Search results for: wireless sensor network

Authors: Kanika Taneja, V. K. Soni, S. D. Attri, Kafeel Ahmad, Shamshad Ahmad

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Aerosols represent an important component of earth-atmosphere system and have a profound impact on the global and regional climate. With the growing population and urbanization, the aerosol load in the atmosphere over the Indian region is found to be increasing. Several studies have reported that the aerosol optical depth over the northern part of India is higher as compared to the southern part. The northern India along the Indo-Gangetic plain is often influenced with dust transported from the Thar Desert in northwestern India and from Arabian Peninsula during the pre-monsoon season. Seasonal variations in aerosol optical and radiative properties were examined using data retrieved from ground based multi-wavelength Prede Sun/sky radiometer (POM-02) over Delhi, Rohtak, Jodhpur and Varanasi for the period April 2011-April 2013. These stations are part of the Skynet-India network of India Meteorological Department. The Sun/sky radiometer (POM-02) has advantage over other instruments that it can be calibrated on-site. These aerosol optical properties retrieved from skyradiometer observations are further used to analyze the Direct Aerosol Radiative Forcing (DARF) over the study locations.

Keywords: aerosol optical properties, indo- gangetic plain, radiative forcing, sky radiometer

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Authors: B. Gopal, Pannala Krishna Murthy, G. N. Sreenivas

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The increasing demand of electric power is giving an emphasis on the need for the maximum utilization of renewable energy sources. On the other hand maintaining power quality to satisfaction of utility is an essential requirement. In this paper the design aspects of a Unified Power Quality Conditioner integrated with photovoltaic system in a distributed generation is presented. The proposed system consist of series inverter, shunt inverter are connected back to back on the dc side and share a common dc-link capacitor with Distributed Generation through a boost converter. The primary task of UPQC is to minimize grid voltage and load current disturbances along with reactive and harmonic power compensation. In addition to primary tasks of UPQC, other functionalities such as compensation of voltage interruption and active power transfer to the load and grid in both islanding and interconnected mode have been addressed. The simulation model is design in MATLAB/ Simulation environment and the results are in good agreement with the published work.

Keywords: distributed generation (DG), interconnected mode, islanding mode, maximum power point tracking (mppt), power quality (PQ), unified power quality conditioner (UPQC), photovoltaic array (PV)

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Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Zuoji Huang, Haiming Qian, Chunlin Wang, Jinyan Sun, Nan Xu

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In this paper, the strategy to extract double road edge lines from acquired road stripe image was explored. The workflow is as follows: the road stripes are acquired by probabilistic boosting tree algorithm and morphological algorithm immediately, and road centerlines are detected by thinning algorithm, so the initial road edge lines can be acquired along the road centerlines. Then we refine the results with big variation of local curvature of centerlines. Specifically, the energy function of edge line is constructed by gradient feature and spectral information, and Dijkstra algorithm is used to optimize the initial road edge lines. The Snake model is constructed to solve the fracture problem of intersection, and the discrete dynamic programming algorithm is used to solve the model. After that, we could get the final road network. Experiment results show that the strategy proposed in this paper can be used to extract the continuous and smooth road edge lines from high-resolution remote sensing images with an accuracy of 88% in our study area.

Keywords: road edge lines extraction, energy function, intersection fracture, Snake model

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Authors: J. Satya Eswari, Ch. Venkateswarlu

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The formulation of a commercial pharmaceutical product involves several composition factors and response characteristics. When the formulation requires to satisfy multiple response characteristics which are conflicting, an optimal solution requires the need for an efficient multiobjective optimization technique. In this work, a regression is combined with a non-dominated sorting differential evolution (NSDE) involving Naïve & Slow and ε constraint techniques to derive different multiobjective optimization strategies, which are then evaluated by means of a trapidil pharmaceutical formulation. The analysis of the results show the effectiveness of the strategy that combines the regression model and NSDE with the integration of both Naïve & Slow and ε constraint techniques for Pareto optimization of trapidil formulation. With this strategy, the optimal formulation at pH=6.8 is obtained with the decision variables of micro crystalline cellulose, hydroxypropyl methylcellulose and compression pressure. The corresponding response characteristics of rate constant and release order are also noted down. The comparison of these results with the experimental data and with those of other multiple regression model based multiobjective evolutionary optimization strategies signify the better performance for optimal trapidil formulation.

