Search results for: wireless sensor networks (WSN)

Authors: Norihiro Kato, Yuriko Takayama

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Some gram-negative bacteria enable the simultaneous activation of gene expression involved in N-acylhomoserine lactone (AHL) dependent cell-to-cell communication system. Such regulatory system for the bacterial group behavior is termed as quorum sensing (QS) because a diffusible AHL signal can accumulate around the cell during the increase of the cell density and trigger activation of the sequential QS process. By blocking the QS, the expression of diverse genes related to infection, antibiotic production, and biofilm formation is inhibited. Conditioning of QS by regulation of the DNA-receptor-AHL interaction is a potential target for enhancing host defenses against pathogenicity. We focused on engineered application of transcriptional regulator SpnR produced in opportunistic human pathogen Serratia marcescens. The SpnR can interact with AHL signals at an N-terminal domain and also with a promoter region of a QS target gene at a C-terminal domain. As the initial process of the QS activation, the SpnR forms a complex with the AHL to enhance the expression of pig cluster; the SpnR normally acts as an activator for the expression of the QS-dependent gene. In this research, we attempt to artificially control QS by changing the role of SpnR. The QS-dependent prodigiosin production is expected to inhibit by externally added SpnR in the culture broth of AS-1 strain because the AHL concentration was kept below the threshold by AHL-SpnR complex formation. Maltose-binding protein (MBP)-tagged SpnR (MBP-SpnR) was overexpressed in Escherichia coli and purified using an affinity chromatography equipped with an amylose resin column. The specific interaction between AHL and MBP-SpnR was demonstrated by quartz crystal microbalance (QCM) sensor. AHL with amino end-group was coupled with COOH-terminated self-assembled monolayer prepared on a gold electrode of 27-MHz quartz crystal sensor using water-soluble carbodiimide. After the injection of MBP-SpnR into a cup-type sensor cell filled with the buffer solution, time course of resonant frequency change (ΔFs) was determined. A decrease of ΔFs clearly showed the uptake of MBP-SpnR onto the AHL-immobilized electrode. Furthermore, no binding affinity was observed after the heat-inactivation of MBP-SpnR at 80ºC. These results suggest that MBP-SpnR possesses a specific affinity for AHL. MBP-SpnR was added to the culture medium as an AHL trap to study inhibitory effects on intracellularly accumulated prodigiosin. With approximately 2 µM MBP-SpnR, the amount of prodigiosin induced was half that of the control without any additives. In conclusion, the function of SpnR could be switched by adding it to the cell culture. Exogenously added MBP-SpnR possesses high affinity for AHL derived from cells and acts as an inhibitor of AHL-mediated QS.

Keywords: intracellular signaling, microbial biotechnology, quorum sensing, transcriptional regulator

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Authors: Mariam A. Abu-Alim

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The purpose of this study was to validate muscle activation amplitudes of two field base dynamic balance tests that are used as strengthen and motor control exercises too in the activation of selected hip and knee stabilizer muscles. Methods: Eighteen college-age females students (21±2 years; 65.6± 8.7 kg; 169.7±8.1 cm) who participated at least for 30 minutes in physical activity most days of the week volunteered. The wireless BIOPAC (MP150, BIOPAC System. Inc, California, USA) surface electromyography system was used to validate the activation of the Gluteus Medius and the Adductor Magnus of hip stabilizer muscles; and the Hamstrings, Quadriceps, and the Gastrocnemius of the knee stabilizer muscles. Surface electrodes (EL 503, BIOPAC, System. Inc) connected to dual wireless EMG BioNormadix Transmitters were place on selected muscles of participants dominate side. Manual muscle testing was performed to obtain the maximal voluntary isometric contraction (MVIC) in which all collected muscle activity data during the three reaching direction: anterior, posteromedial, posterolateral of the Star Excursion Balance Test (SEBT) and the Y-balance Test (YBT) data could be normalized. All participants performed three trials for each reaching direction of the SEBT and the YBT. The domanial leg trial for each participant was selected for analysis which was also the standing leg. Results: the selected hip stabilizer muscles (Gluteus Medius, Adductor Magnus) were both greater than 100%MVIC during the performance of the SEBT and in all three directions. Whereas, selected knee stabilizer muscles had greater activation 0f 100% MVIC and were significantly more activated during the performance of the YBT test in all three reaching directions. The results showed that the posterolateral and the postmedial reaching directions for both dynamic balance tests had greater activation levels and greater than 200%MVIC for all tested muscles expect of the hamstrings. Conclusion: the results of this study showed that the SEBT and the YBT had validated high levels of muscular activity for the hip and the knee stabilizer muscles; which can be used to represent the improvement, strength, control and the decreasing in the injury levels. Since these selected hip and knee stabilizer muscles, represent 35% of all athletic injuries depending on the type of sport.

