Search results for: distributed sensor networks

Authors: Sahabeh Safarpour, Khiruddin Abdullah, Hwee San Lim, Mohsen Dadras

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Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality parameters are important due to health impact. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. Seasonal aerosol optical depth (AOD) values at 550 nm derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA’s Terra satellites, for the 10 years period of 2000-2010 were used to test 7 different spatial interpolation methods in the present study. The accuracy of estimations was assessed through visual analysis as well as independent validation based on basic statistics, such as root mean square error (RMSE) and correlation coefficient. Based on the RMSE and R values of predictions made using measured values from 2000 to 2010, Radial Basis Functions (RBFs) yielded the best results for spring, summer, and winter and ordinary kriging yielded the best results for fall.

Keywords: aerosol optical depth, MODIS, spatial interpolation techniques, Radial Basis Functions

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Authors: Emily Awuor, Helga Huszar, Zoltan Bozoki

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Isotope analysis has found numerous applications in the environmental science discipline, most common being the tracing of environmental contaminants on both regional and global scales. Many environmental contaminants contain ammonia (NH₃) since it is the most abundant gas in the atmosphere and its largest sources are from agricultural and industrial activities. NH₃ isotopes (¹⁴NH₃ and ¹⁵NH₃) are therefore important and can be used in the traceability studies of these atmospheric pollutants. The goal of the project is the construction of a photoacoustic spectroscopy system that is capable of measuring ¹⁵NH₃ isotope selectively in terms of its concentration. A further objective is for the system to be robust, easy-to-use, and automated. This is provided by using two telecommunication type near-infrared distributed feedback (DFB) diode lasers and a laser coupler as the light source in the photoacoustic measurement system. The central wavelength of the lasers in use was 1532 nm, with the tuning range of ± 1 nm. In this range, strong absorption lines can be found for both ¹⁴NH₃ and ¹⁵NH₃. For the selective measurement of ¹⁵NH₃, wavelengths were chosen where the cross effect of ¹⁴NH₃ and water vapor is negligible. We completed the calibration of the photoacoustic system, and as a result, the lowest detectable concentration was 3.32 ppm (3Ϭ) in the case of ¹⁵NH₃ and 0.44 ppm (3Ϭ) in the case of ¹⁴NH₃. The results are most useful in the environmental pollution measurement and analysis.

Keywords: ammonia isotope, near-infrared DFB diode laser, photoacoustic spectroscopy, environmental monitoring

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Authors: Siwei Chang, Heng Li, Haitao Wu, Xin Fang

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Construction robots are receiving more and more attention as a promising solution to the manpower shortage issue in the construction industry. The development of intelligent control techniques that assist in controlling the robots to avoid transparency and reflected building obstacles is crucial for guaranteeing the adaptability and flexibility of mobile construction robots in complex construction environments. With the boom of computer vision techniques, a number of studies have proposed vision-based methods for transparent obstacle avoidance to improve operation accuracy. However, vision-based methods are also associated with disadvantages such as high computational costs. To provide better perception and value evaluation, this study aims to analyze the performance of vision-based techniques for avoiding transparent building obstacles. To achieve this, commonly used sensors, including a lidar, an ultrasonic sensor, and a USB camera, are equipped on the robotic platform to detect obstacles. A Raspberry Pi 3 computer board is employed to compute data collecting and control algorithms. The turtlebot3 burger is employed to test the programs. On-site experiments are carried out to observe the performance in terms of success rate and detection distance. Control variables include obstacle shapes and environmental conditions. The findings contribute to demonstrating how effectively vision-based obstacle avoidance strategies for transparent building obstacle avoidance and provide insights and informed knowledge when introducing computer vision techniques in the aforementioned domain.

