Search results for: Bayesian neural network

Authors: Mahsa Mousavi, Hamid Reza Pourshaghaghi, Mohammad Tahghighi, Henk Corporaal

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Recently, Statistic Random-Access Memory-based (SRAM-based) Field-Programmable Gate Arrays (FPGAs) are widely used in aeronautics and space systems where high dependability is demanded and considered as a mandatory requirement. Since design’s circuit is stored in configuration memory in SRAM-based FPGAs; they are very sensitive to Single Event Upsets (SEUs). In addition, the adverse effects of SEUs on the electronics used in space are much higher than in the Earth. Thus, developing fault tolerant techniques play crucial roles for the use of SRAM-based FPGAs in space. However, fault tolerance techniques introduce additional penalties in system parameters, e.g., area, power, performance and design time. In this paper, an accurate estimation of configuration memory vulnerability to SEUs is proposed for approximate-tolerant applications. This vulnerability estimation is highly required for compromising between the overhead introduced by fault tolerance techniques and system robustness. In this paper, we study applications in which the exact final output value is not necessarily always a concern meaning that some of the SEU-induced changes in output values are negligible. We therefore define and propose Approximate-based Configuration Memory Vulnerability Factor (ACMVF) estimation to avoid overestimating configuration memory vulnerability to SEUs. In this paper, we assess the vulnerability of configuration memory by injecting SEUs in configuration memory bits and comparing the output values of a given circuit in presence of SEUs with expected correct output. In spite of conventional vulnerability factor calculation methods, which accounts any deviations from the expected value as failures, in our proposed method a threshold margin is considered depending on user-case applications. Given the proposed threshold margin in our model, a failure occurs only when the difference between the erroneous output value and the expected output value is more than this margin. The ACMVF is subsequently calculated by acquiring the ratio of failures with respect to the total number of SEU injections. In our paper, a test-bench for emulating SEUs and calculating ACMVF is implemented on Zynq-7000 FPGA platform. This system makes use of the Single Event Mitigation (SEM) IP core to inject SEUs into configuration memory bits of the target design implemented in Zynq-7000 FPGA. Experimental results for 32-bit adder show that, when 1% to 10% deviation from correct output is considered, the counted failures number is reduced 41% to 59% compared with the failures number counted by conventional vulnerability factor calculation. It means that estimation accuracy of the configuration memory vulnerability to SEUs is improved up to 58% in the case that 10% deviation is acceptable in output results. Note that less than 10% deviation in addition result is reasonably tolerable for many applications in approximate computing domain such as Convolutional Neural Network (CNN).

Keywords: fault tolerance, FPGA, single event upset, approximate computing

Procedia PDF Downloads 198

Authors: Javad Taherahmadi, Mohammad Jafarian, Mohammad Naser Asefi

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The global capacity of wind power has dramatically increased in recent years. Therefore, improving the technology of wind turbines to take different advantages of this enormous potential in the power grid, could be interesting subject for scientists. The doubly-fed induction generator (DFIG) wind turbine is a popular system due to its many advantages such as the improved power quality, high energy efficiency and controllability, etc. With an increase in wind power penetration in the network and with regard to the flexible control of wind turbines, the use of wind turbine systems to improve the dynamic stability of power systems has been of significance importance for researchers. Subsynchronous oscillations are one of the important issues in the stability of power systems. Damping subsynchronous oscillations by using wind turbines has been studied in various research efforts, mainly by adding an auxiliary control loop to the control structure of the wind turbine. In most of the studies, this control loop is composed of linear blocks. In this paper, simple adaptive control is used for this purpose. In order to use an adaptive controller, the convergence of the controller should be verified. Since adaptive control parameters tend to optimum values in order to obtain optimum control performance, using this controller will help the wind turbines to have positive contribution in damping the network subsynchronous oscillations at different wind speeds and system operating points. In this paper, the application of simple adaptive control in DFIG wind turbine systems to improve the dynamic stability of power systems is studied and the essential condition for using this controller is considered. It is also shown that this controller has an insignificant effect on the dynamic stability of the wind turbine, itself.

Keywords: almost strictly positive real (ASPR), doubly-fed induction generator (DIFG), simple adaptive control (SAC), subsynchronous oscillations, wind turbine

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Authors: Jossitt Williams Vargas Cruz, Aura Jazmín Pérez Ríos

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The variations of solar dynamics have become a relevant topic of study due to the effects of climate changes generated on the earth. One of the most disconcerting aspects is the variability that the sun has on the climate is the role played by sunspots (extra-atmospheric variable) in the modulation of the Cosmic Rays CR (extra-atmospheric variable). CRs influence the earth's climate by affecting cloud formation (atmospheric variable), and solar cycle influence is associated with the presence of solar storms, and the magnetic activity is greater, resulting in less CR entering the earth's atmosphere. The different methods of climate prediction in Colombia do not take into account the extra-atmospheric variables. Therefore, correlations between atmospheric and extra-atmospheric variables were studied in order to implement a Python code based on neural networks to make the prediction of the extra-atmospheric variable with the highest correlation.

Keywords: correlations, cosmic rays, sun, sunspots and variations.

