Search results for: fast Fourier algorithms

Authors: Reem El Chakik

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The levels of heavy metals in agricultural lands in Lebanon have been witnessing a noticeable increase in the past few years, due to increased anthropogenic pollution sources. Heavy metals pose a serious threat to the environment for being non-biodegradable and persistent, accumulating thus to dangerous levels in the soil. Besides the traditional laboratory and chemical analysis methods, Hyperspectral Imaging (HSI) has proven its efficiency in the rapid detection of HMs contamination. In Lebanon, a continuous environmental monitoring, including the monitoring of levels of HMs in agricultural soils, is lacking. This is due in part to the high cost of analysis. Hence, this proposed research aims at defining the current national status of HMs contamination in agricultural soil, and to evaluate the effectiveness of using HSI in the detection of HM in contaminated agricultural fields. To achieve the two main objectives of this study, soil samples were collected from different areas throughout the country and were analyzed for HMs using Atomic Absorption Spectrophotometry (AAS). The results were compared to those obtained from the HSI technique that was applied using Hyspex SWIR-384 camera. The results showed that the Lebanese agricultural soils contain high contamination levels of Zn, and that the more clayey the soil is, the lower reflectance it has.

Keywords: agricultural soils in Lebanon, atomic absorption spectrophotometer, hyperspectral imaging., heavy metals contamination

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Authors: I-Hui Hsieh, Yu-Hsuan Hu

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The powerful effect of music is often associated with changes in physiological responses such as heart rate and respiration. Previous studies demonstrate that Mayer waves of blood pressure, the spontaneous rhythm occurring at 0.1 Hz, corresponds to a progressive crescendo of the musical phrase. However, music contain dynamic changes in temporal and spectral features. As such, it remains unclear which aspects of musical structures optimally affect synchronization of cardiovascular rhythms. This study investigates the independent contribution of spectral pattern, temporal pattern, and dissonance level on synchronization of cardiovascular rhythms. The regularity of acoustical patterns occurring at a periodic rhythm of 0.1 Hz is hypothesized to elicit the strongest coherence of cardiovascular rhythms. Music excerpts taken from twelve pieces of Western classical repertoire were modulated to contain varying degrees of pattern regularity of the acoustic envelope structure. Three levels of dissonance were manipulated by varying the harmonic structure of the accompanying chords. Electrocardiogram and photoplethysmography signals were recorded for 5 minutes of baseline and simultaneously while participants listen to music excerpts randomly presented over headphones in a sitting position. Participants were asked to indicate the pleasantness of each music excerpt by adjusting via a slider presented on screen. Analysis of the Fourier spectral power of blood pressure around 0.1 Hz showed a significant difference between music excerpts characterized by spectral and temporal pattern regularity compared to the same content in random pattern. Phase coherence between heart rate and blood pressure increased significantly during listening to spectrally-regular phrases compared to its matched control phrases. The degree of dissonance of the accompanying chord sequence correlated with level of coherence between heart rate and blood pressure. Results suggest that low-level auditory features of music can entrain coherence of autonomic physiological variables. These findings have potential implications for using music as a clinical and therapeutic intervention for regulating cardiovascular functions.

Keywords: cardiovascular rhythms, coherence, dissonance, pattern regularity

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Authors: Habibi Said Mustafa, Hiroko Ono

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Since 2006, Afghanistan is dealing with one of the most dramatic trend of urban movement in its history, cities and towns are expanding in size and number. Kabul is the capital of Afghanistan and as well as the fast-growing city in the Asia. The influx of the returnees from neighbor countries and other provinces of Afghanistan caused high rate of artificial growth which slums increased. As an unwanted consequence of this growth, today informal settlements have covered a vast portion of the city. Land Readjustment (LR) has proved to be an important tool for developing informal settlements and reorganizing urban areas but its implementation always varies from country to country and region to region within the countries. Consequently, to successfully develop the informal settlements in Kabul, we need to define an Afghan model of LR specifically for Afghanistan which needs to incorporate all those factors related to the socio-economic condition of the country. For this purpose, a part of the old city of Kabul has selected as a study area which is located near the Central Business District (CBD). After the further analysis and incorporating all needed factors, the result shows a positive potential for the implementation of an adaptable Land Readjustment model for Kabul city which is more sustainable and socio-economically friendly. It will enhance quality of life and provide better urban services for the residents. Moreover, it will set a vision and criteria by which sustainable developments shall proceed in other similar informal settlements of Kabul.