Keywords: dynamic balance tests, electromyography, hip stabilizer muscles, nee stabilizer muscles

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Authors: Amar Partap Singh Pharwaha, Shweta Rani

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This paper is aimed at proposing a rhombus shaped wearable fractal antenna for wireless communication systems. The geometrical descriptors of the antenna have been obtained using bacterial foraging optimization (BFO) for wide band operation. The method of moment based IE3D software has been used to simulate the antenna and observed that miniaturization of 13.08% has been achieved without degrading the resonating properties of the proposed antenna. An analysis with different substrates has also been done in order to evaluate the effectiveness of electrical permittivity on the presented structure. The proposed antenna has low profile, light weight and has successfully demonstrated wideband and multiband characteristics for wearable electronic applications.

Keywords: BFO, bandwidth, electrical permittivity, fractals, wearable antenna

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Authors: Maoxin Ye

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This study examines the relationship between social network sites (SNS) usage and social capital. Because SNS usage can expand the users’ networks, and people who are connected in this networks may become resources to SNS users and lead them to advantage in some situation, it is important to estimate the relationship between SNS usage and ‘who’ is connected or what resources the SNS users can get. Additionally, ‘who’ can be divided in two aspects – people who possess high position and people who are different, hence, it is important to estimate the relationship between SNS usage and high position people and different people. This study adapts Lin’s definition of social capital and the measurement of position generator which tells us who was connected, and can be divided into the same two aspects as well. A national data of America (N = 2,255) collected by Pew Research Center is utilized to do a general regression analysis about SNS usage and social capital. The results indicate that SNS usage is negatively associated with each factor of social capital, and it suggests that, in fact, comparing with non-users, although SNS users can get more connections, the variety and resources of these connections are fewer. For this reason, we could lose benefits through SNS usage.

Keywords: social network sites, social capital, position generator, general regression

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Authors: Chung-Nan Lee, Sheng-Wei Chu, You-Chiun Wang

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LTE (Long Term Evolution) employs the eMBMS (evolved Multimedia Broadcast/Multicast Service) protocol to deliver video streams to a multicast group of users. However, it requires all multicast members to receive a video stream in the same transmission rate, which would degrade the overall service quality when some users encounter bad channel conditions. To overcome this problem, this paper provides two efficient resource allocation schemes in such LTE network: The symbol synchronization (S2) scheme assumes that the macro and pico eNodeBs use the same frequency channel to deliver the video stream to all users. It then adopts a multicast transmission index to guarantee the fairness among users. On the other hand, the resource reuse (R2) scheme allows eNodeBs to transmit data on different frequency channels. Then, by introducing the concept of frequency reuse, it can further improve the overall service quality. Extensive simulation results show that the S2 and R2 schemes can respectively improve around 50% of fairness and 14% of video quality as compared with the common maximum throughput method.

Keywords: LTE networks, multicast, resource allocation, layered video

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Authors: Waleed Ahmed, Huang Xiaohua, Wilayat Ali

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The wheelchair users are generally exposed to physical and psychological health problems, e.g., pressure sores and pain in the hip joint, associated with seating posture or being inactive in a wheelchair for a long time. Reclining Wheelchair with back, thigh, and leg adjustment helps in daily life activities and health preservation. The seat elevating function of an electric wheelchair allows the user (lower limb amputation) to reach different heights. An electric wheelchair is expected to ease the lives of the elderly and disable people by giving them mobility support and decreasing the percentage of accidents caused by users’ narrow sight or joystick operation errors. Thus, this paper proposed the design, analysis and obstacle avoidance control of an electric wheelchair with sit-sleep-seat elevation functions. A 3D model of a wheelchair is designed in SolidWorks that was later used for multi-body dynamic (MBD) analysis and to verify driving control system. The control system uses the fuzzy algorithm to avoid the obstacle by getting information in the form of distance from the ultrasonic sensor and user-specified direction from the joystick’s operation. The proposed fuzzy driving control system focuses on the direction and velocity of the wheelchair. The wheelchair model has been examined and proven in MSC Adams (Automated Dynamic Analysis of Mechanical Systems). The designed fuzzy control algorithm is implemented on Gazebo robotic 3D simulator using Robotic Operating System (ROS) middleware. The proposed wheelchair design enhanced mobility and quality of life by improving the user’s functional capabilities. Simulation results verify the non-accidental behavior of the electric wheelchair.

