Search results for: series active filter

Authors: Raja Rajeswari K

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Orthogonal Frequency Division Multiplexing (OFDM), a multi Carrier transmission technique that has been used in implementing the majority of wireless applications like Wireless Network Protocol Standards (like IEEE 802.11a, IEEE 802.11n), in telecommunications (like LTE, LTE-Advanced) and also in Digital Audio & Video Broadcast standards. The latest research and development in the area of orthogonal frequency division multiplexing, Universal Filtered Multi Carrier (UFMC) & Filtered OFDM (F-OFDM) has attracted lots of attention for wideband wireless communications. In this paper UFMC & F-OFDM system are implemented and comparative analysis are carried out in terms of M-ary QAM modulation scheme over Dolph-chebyshev filter & rectangular window filter and to estimate Bit Error Rate (BER) over Rayleigh fading channel.

Keywords: UFMC, F-OFDM, BER, M-ary QAM

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Authors: Omid Ataei Nia, Karim Salahshoor

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In this paper, a new precise and reliable sensor network methodology is introduced for unit processes and operations using the Constriction Coefficient Particle Swarm Optimization (CPSO) method. CPSO is introduced as a new search engine for optimal sensor network design purposes. Furthermore, a Square Root Unscented Kalman Filter (SRUKF) algorithm is employed as a new data reconciliation technique to enhance the stability and accuracy of the filter. The proposed design procedure incorporates precision, cost, observability, reliability together with importance-of-variables (IVs) as a novel measure in Instrumentation Criteria (IC). To the best of our knowledge, no comprehensive approach has yet been proposed in the literature to take into account the importance of variables in the sensor network design procedure. In this paper, specific weight is assigned to each sensor, measuring a process variable in the sensor network to indicate the importance of that variable over the others to cater to the ultimate sensor network application requirements. A set of distinct scenarios has been conducted to evaluate the performance of the proposed methodology in a simulated Continuous Stirred Tank Reactor (CSTR) as a highly nonlinear process plant benchmark. The obtained results reveal the efficacy of the proposed method, leading to significant improvement in accuracy with respect to other alternative sensor network design approaches and securing the definite allocation of sensors to the most important process variables in sensor network design as a novel achievement.

Keywords: constriction coefficient PSO, importance of variable, MRMSE, reliability, sensor network design, square root unscented Kalman filter

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Authors: Ramesh K. Sunkaria, Puneeta Marwaha

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Nonlinear methods of heart rate variability (HRV) analysis are becoming more popular. It has been observed that complexity measures quantify the regularity and uncertainty of cardiovascular RR-interval time series. In the present work, SampEn has been evaluated in healthy Normal Sinus Rhythm (NSR) male and female subjects for different data lengths and tolerance level r. It is demonstrated that SampEn is small for higher values of tolerance r. Also SampEn value of healthy female group is higher than that of healthy male group for short data length and with increase in data length both groups overlap each other and it is difficult to distinguish them. The SampEn gives inaccurate results by assigning higher value to female group, because male subject have more complex HRV pattern than that of female subjects. Therefore, this traditional algorithm exhibits higher complexity for healthy female subjects than for healthy male subjects, which is misleading observation. This may be due to the fact that SampEn do not account for multiple time scales inherent in the physiologic time series and the hidden spatial and temporal fluctuations remains unexplored.

Keywords: heart rate variability, normal sinus rhythm group, RR interval time series, sample entropy

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Authors: Michelle Bulawan, Mei-Hua Chen