Keywords: construction robot, obstacle avoidance, computer vision, transparent obstacle

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Authors: Negar Ghotbeddin

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In this study, the changes in survival, growth, body composition, hematological, biochemical and immunological parameters of oscar fish (Astronotus ocellatus) have been investigated with dietary spirulina powder supplementation. Total of 300 fish with an initial weight of 8.37 ± 0.36 was distributed to three treatments and one control (0%). The fish were fed 8 weeks with diets containing different concentrations of S. powder: (control (0%), 2.5%, 5%, and 10%). Then sampling was done, and different parameters were measured by standard methods. Growth performance such as weight gain (%), specific growth rate (SGR) and feed conversion ratio (FCR) significantly improved in fish fed with S. powder (p < 0.5). Crude protein significantly increased in the S. powder supplemented groups (p < 0.5). However, crude lipid decreased with the increasing of dietary S. powder levels. Total protein increased in fish fed with 10% S. powder. Triglycerides and cholesterol decreased with the increasing of dietary S. powder levels. Immunological parameters including C3 and C4 increased significantly with the increasing of dietary S. powder levels, and lysozyme was improved in 10% S. powder. Results of this study indicated that S. powder had positive effects on Oscar fish and the best values were observed at 10 % S. powder.

Keywords: spirulina powder, growth performance, body composition, hematology, immunity, Astronotus ocellatus

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Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Ikumapayi Catherine Mayowa

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The well-being of humans on a construction site is critical; abundant manpower had been lost through accidents which kill or make workers physically unfit to carry out construction activities, these, in turn, have multiple effects on the whole economy. Thus, it is necessary to put all safety items and regulations in place before construction activities can commence. This study was carried out in the Ondo state of Nigeria to investigate and analyze the state of health and safety of construction workers in the state. The study was done using first-hand observations, 50 construction project sites were visited in ten major towns of Ondo state, questionnaires were distributed, and the results were analyzed. The result shows that construction workers are being exposed to many construction site hazards due to lack of inadequate safety programs and lack of appropriate safety equipment for workers on site. From the data gotten from each site visited and the statistical analysis, it can be concluded that occurrences of an accident on construction sites depend significantly on the available safety facilities on the sites. The result of the regression statistics shows that the dependence of the frequency of occurrence of an accident on the availability of safety items on the site is 0.0362 which is less than 0.05 maximum significant level allowed. Therefore, a vital way of sustaining our building strategy is given a detail attention to the provision of adequate health and safety items on construction sites which will reduce the occurrence of accident, loss of manpower and death of skilled workers.

Keywords: construction sites, health, safety, welfare

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Authors: H. Hammoudi, S. Bendenia, K. Marouf-Khelifa, R. Marouf, J. Schott, A. Khelifa

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The ionic exchange of the NaX zeolite by Cu2+ and/or Zn2+ cations is progressively driven while following the development of some of its characteristic: crystallinity by XR diffraction, profile of isotherms, RI criterion, isosteric adsorption heat and microporous volume using both the Dubinin–Radushkevich (DR) equation and the t-plot through the Lippens–de Boer method which also makes it possible to determine the external surface area. Results show that the cationic exchange process, in the case of Cu2+ introduced at higher degree, is accompanied by crystalline degradation for Cu(x)X, in contrast to Zn2+-exchanged zeolite X. This degradation occurs without significant presence of mesopores, because the RI criterion values were found to be much lower than 2.2. A comparison between the binary and ternary exchanges shows that the curves of CuZn(x)X are clearly below those of Zn(x)X and Cu(x)X, whatever the examined parameter. On the other hand, the curves relating to CuZn(x)X tend towards those of Cu(x)X. This would again confirm the sensitivity of the crystalline structure of CuZn(x)X with respect to the introduction of Cu2+ cations. An original result is the distortion of the zeolitic framework of X zeolites at middle exchange degree, when Cu2+ competes with another divalent cation, such as Zn2+, for the occupancy of sites distributed within zeolitic cavities. In other words, the ternary exchange accentuates the crystalline degradation of X zeolites. An unexpected result also is the no correlation between crystal damage and the external surface area.