Keywords: fuzzy logic control, joystick, multi body dynamics, obstacle avoidance, scissor mechanism, sensor

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Authors: Yiquan Liao, Quanhong Jiang

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Since terahertz was observed by German scientists, we have obtained terahertz through different generation technologies of broadband and narrowband. Then, with the development of semiconductor and other technologies, the imaging technology of terahertz has become increasingly perfect. From the earliest application of nondestructive testing in aviation to the present application of information transmission and human safety detection, the role of terahertz will shine in various fields. The weapons produced by terahertz were epoch-making, which is a crushing deterrent against technologically backward countries. At the same time, terahertz technology in the fields of imaging, medical and livelihood, communication and communication are for the well-being of the country and the people.

Keywords: terahertz, imaging, communication, medical treatment

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Authors: Jiajun Wang, Xiaoge Li

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The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

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Authors: Farzaneh Tahmoorian, Bijan Samali, John Yeaman

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Road networks are expanding all over the world during the past few decades to meet the increasing freight volumes created by the population growth and industrial development. At the same time, the rate of generation of solid wastes in the society is increasing with the population growth, technological development, and changes in the lifestyle of people. Thus, the management of solid wastes has become an acute problem. Accordingly, there is a need for greater efficiency in the construction and maintenance of road networks, in reducing the overall cost, especially the utilization of natural materials such as aggregates. An efficient means to reduce construction and maintenance costs of road networks is to replace natural (virgin) materials by secondary, recycled materials. Recycling will also help to reduce pressure on landfills and demand for extraction of natural virgin materials thus ensuring sustainability. Application of solid wastes in asphalt layer reduces not only environmental issues associated with waste disposal but also the demand for virgin materials which will subsequently result in sustainability. Therefore, this research aims to investigate the feasibility of the application of some of the waste materials such as glass, construction and demolition wastes, etc. as alternative materials in pavement construction, particularly flexible pavements. To this end, various combination of different waste materials in certain percentages is considered in designing the asphalt mixture. One of the goals of this research is to determine the optimum percentage of all these materials in the mixture. This is done through a series of tests to evaluate the volumetric properties and resilient modulus of the mixture. The information and data collected from these tests are used to select the adequate samples for further assessment through advanced tests such as triaxial dynamic test and fatigue test, in order to investigate the asphalt mixture resistance to permanent deformation and also cracking. This paper presents the results of these investigations on the application of waste materials in asphalt mixture for production of a sustainable asphalt mix.

Keywords: asphalt, glass, pavement, recycled aggregate, sustainability

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Authors: Mohammad Zaeimzade

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The development of communication technologies is clearly and rapidly moving towards reducing the distance between the virtual and real worlds. Of course, living in a two-spatial or two-globalized world or any other interpretation that means mixing real and virtual life is still relevant and debatable. In the present age of communication, where social networks have transformed the message equation and turned the audience out of passivity and turned into a user. Platforms have penetrated widely in various aspects of human life, from culture and education and economy. Among the messengers, Instagram, which is one of the most extensive image-based interactive networks, plays a significant role in the new economic life. It doesn't need much explanation that the era of thinking of every messenger as a non-insulating conductor that is just a neutral load has passed. Every messenger has its own economic, political and of course security background, Instagram is no exception to this rule and of course it leaves its effects in bio-economics as well. Iran, as the 19th largest economy in the world, has not been unaffected by new platforms, including Instagram, and their consequences in the economy. Generally, in the policy-making space, there are two simple and inflexible pessimistic or optimistic views on this issue, and each of the holders of these views usually have their own one-dimensional policy recommendations regarding how to deal with Instagram. Prescriptions that are usually very different and sometimes contradictory. In this article, we show that this confusion of policymakers is the result of not accurately describing the reality of its effect, and the reason for this inaccurate description is the existence of a conflict of interests in the eyes of describers and researchers. In this article, we first take a look at the main indicators of the Iranian economy, estimate the role of the digital economy in Iran's economic growth, then study the conflicting descriptions of the Instagram-based digital economy, the statistics that show the tolerance of economic users of Instagram in Iran. 300 thousand to 9 million have been estimated. Finally, we take a look at the government's actions in this matter, especially in the context of street riots in October and November 2022. And we suggest an intermediate idea.