Procedia PDF Downloads 74

Authors: Anteneh Berhane Yeyi, Tefera Belachew

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Any woman who could become pregnant is at risk of having a baby with neural tube defects (NTDs). A spontaneous aborted women with immediately preceding pregnancy may have an increased risk of develop NTDs. Ethiopia has one of the highest rates of micronutrient deficiencies, including folate and iron deficiency. Currently, in Ethiopia, NTDs is emerged as a public health concern. Even if Ethiopia, has implement different strategies for reducing maternal and neonatal mortality and morbidity, there is no room in the health care system and lack of integration for preventing the risk of NTDs for those women who aborted spontaneously and women who discontinue long acting contraception to become pregnant. The purpose of this study was to evaluate the effect of preconception picture-based nutrition education on knowledge and adherence to iron-folic acid supplement (IFAS) intake to reduce the risk of developing neural tube defects (NTDs) and iron deficiency anemia (IDA) among women who had a planned to pregnancy in Ethiopia, a country with a high burden of NTDs. Methodology: This study was conducted in Eastern Ethiopia. A double blinded parallel randomized controlled trial design was employed among women in the age group of 18-45 years who requested to interrupt modern contraceptive who have an intention to be pregnant and women with spontaneous abortion who refused to take a contraceptive. The interventional arm (n=122) received a preconception picture-based nutrition education with iron-folic acid supplement, and the control arm (n=122) received only preconception IFAS. In this study male partners were participated. Result: After three months of intervention the proportion of adherence to IFAS was 23% (n=56). With regard to adherence within the groups, 42.6% (n=52) in the intervention group and 3.3% (n=4) in the control group and the intervention group were significantly higher than in control group. In the intervention group the proportion of adherence to IFAS intake among participants increased by 40.1% and there were statistically difference (P<0.0001). The difference in difference between the two groups of adherence to IFAS intake was 37.6% and there were a statistical significance (P<0.0001). Level of knowledge between the groups did differ before and after intervention (P= 0.87 Vs P<0.0001). The overall the mean change in knowledge Mean (+SE) between group was 0.9 (+3.04 SE) and there were significant differences between two groups (P<0.001). Conclusion: In general this intervention is effective toward adherence to IFAS and a critical milestone to improve maternal health and reduce the neonate mortality due to NTDs and other advert effect of pregnancy and birth outcomes. This intervention is very short, simple, and cost effective and has potential for adaptation, feasible development to large-scale implementation in the existing health care system. Furthermore, this type of interventional approach has the potential to reduce the country's ANC program dropout rates and increase male partner’s participation on reproductive health.

Keywords: NTDs, IFAS, WRA, Ethiopia

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Authors: M. Pushparani, A. Sagaya

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Embedded systems have emerged as important elements in various domains with extensive applications in automotive, commercial, consumer, healthcare and transportation markets, as there is emphasis on intelligent devices. On the other hand, Business Intelligence (BI) has also been extensively used in a range of applications, especially in the agriculture domain which is the area of this research. The aim of this research is to create a framework for Embedded Weight Comparison Algorithm with Business Intelligence (EWCA-BI). The weight comparison algorithm will be embedded within the plantation management system and the weighbridge system. This algorithm will be used to estimate the weight at the site and will be compared with the actual weight at the plantation. The algorithm will be used to build the necessary alerts when there is a discrepancy in the weight, thus enabling better decision making. In the current practice, data are collected from various locations in various forms. It is a challenge to consolidate data to obtain timely and accurate information for effective decision making. Adding to this, the unstable network connection leads to difficulty in getting timely accurate information. To overcome the challenges embedding is done on a portable device that will have the embedded weight comparison algorithm to also assist in data capture and synchronize data at various locations overcoming the network short comings at collection points. The EWCA-BI will provide real-time information at any given point of time, thus enabling non-latent BI reports that will provide crucial information to enable efficient operational decision making. This research has a high potential in bringing embedded system into the agriculture industry. EWCA-BI will provide BI reports with accurate information with uncompromised data using an embedded system and provide alerts, therefore, enabling effective operation management decision-making at the site.

Keywords: embedded business intelligence, weight comparison algorithm, oil palm plantation, embedded systems

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Authors: Catelyn Coyle, Helena Kwakwa