Keywords: adsorption, crystallinity, ion exchange, zeolite

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Authors: Jyh-Cherng Gu, Ming-Ta Yang, Chao-Fong Yan, Hsin-Yung Chung, Yung-Ruei Chang, Yih-Der Lee, Chen-Min Chan, Chia-Hao Hsu

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There are a number of distributed generations (DGs) installed in microgrid, which may have diverse path and direction of power flow or fault current. The overcurrent protection scheme for the traditional radial type distribution system will no longer meet the needs of microgrid protection. Integrating the intelligent electronic device (IED) and a supervisory control and data acquisition (SCADA) with IEC 61850 communication protocol, the paper proposes a microgrid protection management system (MPMS) to protect power system from the fault. In the proposed method, the MPMS performs logic programming of each IED to coordinate their tripping sequence. The GOOSE message defined in IEC 61850 is used as the transmission information medium among IEDs. Moreover, to cope with the difference in fault current of microgrid between grid-connected mode and islanded mode, the proposed MPMS applies the group setting feature of IED to protect system and robust adaptability. Once the microgrid topology varies, the MPMS will recalculate the fault current and update the group setting of IED. Provided there is a fault, IEDs will isolate the fault at once. Finally, the Matlab/Simulink and Elipse Power Studio software are used to simulate and demonstrate the feasibility of the proposed method.

Keywords: IEC 61850, IED, group Setting, microgrid

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Authors: Jack L. Harris, Marya L. Doerfel, Hyunsook Youn, Minkyung Kim, Kautuki Sunil Jariwala

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Organizational resilience refers to the ability to sustain business or general work functioning despite wide-scale interruptions. We focus on organization and businesses as a pillar of their communities and how they attempt to sustain work when a natural disaster impacts their surrounding regions and economies. While it may be more common to think of resilience as a trait possessed by an organization, an emerging area of research recognizes that for organizations and businesses, resilience is a set of processes that are constituted through communication, social networks, and organizing. Indeed, five processes, robustness, rapidity, resourcefulness, redundancy, and external availability through social media have been identified as critical to organizational resilience. These organizing mechanisms involve multi-level coordination, where individuals intersect with groups, organizations, and communities. Because the nature of such interactions are often networks of people and organizations coordinating material resources, information, and support, they necessarily require some way to coordinate despite being displaced. Little is known, however, if physical and digital spaces can substitute one for the other. We thus are guided by the question, is digital space sufficient when disaster creates a scarcity of physical space? This study presents a cross-case comparison based on field research from four different regions of the United States that were impacted by Hurricanes Katrina (2005), Sandy (2012), Maria (2017), and Harvey (2017). These four cases are used to extend the science of resilience by examining multi-level processes enacted by individuals, communities, and organizations that together, contribute to the resilience of disaster-struck organizations, businesses, and their communities. Using field research about organizations and businesses impacted by the four hurricanes, we code data from interviews, participant observations, field notes, and document analysis drawn from New Orleans (post-Katrina), coastal New Jersey (post-Sandy), Houston Texas (post-Harvey), and the lower keys of Florida (post-Maria). This paper identifies an additional organizing mechanism, networked gathering spaces, where citizens and organizations, alike, coordinate and facilitate information sharing, material resource distribution, and social support. Findings show that digital space, alone, is not a sufficient substitute to effectively sustain organizational resilience during a disaster. Because the data are qualitative, we expand on this finding with specific ways in which organizations and the people who lead them worked around the problem of scarce space. We propose that gatherings after disaster are a sixth mechanism that contributes to organizational resilience.

Keywords: communication, coordination, disaster management, information and communication technologies, interorganizational relationships, resilience, work

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Authors: Ladislav Ptacek, Jan Vanek, Jan Eisner, Alexandra Pruchova, Pavel Linhart, Ludek Muller, Dana Jirotkova

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One of the essential steps of avian song processing is signal filtering. Currently, the standard methods of filtering are the Mel Bank Filter or linear filter distribution. In this article, a new type of bank filter called the Bird-Adapted Filter is introduced; whereby the signal filtering is modifiable, based upon a new mathematical description of audiograms for particular bird species or order, which was named the Avian Audiogram Unified Equation. According to the method, filters may be deliberately distributed by frequency. The filters are more concentrated in bands of higher sensitivity where there is expected to be more information transmitted and vice versa. Further, it is demonstrated a comparison of various filters for automatic individual recognition of chiffchaff (Phylloscopus collybita). The average Equal Error Rate (EER) value for Linear bank filter was 16.23%, for Mel Bank Filter 18.71%, the Bird-Adapted Filter gave 14.29%, and Bird-Adapted Filter with 1/3 modification was 12.95%. This approach would be useful for practical use in automatic systems for avian species and individual identification. Since the Bird-Adapted Filter filtration is based on the measured audiograms of particular species or orders, selecting the distribution according to the avian vocalization provides the most precise filter distribution to date.