Keywords: digital economy, instagram, conflict of interest, social networks

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Luke McDonagh

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Over the past three decades, free and open source software (FOSS) programmers have developed new, innovative and legally binding licences that have in turn enabled the creation of innumerable pieces of everyday software, including Linux, Mozilla Firefox and Open Office. That FOSS has been highly successful in competing with 'closed source software' (e.g. Microsoft Office) is now undeniable, but in noting this success, it is important to examine in detail why this system of FOSS has been so successful. One key reason is the existence of networks or communities of programmers, who are bound together by a key shared social norm of 'reciprocity'. At the same time, these FOSS networks are not unitary – they are highly diverse and there are large divergences of opinion between members regarding which licences are generally preferable: some members favour the flexible ‘free’ or 'no copyleft' licences, such as BSD and MIT, while other members favour the ‘strong open’ or 'strong copyleft' licences such as GPL. This paper argues that without both the existence of the shared norm of reciprocity and the diversity of licences, it is unlikely that the innovative legal framework provided by FOSS would have succeeded to the extent that it has.

Keywords: open source, copyright, licensing, copyleft

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Authors: Zheng Zhihao

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Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph, graph neural networks, structural adaptive receptive fields, information aggregation

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Authors: R. L. P. De Lima, F. C. B. Boogaard, R. E. De Graaf-Van Dinther

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Blue-green algae blooms (cyanobacteria) is currently a relevant ecological problem that is being addressed by most water authorities in the Netherlands. These can affect recreation areas by originating unpleasant smells and toxins that can poison humans and animals (e.g. fish, ducks, dogs). Contamination events usually take place during summer months, and their frequency is increasing with climate change. Traditional monitoring of this bacteria is expensive, labor-intensive and provides only limited (point sampling) information about the spatial distribution of algae concentrations. Recently, a novel handheld sensor allowed water authorities to quicken their algae surveying and alarm systems. This study converted the mentioned algae sensor into a mobile platform, by combining it with an underwater remotely operated vehicle (also equipped with other sensors and cameras). This provides a spatial visualization (mapping) of algae concentrations variations within the area covered with the drone, and also in depth. Measurements took place in different locations in the Netherlands: i) lake with thick silt layers at the bottom, very eutrophic former bottom of the sea and frequent / intense mowing regime; ii) outlet of waste water into large reservoir; iii) urban canal system. Results allowed to identify probable dominant causes of blooms (i), provide recommendations for the placement of an outlet, day-night differences in algae behavior (ii), or the highlight / pinpoint higher algae concentration areas (iii). Although further research is still needed to fully characterize these processes and to optimize the measuring tool (underwater drone developments / improvements), the method here presented can already provide valuable information about algae behavior and spatial / temporal variability and shows potential as an efficient monitoring system.

Keywords: blue-green algae, cyanobacteria, underwater drones / ROV / AUV, water quality monitoring

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Authors: Pin Xiu Chen, Shang Liang Chen

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A virtualized software controller is developed in this research to replace traditional hardware control units. This virtualized software controller transfers motion interpolation calculations from the motion control units of end devices to edge computing platforms, thereby reducing the end devices' computational load and hardware requirements and making maintenance and updates easier. The study also applies the concept of microservices, dividing the control system into several small functional modules and then deploy into a cloud data server. This reduces the interdependency among modules and enhances the overall system's flexibility and scalability. Finally, with containerization technology, the system can be deployed and started in a matter of seconds, which is more efficient than traditional virtual machine deployment methods. Furthermore, this virtualized software controller communicates with end control devices via wireless networks, making the placement of production equipment or the redesign of processes more flexible and no longer limited by physical wiring. To handle the large data flow and maintain low-latency transmission, this study integrates 5G technology, fully utilizing its high speed, wide bandwidth, and low latency features to achieve rapid and stable remote machine control. An experimental setup is designed to verify the feasibility and test the performance of this framework. This study designs a smart manufacturing site with a 5G communication architecture, serving as a field for experimental data collection and performance testing. The smart manufacturing site includes one robotic arm, three Computer Numerical Control machine tools, several Input/Output ports, and an edge computing architecture. All machinery information is uploaded to edge computing servers and cloud servers via 5G communication and the Internet of Things framework. After analysis and computation, this information is converted into motion control commands, which are transmitted back to the relevant machinery for motion control through 5G communication. The communication time intervals at each stage are calculated using the C++ chrono library to measure the time difference for each command transmission. The relevant test results will be organized and displayed in the full-text.