Keywords: adaptation, informal settlements, Kabul, land readjustment, preservation

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Authors: Jugal Bhandari, K. Hari Priya

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The ECG signal provides important clinical information which could be used to pretend the diseases related to heart. Accordingly, delineation of ECG signal is an important task. Whereas delineation of P and T waves is a complex task. This paper deals with the Study of ECG signal and analysis of signal by means of Verilog Design of efficient filters and MATLAB tool effectively. It includes generation and simulation of ECG signal, by means of real time ECG data, ECG signal filtering and processing by analysis of different algorithms and techniques. In this paper, we design a basic particle filter which generates a dynamic model depending on the present and past input samples and then produces the desired output. Afterwards, the output will be processed by MATLAB to get the actual shape and accurate values of the ranges of P-wave and T-wave of ECG signal. In this paper, Questasim is a tool of mentor graphics which is being used for simulation and functional verification. The same design is again verified using Xilinx ISE which will be also used for synthesis, mapping and bit file generation. Xilinx FPGA board will be used for implementation of system. The final results of FPGA shall be verified with ChipScope Pro where the output data can be observed.

Keywords: ECG, MATLAB, Bayesian filtering, particle filter, Verilog hardware descriptive language

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Authors: Hassan Saffari, Reza Askari Moghadam

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In modern communication systems, it is highly demanded to achieve high performance with minimal power consumption. Low actuation voltage RF MEMS (Micro-Electro-Mechanical Systems) switches represent a significant advancement in this regard. These switches, with their ability to operate at lower voltages, offer promising solutions for enhancing connectivity while minimizing energy consumption. Microelectromechanical switches are good alternatives for electronic and mechanical switches due to their low insertion loss, high isolation, and fast switching speeds. They have attracted more attention in recent years. Most of the presented RF MEMS switches use electrostatic actuators due to their low power consumption. Low actuation voltage RF MEMS switches are among the important issues that have been investigated in research articles. The actuation voltage can be reduced by different methods. One usually implemented method is low spring constant structures. However, despite their numerous benefits, challenges remain in the widespread adoption of low-actuation voltage RF MEMS switches. Issues related to reliability, durability, and manufacturing scalability need to be addressed to realize their full potential in commercial applications. While overcoming certain challenges, their exceptional performance characteristics and compatibility with miniaturized electronic systems make them a promising choice for next-generation wireless communication and RF applications. In this paper, some previous works that proposed low-voltage actuation RF MEMS switches are investigated and analyzed.

Keywords: RF MEMS switches, low actuation voltage, small spring constant structures, electrostatic actuation

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Authors: Jamil Ahmed

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Left to Right-Right Most (LR) parsing algorithm is a widely used algorithm of syntax analysis. It is contingent on a parsing table, whereas the parsing tables are extracted from the grammar. The parsing table specifies the actions to be taken during parsing. It requires that the parsing table should have no action conflicts for the same input symbol. This requirement imposes a condition on the class of grammars over which the LR algorithms work. However, there are grammars for which the parsing tables hold action conflicts. In such cases, the algorithm needs a capability of scanning (looking-ahead) next input symbols ahead of the current input symbol. In this paper, a ‘Left to Right’-‘Right Most’ parsing algorithm with lookahead capability is introduced. The 'look-ahead' capability in the LR parsing algorithm is the major contribution of this paper. The practicality of the proposed algorithm is substantiated by the parser implementation of the Context Free Grammar (CFG) of an already proposed programming language 'State Controlled Object Oriented Programming' (SCOOP). SCOOP’s Context Free Grammar has 125 productions and 192 item sets. This algorithm parses SCOOP while the grammar requires to ‘look ahead’ the input symbols due to action conflicts in its parsing table. Proposed LR parsing algorithm with lookahead capability can be viewed as an optimization of ‘Simple Left to Right’-‘Right Most’ (SLR) parsing algorithm.