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Reading comprehension is crucial for acquiring information, analyzing critically, and achieving academic proficiency. However, there is a lack of growth in reading comprehension skills beyond fourth grade. The developmental shift from "learning to read" to "reading to learn" occurs around this stage. Factual knowledge and diverse views in articles enhance reading comprehension abilities. Nevertheless, some face difficulties due to evolving textual requirements, such as expanding vocabulary and using longer, more complex terminology. Most research on reading strategies has been conducted at the tertiary and secondary levels, while few have focused on the elementary levels. Furthermore, the use of character, ask, problem, solution (CAPS) charts in teaching reading has also been hardly explored. Thus, the researcher decided to explore the facilitation of active reading strategies through the CAPS chart and address the following research questions: a) What differences existed in elementary EFL learners' reading competency among those who engaged in active reading strategies and those who did not? b) What are the learners’ metacognitive skills of those who engage in active reading strategies and those who do not, and what are their effects on their reading competency? c) For those participants who engage in active reading activities, what are their perceptions about incorporating active reading activities into their English classroom learning? Two groups of elementary EFL learners, each with 18 students of the same level of English proficiency, participated in this study. Group A served as the control group, while Group B served as the experimental group. Two teachers also participated in this research; one of them was the researcher who handled the experimental group. The treatment lasts for one whole semester or seventeen weeks. In addition to the CAPS chart, the researcher also used the metacognitive awareness of reading strategy inventory (MARSI) and a ten-item, five-point Likert scale survey.

Keywords: active reading, EFL learners, metacognitive skills, reading competency, student’s perception

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Prasannakumar Palaniappan, Dong Ho Shin, Chul Gyu Song

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The photoacoustic images are obtained from a custom developed linear array photoacoustic tomography system. The biological specimens are imitated by conducting phantom tests in order to retrieve a fully functional photoacoustic image. The acquired image undergoes the active region based contour filtering to remove the noise and accurately segment the object area for further processing. The universal back projection method is used as the image reconstruction algorithm. The active contour filtering is analyzed by evaluating the signal to noise ratio and comparing it with the other filtering methods.

Keywords: contour filtering, linear array, photoacoustic tomography, universal back projection

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Authors: Aziz Sezgin, Yuksel Hacioglu, Nurkan Yagiz

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Sliding mode controller for a vehicle active suspension system is designed in this study. The widely used quarter car model is preferred and it is aimed to improve the ride comfort of the passengers. The effect of the actuator time delay, which may arise due to the information processing, sensors or actuator dynamics, is also taken into account during the design of the controller. A sliding mode controller was designed that has taken into account the actuator time delay by using Smith predictor. The successful performance of the designed controller is confirmed via numerical results.

Keywords: sliding mode control, active suspension system, actuator, time delay, vehicle

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Authors: Akbar Esmaeili, Zahra Salarieh

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Cerium oxide nanoparticles (nano-series) are used as catalysts in industrial applications due to their free radical scavenging properties. Given that free radicals play an essential role in the pathology of many neurological diseases, we investigated the use of nanocrystals as a potential therapeutic agent for oxidative damage. This project synthesized nano-series from a new and environmentally friendly bio-pathway. Investigation of cerium nitrate in culture medium containing inoculated Lactobacillus acidophilus strain before incubation produces nano-series. Loaded with glatiramer acetate (GA) was formed by coating carboxymethylcellulose (CMC) and CeO2. FE-SEM analysis showed nano-series in the 9-11 nm range, spherical shape, and uniform particle size distribution. Cubic nanoparticles containing anti-multiple sclerosis (anti-Ms) treatment called GA were used. Glycerol monostearate (GMS) was used as a fat base, and evening primrose extract was used as an anti-inflammatory in cubosomes. Design-Expert® software was used to study the effects of different formulation factors on the properties of GAloaded cubic dispersions. Thirty GA-labeled cubic dispersions were prepared with GA-labeled carboxymethylcellulose and evaluated in vitro. The results showed an average nano-series size of 89.02 and a zeta potential of -49.9. Cubosomes containing GA-CMC/CeO2 showed a stable release profile for 180 min. The results showed that cubosomes containing GA-CMC/CeO2 could be a promising drug carrier with normal release behavior.