Keywords: 5G, MEC, microservices, virtualized software controller, smart manufacturing

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Authors: Shokoufeh Abrisham, Seyed Mahmoud Hossieni, Elham Pishbin

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In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures.

Keywords: advanced cardiac life support, fault tree analysis, Bayesian belief networks, numan performance, healthcare systems

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Authors: Maja Ruzic-Baf, Vedrana Keteles, Andrea Debeljuh

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Younger and younger children are now using a smartphone, a device which has become ‘a must have’ and the life of children would be almost ‘unthinkable’ without one. Devices are becoming lighter and lighter but offering an array of options and applications as well as the unavoidable access to the Internet, without which it would be almost unusable. Numerous features such as taking of photographs, listening to music, information search on the Internet, access to social networks, usage of some of the chatting and messaging services, are only some of the numerous features offered by ‘smart’ devices. They have replaced the alarm clock, home phone, camera, tablet and other devices. Their use and possession have become a part of the everyday image of young people. Apart from the positive aspects, the use of smartphones has also some downsides. For instance, free time was usually spent in nature, playing, doing sports or other activities enabling children an adequate psychophysiological growth and development. The greater usage of smartphones during classes to check statuses on social networks, message your friends, play online games, are just some of the possible negative aspects of their application. Considering that the age of the population using smartphones is decreasing and that smartphones are no longer ‘foreign’ to children of pre-school age (smartphones are used at home or in coffee shops or shopping centers while waiting for their parents, playing video games often inappropriate to their age), particular attention must be paid to a very sensitive group, the teenagers who almost never separate from their ‘pets’. This paper is divided into two sections, theoretical and empirical ones. The theoretical section gives an overview of the pros and cons of the usage of smartphones, while the empirical section presents the results of a research conducted in three elementary schools regarding the usage of smartphones and, specifically, their usage during classes, during breaks and to search information on the Internet, check status updates and 'likes’ on the Facebook social network.

Keywords: education, smartphone, social networks, teenagers

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Authors: Yong-Jun Kim, Jae-Hyun Ro, Chang-Bin Ha, Hyoung-Kyu Song

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Because current wireless communication requires high reliability in a limited bandwidth environment, this paper proposes the variable modulation scheme based on the codebook. The variable modulation scheme adjusts transmission power using the codebook in accordance with hannel state. Also, if the codebook is composed of many bits, the reliability is more improved by the proposed scheme. The simulation results show that the performance of proposed scheme has better reliability than the the performance of conventional scheme.

Keywords: MIMO-OFDM, variable modulation, codebook, channel state

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

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Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: multi-agent system, safety analysis, safety model, integration map

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Authors: Manon Giraud, Francois-Damien Delapierre, Guenaelle Jasmin-Lebras, Cecile Feraudet-Tarisse, Stephanie Simon, Claude Fermon

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Early diagnosis or detection of harmful substances at low level is a growing field of high interest. The ideal test should be cheap, easy to use, quick, reliable, specific, and with very low detection limit. Combining the high specificity of antibodies-functionalized magnetic beads used to immune-capture biologic objects and the high sensitivity of a GMR-based sensors, it is possible to even detect these biologic objects one by one, such as a cancerous cell, a bacteria or a disease biomarker. The simplicity of the detection process makes its use possible even for untrained staff. Giant Magneto Resistance (GMR) is a recently discovered effect consisting in the electrical resistance modification of some conductive layers when exposed to a magnetic field. This effect allows the detection of very low variations of magnetic field (typically a few tens of nanoTesla). Magnetic nanobeads coated with antibodies targeting the analytes are mixed with a biological sample (blood, saliva) and incubated for 45 min. Then the mixture is injected in a very simple microfluidic chip and circulates above a GMR sensor that detects changes in the surrounding magnetic field. Magnetic particles do not create a field sufficient to be detected. Therefore, only the biological objects surrounded by several antibodies-functionalized magnetic beads (that have been captured by the complementary antigens) are detected when they move above the sensor. Proof of concept has been carried out on NS1 mouse cancerous cells diluted in PBS which have been bonded to magnetic 200nm particles. Signals were detected in cells-containing samples while none were recorded for negative controls. Binary response was hence assessed for this first biological model. The precise quantification of the analytes and its detection in highly diluted solution is the step now in progress.