Keywords: left to right-right most parsing, syntax analysis, bottom-up parsing algorithm

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Authors: Mahmoud Nadir, Adel Ghenaiet

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The main purpose of this study is the thermodynamic modeling, the parametric analysis, and the optimization of three pressure level reheat HRSG (Heat Recovery Steam Generator) using PSO method (Particle Swarm Optimization). In this paper, a parametric analysis followed by a thermodynamic optimization is presented. The chosen objective function is the specific work of the steam cycle that may be, in the case of combined cycle (CC), a good criterion of thermodynamic performance analysis, contrary to the conventional steam turbines in which the thermal efficiency could be also an important criterion. The technologic constraints such as maximal steam cycle temperature, minimal steam fraction at steam turbine outlet, maximal steam pressure, minimal stack temperature, minimal pinch point, and maximal superheater effectiveness are also considered. The parametric analyses permitted to understand the effect of design parameters and the constraints on steam cycle specific work variation. PSO algorithm was used successfully in HRSG optimization, knowing that the achieved results are in accordance with those of the previous studies in which genetic algorithms were used. Moreover, this method is easy to implement comparing with the other methods.

Keywords: combined cycle, HRSG thermodynamic modeling, optimization, PSO, steam cycle specific work

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Authors: P. Jayashree, S. Rajkumar

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With social networks and smart devices generating a multitude of data, effective data management is the need of the hour for networks and cloud applications. Some applications need effective storage while some other applications need effective communication over networks and data reduction comes as a handy solution to meet out both requirements. Most of the data compression techniques are based on data statistics and may result in either lossy or lossless data reductions. Though lossy reductions produce better compression ratios compared to lossless methods, many applications require data accuracy and miniature details to be preserved. A variety of data compression algorithms does exist in the literature for different forms of data like text, image, and multimedia data. In the proposed work, a generic progressive compression algorithm, based on probabilistic encoding, called PEINS is projected as an enhancement over irrational number stored coding technique to cater to storage issues of increasing data volumes as a cost effective solution, which also offers data security as a secondary outcome to some extent. The proposed work reveals cost effectiveness in terms of better compression ratio with no deterioration in compression time.

Keywords: compression ratio, generic compression, irrational number storage, probabilistic encoding

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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Authors: Muhammad Ather Nadeem, Bilal Ahmad Khan, Tasawer Abbas

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Nanoherbicides utilize nanotechnology to enhance the delivery of biological or chemical herbicides using combinations of nanomaterials. The aim of this research was to examine the efficacy of chitosan nanoparticles containing MCPA herbicide as a potential eco-friendly alternative for weed control in wheat crops. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and ultraviolet absorbance were used to analyze the developed nanoparticles. The SEM analysis indicated that the average size of the particles was 35 nm, forming clusters with a porous structure. Both nanoparticles of fluroxyper + MCPA exhibited maximal absorption peaks at a wavelength of 320 nm. The compound fluroxyper +MCPA has a strong peak at a 2θ value of 30.55°, which correlates to the 78 plane of the anatase phase. The weeds, including Chenopodium album, Lathyrus aphaca, Angalis arvensis, and Melilotus indica, were sprayed with the nanoparticles while they were in the third or fourth leaf stage. There were seven distinct dosages used: doses (D0 (Check weeds), D1 (Recommended dose of traditional herbicide, D2 (Recommended dose of Nano-herbicide (NPs-H)), D3 (NPs-H with 05-fold lower dose), D4 ((NPs-H) with 10-fold lower dose), D5 (NPs-H with 15-fold lower dose), and D6 (NPs-H with 20-fold lower dose)). The chitosan-based nanoparticles of MCPA at the prescribed dosage of conventional herbicide resulted in complete death and visual damage, with a 100% fatality rate. The dosage that was 5-fold lower exhibited the lowest levels of plant height (3.95 cm), chlorophyll content (5.63%), dry biomass (0.10 g), and fresh biomass (0.33 g) in the broad-leaved weed of wheat. The herbicide nanoparticles, when used at a dosage 10-fold lower than that of conventional herbicides, had a comparable impact on the prescribed dosage. Nano-herbicides have the potential to improve the efficiency of standard herbicides by increasing stability and lowering toxicity.