Keywords: pharmaceutical formulation, multiple regression model, response surface method, radial basis function network, differential evolution, multiobjective optimization

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Authors: Kwedi L. M. Nsah, Hisao Uchiki

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Quantum computation is a new and exciting field making use of quantum mechanical phenomena. In classical computers, information is represented as bits, with values either 0 or 1, but a quantum computer uses quantum bits in an arbitrary superposition of 0 and 1, enabling it to reach beyond the limits predicted by classical information theory. lanthanide ion quantum computer is an organic crystal, having a lanthanide ion. Europium is a favored lanthanide, since it exhibits nuclear spin coherence times, and Eu(III) is photo-stable and has two stable isotopes. In a europium organic crystal, the key factor is the mutual dipole-dipole interaction between two europium atoms. Crystals of the complex were formed by making a 2 :1 reaction of Eu(fod)3 and bpm. The transparent white crystals formed showed brilliant red luminescence with a 405 nm laser. The photoluminescence spectroscopy was observed both at room and cryogenic temperatures (300-14 K). The luminescence spectrum of [Eu(fod)3(μ-bpm) Eu(fod)3] showed characteristic of Eu(III) emission transitions in the range 570–630 nm, due to the deactivation of 5D0 emissive state to 7Fj. For the application of dinuclear Eu3+ complex to q-bit device, attention was focused on 5D0 -7F0 transition, around 580 nm. The presence of 5D0 -7F0 transition at room temperature revealed that at least one europium symmetry had no inversion center. Since the line was unsplit by the crystal field effect, any multiplicity observed was due to a multiplicity of Eu3+ sites. For q-bit element, more narrow line width of 5D0 → 7F0 PL band in Eu3+ ion was preferable. Cryogenic temperatures (300 K – 14 K) was applicable to reduce inhomogeneous broadening and distinguish between ions. A CCD image sensor was used for low temperature Photoluminescence measurement, and a far better resolved luminescent spectrum was gotten by cooling the complex at 14 K. A red shift by 15 cm-1 in the 5D0 - 7F0 peak position was observed upon cooling, the line shifted towards lower wavenumber. An emission spectrum at the 5D0 - 7F0 transition region was obtained to verify the line width. At this temperature, a peak with magnitude three times that at room temperature was observed. The temperature change of the 5D0 state of Eu(fod)3(μ-bpm)Eu(fod)3 showed a strong dependence in the vicinity of 60 K to 100 K. Thermal quenching was observed at higher temperatures than 100 K, at which point it began to decrease slowly with increasing temperature. The temperature quenching effect of Eu3+ with increase temperature was caused by energy migration. 100 K was the appropriate temperature for the observation of the 5D0 - 7F0 emission peak. Europium dinuclear complex bridged by bpm was successfully prepared and monitored at cryogenic temperatures. At 100 K the Eu3+-dope complex has a good thermal stability and this temperature is appropriate for the observation of the 5D0 - 7F0 emission peak. Sintering the sample above 600o C could also be a method to consider but the Eu3+ ion can be reduced to Eu2+, reasons why cryogenic temperature measurement is preferably over other methods.

Keywords: Eu(fod)₃, europium dinuclear complex, europium ion, quantum bit, quantum computer, 2, 2-bipyrimidine

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Authors: Chiung-Hui Chen

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The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services.

Keywords: behavior, big data, hierarchical hidden Markov model, intelligent object

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Authors: F. A. Olutoge, O.R. Olulope, M. O. Odelola

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This study investigates the strength potentials of groundnut husk ash as partial cement replacement in mortar and also develops a predictive model using Artificial Neural Network. Groundnut husks sourced from Ogbomoso, Nigeria, was sun dried, calcined to ash in a furnace at a controlled temperature of 600⁰ C for a period of 6 hours, and sieved through the 75 microns. The ash was subjected to chemical analysis and setting time test. Fine aggregate (sand) for the mortar was sourced from Ado Ekiti, Nigeria. The cement: GHA constituents were blended in ratios 100:0, 95:5, 90:10, 85:15 and 80:20 %. The sum of SiO₂, Al₂O₃, and Fe₂O₃ content in GHA is 26.98%. The compressive strength for mortars PC, GHA5, GHA10, GHA15, and GHA20 ranged from 6.3-10.2 N/mm² at 7days, 7.5-12.3 N/mm² at 14 days, 9.31-13.7 N/mm² at 28 days, 10.4-16.7 N/mm² at 56days and 13.35- 22.3 N/mm² at 90 days respectively, PC, GHA5 and GHA10 had competitive values up to 28 days, but GHA10 gave the highest values at 56 and 90 days while GHA20 had the lowest values at all ages due to dilution effect. Flexural strengths values at 28 days ranged from 1.08 to 1.87 N/mm² and increased to a range of 1.53-4.10 N/mm² at 90 days. The ANN model gave good prediction for compressive strength of the mortars. This study has shown that groundnut husk ash as partial cement replacement improves the strength properties of mortar.