Keywords: avian audiogram, bird individual identification, bird song processing, bird species recognition, filter bank

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Authors: Sofiane Bendoukha, Daniel Moldt, Hayat Bendoukha

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Workflow concepts are essential for the development of remote sensing applications. They can help users to manage and process satellite data and execute scientific experiments on distributed resources. The objective of this paper is to introduce an approach for the specification and the execution of complex scientific workflows in Cloud-like environments. The approach strives to support scientists during the modeling, the deployment and the monitoring of their workflows. This work takes advantage from Petri nets and more pointedly the so-called reference nets formalism, which provides a robust modeling/implementation technique. RENEWGRASS is a tool that we implemented and integrated into the Petri nets editor and simulator RENEW. It provides an easy way to support not experienced scientists during the specification of their workflows. It allows both modeling and enactment of image processing workflows from the remote sensing domain. Our case study is related to the implementation of vegetation indecies. We have implemented the Normalized Differences Vegetation Index (NDVI) workflow. Additionally, we explore the integration possibilities of the Cloud technology as a supplementary layer for the deployment of the current implementation. For this purpose, we discuss migration patterns of data and applications and propose an architecture.

Keywords: cloud computing, scientific workflows, petri nets, RENEWGRASS

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Authors: N. Tahouni, M. Gholami, M. H. Panjeshahi

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Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.

Keywords: flaring, fuel gas network, GHG emissions, stream

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Authors: C. Pislaru, A. Shebani

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This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the K-Means clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: system identification, nonlinear systems, neural networks, radial basis function, K-means clustering algorithm

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Khaled S. Al Salhen

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Aldehyde oxidase is molybdo-flavoenzyme involved in the oxidation of hundreds of endogenous and exogenous and N-heterocyclic compounds and environmental pollutants. Uncharged N-heterocyclic aromatic compounds such phenanthridine are commonly distributed pollutants in soil, air, sediments, surface water and groundwater, and in animal and plant tissues. Phenanthridine as uncharged N-heterocyclic aromatic compound was incubated with partially purified aldehyde oxidase from rainbow trout fish liver. Reversed-phase HLPC method was used to separate the oxidation products from phenanthridine and the metabolite was identified. The 6(5H)-phenanthridinone was identified the major metabolite by partially purified aldehyde oxidase from fish liver. Kinetic constant for the oxidation reactions were determined spectrophotometrically and showed that this substrate has a good affinity (Km = 78 ± 7.6 µM) for hepatic aldehyde oxidase, coupled with a relatively high oxidation rate (0.77± 0.03 nmol/min/mg protein). In addition, the kinetic parameters of hepatic fish aldehyde oxidase towards the phenanthridine substrate indicate that in vitro biotransformation by hepatic fish aldehyde oxidase will be a significant pathway. This study confirms that partially purified aldehyde oxidase from fish liver is indeed the enzyme responsible for the in vitro production 6(5H)-phenanthridinone metabolite as it is a major metabolite by mammalian aldehyde oxidase.

Keywords: aldehyde oxidase, fish, phenanthridine, specificity

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Authors: Michał Witas

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This paper presents a technical approach to analysis of security of SCADA systems. Main goal of the paper is to make SCADA administrators aware of risks resulting from SCADA systems usage and to familiarize with methods that can be adopt to existing or planned system, to increase overall system security level. Because SCADA based systems become a industrial standard, more attention should be paid to the security of that systems. Industrial Control Systems (ICS) like SCADA are responsible for controlling crucial aspects of wide range of industrial processes. In pair with that responsibility, goes a lot of money that can be earned or lost – this fact is main reason of increased interest of attackers. Additionally ICS are often responsible for maintaining resources strategic from the point of view of national economy, like electricity (including nuclear power plants), heating, water resources or military facilities, so they can be targets of terrorist cybernetic attacks. Without proper risk analysis and management, vulnerabilities resulting from the usage of SCADA can be easily exploited by potential attacker. Paper is based mostly on own experience in systems security, gathered during academic studies and professional work in international company. As title suggests, it will cover only basics of topic, because every of points mentioned in the document can be base for additional research and papers.