Keywords: mortality, visual injury, chlorophyl contents, chitosan-based nanoparticles

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Authors: Hadeel Sulaiman Alamri, Mohamed Maher Ben Ismail, Ouiem Bchir

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Abstract— The continued advances in Artificial Intelligence (AI) and Machine Learning (ML) have boosted the interest of tax authorities in developing smart solutions as efficient alternatives to their actual fraud detection mechanisms. In particular, the real estate data collected by the administrations promoted the efforts to develop advanced analytics models aimed at detecting fraudulent real estate transactions. Specifically, supervised and unsupervised Machine Learning techniques have been associated with the available large datasets to improve overall taxpayer compliance. This research introduces a machine-learning approach intended to classify land and building prices in Saudi Arabia. Specifically, it intends to group real estate transactions reported into homogeneous groups based on relevant features. Moreover, the proposed solution classifies the lands and buildings prices in Saudi city, neighborhood, and schema. In fact, the outcomes of the clustering task are fed into a supervised machine learning process to categorize future real estate transactions into “Fair”, “Under-valued” or “Over-valued” classes. In particular, the experimental findings indicate that associating clustering algorithms with Random Forest (RF) model yields an accuracy of 99%.

Keywords: classification, clustering, machine learning, real estate price

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Authors: Monica Gonzalez Machorro

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Dementia is hard to diagnose because of the lack of early physical symptoms. Early dementia recognition is key to improving the living condition of patients. Speech technology is considered a valuable biomarker for this challenge. Recent works have utilized conventional acoustic features and machine learning methods to detect dementia in speech. BERT-like classifiers have reported the most promising performance. One constraint, nonetheless, is that these studies are either based on human transcripts or on transcripts produced by automatic speech recognition (ASR) systems. This research contribution is to explore a method that does not require transcriptions to detect early Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This is achieved by fine-tuning a pre-trained ASR model for the downstream early AD and MCI tasks. To do so, a subset of the thoroughly studied Pitt Corpus is customized. The subset is balanced for class, age, and gender. Data processing also involves cropping the samples into 10-second segments. For comparison purposes, a baseline model is defined by training and testing a Random Forest with 20 extracted acoustic features using the librosa library implemented in Python. These are: zero-crossing rate, MFCCs, spectral bandwidth, spectral centroid, root mean square, and short-time Fourier transform. The baseline model achieved a 58% accuracy. To fine-tune HuBERT as a classifier, an average pooling strategy is employed to merge the 3D representations from audio into 2D representations, and a linear layer is added. The pre-trained model used is ‘hubert-large-ls960-ft’. Empirically, the number of epochs selected is 5, and the batch size defined is 1. Experiments show that our proposed method reaches a 69% balanced accuracy. This suggests that the linguistic and speech information encoded in the self-supervised ASR-based model is able to learn acoustic cues of AD and MCI.