Keywords: compressive strength, groundnut husk ash, mortar, pozzolanic index

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Authors: Shahadat Uddin, Md Ekramul Hossain, Arif Khan

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The collaboration among physicians during episodes of care for a hospitalised patient has a significant contribution towards effective health outcome. This research aims at improving this health outcome by analysing the attributes of patient-sharing physician collaboration network (PCN) on hospital data. To accomplish this goal, we present a research framework that explores the impact of several types of attributes (such as clique and clan) of PCN on hospitalisation cost and hospital length of stay. We use electronic health insurance claim dataset to construct and explore PCNs. Each PCN is categorised as ‘low’ and ‘high’ in terms of hospitalisation cost and length of stay. The results from the proposed model show that the clique and clan of PCNs affect the hospitalisation cost and length of stay. The clique and clan of PCNs show the difference between ‘low’ and ‘high’ PCNs in terms of hospitalisation cost and length of stay. The findings and insights from this research can potentially help the healthcare stakeholders to better formulate the policy in order to improve quality of care while reducing cost.

Keywords: clique, clan, electronic health records, physician collaboration

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Authors: Ioan Corneliu Salisteanu, Valentin Dogaru Ulieru, Mihaita Nicolae Ardeleanu, Alin Pohoata, Bogdan Salisteanu, Stefan Broscareanu

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Creating a personalized statistic for an individual within the population using IT systems, based on the searches and intercepted spheres of interest they manifest, is just one 'atom' of the artificial intelligence analysis network. However, having the ability to generate statistics based on individual data intercepted from large demographic areas leads to reasoning like that issued by a human mind with global strategic ambitions. The DiaMOTO device is a technical sensory system that allows the interception of car events caused by a driver, positioning them in time and space. The device's connection to the vehicle allows the creation of a source of data whose analysis can create psychological, behavioural profiles of the drivers involved. The SiaMOTO system collects data from many vehicles equipped with DiaMOTO, driven by many different drivers with a unique fingerprint in their approach to driving. In this paper, we aimed to explain the software infrastructure of the SiaMOTO system, a system designed to monitor and improve driver driving behaviour, as well as the criteria and algorithms underlying the intelligent analysis process.

Keywords: artificial intelligence, data processing, driver behaviour, driver monitoring, SiaMOTO

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Authors: Christina Adly, Meena Abdelmeseeh, Tamer Basha

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This paper proposes a system for hand movement recognition using multichannel surface EMG(sEMG) signals obtained from 40 subjects using 40 different exercises, which are available on the Ninapro(Non-Invasive Adaptive Prosthetics) database. First, we applied processing methods to the raw sEMG signals to convert them to their amplitudes. Second, we used deep learning methods to solve our problem by passing the preprocessed signals to Fully connected neural networks(FCNN) and recurrent neural networks(RNN) with Long Short Term Memory(LSTM). Using intrasubject evaluation, The accuracy using the FCNN is 72%, with a processing time for training around 76 minutes, and for RNN's accuracy is 79.9%, with 8 minutes and 22 seconds processing time. Third, we applied some postprocessing methods to improve the accuracy, like majority voting(MV) and Movement Error Rate(MER). The accuracy after applying MV is 75% and 86% for FCNN and RNN, respectively. The MER value has an inverse relationship with the prediction delay while varying the window length for measuring the MV. The different part uses the RNN with the intersubject evaluation. The experimental results showed that to get a good accuracy for testing with reasonable processing time, we should use around 20 subjects.