Keywords: ciochemistry, biotechnology, molecular, biology

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Authors: Austin Jiang, Richard M. Salmon, Nicholas W. Morrell, Wei Li

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BMP10 is highly expressed in the developing heart and plays essential roles in cardiogenesis. BMP10 deletion in mice results in embryonic lethality due to impaired cardiac development. In adults, BMP10 expression is restricted to the right atrium, though ventricular hypertrophy is accompanied by increased BMP10 expression in a rat hypertension model. However, reports of BMP10 activity in the circulation are inconclusive. In particular it is not known whether in vivo secreted BMP10 is active or whether additional factors are required to achieve its bioactivity. It has been shown that high-affinity binding of the BMP10 prodomain to the mature ligand inhibits BMP10 signaling activity in C2C12 cells, and it was proposed that prodomain-bound BMP10 (pBMP10) complex is latent. In this study, we demonstrated that the BMP10 prodomain did not inhibit BMP10 signaling activity in multiple endothelial cells, and that recombinant human pBMP10 complex, expressed in mammalian cells and purified under native conditions, was fully active. In addition, both BMP10 in human plasma and BMP10 secreted from the mouse right atrium were fully active. Finally, we confirmed that active BMP10 secreted from mouse right atrium was in the prodomain-bound form. Our data suggest that circulating BMP10 in adults is fully active and that the reported vascular quiescence function of BMP10 in vivo is due to the direct activity of pBMP10 and does not require an additional activation step. Moreover, being an active ligand, recombinant pBMP10 may have therapeutic potential as an endothelial-selective BMP ligand, in conditions characterized by loss of BMP9/10 signaling.

Keywords: bone morphogenetic protein 10 (BMP10), endothelial cell, signal transduction, transforming growth factor beta (TGF-B)

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Authors: Shafiullah Soomro

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Active contour is one of the image segmentation techniques and its goal is to capture required object boundaries within an image. In this paper, we propose a novel image segmentation method by using an active contour method based on anisotropic diffusion feature enhancement technique. The traditional active contour methods use only pixel information to perform segmentation, which produces inaccurate results when an image has some noise or complex background. We use Perona and Malik diffusion scheme for feature enhancement, which sharpens the object boundaries and blurs the background variations. Our main contribution is the formulation of a new SPF (signed pressure force) function, which uses global intensity information across the regions. By minimizing an energy function using partial differential framework the proposed method captures semantically meaningful boundaries instead of catching uninterested regions. Finally, we use a Gaussian kernel which eliminates the problem of reinitialization in level set function. We use several synthetic and real images from different modalities to validate the performance of the proposed method. In the experimental section, we have found the proposed method performance is better qualitatively and quantitatively and yield results with higher accuracy compared to other state-of-the-art methods.

Keywords: active contours, anisotropic diffusion, level-set, partial differential equations

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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Authors: Ali Alzeyadi, Edward Loffill, Rafid Alkhaddar Ali Alattabi

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Phosphate, ammonium, and nitrates are forms of nutrients; they are released from different sources. High nutrient levels contribute to the eutrophication of water bodies by accelerating the extraordinary growth of algae. Recently, many filtration and treatment systems were developed and used for different removal processes. Due to enhanced operational aspects for the up-flow, continuous, granular Media filter researchers became more interested in further developing this technology and its performance for nutrient removal from wastewater. Environmental factors significantly affect the filtration process performance, and understanding their impact will help to maintain the nutrient removal process. Phosphate removal by phosphate sorption materials PSMs and nitrogen removal biologically are the methods of nutrient removal that have been discussed in this paper. Hence, the focus on the factors that influence these processes is the scope of this work. The finding showed the presence of factors affecting both removal processes; the size, shape, and roughness of the filter media particles play a crucial role in supporting biofilm formation. On the other hand, all of which are effected on the reactivity of surface between the media and phosphate. Many studies alluded to factors that have significant influence on the biological removal for nitrogen such as dissolved oxygen, temperature, and pH; this is due to the sensitivity of biological processes while the phosphate removal by PSMs showed less affected by these factors. This review work provides help to the researchers in create a comprehensive approach in regards study the nutrient removal in up flow filtration systems.