Keywords: denial of service, SCADA, security policy, distributed network

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Authors: O. Gsib, U. Peirera, C. Egles, S. A. Bencherif

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In skin regenerative medicine, one of the critical issues is to produce a three-dimensional scaffold with optimized porosity for dermal fibroblast infiltration and neovascularization, which exhibits high mechanical properties and displays sufficient wound healing characteristics. In this study, we report on the synthesis and characterization of macroporous sequential interpenetrating polymer networks (IPNs) combining skin wound healing properties of fibrin with the excellent physical properties of polyethylene glycol (PEG). Fibrin fibers serve as a provisional biologically active network to promote cell adhesion and proliferation while PEG provides the mechanical stability to maintain the entire 3D construct. After having modified both PEG and Serum Albumin (used for promoting enzymatic degradability) by adding methacrylate residues (PEGDM and SAM, respectively), Fibrin/PEGDM-SAM sequential IPNs were synthesized as follows: Macroporous sponges were first produced from PEGDM-SAM hydrogels by a freeze-drying technique and then rehydrated by adding the fibrin precursors. Environmental Scanning Electron Microscopy (ESEM) and Confocal Laser Scanning Microscopy (CLSM) were used to characterize their microstructure. Human dermal fibroblasts were cultivated during one week in the constructs and different cell culture parameters (viability, morphology, proliferation) were evaluated. Subcutaneous implantations of the scaffolds were conducted on five-week old male nude mice to investigate their biocompatibility in vivo. We successfully synthesized interconnected and macroporous Fibrin/PEGDM-SAM sequential IPNs. The viability of primary dermal fibroblasts was well maintained (above 90%) after 2 days of culture. Cells were able to adhere, spread and proliferate in the scaffolds suggesting the suitable porosity and intrinsic biologic properties of the constructs. The fibrin network adopted a spider web shape that covered partially the pores allowing easier cell infiltration into the macroporous structure. To further characterize the in vitro cell behavior, cell proliferation (EdU incorporation, MTS assay) is being studied. Preliminary histological analysis of animal studies indicated the persistence of hydrogels even after one-month post implantation and confirmed the absence of inflammation response, good biocompatibility and biointegration of our scaffolds within the surrounding tissues. These results suggest that our Fibrin/PEGDM-SAM IPNs could be considered as potential candidates for dermis regenerative medicine. Histological analysis will be completed to further assess scaffold remodeling including de novo extracellular matrix protein synthesis and early stage angiogenesis analysis. Compression measurements will be conducted to investigate the mechanical properties.

Keywords: fibrin, hydrogels for dermal reconstruction, polyethylene glycol, semi-interpenetrating polymer network

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Authors: Jerry John T. M., Sylas V. P., Shijo Joy

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Hydrogen is the most essential gas that can be used for many purposes. During the production of hydrogen using raw materials like Soil and leftover cooked rice water (kanjivellam), the major by-product formed is water. Soil is collected from three different places in kottayam district: Kallara, Meenachilar, and Athirampuzha. Collected samples are mixed with rice water and tested for traces of hydrogen using a biohydrogen sensor after 72 hours. The result was the presence of hydrogen in all the 3 samples. After streaking, PCR and gel electrophoresis detected the bacteria which produced the hydrogen. RGCB Thiruvananthapuram conducted the sequencing of the PCR resultant. And identified the bacterial strains. Five variants of Bacillus bacteria ( (1) Bacillus cereus strain JTM GenBank: OP278839.1 (2) Bacillus toyonensis strain JTM2 GenBank: OP278841.1 (3) Bacillus anthracis strain JTM_SR2989-3-R_H08 GenBank: OP278960.1 (4) Bacillus thuringiensis strain JRY1 GenBank: OP278976.1 (5) Bacillus anthracis strain JTM_SR2989-3-F_H07 GenBank: OP278959.1 ) are identified and successfully registered in NCBI Gen bank. These Bacillus bacteria are major types of Rhizobacteria that can form spores and can survive in the soil for a long time period under harsh environmental conditions. Also, plant growth is enhanced by PGPR (Plant growth promoting rhizobacteria) through the induction of systemic resistance, antibiosis, and competitive omission. The molecular sequencing was submitted to the NCBI Gen Bank, and the accession numbers were allotted for the bacterial cultures.