Keywords: hand movement recognition, recurrent neural network, movement error rate, intrasubject evaluation, intersubject evaluation

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Authors: Kanti Sapkota, Do Hyun Lee, Sung Soo Han

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Nanocomposites have been widely used in various fields such as electronics, catalysis, and in chemical, biological, biomedical and optical fields. They display broad biomedical properties like antidiabetic, anticancer, antioxidant, antimicrobial and antibacterial activities. Moreover, nanomaterials have been used for wastewater treatment. Particularly, bimetallic hybrid nanocomposites exhibit unique features as compared to their monometallic components. Hybrid nanomaterials not only afford the multifunctionality endowed by their constituents but can also show synergistic properties. In addition, these hybrid nanomaterials have noteworthy catalytic and optical properties. Notably, Au−Ag based nanoparticles can be employed in sensor and catalysis due to their characteristic composition-tunable plasmonic properties. Due to their importance and usefulness, various efforts were developed for their preparation. Generally, chemical methods have been described to synthesize such bimetallic nanocomposites. In such chemical synthesis, harmful and hazardous chemicals cause environmental contamination and increase toxicity levels. Therefore, ecologically benevolent processes for the synthesis of nanomaterials are highly desirable to diminish such environmental and safety concerns. In this regard, here we disclose a simple, cost-effective, external additive free and eco-friendly method for the synthesis of Au-Ag@AgCl nanocomposites using Nephrolepis cordifolia root extract. Au-Ag@AgCl NCs were obtained by the simultaneous reduction of cationic Ag and Au into AgCl in the presence of plant extract. The particle size of 10 to 50 nm was observed with the average diameter of 30 nm. The synthesized nanocomposite was characterized by various modern characterization techniques. For example, UV−visible spectroscopy was used to determine the optical activity of the synthesized NCs, and Fourier transform infrared (FT-IR) spectroscopy was employed to investigate the functional groups present in the biomolecules that were responsible for both reducing and capping agents during the formation of nanocomposites. Similarly, powder X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA) and energy-dispersive X-ray (EDX) spectroscopy were used to determine crystallinity, size, oxidation states, thermal stability and weight loss of the synthesized nanocomposites. As a synthetic application, the synthesized nanocomposite exhibited excellent catalytic activity for the multicomponent synthesis of biologically interesting quinoline molecules via domino annulation-aromatization reaction of aniline, arylaldehyde, and phenyl acetylene derivatives. Interestingly, the nanocatalyst was efficiently recycled for five times without substantial loss of catalytic properties.

Keywords: nanoparticles, catalysis, multicomponent, quinoline

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Authors: Sanat K. Dash, Rama S. Verma, Sarit K. Das

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A logarithmic microfluidic shear device was designed and fabricated for cellular mechanotransduction studies. The device contains four cell culture chambers in which flow was modulated to achieve a logarithmic increment. Resistance values were optimized to make the device compact. The network of resistances was developed according to a unique combination of series and parallel resistances as found via optimization. Simulation results done in Ansys 16.1 matched the analytical calculations and showed the shear stress distribution at different inlet flow rates. Fabrication of the device was carried out using conventional photolithography and PDMS soft lithography. Flow profile was validated taking DI water as working fluid and measuring the volume collected at all four outlets. Volumes collected at the outlets were in accordance with the simulation results at inlet flow rates ranging from 1 ml/min to 0.1 ml/min. The device can exert fluid shear stresses ranging four orders of magnitude on the culture chamber walls which will cover shear stress values from interstitial flow to blood flow. This will allow studying cell behavior in the long physiological range of shear stress in a single run reducing number of experiments.

Keywords: microfluidics, mechanotransduction, fluid shear stress, physiological shear

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Authors: Maria M. Giannakou, Athanasios K. Ziliaskopoulos

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Transmission pipelines carrying natural gas are often routed through populated cities, industrial and environmentally sensitive areas. While the need for these networks is unquestionable, there are serious concerns about the risk these lifeline networks pose to the people, to their habitat and to the critical infrastructures, especially in view of natural disasters such as earthquakes. This work presents an Integrated Pipeline Risk Management methodology (IPRM) for assessing the hazard associated with a natural gas pipeline failure due to natural or manmade disasters. IPRM aims to optimize the allocation of the available resources to countermeasures in order to minimize the impacts of pipeline failure to humans, the environment, the infrastructure and the economic activity. A proposed knapsack mathematical programming formulation is introduced that optimally selects the proper mitigation policies based on the estimated cost – benefit ratios. The proposed model is demonstrated with a small numerical example. The vulnerability analysis of these pipelines and the quantification of consequences from such failures can be useful for natural gas industries on deciding which mitigation measures to implement on the existing pipeline networks with the minimum cost in an acceptable level of hazard.