Keywords: nitrogen biological treatment, nutrients, psms, upflow filter, wastewater treatment

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Authors: Jamileh Vasheghani Farahani

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The main purpose of this research is to show the current active faults and active tectonic of the area by three seismic networks in Tehran region: 1-Tehran Disaster Mitigation and Management Organization (TDMMO), 2-Broadband Iranian National Seismic Network Center (BIN), 3-Iranian Seismological Center (IRSC). In this study, we analyzed microearthquakes happened in Tehran city and its surroundings using the Tehran networks from 1996 to 2015. We found some active faults and trends in the region. There is a 200-year history of historical earthquakes in Tehran. Historical and instrumental seismicity show that the east of Tehran is more active than the west. The Mosha fault in the North of Tehran is one of the active faults of the central Alborz. Moreover, other major faults in the region are Kahrizak, Eyvanakey, Parchin and North Tehran faults. An important seismicity region is an intersection of the Mosha and North Tehran fault systems (Kalan village in Lavasan). This region shows a cluster of microearthquakes. According to the historical and microseismic events analyzed in this research, there is a seismic gap in SE of Tehran. The empirical relationship is used to assess the Mmax based on the rupture length. There is a probability of occurrence of a strong motion of 7.0 to 7.5 magnitudes in the region (based on the assessed capability of the major faults such as Parchin and Eyvanekey faults and historical earthquakes).

Keywords: Iran, major faults, microseismicity, Tehran

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Hare Krishna Nigam

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In this paper, for the first time, (E,q)(C,2) product summability method is introduced and two quite new results on degree of approximation of the function f belonging to Lip (alpha,r)class and W(L(r), xi(t)) class by (E,q)(C,2) product means of Fourier series, has been obtained.

Keywords: Degree of approximation, (E, q)(C, 2) means, Fourier series, Lebesgue integral, Lip (alpha, r)class, W(L(r), xi(t))class of functions

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Authors: Zizouni Khaled, Fali Leyla, Sadek Younes, Bousserhane Ismail Khalil

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In structural vibration caused principally by an earthquake excitation, the most vibration’s attenuation system used recently is the semi active control with a Magneto Rheological Damper device. This control was a subject of many researches and works in the last years. The big challenges of searchers in this case is to propose an adequate controller with a robust algorithm of current or tension adjustment. In this present paper, a linear controller is proposed to control the MR damper using to reduce a vibrations of three story structure exposed to El Centro’s 1940 and Boumerdès 2003 earthquakes. In this example, the MR damper is installed in the first floor of the structure. The numerical simulations results of the proposed linear control with a feedback law based on clipped optimal algorithm showed the feasibility of the semi active control to protecting civil structures. The comparison of the controlled structure and uncontrolled structures responses illustrate clearly the performance and the effectiveness of the simple proposed approach.

Keywords: MR damper, seismic vibration, semi-active control

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Authors: Yixuan Wang, A. C. Y. Wong, J. M. Y. Chiu, S. G. Cheung

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After being released into the ocean, microplastics (MPs) are quickly colonized by microbes. The biofilm that forms on MPs alters their characteristics and perplexes users, including filter-feeders, some of whom choose to eat MPs that have biofilm. It has been proposed that filter feeders like mussels and other bivalves could serve as bioindicators of MP pollution. Mussels are considered selective feeders with particle sorting capability. Two sizes (27-32 µm and 90-106 µm), shapes (microspheres and microfibers), and types (polyethylene, polystyrene and polyester) of MPs were available for the green mussel, Perna viridis, at three concentrations (100 P/ml, 1000 P/ml and 10,000 P/ml). These MPs were incubated in the sea for 0, 3 or 14 days for biofilm development. The presence of the biofilm significantly affected the ingestion of MPs, and the mussels preferred MPs with biofilm, with a higher preference observed for biofilm with a longer incubation period. Additionally, the ingestion rate varied with the interaction between the concentration, size and form of MPs. The findings are discussed in relation to the possibility that mussels serve as MP bioindicators.