Keywords: bio hydrogen production, bacterial bio hydrogen production, plant related to bacillus bacteria., bacillus bacteria study

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Authors: Susan Catapano, Meredith Jones, Carol McNulty

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Analysis of one state’s collaborative effort to support teachers, in both public and private schools, as they moved from face-to-face teaching to remote teaching during the Covid pandemic to identify lessons learned and materials put into place to support teachers and families. Surveys were created, distributed, and analyzed throughout the three months of remote teaching, documents and lesson plans were developed, and training materials were created. All data collected and materials developed were analyzed to identify supports teachers used and needed for successful remote teaching. Researchers found most teachers easily moved to online teaching; however, many families did not have access to technology, so teachers needed to develop non-technology-based access and support for remote teaching. Teachers also reported the need to prepare to teach remotely as part of their teaching training, so they were prepared in the future. Finally, data indicated teachers were able to establish stronger relationships with families than usual as a result of remote teaching. The lessons learned and support developed are part of the state’s ongoing policy for online teaching in the event of disasters and pandemics in the future.

Keywords: remote learning, teacher education, pandemic, families

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Sanghoon Shim, Jaehwan Jung, Sung-Ah Kim

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As the digital technology develops, studies on the TUI (Tangible User Interface) that links the physical environment utilizing the human senses with the virtual environment through the computer are actively being conducted. In addition, there has been a tremendous advance in computer design making through the use of computer-aided design techniques, which enable optimized decision-making through comparison with machine learning and parallel comparison of alternatives. However, a complex design that can respond to user requirements or performance can emerge through the intuition of the designer, but it is difficult to actualize the emerged design by the designer's ability alone. Ancillary tools such as Gaudí's Sandbag can be an instrument to reinforce and evolve emerged ideas from designers. With the advent of many commercial tools that support 3D objects, designers' intentions are easily reflected in their designs, but the degree of their reflection reflects their intentions according to the proficiency of design tools. This study embodies the environment in which the form can be implemented by the fingers of the most basic designer in the initial design phase of the complex type building design. Leapmotion is used as a sensor to recognize the hand motions of the designer, and it is converted into digital information to realize an environment that can be linked in real time in virtual reality (VR). In addition, the implemented design can be linked with Rhino™, a 3D authoring tool, and its plug-in Grasshopper™ in real time. As a result, it is possible to design sensibly using TUI, and it can serve as a tool for assisting designer intuition.

Keywords: design environment, digital modeling, hand gesture, TUI, virtual reality

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Authors: Kamel Laidi, Khelifa Benmansour, Ouahid Bouchhida

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Neural networks-based approach for 2-cells serial converter has been developed and implemented. The approach is based on a behavioural description of the different operating modes of the converter. Each operating mode represents a well-defined configuration, and for which is matched an operating zone satisfying given invariance conditions, depending on the capacitors' voltages and the load current of the converter. For each mode, a control vector whose components are the control signals to be applied to the converter switches has been associated. Therefore, the problem is reduced to a classification task of the different operating modes of the converter. The artificial neural nets-based approach, which constitutes a powerful tool for this kind of task, has been adopted and implemented. The application to a 2-cells chopper has allowed ensuring efficient and robust control of the load current and a high capacitors voltages balancing.