Keywords: early diagnosis, giant magnetoresistance, lab-on-a-chip, submicron particle

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Authors: Wissal Ben Arfi, Lubica Hikkerova, Jean-Michel Sahut

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This study examines interfirm networks in the digital transformation era, and in particular, how tacit knowledge codification affects innovation success within digital platforms. Hence, one of the most important features of digital transformation and innovation process outcomes is the emergence of digital platforms, as an interfirm network, at the heart of open innovation. This research aims to illuminate how digital platforms influence inter-organizational innovation through virtual team interactions and knowledge sharing practices within an interfirm network. Consequently, it contributes to the respective strategic management literature on new product development (NPD), open innovation, industrial management, and its emerging interfirm networks’ management. The empirical findings show, on the one hand, that knowledge conversion may be enhanced, especially by the socialization which seems to be the most important phase as it has played a crucial role to hold the virtual team members together. On the other hand, in the process of socialization, the tacit knowledge codification is crucial because it provides the structure needed for the interfirm network actors to interact and act to reach common goals which favor the emergence of open innovation. Finally, our results offer several conditions necessary, but not always sufficient, for interfirm managers involved in NPD and innovation concerning strategies to increasingly shape interconnected and borderless markets and business collaborations. In the digital transformation era, the need for adaptive and innovative business models as well as new and flexible network forms is becoming more significant than ever. Supported by technological advancements and digital platforms, companies could benefit from increased market opportunities and creating new markets for their innovations through alliances and collaborative strategies, as a mode of reducing or eliminating uncertainty environments or entry barriers. Consequently, an efficient and well-structured interfirm network is essential to create network capabilities, to ensure tacit knowledge sharing, to enhance organizational learning and to foster open innovation success within digital platforms.

Keywords: interfirm networks, digital platform, virtual teams, open innovation, knowledge sharing

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Authors: Fengqing Li, Petra Schneider

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Failure of tailings management facilities (TMF) of radioactive residues is an enormous challenge worldwide and can result in major catastrophes. Particularly in transboundary regions, such failure is most likely to lead to international conflict. This risk occurs in Kyrgyzstan and Uzbekistan, where the current major challenge is the quantification of impacts due to pollution from uranium legacy sites and especially the impact on river basins after natural hazards (i.e., landslides). By means of GoldSim, a probabilistic simulation model, the amount of tailing material that flows into the river networks of Mailuu Suu in Kyrgyzstan after pond failure was simulated for three scenarios, namely 10%, 20%, and 30% of material inputs. Based on Muskingum-Cunge flood routing procedure, the peak value of uranium flood wave along the river network was simulated. Among the 23 TMF, 19 ponds are close to the river networks. The spatiotemporal distributions of uranium along the river networks were then simulated for all the 19 ponds under three scenarios. Taking the TP7 which is 30 km far from the Kyrgyzstan-Uzbekistan border as one example, the uranium concentration decreased continuously along the longitudinal gradient of the river network, the concentration of uranium was observed at the border after 45 min of the pond failure and the highest value was detected after 69 min. The highest concentration of uranium at the border were 16.5, 33, and 47.5 mg/L under scenarios of 10%, 20%, and 30% of material inputs, respectively. In comparison to the guideline value of uranium in drinking water (i.e., 30 µg/L) provided by the World Health Organization, the observed concentrations of uranium at the border were 550‒1583 times higher. In order to mitigate the transboundary impact of a radioactive pollutant release, an integrated framework consisting of three major strategies were proposed. Among, the short-term strategy can be used in case of emergency event, the medium-term strategy allows both countries handling the TMF efficiently based on the benefit-sharing concept, and the long-term strategy intends to rehabilitate the site through the relocation of all TMF.

Keywords: Central Asia, contaminant transport modelling, radioactive residue, transboundary conflict

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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.