Keywords: marine miroplastics, biofilm, bioindicator, green mussel perna viridis

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Authors: Yu-ding Du, Qi-lian Bao, Nassim Bessaad, Lin Liu

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The Global Navigation Satellite System (GNSS) is regarded as an effective approach for the purpose of replacing the large amount used track-side balises in modern train localization systems. This paper describes a method based on the data fusion of a GNSS receiver sensor and an odometer sensor that can significantly improve the positioning accuracy. A digital track map is needed as another sensor to project two-dimensional GNSS position to one-dimensional along-track distance due to the fact that the train’s position can only be constrained on the track. A model trained by BP neural network is used to estimate the trend positioning error which is related to the specific location and proximate processing of the digital track map. Considering that in some conditions the satellite signal failure will lead to the increase of GNSS positioning error, a detection step for GNSS signal is applied. An adaptive weighted fusion algorithm is presented to reduce the standard deviation of train speed measurement. Finally an Extended Kalman Filter (EKF) is used for the fusion of the projected 1-D GNSS positioning data and the 1-D train speed data to get the estimate position. Experimental results suggest that the proposed method performs well, which can reduce positioning error notably.

Keywords: multi-sensor data fusion, train positioning, GNSS, odometer, digital track map, map matching, BP neural network, adaptive weighted fusion, Kalman filter

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Authors: F. J. Adeyeye, D. Eni, K. M. Okedoye

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In this paper we present a Hybrid of Adomian decomposition method (ADM). This is the substitution of a One-step method of Taylor’s series approximation of orders I and II, into the nonlinear part of Adomian decomposition method resulting in a convergent series scheme. This scheme is applied to solve some Logistic problems represented as Abelian differential equation and the results are compared with the actual solution and Runge-kutta of order IV in order to ascertain the accuracy and efficiency of the scheme. The findings shows that the scheme is efficient enough to solve logistic problems considered in this paper.

Keywords: Adomian decomposition method, nonlinear part, one-step method, Taylor series approximation, hybrid of Adomian polynomial, logistic problem, Malthusian parameter, Verhulst Model

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Lijuan Li

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Nitric oxide (NO) has become a fascinating entity in biological chemistry over the past few years. It is a gaseous lipophilic radical molecule that plays important roles in several physiological and pathophysiological processes in mammals, including activating the immune response, serving as a neurotransmitter, regulating the cardiovascular system, and acting as an endothelium-derived relaxing factor. NO functions in eukaryotes both as a signal molecule at nanomolar concentrations and as a cytotoxic agent at micromolar concentrations. The latter arises from the ability of NO to react readily with a variety of cellular targets leading to thiol S-nitrosation, amino acid N-nitrosation, and nitrosative DNA damage. Nitric oxide can readily bind to metals to give metal-nitrosyl (M-NO) complexes. Some of these species are known to play roles in biological NO storage and transport. These complexes have different biological, photochemical, or spectroscopic properties due to distinctive structural features. These recent discoveries have spawned a great interest in the development of transition metal complexes containing NO, particularly its iron complexes that are central to the role of nitric oxide in the body. Spectroscopic evidence would appear to implicate species of “Fe(NO)2+” type in a variety of processes ranging from polymerization, carcinogenesis, to nitric oxide stores. Our research focuses on isolation and structural studies of non-heme iron nitrosyls that mimic biologically active compounds and can potentially be used for anticancer drug therapy. We have shown that reactions between Fe(NO)2(CO)2 and a series of imidazoles generated new non-heme iron nitrosyls of the form Fe(NO)2(L)2 [L = imidazole, 1-methylimidazole, 4-methylimidazole, benzimidazole, 5,6-dimethylbenzimidazole, and L-histidine] and a tetrameric cluster of [Fe(NO)2(L)]4 (L=Im, 4-MeIm, BzIm, and Me2BzIm), resulted from the interactions of Fe(NO)2 with a series of substituted imidazoles was prepared. Recently, a series of sulfur bridged iron di nitrosyl complexes with the general formula of [Fe(µ-RS)(NO)2]2 (R = n-Pr, t-Bu, 6-methyl-2-pyridyl, and 4,6-dimethyl-2-pyrimidyl), were synthesized by the reaction of Fe(NO)2(CO)2 with thiols or thiolates. Their structures and properties were studied by IR, UV-vis, 1H-NMR, EPR, electrochemistry, X-ray diffraction analysis and DFT calculations. IR spectra of these complexes display one weak and two strong NO stretching frequencies (νNO) in solution, but only two strong νNO in solid. DFT calculations suggest that two spatial isomers of these complexes bear 3 Kcal energy difference in solution. The paramagnetic complexes [Fe2(µ-RS)2(NO)4]-, have also been investigated by EPR spectroscopy. Interestingly, the EPR spectra of complexes exhibit an isotropic signal of g = 1.998 - 2.004 without hyperfine splitting. The observations are consistent with the results of calculations, which reveal that the unpaired electron dominantly delocalize over the two sulfur and two iron atoms. The difference of the g values between the reduced form of iron-sulfur clusters and the typical monomeric di nitrosyl iron complexes is explained, for the first time, by of the difference in unpaired electron distributions between the two types of complexes, which provides the theoretical basis for the use of g value as a spectroscopic tool to differentiate these biologically active complexes.