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Objective: As novel and better tolerated therapies become available, effective HCV testing and care models become increasingly necessary to not only identify individuals with active infection but also link them to HCV providers for medical evaluation and treatment. Our aim is to describe an effective HCV testing and linkage to care model piloted in a network of five community health centers located in Philadelphia, PA. Methods: In October 2012, National Nursing Centers Consortium piloted a routine opt-out HCV testing model in a network of community health centers, one of which treats HCV, HIV, and co-infected patients. Key aspects of the model were medical assistant initiated testing, the use of laboratory-based reflex test technology, and electronic medical record modifications to prompt, track, report and facilitate payment of test costs. Universal testing on all adult patients was implemented at health centers serving patients at high-risk for HCV. The other sites integrated high-risk based testing, where patients meeting one or more of the CDC testing recommendation risk factors or had a history of homelessness were eligible for HCV testing. Mid-course adjustments included the integration of dual HIV testing, development of a linkage to care coordinator position to facilitate the transition of HIV and/or HCV-positive patients from primary to specialist care, and the transition to universal HCV testing across all testing sites. Results: From October 2012 to June 2015, the health centers performed 7,730 HCV tests and identified 886 (11.5%) patients with a positive HCV-antibody test. Of those with positive HCV-antibody tests, 838 (94.6%) had an HCV-RNA confirmatory test and 590 (70.4%) progressed to current HCV infection (overall prevalence=7.6%); 524 (88.8%) received their RNA-positive test result; 429 (72.7%) were referred to an HCV care specialist and 271 (45.9%) were seen by the HCV care specialist. The best linkage to care results were seen at the test and treat the site, where of the 333 patients were current HCV infection, 175 (52.6%) were seen by an HCV care specialist. Of the patients with active HCV infection, 349 (59.2%) were unaware of their HCV-positive status at the time of diagnosis. Since the integration of dual HCV/HIV testing in September 2013, 9,506 HIV tests were performed, 85 (0.9%) patients had positive HIV tests, 81 (95.3%) received their confirmed HIV test result and 77 (90.6%) were linked to HIV care. Dual HCV/HIV testing increased the number of HCV tests performed by 362 between the 9 months preceding dual testing and first 9 months after dual testing integration, representing a 23.7% increment. Conclusion: Our HCV testing model shows that integrated routine testing and linkage to care is feasible and improved detection and linkage to care in a primary care setting. We found that prevalence of current HCV infection was higher than that seen in locally in Philadelphia and nationwide. Intensive linkage services can increase the number of patients who successfully navigate the HCV treatment cascade. The linkage to care coordinator position is an important position that acts as a trusted intermediary for patients being linked to care.

Keywords: HCV, routine testing, linkage to care, community health centers

Procedia PDF Downloads 357

Authors: Namugenyi Ephrance Eunice, Julianne Sansa Otim, Marco Zennaro, Stephen D. Wolthusen

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The Internet of Things (IoT) is a network of connected data processing devices, mechanical and digital machinery, items, animals, or people that may send data across a network without requiring human-to-human or human-to-computer interaction. Each component has sensors that can pick up on specific phenomena, as well as processing software and other technologies that can link to and communicate with other systems and/or devices over the Internet or other communication networks and exchange data with them. IoT is increasingly being used in fields other than consumer electronics, such as public safety, emergency response, industrial automation, autonomous vehicles, the Internet of Medical Things (IoMT), and general environmental monitoring. Consumer-based IoT applications, like smart home gadgets and wearables, are also becoming more prevalent. This paper presents the main IoT deployment areas for environmental monitoring in developing regions and the backhaul options suitable for them. A detailed review of each of the list of papers selected for the study is included in section III of this document. The study includes an overview of existing IoT deployments, the underlying communication architectures, protocols, and technologies that support them. This overview shows that Low Power Wireless Area Networks (LPWANs), as summarized in Table 1, are very well suited for monitoring environment architectures designed for remote locations. LoRa technology, particularly the LoRaWAN protocol, has an advantage over other technologies due to its low power consumption, adaptability, and suitable communication range. The prevailing challenges of the different architectures are discussed and summarized in Table 3 of the IV section, where the main problem is the obstruction of communication paths by buildings, trees, hills, etc.

Keywords: communication technologies, environmental monitoring, Internet of Things, IoT deployment challenges

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Authors: Nadir Atayev, Mehman Hasanov

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Future spacecraft will require a structural change in the way data is transmitted due to the increase in the volume of data required for space communication. Current radio frequency communication systems are already facing a bottleneck in the volume of data sent to the ground segment due to their technological and regulatory characteristics. To overcome these issues, free space optics communication plays an important role in the integrated terrestrial space network due to its advantages such as significantly improved data rate compared to traditional RF technology, low cost, improved security, and inter-satellite free space communication, as well as uses a laser beam, which is an optical signal carrier to establish satellite-ground & ground-to-satellite links. In this approach, there is a need for high-speed and energy-efficient systems as a base platform for sending high-volume video & audio data. Nano Satellite and its branch CubeSat platforms have more technical functionality than large satellites, wheres cover an important part of the space sector, with their Low-Earth-Orbit application area with low-cost design and technical functionality for building networks using different communication topologies. Along the research theme developed in this regard, the output parameter indicators for the FSO of the optical communication transceiver subsystem on the existing CubeSat platforms, and in the direction of improving the mentioned parameters of this communication methodology, 3D optical switch and laser beam controlled optical transponder with 2U CubeSat structural subsystems and application in the Low Earth Orbit satellite network topology, as well as its functional performance and structural parameters, has been studied accordingly.

Keywords: cubesat, free space optics, nano satellite, optical laser communication.