Keywords: neural nets, control, multicellular converters, 2-cells chopper

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Authors: Oluwayemisi Onyenankeya

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The popularity and viewership of Nollywood films have expanded across African countries as well as European and American cities especially those with considerable Diaspora population. This appears to underscore the assumption that Nollywood films not only project or transmit the embedded Nigerian cultural values to audience members but also actually make appreciable impression on those audience members. This study sought to ascertain the role of Nollywood film in the transference of Nigerian cultural heritage across other cultures using students at the University of Fort Hare as a case study. This study sought to determine if audience members learn, imbibe or manifest any behavioural tendencies that can be ascribed to the influence of watching Nollywood films. Specifically, the study sought to establish the aspect of Nigerian culture that Nollywood films transmit to audience members. The survey research method was used while data was analysed using descriptive statistics. In all, 400 questionnaires were distributed while 370 were returned. Results show that students who were exposed to Nollywood film could mimic some common Nigerian expressions and exclamations. A small percentage of viewers could actually speak a smattering of some Nigerian language while a few have bought or made dresses in the pattern of what they saw in the films. It could be concluded that Nollywood films do exert significant influence on the viewers in the University of Fort Hare.

Keywords: culture, film, Nollywood, transmission

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Authors: Walaa Al-Maghraby

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Endophytes are organisms that colonize internal plant tissues without causing apparent harm to their host. Almost all groups of microorganisms have been found in endophytic association with plants may be fungi. They stimulate the production of secondary metabolites with a diverse range of biological activities. Leptadenia pyrotechnica is a more or less leafless, erect shrub with straight stems which is highly distributed in Saudi Arabia. Four endophytes fungi were isolated from Leptadenia pyrotechnica and identified using 18S ribosomal RNA sequences, which revealed four fungi genuses, namely Aspergillus terreus; Aspergillus welwitschiae; Aspergillus fumigatus and Aspergillus flavus. In this present study, four endophytic fungi from Leptadenia pyrotechnica were used for obtaining crude aqueous and ethyl acetate extracts for antimicrobial screening against 6 human pathogens, the antibacterial tests presented satisfactory results, where the pathogenic bacteria were inhibited by the four extracts tested, except for Escherichia coli that was inhibited by all extracts except ethyl acetate extract of Aspergillus terreus. Analysis of variance showed that the extract produced by endophyte Leptadenia pyrotechnica was the most effective against all bacteria, either gram-negative or positive. However, the extract was not efficient against pathogenic fungi. Therefore, this study indicates that endophytes from medicinal plant Leptadenia pyrotechnica could be potential sources of antibacterial substances.

Keywords: antimicrobial activity, Aspergillus sp, endophytes, Leptadenia pyrotechnica

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Authors: Emad A. Mohammed

Abstract:

Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Manikandan S. P., Sharmila N.

Abstract:

Pain in the neck, intermediate and anterior, and even low back may occur in one or more locations. Numerous factors can lead to back discomfort, which can manifest into sensations in the other parts of your body. Up to 80% of people will have low back problems at a certain stage of their lives, making spine-related pain a highly prevalent ailment. Roughly twice as commonly as neck pain, low back discomfort also happens about as often as knee pain. According to current studies, using digital devices for extended periods of time and poor sitting posture are the main causes of neck and low back pain. There are numerous monitoring techniques provided to enhance the sitting posture for the aforementioned problems. A sophisticated technique to monitor the extended sitting position is suggested in this research based on this problem. The system is made up of an inertial measurement unit, a T-shirt, an Arduino board, a buzzer, and a mobile app with cloud services. Based on the anatomical position of the spinal cord, the inertial measurement unit was positioned on the inner back side of the T-shirt. The IMU (inertial measurement unit) sensor will evaluate the hip position, imbalanced shoulder, and bending angle. Based on the output provided by the IMU, the data will be analyzed by Arduino, supplied through the cloud, and shared with a mobile app for continuous monitoring. The buzzer will sound if the measured data is mismatched with the human body's natural position. The implementation and data prediction with design to identify balanced and unbalanced posture using a posture monitoring t-shirt will be further discussed in this research article.