Keywords: di nitrosyl iron complex, metal nitrosyl, non-heme iron, nitric oxide

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Authors: Hueiwang Anna Jeng, Norou Diawara, Nancy Welch, Cynthia Jackson, Rekha Singh, Kyle Curtis, Raul Gonzalez, David Jurgens, Sasanka Adikari

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Modeling effort is needed to predict the COVID-19 trends for developing management strategies and adaptation measures. The objective of this study was to assess whether SARS-CoV-2 viral load in wastewater could serve as a predictor for forecasting COVID-19 cases, hospitalization cases, and death cases using copula-based time series modeling. SARS-CoV-2 RNA load in raw wastewater in Chesapeake VA was measured using the RT-qPCR method. Gaussian copula time series marginal regression model, incorporating an autoregressive moving average model and the copula function, served as a forecasting model. COVID-19 cases were correlated with wastewater viral load, hospitalization cases, and death cases. The forecasted trend of COVID-19 cases closely paralleled one of the reported cases, with over 90% of the forecasted COVID-19 cases falling within the 99% confidence interval of the reported cases. Wastewater SARS-CoV-2 viral load could serve as a predictor for COVID-19 cases and hospitalization cases.

Keywords: COVID-19, modeling, time series, copula function

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Authors: Mounir Belattar

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In this paper the Analysis of voltage waves that propagate along a lossless exponential nonuniform line is presented. For this analysis the parameters of this line are assumed to be varying function of the distance x along the line from the source end. The approach is based on the tow-port networks cascading presentation to derive the ABDC parameters of transmission using Picard-Carson Method which is a powerful method in getting a power series solution for distributed network because it is easy to calculate poles and zeros and solves differential equations such as telegrapher equations by an iterative sequence. So the impedance, admittance voltage and current along the line are expanded as a Taylor series in x/l where l is the total length of the line to obtain at the end, the main transmission line parameters such as voltage response and transmission and reflexion coefficients represented by scattering parameters in frequency domain.

Keywords: ABCD parameters, characteristic impedance exponential nonuniform transmission line, Picard-Carson's method, S parameters, Taylor's series

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi

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This study was designed to find the best stochastic model (using of time series analysis) for annual extreme streamflow (peak and maximum streamflow) of Karkheh River at Iran. The Auto-regressive Integrated Moving Average (ARIMA) model used to simulate these series and forecast those in future. For the analysis, annual extreme streamflow data of Jelogir Majin station (above of Karkheh dam reservoir) for the years 1958–2005 were used. A visual inspection of the time plot gives a little increasing trend; therefore, series is not stationary. The stationarity observed in Auto-Correlation Function (ACF) and Partial Auto-Correlation Function (PACF) plots of annual extreme streamflow was removed using first order differencing (d=1) in order to the development of the ARIMA model. Interestingly, the ARIMA(4,1,1) model developed was found to be most suitable for simulating annual extreme streamflow for Karkheh River. The model was found to be appropriate to forecast ten years of annual extreme streamflow and assist decision makers to establish priorities for water demand. The Statistical Analysis System (SAS) and Statistical Package for the Social Sciences (SPSS) codes were used to determinate of the best model for this series.