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Authors: Hang Yang, Jichao Li, Kewei Yang, Tianyang Lei

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Multivariate time series anomaly detection is a significant research topic in the field of data mining, encompassing a wide range of applications across various industrial sectors such as traffic roads, financial logistics, and corporate production. The inherent spatial dependencies and temporal characteristics present in multivariate time series introduce challenges to the anomaly detection task. Previous studies have typically been based on the assumption that all variables belong to the same spatial hierarchy, neglecting the multi-level spatial relationships. To address this challenge, this paper proposes a multi-scale spatial and unified temporal feature fusion network, denoted as MSUT-Net, for multivariate time series anomaly detection. The proposed model employs a multi-level modeling approach, incorporating both temporal and spatial modules. The spatial module is designed to capture the spatial characteristics of multivariate time series data, utilizing an adaptive graph structure learning model to identify the multi-level spatial relationships between data variables and their attributes. The temporal module consists of a unified temporal processing module, which is tasked with capturing the temporal features of multivariate time series. This module is capable of simultaneously identifying temporal dependencies among different variables. Extensive testing on multiple publicly available datasets confirms that MSUT-Net achieves superior performance on the majority of datasets. Our method is able to model and accurately detect systems data with multi-level spatial relationships from a spatial-temporal perspective, providing a novel perspective for anomaly detection analysis.

Keywords: data mining, industrial system, multivariate time series, anomaly detection

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Authors: Shahed Vahedi, Mohd Salmi Md Noorani

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This paper investigates cluster synchronization phenomena between community networks. We focus on the situation where a variety of dynamics occur in the clusters. In particular, we show that different synchronization states simultaneously occur between the networks. The controller is designed having an adaptive control gain, and theoretical results are derived via Lyapunov stability. Simulations on well-known dynamical systems are provided to elucidate our results.

Keywords: cluster synchronization, adaptive control, community network, simulation

Procedia PDF Downloads 475

Authors: Shayli Varasteh Moradi, Wayne A. Johnston, Dejan Gagoski, Kirill Alexandrov

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The demand for a rapid and more efficient technique to identify protein-protein interaction particularly in the areas of therapeutics and diagnostics development is growing. The method described here is a rapid in vitro protein-protein interaction analysis approach based on AlphaLISA technology combined with Leishmania tarentolae cell-free protein production (LTE) system. Cell-free protein synthesis allows the rapid production of recombinant proteins in a multiplexed format. Among available in vitro expression systems, LTE offers several advantages over other eukaryotic cell-free systems. It is based on a fast growing fermentable organism that is inexpensive in cultivation and lysate production. High integrity of proteins produced in this system and the ability to co-express multiple proteins makes it a desirable method for screening protein interactions. Following the translation of protein pairs in LTE system, the physical interaction between proteins of interests is analysed by AlphaLISA assay. The assay is performed using unpurified in vitro translation reaction and therefore can be readily multiplexed. This approach can be used in various research applications such as epitope mapping, antigen-antibody analysis and protein interaction network mapping. The intra-viral protein interaction network of Zika virus was studied using the developed technique. The viral proteins were co-expressed pair-wise in LTE and all possible interactions among viral proteins were tested using AlphaLISA. The assay resulted to the identification of 54 intra-viral protein-protein interactions from which 19 binary interactions were found to be novel. The presented technique provides a powerful tool for rapid analysis of protein-protein interaction with high sensitivity and throughput.

Keywords: AlphaLISA technology, cell-free protein expression, epitope mapping, Leishmania tarentolae, protein-protein interaction

Procedia PDF Downloads 237

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

Procedia PDF Downloads 260

Authors: Rezaei Hachesu Peyman, Oliyaee Azadeh, Salahzadeh Zahra, Alizadeh Somayyeh, Safaei Naser

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Coronary Artery Disease (CAD) is one major cause of disability in adults and one main cause of death in developed. In this study, data mining techniques including Decision Trees, Artificial neural networks (ANNs), and Support Vector Machine (SVM) analyze CAD data. Data of 4948 patients who had suffered from heart diseases were included in the analysis. CAD is the target variable, and 24 inputs or predictor variables are used for the classification. The performance of these techniques is compared in terms of sensitivity, specificity, and accuracy. The most significant factor influencing CAD is chest pain. Elderly males (age > 53) have a high probability to be diagnosed with CAD. SVM algorithm is the most useful way for evaluation and prediction of CAD patients as compared to non-CAD ones. Application of data mining techniques in analyzing coronary artery diseases is a good method for investigating the existing relationships between variables.

Keywords: classification, coronary artery disease, data-mining, knowledge discovery, extract

Procedia PDF Downloads 657

Authors: Mubarak Suliman, Mohamed Hassan

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Detection of downed conductors occurring with high fault resistance (reaching kilo-ohms) has always been a challenge, especially in countries like Saudi Arabia, on which earth resistivity is very high in general (reaching more than 1000 Ω-meter). The new approaches for the detection of resistive and high impedance faults are based on the analysis of the fault current waveform. These methods are still under research and development, and they are currently lacking security and dependability. The other approach is communication-based solutions which depends on voltage measurement at the end of overhead line branches and communicate the measured signals to substation feeder relay or a central control center. However, such a detection method is costly and depends on the availability of communication medium and infrastructure. The main objective of this research is to utilize the available standard protection schemes to increase the probability of detection of downed conductors occurring with a low magnitude of fault currents and at the same time avoiding unwanted tripping in healthy conditions and feeders. By specifying the operating region of the faulty feeder, use of tripping curve for discrimination between faulty and healthy feeders, and with proper selection of core balance current transformer (CBCT) and voltage transformers with fewer measurement errors, it is possible to set the pick-up of sensitive earth fault current to minimum values of few amps (i.e., Pick-up Settings = 3 A or 4 A, …) for the detection of earth faults with fault resistance more than (1 - 2 kΩ) for 13.8kV overhead network and more than (3-4) kΩ fault resistance in 33kV overhead network. By implementation of the outcomes of this study, the probability of detection of downed conductors is increased by the utilization of existing schemes (i.e., Directional Sensitive Earth Fault Protection).