Keywords: cost benefit analysis, knapsack problem, natural gas distribution network, risk management, risk mitigation

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Authors: Giovanni Menichini, Salvatore Giacomo Morano, Gloria Terenzi, Luca Salvatori, Maurizio Orlando

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The “Guidelines for Risk Classification, Safety Assessment, and Structural Health Monitoring of Existing Bridges” were recently approved by the Italian Government to define technical standards for managing the national network of existing bridges. These guidelines provide a framework for risk mitigation and safety assessment of bridges, which are essential elements of the built environment and form the basis for the operation of transport systems. Within the guideline framework, a workflow based on three main points was proposed: (1) risk-based, i.e., based on typical parameters of hazard, vulnerability, and exposure; (2) multi-level, i.e., including six assessment levels of increasing complexity; and (3) multirisk, i.e., assessing structural/foundational, seismic, hydrological, and landslide risks. The paper focuses on applying the Italian Guidelines to specific case studies, aiming to identify the parameters that predominantly influence the determination of the “class of attention”. The significance of each parameter is determined via sensitivity analysis. Additionally, recommendations for enhancing the process of assigning the class of attention are proposed.

Keywords: bridge safety assessment, Italian guidelines implementation, risk classification, structural health monitoring

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

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It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: cancer classification, feature selection, deep learning, genetic algorithm

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Authors: Zahra Abdolkarimi, Naser Zourikalatehsamad,

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Up to 40 years ago, after recognition of epilepsy, it was generally believed that these attacks occurred randomly and suddenly. However, thanks to the advance of mathematics and engineering, such attacks can be predicted within a few minutes or hours. In this way, various algorithms for long-term prediction of the time and frequency of the first attack are presented. In this paper, by considering the nonlinear nature of brain signals and dynamic recorded brain signals, ANFIS model is presented to predict the brain signals, since according to physiologic structure of the onset of attacks, more complex neural structures can better model the signal during attacks. Contribution of this work is the co-evolution algorithm for optimization of ANFIS network parameters. Our objective is to predict brain signals based on time series obtained from brain signals of the people suffering from epilepsy using ANFIS. Results reveal that compared to other methods, this method has less sensitivity to uncertainties such as presence of noise and interruption in recorded signals of the brain as well as more accuracy. Long-term prediction capacity of the model illustrates the usage of planted systems for warning medication and preventing brain signals.

Keywords: co-evolution algorithms, brain signals, time series, neural networks, ANFIS model, physiologic structure, time prediction, epilepsy suffering, illustrates model

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Authors: Tingkai Zhao, Jingtian Hu, Xiarong Peng, Wenbo Yang, Tiehu Li

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Homogeneous amorphous carbon nanotube (ACNT) networks have been synthesized using floating catalyst chemical vapor deposition method on a three-dimensional (3D) graphene aerogel (GA)/BaFe₁₂O₁₉ nanorod (BNR) composite which prepared by a self-propagating combustion process. The as-synthesized ACNT/GA/BNR composite which has 3D network structures could be directly used as a good absorber in the electromagnetic wave absorbent materials. The experimental results indicated that the maximum absorbing peak of ACNT/GA/BNR composite with a thickness of 2 mm was -18.35 dB at 10.64 GHz in the frequency range of 2-18 GHz. The bandwidth of the reflectivity below -10 dB is 3.32 GHz. The 3D graphene aerogel structures which composed of dense interlined tubes and amorphous structure of ACNTs bearing quantities of dihedral angles could consume the incident waves through multiple reflection and scattering inside the 3D web structures. The interlinked ACNTs have both the virtues of amorphous CNTs (multiple reflections inside the wall) and crystalline CNTs (high conductivity), consuming the electromagnetic wave as resistance heat. ACNT/GA/BNR composite has a good electromagnetic wave absorbing performance.