Keywords: stochastic models, ARIMA, extreme streamflow, Karkheh river

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Prangya Ranjan Rout, Puspendu Bhunia, Rajesh Roshan Dash

Abstract:

In a developing country like India, providing reliable and affordable wastewater treatment facilities in rural areas is a huge challenge. With the aim of enhancing the nutrient removal from rural domestic wastewater while reducing the cost of treatment process, a novel, integrated treatment system consisting of a multistage bio-filter with drop aeration and a post positioned attached growth carbonaceous denitrifying-bioreactor was designed and developed in this work. The bio-filter was packed with ‘dolochar’, a sponge iron industry waste, as an adsorbent mainly for phosphate removal through physiochemical approach. The Denitrifying bio-reactor was packed with many waste organic solid substances (WOSS) as carbon sources and substrates for biomass attachment, mainly to remove nitrate in biological denitrification process. The performance of the modular system, treating real domestic wastewater was monitored for a period of about 60 days and the average removal efficiencies during the period were as follows: phosphate, 97.37%; nitrate, 85.91%, ammonia, 87.85%, with mean final effluent concentration of 0.73, 9.86, and 9.46 mg/L, respectively. The multistage bio-filter played an important role in ammonium oxidation and phosphate adsorption. The multilevel drop aeration with increasing oxygenation, and the special media used, consisting of certain oxides were likely beneficial for nitrification and phosphorus removal, respectively, whereas the nitrate was effectively reduced by biological denitrification in the carbonaceous bioreactor. This treatment system would allow multipurpose reuse of the final effluent. Moreover, the saturated dolochar can be used as nutrient suppliers in agricultural practices and the partially degraded carbonaceous substances can be subjected to composting, and subsequently used as an organic fertilizer. Thus, the system displays immense potential for treating domestic wastewater significantly decreasing the concentrations of nutrients and more importantly, facilitating the conversion of the waste materials into usable ones.

Keywords: nutrient removal, denitrifying bioreactor, multi-stage bio-filter, dolochar, waste organic solid substances

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Authors: Asif Zaman, Moinul Islam Zaber, Amin Ahsan Ali

Abstract:

In recent years of industrialization, the South Asian countries are being affected by air pollution due to a severe increase in fine particulate matter 2.5 (PM₂.₅). Among them, Bangladesh is one of the most polluting countries. In this paper, statistical analyses were conducted on the time series of PM₂.₅ from various districts in Bangladesh, mostly around Dhaka city. Research has been conducted on the dynamic interactions and relationships between PM₂.₅ concentrations in different zones. The study is conducted toward understanding the characteristics of PM₂.₅, such as spatial-temporal characterization, correlation of other contributors behind air pollution such as human activities, driving factors and environmental casualties. Clustering on the data gave an insight on the districts groups based on their AQI frequency as representative districts. Seasonality analysis on hourly and monthly frequency found higher concentration of fine particles in nighttime and winter season, respectively. Cross correlation analysis discovered a phenomenon of correlations among cities based on time-lagged series of air particle readings and visualization framework is developed for observing interaction in PM₂.₅ concentrations between cities. Significant time-lagged correlations were discovered between the PM₂.₅ time series in different city groups throughout the country by cross correlation analysis. Additionally, seasonal heatmaps depict that the pooled series correlations are less significant in warmer months, and among cities of greater geographic distance as well as time lag magnitude and direction of the best shifted correlated particulate matter time series among districts change seasonally. The geographic map visualization demonstrates spatial behaviour of air pollution among districts around Dhaka city and the significant effect of wind direction as the vital actor on correlated shifted time series. The visualization framework has multipurpose usage from gathering insight of general and seasonal air quality of Bangladesh to determining the pathway of regional transportation of air pollution.