Keywords: automatic speech recognition, early Alzheimer’s recognition, mild cognitive impairment, speech impairment

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Authors: G. K. Viswanadh, B. Rajasekhar, G. Venkata Ramana

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Water distribution networks play a vital role in ensuring a reliable supply of clean water to urban areas. However, they face several challenges, including pressure control, pump speed optimization, and burst event detection. This paper combines insights from two studies to address these critical issues in Water distribution networks, focusing on the specific context of Kapra Municipality, India. The first part of this research concentrates on optimizing pressure control and pump speed in complex Water distribution networks. It utilizes the EPANET- MATLAB Toolkit to integrate EPANET functionalities into the MATLAB environment, offering a comprehensive approach to network analysis. By optimizing Pressure Reduce Valves (PRVs) and variable speed pumps (VSPs), this study achieves remarkable results. In the Benchmark Water Distribution System (WDS), the proposed PRV optimization algorithm reduces average leakage by 20.64%, surpassing the previous achievement of 16.07%. When applied to the South-Central and East zone WDS of Kapra Municipality, it identifies PRV locations that were previously missed by existing algorithms, resulting in average leakage reductions of 22.04% and 10.47%. These reductions translate to significant daily Water savings, enhancing Water supply reliability and reducing energy consumption. The second part of this research addresses the pressing issue of burst event detection and localization within the Water Distribution System. Burst events are a major contributor to Water losses and repair expenses. The study employs wireless sensor technology to monitor pressure and flow rate in real time, enabling the detection of pipeline abnormalities, particularly burst events. The methodology relies on transient analysis of pressure signals, utilizing Cumulative Sum and Wavelet analysis techniques to robustly identify burst occurrences. To enhance precision, burst event localization is achieved through meticulous analysis of time differentials in the arrival of negative pressure waveforms across distinct pressure sensing points, aided by nodal matrix analysis. To evaluate the effectiveness of this methodology, a PVC Water pipeline test bed is employed, demonstrating the algorithm's success in detecting pipeline burst events at flow rates of 2-3 l/s. Remarkably, the algorithm achieves a localization error of merely 3 meters, outperforming previously established algorithms. This research presents a significant advancement in efficient burst event detection and localization within Water pipelines, holding the potential to markedly curtail Water losses and the concomitant financial implications. In conclusion, this combined research addresses critical challenges in Water distribution networks, offering solutions for optimizing pressure control, pump speed, burst event detection, and localization. These findings contribute to the enhancement of Water Distribution System, resulting in improved Water supply reliability, reduced Water losses, and substantial cost savings. The integrated approach presented in this paper holds promise for municipalities and utilities seeking to improve the efficiency and sustainability of their Water distribution networks.

Keywords: pressure reduce valve, complex networks, variable speed pump, wavelet transform, burst detection, CUSUM (Cumulative Sum), water pipeline monitoring

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Authors: Jorge Lus Nisperuza Toledo, Juan Pablo Rubio Ospina, Daniel Santiago Umana, Hector Alejandro Alvarez

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This work develops and implements computational codes to calculate the optimal launch trajectories for sending a probe from the earth to different planets of the Solar system, making use of trajectories of the Hohmann and No-Hohmann type and gravitational assistance in intermediate steps. Specifically, the orbital elements, the graphs and the dynamic simulations of the trajectories for sending a probe from the Earth towards the planets Mercury, Venus, Mars, Jupiter, and Saturn are obtained. A detailed study was made of the state vectors of the position and orbital velocity of the considered planets in order to determine the optimal trajectories of the probe. For this purpose, computer codes were developed and implemented to obtain the orbital elements of the Mariner 10 (Mercury), Magellan (Venus), Mars Global Surveyor (Mars) and Voyager 1 (Jupiter and Saturn) missions, as an exercise in corroborating the algorithms. This exercise gives validity to computational codes, allowing to find the orbital elements and the simulations of trajectories of three future interplanetary missions with specific launch windows.