Keywords: amorphous carbon nanotubes, graphene aerogel, barium ferrite nanorod, electromagnetic wave absorption

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Authors: Mahmoud Fouad, Mervat Badr, Marwa Ibrahim

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Micro-grid system is presently considered a reliable solution for the expected deficiency in the power required from future power systems. Renewable power sources such as wind, solar and hydro offer high potential of benign power for future micro-grid systems. Micro-Grid (MG) is basically a low voltage (LV) or medium voltage (MV) distribution network which consists of a number of called distributed generators (DG’s); micro-sources such as photovoltaic array, fuel cell, wind turbine etc. energy storage systems and loads; operating as a single controllable system, that could be operated in both grid-connected and islanded mode. The capacity of the DG’s is sufficient to support all; or most, of the load connected to the micro-grid. This paper presents a micro-grid system based on wind and solar power sources and addresses issues related to operation, control, and stability of the system. Using Matlab/Simulink, the system is modeled and simulated to identify the relevant technical issues involved in the operation of a micro-grid system based on renewable power generation units.

Keywords: micro-grid system, photovoltaic, wind turbine, energy storage, distributed generation, modeling

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Authors: Ying-Chang Yu, Yuan-Lung Lo

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Building envelope design is one of the most popular design fields of architectural profession in nowadays. The main design trend of such system is to highlight the designer's aesthetic intention from the outlook of building project. Due to the trend of current façade design, the building envelope contains more and more layers of components, such as double skin façade, photovoltaic panels, solar control system, or even ornamental components. These exterior components are designed for various functional purposes. Most researchers focus on how these exterior elements should be structurally sound secured. However, not many researchers consider these elements would help to improve the performance of façade system. When the exterior elements are deployed in large scale, it creates an additional layer outside of original façade system and acts like a porous interface which would interfere with the aerodynamic of façade surface in micro-scale. A standard façade performance consists with 'water penetration, air infiltration rate, operation force, and component deflection ratio', and these key performances are majorly driven by the 'Design Wind Load' coded in local regulation. A design wind load is usually determined by the maximum wind pressure which occurs on the surface due to the geometry or location of building in extreme conditions. This research was designed to identify the air damping phenomenon of micro turbulence caused by porous exterior layer leading to surface wind load reduction for improvement of façade system performance. A series of wind tunnel test on dynamic pressure sensor array covered by various scale of porous exterior skin was conducted to verify the effect of wind pressure reduction. The testing specimens were designed to simulate the typical building with two-meter extension offsetting from building surface. Multiple porous exterior skins were prepared to replicate various opening ratio of surface which may cause different level of damping effect. This research adopted 'Pitot static tube', 'Thermal anemometers', and 'Hot film probe' to collect the data of surface dynamic pressure behind porous skin. Turbulence and distributed resistance are the two main factors of aerodynamic which would reduce the actual wind pressure. From initiative observation, the reading of surface wind pressure was effectively reduced behind porous media. In such case, an actual building envelope system may be benefited by porous skin from the reduction of surface wind pressure, which may improve the performance of envelope system consequently.

Keywords: multi-layer facade, porous media, facade performance, turbulence and distributed resistance, wind tunnel test

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Authors: Aniket Ganvir, Ritarani Sahu, Suchismita Chinara

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The rapid digitization of various aspects of life is leading to the creation of smart IoT ecosystems, where interconnected devices generate significant amounts of valuable data. However, these IoT devices face constraints such as limited computational resources and bandwidth. Cloud computing emerges as a solution by offering ample resources for offloading tasks efficiently despite introducing latency issues, especially for time-sensitive applications like fog computing. Fog computing (FC) addresses latency concerns by bringing computation and storage closer to the network edge, minimizing data travel distance, and enhancing efficiency. Offloading tasks to fog nodes or the cloud can conserve energy and extend IoT device lifespan. The offloading process is intricate, with tasks categorized as full or partial, and its optimization presents an NP-hard problem. Traditional greedy search methods struggle to address the complexity of task offloading efficiently. To overcome this, the efficient crow search algorithm (ECSA) has been proposed as a meta-heuristic optimization algorithm. ECSA aims to effectively optimize computation offloading, providing solutions to this challenging problem.

Keywords: IoT, fog computing, task offloading, efficient crow search algorithm

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Authors: Peter J. Glinos

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The Alternative Education Resource Organization (AERO), one of the largest international hubs for alternative educators led by Jerry Mintz, has had a major impact on the global alternative education movement. The organization’s publications, like the AERO-Gramme Newsletter and its successor, the Education Revolution Magazine, allowed members across the globe to discuss issues, share support, and submit writings on policies and reforms. Stored on AERO's online digital archive, this work uses these publications from 1989 to 2011 to investigate the network's entanglements with America, Canada, Russia, Ukraine, Israel, Palestine, Japan, India, and Guatemala. Inspired by Reinhart Koselleck, this historical analysis will trace AERO’s entanglements within the United States, Japan, and Russia, contextualizing each of these multiple temporalities within the history of each nation’s education system, the developments within AERO, and the global geo-political climate at the time of AERO’s expansion. To help remedy the lack of attention paid by global historians to the role state organizations play supporting global networks, as noted in What is Global History? by Sebastian Conrad, this work will focus on the relationship between AERO and state actors.

