads 83

Authors: Antoaneta Stefanova, Rositsa Gencheva, Pavlin Groudev

Abstract:

This paper presents the modeling approach in SBO sequence for VVER 1000 reactors and describes the reactor core behavior at late in-vessel phase in case of late reflooding by HPIS and gives preliminary results for the ASTECv2 validation. The work is focused on investigation of plant behavior during total loss of power and the operator actions. The main goal of these analyses is to assess the phenomena arising during the Station blackout (SBO) followed by primary side high pressure injection system (HPIS) reflooding of already damaged reactor core at very late ‘in-vessel’ phase. The purpose of the analysis is to define how the later HPIS switching on can delay the time of vessel failure or possibly avoid vessel failure. For this purpose has been simulated an SBO scenario with injection of cold water by a high pressure pump (HPP) in cold leg at different stages of core degradation. The times for HPP injection were chosen based on previously performed investigations.

Keywords: VVER, operator action validation, reflooding of overheated reactor core, ASTEC computer code

Procedia PDF Downloads 404

Authors: Bashayer Alsana

Abstract:

Emerging technologies present human interaction with new challenges. Individuals are required to interact and collaborate to achieve mutual gain. Accomplishing shared goals requires all parties involved to trust others commitment to fulfill their specified obligations. Trust is harder to establish when groups work virtually and members transcend time, space, and culture. This paper identifies the importance of trust in virtual groups of students at Cairo University by exposing them to electronic projects on which they collaborate.Students respond to a survey to assess their range of trust within their teams and how the outcome is affected. Gender differences and other demographic factors are analyzed to understand results and rates of trust. The paper concludes with summarizing factors influencing trust development and possible implications.

Keywords: students, teams, trust, virtual

Procedia PDF Downloads 248

Authors: Ahmad Fahad Alotaibi, Khalid Hamed Hajri, Humoud Hudiban Rashidi

Abstract:

Intelligent information technology infrastructure is considered one of the enablers to enhance digital transformation in the gas and oil fields to optimize IT infrastructure reliability by supporting operations and maintenance in a safe and secure method to optimize resources. Smart IT buildings, communication rooms and shelters with intelligent technologies can strengthen the performance and profitability of gas and oil companies by ensuring business continuity. This paper describes the advantages of deploying intelligent IT infrastructure in the oil and gas industry by illustrating its positive impacts on some development aspects, for instance, operations, maintenance, safety, security and resource optimization. Moreover, it highlights the challenges and difficulties of providing smart IT services in a remote area and proposes solutions to overcome such difficulties.

Keywords: intelligent IT infrastructure, remote areas, oil and gas field, digitalization

Procedia PDF Downloads 45

Authors: Uvais Qidwai, Amor Moursi, Mohamed Tahar, Malek Hamad, Hamad Alansi

Abstract:

This paper focuses on the development and implementation of an advanced plant health monitoring system with an AI backbone and IoT sensory network. Our approach involves addressing the critical environmental factors essential for preserving a plant’s well-being, including air temperature, soil moisture, soil temperature, soil conductivity, pH, water levels, and humidity, as well as the presence of essential nutrients like nitrogen, phosphorus, and potassium. Central to our methodology is the utilization of computer vision technology, particularly a night vision camera. The captured data is then compared against a reference database containing different health statuses. This comparative analysis is implemented using an AI deep learning model, which enables us to generate accurate assessments of plant health status. By combining the AI-based decision-making approach, our system aims to provide precise and timely insights into the overall health and well-being of plants, offering a valuable tool for effective plant care and management.

Keywords: deep learning image model, IoT sensing, cloud-based analysis, remote monitoring app, computer vision, fuzzy control

Procedia PDF Downloads 25