Keywords: gravitational assistance, Hohmann’s trajectories, interplanetary mission, orbital elements

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Authors: Jyoti Singh, Swati Dubey, Mukta Singh, R. P. Singh

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The freshwater microalgae Chlorella sp. is alluring considerable interest as a source for biofuel production due to its fast growth rate and high lipid content. Under nitrogen limited conditions, they can accumulate significant amounts of lipids. Thus, it is important to gain insight into the molecular mechanism of their lipid metabolism. In this study under nitrogen limited conditions, regular pattern of growth characteristics lipid accumulation and gene expression analysis of key regulatory genes of lipid biosynthetic pathway were carried out in microalgae Chlorella sp 3. Our results indicated that under nitrogen limited conditions there is a significant increase in the lipid content and lipid productivity, achieving 44.21±2.64 % and 39.34±0.66 mg/l/d at the end of the cultivation, respectively. Time-course transcript patterns of lipid biosynthesis genes i.e. acetyl coA carboxylase (accD) and diacylglycerol acyltransferase (dgat) showed that during late log phase of microalgae Chlorella sp.3 both the genes were significantly up regulated as compared to early log phase. Moreover, the transcript level of the dgat gene is two-fold higher than the accD gene. The results suggested that both the genes responded sensitively to the nitrogen limited conditions during the late log stage, which proposed their close relevance to lipid biosynthesis. Further, this transcriptome data will be useful for engineering microalgae species by targeting these genes for genetic modification to improve microalgal biofuel quality and production.

Keywords: biofuel, gene, lipid, microalgae

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Authors: Huang Haoxin

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Large dark current at room temperature has long been the major bottleneck that impedes the development of high-performance infrared photodetectors towards miniaturization and integration. Although infrared photodetectors based on layered 2D narrow bandgap semiconductors have shown admirable advantages compared with those based on conventional compounds, which typically suffer from expensive cryogenic operations, it is still urgent to develop a simple but effective strategy to further reduce the dark current. Herein, a tellurium (Te) based infrared photodetector is reported with a specifically designed asymmetric electrical contact area. The deliberately introduced asymmetric electrical contact raises the electric field intensity difference in the Te channel near the drain and the source electrodes, resulting in spontaneous asymmetric carrier diffusion under global infrared light illumination under zero bias. Specifically, the Te-based photodetector presents promising detector performance at room temperature, including a low dark current of≈1 nA, an ultrahigh photocurrent/dark current ratio of 1.57×10⁴, a high specific detectivity (D*) of 3.24×10⁹ Jones, and relatively fast response speed of ≈720 μs at zero bias. The results prove that the simple design of asymmetric electrical contact areas can provide a promising solution to high-performance 2D semiconductor-based infrared photodetectors working at room temperature.

Keywords: asymmetrical contact, tellurium, dark current, infrared photodetector, sensitivity

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Authors: Sajjad Baghernezhad

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Meta-heuristic and hybrid algorithms have high adeer in modeling medical problems. In this study, a neural network was used to predict covid-19 among high-risk and low-risk patients. This study was conducted to collect the applied method and its target population consisting of 550 high-risk and low-risk patients from the Kerman University of medical sciences medical center to predict the coronavirus. In this study, the memetic algorithm, which is a combination of a genetic algorithm and a local search algorithm, has been used to update the weights of the neural network and develop the accuracy of the neural network. The initial study showed that the accuracy of the neural network was 88%. After updating the weights, the memetic algorithm increased by 93%. For the proposed model, sensitivity, specificity, positive predictivity value, value/accuracy to 97.4, 92.3, 95.8, 96.2, and 0.918, respectively; for the genetic algorithm model, 87.05, 9.20 7, 89.45, 97.30 and 0.967 and for logistic regression model were 87.40, 95.20, 93.79, 0.87 and 0.916. Based on the findings of this study, neural network models have a lower error rate in the diagnosis of patients based on individual variables and vital signs compared to the regression model. The findings of this study can help planners and health care providers in signing programs and early diagnosis of COVID-19 or Corona.