Keywords: sensitive earth fault, zero sequence current, grounded system, resistive fault detection, healthy feeder

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Gül Fatma Türker, Hatice Akman

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Portable muscle stimulators for real-time applications has first introduced by Liberson in 1961. Now these systems has been advanced. In this study, FES (Functional Electrical Stimulator) and NMES (Neuromuscular Electrostimulator) systems are analyzed through their hardware and their quality of life improvements for foot drop patients. FES and NMES systems are used for people whose leg muscles and leg neural connections are healty but not able to walk properly because of their injured central nervous system like spinal cord injuries. These systems are used to stimulate neurons or muscles by getting information from other movements and programming these stimulations to get natural walk and it is accepted as a rehabilitation method for the correction of drop foot. This systems support person to approach natural form of walking. Foot drop is characterized by steppage gait. It is a gait abnormality. This systems helps to person for plantar and dorse reflection movements which are hard to done for foot drop patients.

Keywords: FES, foot drop, NMES, stimulator

Procedia PDF Downloads 388

Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

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Authors: JingWei Yu, Hong Yang Yu

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At present, the increasingly serious air pollution in various cities of China has made people pay more attention to the air quality index(hereinafter referred to as AQI) of their living areas. To face this situation, it is of great significance to predict air pollution in heavily polluted areas. In this paper, based on the time series model of LSTM, a spatiotemporal prediction model of PM2.5 concentration in Mianyang, Sichuan Province, is established. The model fully considers the temporal variability and spatial distribution characteristics of PM2.5 concentration. The spatial correlation of air quality at different locations is based on the Air quality status of other nearby monitoring stations, including AQI and meteorological data to predict the air quality of a monitoring station. The experimental results show that the method has good prediction accuracy that the fitting degree with the actual measured data reaches more than 0.7, which can be applied to the modeling and prediction of the spatial and temporal distribution of regional PM2.5 concentration.

Keywords: LSTM, PM2.5, neural networks, spatio-temporal prediction

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Authors: Alejandra Betancur Sánchez, Carmen Elena Zapata Sánchez, Juan Bautista López Ortiz

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Adverse effects and increased air pollution has raised concerns about regulatory policies and has fostered the development of new air quality standards; this is due to the complexity of the composition and the poorly understood reactions in the atmospheric environment. Toxic compounds act as environmental agents having various effects, from irritation to death of cells and tissues. A toxic agent is defined an adverse response in a biological system. There is a particular class that produces some kind of alteration in the genetic material or associated components, so they are recognized as genotoxic agents. Within cells, they interact directly or indirectly with DNA, causing mutations or interfere with some enzymatic repair processes or in the genesis or polymerization of proteinaceous material involved in chromosome segregation. An air pollutant may cause or contribute to increased mortality or serious illness and even pose a potential danger to human health. The aim of this study was to evaluate the effect on the viability and the genotoxic potential on the cell lines CHO-K1 and Jurkat and peripheral blood of particulate matter PM T lymphocytes 2.5 obtained from filters collected three monitoring stations network air quality Aburrá Valley. Tests, reduction of MTT, trypan blue, NRU, comet assay, sister chromatid exchange (SCE) and chromosomal aberrations allowed evidence reduction in cell viability in cell lines CHO-K1 and Jurkat and damage to the DNA from cell line CHOK1, however, no significant effects were observed in the number of SCEs and chromosomal aberrations. The results suggest that PM2.5 material has genotoxic potential and can induce cancer development, as has been suggested in other studies.

Keywords: PM2.5, cell line Jurkat, cell line CHO-K1, cytotoxicity, genotoxicity

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Authors: A. Maghoul, A. Rostampouryasouri, MR. Maghami