Keywords: global history, history of education, neoliberalism, transnational history, alternative education

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Authors: Salha Abdullah Ali Al-Shamrani, Maha Muhammad Dhaher Aljuhani, Eman Ali Ahmed Aldhaheri

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With the proliferation of Internet of Things (IoT) devices in various industries, there has been a concurrent rise in security vulnerabilities, particularly spoofing attacks. This study explores the potential of blockchain technology in enhancing the security of IoT systems and mitigating these attacks. Blockchain's decentralized and immutable ledger offers significant promise for improving data integrity, transaction transparency, and tamper-proofing. This research develops and implements a blockchain-based IoT architecture and a reference network to simulate real-world scenarios and evaluate a blockchain-integrated intrusion detection system. Performance measures including time delay, security, and resource utilization are used to assess the system's effectiveness, comparing it to conventional IoT networks without blockchain. The results provide valuable insights into the practicality and efficacy of employing blockchain as a security mechanism, shedding light on the trade-offs between speed and security in blockchain deployment for IoT. The study concludes that despite minor increases in time consumption, the security benefits of incorporating blockchain technology into IoT systems outweigh potential drawbacks, demonstrating a significant potential for blockchain in bolstering IoT security.

Keywords: internet of things, spoofing, IoT, access control, blockchain, raspberry pi

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Authors: Richard Maguire

Abstract:

This research evaluates the efficiency of using mobile HEPA filters to reduce localized Particulate Matter (PM), Total Volatile Organic Chemical (TVOC) and Formaldehyde (HCHO) Air Pollution. The research is being performed using a standard HEPA filter that is tube fitted and attached to a motor vehicle. The velocity of the vehicle is used to generate the pressure difference that allows the filter to remove PM, VOC and HCOC pollution from the localized atmosphere of a road transport traffic route. The testing has been performed on a sample of traffic routes in Non-Sparse and Sparse urban environments within the UK. Pre and Post filter measuring of the PM2.5 Air Quality has been carried out along with demographics of the climate environment, including live filming of the traffic conditions. This provides a base line for future national and international research. The effectiveness measurement is generated through evaluating the difference in PM2.5 Air Quality measured pre- and post- the mobile filter test equipment. A series of further research opportunities and future exploitation options are made based on the results of the research.

Keywords: high efficiency particulate air, HEPA filter, particulate matter, traffic pollution

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Authors: Marvin Ricardo Awarab

Abstract:

There has never been a greater need for telecom services. The Internet of Things (IoT), 5G networking, and edge computing are the driving forces behind this increased demand. The fierce demand offers communications service providers significant income opportunities. The telecom sector is centered on automation, and realizing a digital operation that functions as a real-time business will be crucial for the industry as a whole. Automation in telecom refers to the application of technology to create a more effective, quick, and scalable alternative to the conventional method of operating the telecom industry. With the promotion of 5G and the Internet of Things (IoT), telecom companies will continue to invest extensively in telecom automation technology. Automation offers benefits in the telecom industry; developing countries such as Namibia may not fully tap into such benefits because of the lack of funds and infrastructural resources to invest in automation. This paper fully investigates the benefits of automation in the telecom industry. Furthermore, the paper identifies hiccups that developing countries such as Namibia face in their quest to fully introduce automation in the telecom industry. Additionally, the paper proposes possible avenues that Namibia, as a developing country, adopt investing in automation infrastructural resources with the aim of reaping the full benefits of automation in the telecom industry.