Keywords: COVID-19, decision support technique, neural network, genetic algorithm, memetic algorithm

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Authors: Malgorzata J. Rybak-Smith, Benedicte Thiebaut, Simon Johnson, Peter Bishop, Helen E. Townley

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Gadolinium doped titania (TiO2:Gd) nanoparticles (NPs) can be activated by X-ray radiation to generate Reactive Oxygen Species (ROS), which can be effective in killing cancer cells. As such, treatment with these NPs can be used to enhance the efficacy of conventional radiotherapy. Incorporation of the NPs in to tumour tissue will permit the extension of radiotherapy to currently untreatable tumours deep within the body, and also reduce damage to neighbouring healthy cells. In an attempt to find a fast and scalable method for the synthesis of the TiO2:Gd NPs, the use of Flame Spray Pyrolysis (FSP) was investigated. A series of TiO2 NPs were generated with 1, 2, 5 and 7 mol% gadolinium dopant. Post-synthesis, the TiO2:Gd NPs were silica-coated to improve their biocompatibility. Physico-chemical characterisation was used to determine the size and stability in aqueous suspensions of the NPs. All analysed TiO2:Gd NPs were shown to have relatively high photocatalytic activity. Furthermore, the FSP synthesized silica-coated TiO2:Gd NPs generated enhanced ROS in chemico. Studies on rhabdomyosarcoma (RMS) cell lines (RD & RH30) demonstrated that in the absence of irradiation all TiO2:Gd NPs were inert. However, application of TiO2:Gd NPs to RMS cells, followed by irradiation, showed a significant decrease in cell proliferation. Consequently, our studies showed that the X-ray-activatable TiO2:Gd NPs can be prepared by a high-throughput scalable technique to provide a novel and affordable anticancer therapy.

Keywords: cancer, gadolinium, ROS, titania nanoparticles, X-ray

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Authors: Yilin Liao, Hewen Li, Paula McConvey

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Speed skaters often face a risk of concussion when they fall on the ice floor and impact crash mats during practices and competitive races. Several variables, including those related to the skater, the crash mat, and the impact position (body side/head/feet impact), are believed to influence the severity of the skater's concussion. While computer simulation modeling can be employed to analyze these accidents, the simulation process is time-consuming and does not provide rapid information for coaches and teams to assess the skater's injury risk in competitive events. This research paper promotes the exploration of the feasibility of using AI techniques for evaluating skater’s potential concussion severity, and to develop a fast concussion prediction tool using artificial neural networks to reduce the risk of treatment delays for injured skaters. The primary data is collected through virtual tests and physical experiments designed to simulate skater-mat impact. It is then analyzed to identify patterns and correlations; finally, it is used to train and fine-tune the artificial neural networks for accurate prediction. The development of the prediction tool by employing machine learning strategies contributes to the application of AI methods in sports science and has theoretical involvements for using AI techniques in predicting and preventing sports-related injuries.

Keywords: artificial neural networks, concussion, machine learning, impact, speed skater

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Authors: Meziane Rachid, Boufala Seddik, Hamzi Amar, Amara Mohamed

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Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented.

Keywords: reliability optimization, harmony search optimization (HSA), universal generating function (UMGF)

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Authors: Ghulam Haider Haidaree, Nsenda Lukumwena

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Today the world of research enjoys abundant data, available in virtually any field, technology, science, and business, politics, etc. This is commonly referred to as big data. This offers a great deal of precision and accuracy, supportive of an in-depth look at any decision-making process. When and if well used, Big Data affords its users with the opportunity to produce substantially well supported and good results. This paper leans extensively on big data to investigate possible smart solutions to urban mobility and related issues, namely road traffic accidents, its casualties, and fatalities based on multiple factors, including age, gender, location occurrences of accidents, etc. Multiple technologies were used in combination to produce an Information Communication Technology (ICT) based solution with embedded technology. Those technologies include principally Geographic Information System (GIS), Orange Data Mining Software, Bayesian Statistics, to name a few. The study uses the Leeds accident 2016 to illustrate the thinking process and extracts thereof a model that can be tested, evaluated, and replicated. The authors optimistically believe that the proposed model will significantly and smartly help to flatten the curve of road traffic accidents in the fast-growing population densities, which increases considerably motor-based mobility.