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The development of an urban design and power network planning has been gaining momentum in recent years. The integration of renewable energy with urban design has been widely regarded as an increasingly important solution leading to climate change and energy security. Through the use of passive strategies and solar integration with Urban Building Energy Modeling (UBEM) tools, architects and designers can create high-quality designs that meet the needs of clients and stakeholders. To determine the most effective ways of combining renewable energy with urban development, we analyze the relationship between urban form and renewable energy production. The procedure involved in this practice include passive solar gain (in building design and urban design), solar integration, location strategy, and 3D models with a case study conducted in Tehran, Iran. The study emphasizes the importance of spatial and temporal considerations in the development of sector coupling strategies for solar power establishment in arid and semi-arid regions. The substation considered in the research consists of two parallel transformers, 13 lines, and 38 connection points. Each urban load connection point is equipped with 500 kW of solar PV capacity and 1 kWh of battery Energy Storage (BES) to store excess power generated from solar, injecting it into the urban network during peak periods. The simulations and analyses have occurred in EnergyPlus software. Passive solar gain involves maximizing the amount of sunlight that enters a building to reduce the need for artificial lighting and heating. Solar integration involves integrating solar photovoltaic (PV) power into smart grids to reduce emissions and increase energy efficiency. Location strategy is crucial to maximize the utilization of solar PV in an urban distribution feeder. Additionally, 3D models are made in Revit, and they are keys component of decision-making in areas including climate change mitigation, urban planning, and infrastructure. we applied these strategies in this research, and the results show that it is possible to create sustainable and energy-efficient urban environments. Furthermore, demand response programs can be used in conjunction with solar integration to optimize energy usage and reduce the strain on the power grid. This study highlights the influence of ancient Persian architecture on Iran's urban planning system, as well as the potential for reducing pollutants in building construction. Additionally, the paper explores the advances in eco-city planning and development and the emerging practices and strategies for integrating sustainability goals.

Keywords: energy-efficient urban planning, sustainable architecture, solar energy, sustainable urban design

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Authors: Yifan Hou, Haibo Liu, Le Jiang, Wandong Su, Binqing Wang

Abstract:

Road defect detection is crucial for modern urban management and infrastructure maintenance. Traditional road defect detection methods mostly rely on manual labor, which is not only inefficient but also difficult to ensure their reliability. However, existing deep learning-based road defect detection models have poor detection performance in complex environments and lack robustness to multi-scale targets. To address this challenge, this paper proposes a distinct detection framework based on the one stage algorithm network structure. This article designs a deep feature extraction network based on RCSDarknet, which applies channel shuffling to enhance information fusion between tensors. Through repeated stacking of RCS modules, the information flow between different channels of adjacent layer features is enhanced to improve the model's ability to capture target spatial features. In addition, a multi-scale feature fusion mechanism with weighted dual flow paths was adopted to fuse spatial features of different scales, thereby further improving the detection performance of the model at different scales. To validate the performance of the proposed algorithm, we tested it using the RDD2022 dataset. The experimental results show that the enhancement algorithm achieved 84.14% mAP, which is 1.06% higher than the currently advanced YOLOv8 algorithm. Through visualization analysis of the results, it can also be seen that our proposed algorithm has good performance in detecting targets of different scales in complex scenes. The above experimental results demonstrate the effectiveness and superiority of the proposed algorithm, providing valuable insights for advancing real-time road defect detection methods.

Keywords: roads, defect detection, visualization, deep learning

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Authors: David Tena-Gago, Jose M. Alcaraz Calero, Qi Wang

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The evolution and integration of automated vehicles have become more and more tangible in recent years. State-of-the-art technological advances in the field of camera-based Artificial Intelligence (AI) and computer vision greatly favor the performance and reliability of the Advanced Driver Assistance System (ADAS), leading to a greater knowledge of vehicular operation and resembling human behavior. However, the exclusive use of this technology still seems insufficient to control vehicular operation at 100%. To reveal the degree of accuracy of the current camera-based automated driving AI modules, this paper studies the structure and behavior of one of the main solutions in a controlled testing environment. The results obtained clearly outline the lack of reliability when using exclusively the AI model in the perception stage, thereby entailing using additional complementary sensors to improve its safety and performance.

Keywords: accuracy assessment, AI-driven mobility, artificial intelligence, automated vehicles

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Authors: Samira Kalantary, Sara Taghipour, Mansoure Ghias Abadi

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The Internet of Things (IOT) is a technological revolution that represents the future of computing and communications. IOT is the convergence of Internet with RFID, NFC, Sensor, and smart objects. Fog Computing is the natural platform for IOT. At present, the IOT as a new network communication technology has rapidly shifted from concept to application under fog computing virtual storage computing platform. In this paper, we describe everything about IOT and difference between cloud computing and fog computing.

Keywords: cloud computing, fog computing, Internet of Things (IoT), IOT application

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Authors: Craig Mahlasi

Abstract:

The Subtropical Thicket Biome has been in peril of transformation. Estimates indicate that as much as 63% of the Subtropical Thicket Biome is severely degraded. Agricultural expansion is the main driver of transformation. While several studies have sought to document and map the long term transformations, there is a lack of information on disturbance events that allow for timely intervention by authorities. Furthermore, tools that seek to perform continuous land cover change detection are often developed for forests and thus tend to perform poorly in thicket ecosystems. This study investigates the utility of Earth Observation data for continuous land cover change detection in Subtropical Thicket ecosystems. Temporal Neural Networks are implemented on a time series of Sentinel-2 observations. The model obtained 0.93 accuracy, a recall score of 0.93, and a precision score of 0.91 in detecting Thicket disturbances. The study demonstrates the potential of continuous land cover change in Subtropical Thicket ecosystems.