Keywords: automation, development, internet, internet of things, network, telecom, telecommunications policy, 5G

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Authors: Jiaqi Yan, Yani Shi

Abstract:

The blockchain technology is regarded as the most promising technology that has the potential to trigger a technological revolution. However, besides the bitcoin industry, we have not yet seen a large-scale application of blockchain in those domains that are supposed to be impacted, such as supply chain, financial network, and intelligent manufacturing. The reasons not only lie in the difficulties of blockchain implementation, but are also root in the challenges of blockchain-oriented information systems design. As the blockchain members are self-interest actors that belong to organizations with different existing information systems. As they expect different information inputs and outputs of the blockchain application, a common language protocol is needed to facilitate communications between blockchain members. Second, considering the decentralization of blockchain organization, there is not any central authority to organize and coordinate the business processes. Thus, the information systems built on blockchain should support more adaptive business process. This paper aims to address these difficulties by providing a modeling approach for blockchain-oriented information systems design. We will investigate the information structure of distributed-ledger data with conceptual modeling techniques and ontology theories, and build an effective ontology mapping method for the inter-organization information flow and blockchain information records. Further, we will study the distributed-ledger-ontology based business process modeling to support adaptive enterprise on blockchain.

Keywords: blockchain, ontology, information systems modeling, business process

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Authors: Mardiana Ahamad Zabidi, Akmalluddin Md. Yunus

Abstract:

Soursop (Anona muricata) is one of the underutilized tropical fruits containing nutrients, particularly dietary fibre and antioxidant properties that are beneficial to human health. This objective of this study is to investigate the feasibility of matured soursop pulp flour (SPF) to be substituted with high-protein wheat flour in bread. Bread formulation was substituted with different levels of SPF (0%, 5%, 10% and 15%). The effect on physicochemical properties and sensory attributes were evaluated. Higher substitution level of SPF resulted in significantly higher (p<0.05) fibre, protein and ash content, while fat and carbohydrate content reduced significantly (p<0.05). FESEM showed that the bread crumb surface of control and 5% SPF appeared to distribute evenly and coalesced by thin gluten film. However, higher SPF substitution level in bread formulation exhibited a deleterious effect by formation of discontinuous gluten network. For texture profile analysis, 5% SPF bread resulted in the lowest value of hardness. The score of sensory evaluation showed that 5% SPF bread received good acceptability and is comparable with control bread.

Keywords: soursop pulp flour, bread, physicochemical properties, sensory attributes, scanning electron microscopy (SEM)

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Authors: Stuti Modi, Aditi Kambli

Abstract:

This paper addresses the need for intelligent water management and distribution system in smart cities to ensure optimal consumption and distribution of water for drinking and sanitation purposes. Water being a limited resource in cities require an effective system for collection, storage and distribution. In this paper, applications of two mostly widely used particular types of data-driven models, namely artificial neural networks (ANN) and fuzzy logic-based models, to modelling in the water resources management field are considered. The objective of this paper is to review the principles of various types and architectures of neural network and fuzzy adaptive systems and their applications to integrated water resources management. Final goal of the review is to expose and formulate progressive direction of their applicability and further research of the AI-related and data-driven techniques application and to demonstrate applicability of the neural networks, fuzzy systems and other machine learning techniques in the practical issues of the regional water management. Apart from this the paper will deal with water storage, using ANN to find optimum reservoir level and predicting peak daily demands.

Keywords: artificial neural networks, fuzzy systems, peak daily demand prediction, water management and distribution

Procedia PDF Downloads 187

Authors: Lindani Mbatha, Suvendi Rimer, Mpho Gololo

Abstract:

Image segmentation is the process of dividing an image into several sections, such as the object's background and the foreground. It is a critical technique in both image-processing tasks and computer vision. Most of the image segmentation algorithms have been developed for gray-scale images and little research and algorithms have been developed for the color images. Most image segmentation algorithms or techniques vary based on the input data and the application. Nearly all of the techniques are not suitable for noisy environments. Most of the work that has been done uses the Markov Random Field (MRF), which involves the computations and is said to be robust to noise. In the past recent years' image segmentation has been brought to tackle problems such as easy processing of an image, interpretation of the contents of an image, and easy analysing of an image. This article reviews and summarizes some of the image segmentation techniques and algorithms that have been developed in the past years. The techniques include neural networks (CNN), edge-based techniques, region growing, clustering, and thresholding techniques and so on. The advantages and disadvantages of medical ultrasound image segmentation techniques are also discussed. The article also addresses the applications and potential future developments that can be done around image segmentation. This review article concludes with the fact that no technique is perfectly suitable for the segmentation of all different types of images, but the use of hybrid techniques yields more accurate and efficient results.

Keywords: clustering-based, convolution-network, edge-based, region-growing

Procedia PDF Downloads 98