Keywords: IMU, posture, IOT, textile

Procedia PDF Downloads 92

Authors: Mohamed A. Almoghalis, Osman M. Hegazy, Ibrahim F. Imam, Ali H. Elbastawessy

Abstract:

This paper presents a new hyperspectral image segmentation scheme with respect to both spatial and spectral contexts. The scheme uses the 8-pixels spatial pattern to build a weight structure that holds the number of outlier bands for each pixel among its neighborhood windows in different directions. The number of outlier bands for a pixel is obtained using bands quartile box plots profile among spatial 8-pixels pattern windows. The quartile box plot weight structure represents the spatial-spectral context in the image. Instead of starting segmentation process by single pixels, the proposed methodology starts by pixels groups that proved to share the same spectral features with respect to their spatial context. As a result, the segmentation scheme starts with Jigsaw pieces that build a mosaic image. The following step builds a model for each Jigsaw piece in the mosaic image. Each Jigsaw piece will be merged with another Jigsaw piece using KNN applied to their bands' quartile box plots profiles. The scheme iterates till required number of segments reached. Experiments use two data sets obtained from Earth Observer 1 (EO-1) sensor for Egypt and France. Initial results qualitative analysis showed encouraging results compared with ground truth. Quantitative analysis for the results will be included in the final paper.

Keywords: hyperspectral image segmentation, image processing, remote sensing, box plot

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Authors: Ritwik Dutta, Marylin Wolf

Abstract:

This paper describes the trade-offs and the design from scratch of a self-contained, easy-to-use health dashboard software system that provides customizable data tracking for patients in smart homes. The system is made up of different software modules and comprises a front-end and a back-end component. Built with HTML, CSS, and JavaScript, the front-end allows adding users, logging into the system, selecting metrics, and specifying health goals. The back-end consists of a NoSQL Mongo database, a Python script, and a SimpleHTTPServer written in Python. The database stores user profiles and health data in JSON format. The Python script makes use of the PyMongo driver library to query the database and displays formatted data as a daily snapshot of user health metrics against target goals. Any number of standard and custom metrics can be added to the system, and corresponding health data can be fed automatically, via sensor APIs or manually, as text or picture data files. A real-time METAR request API permits correlating weather data with patient health, and an advanced query system is implemented to allow trend analysis of selected health metrics over custom time intervals. Available on the GitHub repository system, the project is free to use for academic purposes of learning and experimenting, or practical purposes by building on it.

Keywords: flask, Java, JavaScript, health monitoring, long-term care, Mongo, Python, smart home, software engineering, webserver

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Authors: Shalini Singh, Sanjay K. Srivastava, Govind, Mukhtar. A. Khan, P. K. Singh

Abstract:

Nanotechnology is revolutionizing the development of biosensors. Nanomaterials and nanofabrication technologies are increasingly being used to design novel biosensors. Sensitivity and other attributes of biosensors can be improved by using nanomaterials with unique chemical, physical, and mechanical properties in their construction. Silicon is a promising biomaterial that is non-toxic and biodegradable and can be exploited in chemical and biological sensing. Present study demonstrated the streptavidin–biotin interaction on silicon surfaces with different topographies such as flat and nanostructured silicon (nanowires) surfaces. Silicon nanowires with wide range of surface to volume ratio were prepared by electrochemical etching of silicon wafer. The large specific surface of silicon nanowires can be chemically modified to link different molecular probes (DNA strands, enzymes, proteins and so on), which recognize the target analytes, in order to enhance the selectivity and specificity of the sensor device. The interaction of streptavidin with biotin was carried out on 3-aminopropyltriethoxysilane (APTS) functionalized silicon surfaces. Fourier Transform Infrared Spectroscopy (FTIR) and X-ray Photoelectron Spectroscopy (XPS) studies have been performed to characterize the surface characteristics to ensure the protein attachment. Silicon nanowires showed the enhance protein attachment, as compared to flat silicon surface due to its large surface area and good molecular penetration to its surface. The methodology developed herein could be generalized to a wide range of protein-ligand interactions, since it is relatively easy to conjugate biotin with diverse biomolecules such as antibodies, enzymes, peptides, and nucleotides.

Keywords: FTIR, silicon nanowires, streptavidin-biotin, XPS

Procedia PDF Downloads 423