Keywords: remote sensing, land cover change detection, subtropical thickets, near-real time

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Authors: Rafiu Babatunde Ibrahim, Richard Oluseyi Taiwo, Abiodun Toheeb Akintunde

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The relationship between road transport infrastructure and commercial activities in Nigeria has been a topical issue and identified as one of the crucial components for economic development in the country. This study examines road transport infrastructure and commercial activities along selected roads in Ede, Nigeria. The study assesses the characteristics of the selected roads, the condition of road infrastructure, the degree of road network connectivity, maintenance culture for the road infrastructure as well as commercial activities along identified roads in the study area. Stratified Sampling Techniques were used to partition the study area into core, Intermediate and Suburb Township zones. Roads were also classified into Major, Distributor and Access Roads. Field observation and measurement, as well as a questionnaire, were used to obtain primary data from 246 systematically sampled respondents along the roads selected, and they were analyzed using descriptive and inferential statistics. The study revealed that most of the roads were characterized by an incidence of potholes. A total of 448 potholes were observed, where Olowoibida Road accounted for (19.0%), Federal Polytechnic Road (17.4%), and Back to Land Road (16.3%). The majority of the selected roads have no street lights and are of open drainage systems. Also, the condition of road surfaces was observed to be deteriorating. Road network connectivity of the study area was found to be poorly connected with 11% using the alpha index and 40% of Gamma index. It was found that the tailoring business (39) is predominant on major roads and Distributor Roads, while petty trading (35) is dominant on the access road. Results of correlation analysis (r = 0.242) show that there is a low positive correlation between road infrastructure and commercial activities; the significant relationships have indeed explained how important it is in influencing commercial activities across the study area. The study concluded by emphasizing the need for the provision of more roads and proper maintenance of the existing ones. This will no doubt improve the commercial activities along the roads in the study area.

Keywords: road transport, infrastructure, commercial activities, maintenance culture

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Authors: Ciaran Smiddy, Fergus Nugent, Karen Fitzmaurice

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A measure of prescribing and administration of Antimicrobial Prophylaxis before and during Covid-19(2019 vs. 2021) was desired to assess how these were affected by Covid-19. Antimicrobial Prophylaxis was assessed for 60 patients, under 3 Orthopaedic Consultants, against local guidelines. The study found that compliance with guidelines improved significantly, from 60% to 83%, but Appropriate use of Vancomycin reduced from 37% to 29%.

Keywords: antimicrobial stewardship, prescribing, spinal surgery, vancomycin

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Authors: Eugene Stepanov, Arcady Ponosov

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To model gene regulatory networks one uses ordinary differential equations with switching nonlinearities, where the initial value problem is known to be well-posed if the trajectories cross the discontinuities transversally. Otherwise, the initial value problem is usually ill-posed, which lead to theoretical and numerical complications. In the presentation, it is proposed to apply the theory of hybrid dynamical systems, rather than switched ones, to regularize the problem. 'Hybridization' of the switched system means that one attaches a dynamic discrete component ('automaton'), which follows the trajectories of the original system and governs its dynamics at the points of ill-posedness of the initial value problem making it well-posed. The construction of the automaton is based on the classification of the attractors of the specially designed adjoint dynamical system. Several examples are provided in the presentation, which support the suggested analysis. The method can also be of interest in other applied fields, where differential equations contain switchings, e.g. in neural field models.

Keywords: hybrid dynamical systems, ill-posed problems, singular perturbation analysis, switching nonlinearities

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Authors: Nilki Upathissa, Torin Wirasinghe

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This solution addresses the challenges faced by traditional bookstores and the limitations of digital media, striking a balance between the tactile experience of printed books and the convenience of modern technology. The book exchange system offers a sustainable alternative, empowering users to access a diverse range of books while promoting community engagement. The user-friendly interfaces incorporated into the book exchange system ensure a seamless and enjoyable experience for users. Intuitive features for book management, search, and messaging facilitate effortless exchanges and interactions between users. By streamlining the process, the system encourages readers to explore new books aligned with their interests, enhancing the overall reading experience. Central to the system's success is the hybrid recommendation engine, which leverages advanced technologies such as Long Short-Term Memory (LSTM) models. By analyzing user input, the engine accurately predicts genre preferences, enabling personalized book recommendations. The hybrid approach integrates multiple technologies, including user interfaces, machine learning models, and recommendation algorithms, to ensure the accuracy and diversity of the recommendations. The evaluation of the book exchange system with the hybrid recommendation engine demonstrated exceptional performance across key metrics. The high accuracy score of 0.97 highlights the system's ability to provide relevant recommendations, enhancing users' chances of discovering books that resonate with their interests. The commendable precision, recall, and F1score scores further validate the system's efficacy in offering appropriate book suggestions. Additionally, the curve classifications substantiate the system's effectiveness in distinguishing positive and negative recommendations. This metric provides confidence in the system's ability to navigate the vast landscape of book choices and deliver recommendations that align with users' preferences. Furthermore, the implementation of this book exchange system with a hybrid recommendation engine has the potential to revolutionize the way readers interact with printed books. By facilitating book exchanges and providing personalized recommendations, the system encourages a sense of community and exploration within the reading community. Moreover, the emphasis on sustainability aligns with the growing global consciousness towards eco-friendly practices. With its robust technical approach and promising evaluation results, this solution paves the way for a more inclusive, accessible, and enjoyable reading experience for book lovers worldwide. In conclusion, the developed book exchange system with a hybrid recommendation engine represents a progressive solution to the challenges faced by traditional bookstores and the limitations of digital media. By promoting sustainability, widening access to printed books, and fostering engagement with reading, this system addresses the evolving needs of book enthusiasts. The integration of user-friendly interfaces, advanced machine learning models, and recommendation algorithms ensure accurate and diverse book recommendations, enriching the reading experience for users.