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Authors: Seong-Jin Cho, Jin Ho Kim

Abstract:

Energy harvesting is the technology which gathers and converts external energies such as light, vibration and heat which are disposed into reusable electrical energy and uses such electrical energy. The vibration energy harvesting is very interesting technology because it produces very high density of energy and unaffected by the climate. Vibration energy can be harvested by the electrostatic, electromagnetic and piezoelectric systems. The electrostatic system has low energy conversion efficiency, and the piezoelectric system is expensive and needs the frequent maintenance because it is made of piezoelectric ceramic. On the other hand, the electromagnetic system has a long life time and high harvesting efficiency, and it is relatively cheap. The electromagnetic harvesting system includes the linear generator and the rotary-type generator. The rotary-type generators require the additional mechanical conversion device if it uses linear motion of vibration. But, the linear generator uses directly linear motion of vibration without a mechanical conversion device, and it has uncomplicated structure and light weight compared with the rotary-type generator. Therefore, the linear electromagnetic generator can be useful in using vibration energy harvesting. The pole transformer systems need electricity sensor system for sending voltage and power information to administrator. Therefore, the battery is essential, and its regular maintenance of replacement is required. In case of the transformer of high location in mountainous areas, the person can’t easily access it resulting in high maintenance cost. To overcome these problems, we designed and developed the linear electromagnetic generator which can replace battery in electricity sensor system for sending voltage and power information of the pole transformer. And, it uses vibration energy of frequency more than 50 Hz by the pole transformer. In order to analyze the electromagnetic characteristics of small linear electric generator, a commercial electromagnetic finite element analysis program "MAXWELL" was used. Then, through the actual production and experiment of linear generator, we confirmed output power of linear generator.

Keywords: energy harvesting, frequency, linear generator, experiment

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Authors: Azam Abkhiz, Abolghasem Nasir

Abstract:

To achieve sustainable competitive advantage in the market, it is necessary to provide and improve customer satisfaction and Loyalty. To reach this objective, companies need to identify and analyze their customers. Thus, it is critical to measure the level of customer satisfaction and Loyalty very carefully. This study attempts to build a conceptual model to provide clear insights of customer loyalty. Using Bayesian networks (BNs), a model is proposed to evaluate customer loyalty and its consequences, such as repurchase and positive word-of-mouth. BN is a probabilistic approach that predicts the behavior of a system based on observed stochastic events. The most relevant determinants of customer loyalty are identified by the literature review. Perceived value, service quality, trust, corporate image, satisfaction, and switching costs are the most important variables that explain customer loyalty. The data are collected by use of a questionnaire-based survey from 1430 customers of a home appliances manufacturer in Iran. Four scenarios and sensitivity analyses are performed to run and analyze the impact of different determinants on customer loyalty. The proposed model allows businesses to not only set their targets but proactively manage their customer behaviors as well.

Keywords: customer satisfaction, customer loyalty, Bayesian networks, home appliances industry

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Authors: Vethathirri Ramanujam Srinivasan, S. M. Shiva Nagendra

Abstract:

Air quality management in urban areas is a serious concern in both developed and developing countries. In this regard, more number of air quality monitoring stations are planned to mitigate air pollution in urban areas. In India, Central Pollution Control Board has set up 574 air quality monitoring stations across the country and proposed to set up another 500 stations in the next few years. The number of monitoring stations for each city has been decided based on population data. The setting up of ambient air quality monitoring stations and their operation and maintenance are highly expensive. Therefore, there is a need to optimize monitoring networks for air quality management. The present paper discusses the various methods such as Indian Standards (IS) method, US EPA method and European Union (EU) method to arrive at the minimum number of air quality monitoring stations. In addition, optimization of rain-gauge method and Inverse Distance Weighted (IDW) method using Geographical Information System (GIS) are also explored in the present work for the design of air quality network in Chennai city. In summary, additionally 18 stations are required for Chennai city, and the potential monitoring locations with their corresponding land use patterns are ranked and identified from the 1km x 1km sized grids.

Keywords: air quality monitoring network, inverse distance weighted method, population based method, spatial variation

Procedia PDF Downloads 189

Authors: Angelo Lerro, Manuela Battipede, Piero Gili, Alberto Brandl

Abstract:

Redundancy requirements for UAV (Unmanned Aerial Vehicle) are hardly faced due to the generally restricted amount of available space and allowable weight for the aircraft systems, limiting their exploitation. Essential equipment as the Air Data, Attitude and Heading Reference Systems (ADAHRS) require several external probes to measure significant data as the Angle of Attack or the Sideslip Angle. Previous research focused on the analysis of a patented technology named Smart-ADAHRS (Smart Air Data, Attitude and Heading Reference System) as an alternative method to obtain reliable and accurate estimates of the aerodynamic angles. This solution is based on an innovative sensor fusion algorithm implementing soft computing techniques and it allows to obtain a simplified inertial and air data system reducing external devices. In fact, only one external source of dynamic and static pressures is needed. This paper focuses on the benefits which would be gained by the implementation of this system in UAV applications. A simplification of the entire ADAHRS architecture will bring to reduce the overall cost together with improved safety performance. Smart-ADAHRS has currently reached Technology Readiness Level (TRL) 6. Real flight tests took place on ultralight aircraft equipped with a suitable Flight Test Instrumentation (FTI). The output of the algorithm using the flight test measurements demonstrates the capability for this fusion algorithm to embed in a single device multiple physical and virtual sensors. Any source of dynamic and static pressure can be integrated with this system gaining a significant improvement in terms of versatility.

Keywords: aerodynamic angles, air data system, flight test, neural network, unmanned aerial vehicle, virtual sensor

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Authors: David Pham, Yongfeng Zhang

Abstract:

Graphs are an important structure for data storage and computation. Recent years have seen the success of deep learning on graphs such as Graph Neural Networks (GNN) on various data mining and machine learning tasks. However, most of the deep learning models on graphs cannot easily explain their predictions and are thus often labelled as “black boxes.” For example, Graph Attention Network (GAT) is a frequently used GNN architecture, which adopts an attention mechanism to carefully select the neighborhood nodes for message passing and aggregation. However, it is difficult to explain why certain neighbors are selected while others are not and how the selected neighbors contribute to the final classification result. In this paper, we present a graph learning model called Explainable Graph Attention Network (XGAT), which integrates graph attention modeling and explainability. We use a single model to target both the accuracy and explainability of problem spaces and show that in the context of graph attention modeling, we can design a unified neighborhood selection strategy that selects appropriate neighbor nodes for both better accuracy and enhanced explainability. To justify this, we conduct extensive experiments to better understand the behavior of our model under different conditions and show an increase in both accuracy and explainability.

Keywords: explainable AI, graph attention network, graph neural network, node classification

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Authors: Martin Arguelles Perez, Woosoon Yim, Alexander Barzilov

Abstract:

High-fidelity radiation monitoring is an essential component in the enhancement of the situational awareness capabilities of the Department of Energy’s Office of Environmental Management (DOE-EM) personnel. In this paper, multiple units of unmanned aerial vehicles (UAVs) each equipped with a cadmium zinc telluride (CZT) gamma-ray sensor are used for radiation source localization, which can provide vital real-time data for the EM tasks. To achieve this goal, a fully autonomous system of multicopter-based UAV swarm in 3D tetrahedron formation is used for surveying the area of interest and performing radiation source localization. The CZT sensor used in this study is suitable for small-size multicopter UAVs due to its small size and ease of interfacing with the UAV’s onboard electronics for high-resolution gamma spectroscopy enabling the characterization of radiation hazards. The multicopter platform with a fully autonomous flight feature is suitable for low-altitude applications such as radiation contamination sites. The conventional approach uses a single UAV mapping in a predefined waypoint path to predict the relative location and strength of the source, which can be time-consuming for radiation localization tasks. The proposed UAV swarm-based approach can significantly improve its ability to search for and track radiation sources. In this paper, two approaches are developed using (a) 2D planar circular (3 UAVs) and (b) 3D tetrahedron formation (4 UAVs). In both approaches, accurate estimation of the gradient vector is crucial for heading angle calculation. Each UAV carries the CZT sensor; the real-time radiation data are used for the calculation of a bulk heading vector for the swarm to achieve a UAV swarm’s source-seeking behavior. Also, a spinning formation is studied for both cases to improve gradient estimation near a radiation source. In the 3D tetrahedron formation, a UAV located closest to the source is designated as a lead unit to maintain the tetrahedron formation in space. Such a formation demonstrated a collective and coordinated movement for estimating a gradient vector for the radiation source and determining an optimal heading direction of the swarm. The proposed radiation localization technique is studied by computer simulation and validated experimentally in the indoor flight testbed using gamma sources. The technology presented in this paper provides the capability to readily add/replace radiation sensors to the UAV platforms in the field conditions enabling extensive condition measurement and greatly improving situational awareness and event management. Furthermore, the proposed radiation localization approach allows long-term measurements to be efficiently performed at wide areas of interest to prevent disasters and reduce dose risks to people and infrastructure.

Keywords: radiation, unmanned aerial system(UAV), source localization, UAV swarm, tetrahedron formation

Procedia PDF Downloads 99