Keywords: air quality, particles, cross correlation, seasonality

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Authors: D. Shakirah Shukor, A. Aminudin, Hashim U. A., Waziralilah N. Fathiah, T. Vikneshvaran

Abstract:

The interior noise control is essential to be explored due to the interior acoustic analysis is significant in the systems such as automobiles, aircraft, air-handling system and diesel engine exhausts system. In this research, experimental work was undertaken for canceling an active noise in the rectangular enclosure. The rectangular enclosure was fabricated with multiple speakers and microphones inside the enclosure. A software program using digital signal processing is implemented to evaluate the proposed method. Experimental work was conducted to obtain the acoustic behavior and characteristics of the rectangular enclosure and noise cancellation based on active noise control in low-frequency range. Noise is generated by using multispeaker inside the enclosure and microphones are used for noise measurements. The technique for noise cancellation relies on the principle of destructive interference between two sound fields in the rectangular enclosure. One field is generated by the original or primary sound source, the other by a secondary sound source set up to interfere with, and cancel, that unwanted primary sound. At the end of this research, the result of output noise before and after cancellation are presented and discussed. On the basis of the findings presented in this research, an active noise cancellation in the rectangular enclosure is worth exploring in order to improve the noise control technologies.

Keywords: active noise control, digital signal processing, noise cancellation, rectangular enclosure

Procedia PDF Downloads 271

Authors: Reji Thankachan, Varsha PS

Abstract:

Both image capturing devices and human visual systems are nonlinear. Hence nonlinear filtering methods outperforms its linear counterpart in many applications. Linear methods are unable to remove impulsive noise in images by preserving its edges and fine details. In addition, linear algorithms are unable to remove signal dependent or multiplicative noise in images. This paper presents an approach to denoise and smoothen the Bipolar impulse noised images and videos using improved Kuwahara filter. It involves a 2 stage algorithm which includes a noise detection followed by filtering. Numerous simulation demonstrate that proposed method outperforms the existing method by eliminating the painting like flattening effect along the local feature direction while preserving edge with improvement in PSNR and MSE.

Keywords: bipolar impulse noise, Kuwahara, PSNR MSE, PDF

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Authors: Bander Alzahrani

Abstract:

Information-centric networking (ICN) using architectures such as Publish-Subscribe Internet Routing Paradigm (PSIRP) is one of the promising candidates for a future Internet, has recently been under the spotlight by the research community to investigate the possibility of redesigning the current Internet architecture to solve many issues such as routing scalability, security, and quality of services issues.. The Bloom filter-based forwarding is a source-routing approach that is used in the PSIRP architecture. This mechanism is vulnerable to brute force attacks which may lead to denial-of-service (DoS) attacks. In this work, we present a new forwarding approach that keeps the advantages of Bloom filter-based forwarding while mitigates attacks on the forwarding mechanism. In practice, we introduce a special type of forwarding nodes called Edge-FW to be placed at the edge of the network. The role of these node is to add an extra security layer by validating and inspecting packets at the edge of the network against brute-force attacks and check whether the packet contains a legitimate forwarding identifier (FId) or not. We leverage Certificateless Aggregate Signature (CLAS) scheme with a small size of 64-bit which is used to sign the FId. Hence, this signature becomes bound to a specific FId. Therefore, malicious nodes that inject packets with random FIds will be easily detected and dropped at the Edge-FW node when the signature verification fails. Our preliminary security analysis suggests that with the proposed approach, the forwarding plane is able to resist attacks such as DoS with very high probability.

Keywords: bloom filter, certificateless aggregate signature, denial-of-service, information centric network

Procedia PDF Downloads 198