Keywords: recommendation systems, hybrid recommendation systems, machine learning, data science, long short-term memory, recurrent neural network

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Authors: Manel Hammami, Gabriele Grandi

Abstract:

In recent years, multilevel inverters have become more attractive for single-phase photovoltaic (PV) systems, due to their known advantages over conventional H-bridge pulse width-modulated (PWM) inverters. They offer improved output waveforms, smaller filter size, lower total harmonic distortion (THD), higher output voltages and others. The most common multilevel converter topologies, presented in literature, are the neutral-point-clamped (NPC), flying capacitor (FC) and Cascaded H-Bridge (CHB) converters. In both NPC and FC configurations, the number of components drastically increases with the number of levels what leads to complexity of the control strategy, high volume, and cost. Whereas, increasing the number of levels in case of the cascaded H-bridge configuration is a flexible solution. However, it needs isolated power sources for each stage, and it can be applied to PV systems only in case of PV sub-fields. In order to improve the ratio between the number of output voltage levels and the number of components, several hybrids and asymmetric topologies of multilevel inverters have been proposed in the literature such as the FC asymmetric H-bridge (FCAH) and the NPC asymmetric H-bridge (NPCAH) topologies. Another asymmetric multilevel inverter configuration that could have interesting applications is the cascaded asymmetric H-bridge (CAH), which is based on a modular half-bridge (two switches and one capacitor, also called level doubling network, LDN) cascaded to a full H-bridge in order to double the output voltage level. This solution has the same number of switches as the above mentioned AH configurations (i.e., six), and just one capacitor (as the FCAH). CAH is becoming popular, due to its simple, modular and reliable structure, and it can be considered as a retrofit which can be added in series to an existing H-Bridge configuration in order to double the output voltage levels. In this paper, an original and effective method for the analysis of the DC-link voltage ripple is given for single-phase asymmetric H-bridge multilevel inverters based on level doubling network (LDN). Different possible configurations of the asymmetric H-Bridge multilevel inverters have been considered and the analysis of input voltage and current are analytically determined and numerically verified by Matlab/Simulink for the case of cascaded asymmetric H-bridge multilevel inverters. A comparison between FCAH and the CAH configurations is done on the basis of the analysis of the DC and voltage ripple for the DC source (i.e., the PV system). The peak-to-peak DC and voltage ripple amplitudes are analytically calculated over the fundamental period as a function of the modulation index. On the basis of the maximum peak-to-peak values of low frequency and switching ripple voltage components, the DC capacitors can be designed. Reference is made to unity output power factor, as in case of most of the grid-connected PV generation systems. Simulation results will be presented in the full paper in order to prove the effectiveness of the proposed developments in all the operating conditions.

Keywords: asymmetric inverters, dc-link voltage, level doubling network, single-phase multilevel inverter

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Authors: Rashid S. Mohammad, Shicheng Zhang, Xinzhe Zhao

Abstract:

In tight oil reservoirs, wettability alteration has generally been considered as an effective way to remove fracturing fluid retention on the surface of the fracture and consequently improved oil production. However, there is a lack of a reliable productivity prediction model to show the relationship between the wettability and oil production in tight oil well. In this paper, a new oil productivity prediction model of immiscible oil-water flow and miscible CO₂-oil flow accounting for wettability is developed. This mathematical model is established by considering two different length scales: nonporous network and propped fractures. CO₂ flow diffuses in the nonporous network and high velocity non-Darcy flow in propped fractures are considered by taking into account the effect of wettability alteration on capillary pressure and relative permeability. A laboratory experiment is also conducted here to validate this model. Laboratory experiments have been designed to compare the water saturation profiles for different contact angle, revealing the fluid retention in rock pores that affects capillary force and relative permeability. Four kinds of brines with different concentrations are selected here to create different contact angles. In water-wet porous media, as the system becomes more oil-wet, water saturation decreases. As a result, oil relative permeability increases. On the other hand, capillary pressure which is the resistance for the oil flow increases as well. The oil production change due to wettability alteration is the result of the comprehensive changes of oil relative permeability and capillary pressure. The results indicate that wettability is a key factor for fracturing fluid retention removal and oil enhancement in tight reservoirs. By incorporating laboratory test into a mathematical model, this work shows the relationship between wettability and oil production is not a simple linear pattern but a parabolic one. Additionally, it can be used for a better understanding of optimization design of fracturing fluids.

Keywords: wettability, relative permeability, fluid retention, oil production, unconventional and tight reservoirs

Procedia PDF Downloads 236