Keywords: accident factors, geographic information system, information communication technology, mobility

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Authors: Uttam Kumar Ghorai, Madhupriya Samanta, Subhajit Saha, Swati Das, Nilesh Mazumder, Kalyan Kumar Chattopadhyay

Abstract:

Organic semiconductors have gained potential interest in the last few decades for their significant contributions in the various fields such as solar cell, non-volatile memory devices, field effect transistors and light emitting diodes etc. The most important advantages of using organic materials are mechanically flexible, light weight and low temperature depositing techniques. Recently with the advancement of nanoscience and technology, one dimensional organic and inorganic nanostructures such as nanowires, nanorods, nanotubes have gained tremendous interests due to their very high aspect ratio and large surface area for electron transport etc. Among them, self-assembled organic nanostructures like Copper, Zinc Phthalocyanine have shown good transport property and thermal stability due to their π conjugated bonds and π-π stacking respectively. Field emission properties of inorganic and carbon based nanostructures are reported in literatures mostly. But there are few reports in case of cold cathode emission characteristics of organic semiconductor nanostructures. In this work, the authors report the field emission characteristics of chemically and physically synthesized Copper Phthalocyanine (CuPc) nanostructures such as nanowires, nanotubes and nanotips. The as prepared samples were characterized by X-Ray diffraction (XRD), Ultra Violet Visible Spectrometer (UV-Vis), Fourier Transform Infra-red Spectroscopy (FTIR), and Field Emission Scanning Electron Microscope (FESEM) and Transmission Electron Microscope (TEM). The field emission characteristics were measured in our home designed field emission set up. The registered turn-on field and local field enhancement factor are found to be less than 5 V/μm and greater than 1000 respectively. The field emission behaviour is also stable for 200 minute. The experimental results are further verified by theoretically using by a finite displacement method as implemented in ANSYS Maxwell simulation package. The obtained results strongly indicate CuPc nanostructures to be the potential candidate as an electron emitter for field emission based display device applications.

Keywords: organic semiconductor, phthalocyanine, nanowires, nanotubes, field emission

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Authors: Purnendu Bose, Jyoti Kainthola

Abstract:

Due to recent advances in illumination technology, artificially illuminated algal-bacterial photo bioreactors are now a potentially feasible option for simultaneous and comprehensive organic carbon and nutrients removal from secondary treated domestic sewage. The experiments described herein were designed to determine the extent of nutrient uptake in photo bioreactors through algal assimilation. Accordingly, quasi steady state data on algal photo bioreactor performance was obtained under 20 different conditions. Results indicated that irrespective of influent N and P levels, algal biomass recycling resulted in superior performance of algal photo bioreactors in terms of both N and P removals. Further, both N and P removals were positively related to the growth of algal biomass in the reactor. Conditions in the reactor favouring greater algal growth also resulted in greater N and P removals. N and P removals were adversely impacted in reactors with low algal concentrations due to the inability of the algae to grow fast enough under the conditions provided. Increasing algal concentrations in reactors over a certain threshold value through higher algal biomass recycling was also not fruitful, since algal growth slowed under such conditions due to reduced light availability due to algal ‘self-shading’. It was concluded that N removals greater than 80% at high influent N concentrations is not possible with the present reactor configuration. Greater than 80% N removals may however be possible in similar reactors if higher light intensity is provided. High P removal is possible only if the influent N: P ratio in the reactor is aligned closely with the algal stoichiometric requirements for P.

Keywords: nutrients, algae, photo, bioreactor

Procedia PDF Downloads 218

Authors: Chun-Lang Chang, Chun-Kai Liu

Abstract:

In this study, the patients that have undergone total knee replacement surgery from the 2010 National Health Insurance database were adopted as the study participants. The important factors were screened and selected through literature collection and interviews with physicians. Through the Cross Entropy Method (CE), Genetic Algorithm Logistic Regression (GALR), and Particle Swarm Optimization (PSO), the weights of the factors were obtained. In addition, the weights of the respective algorithms, coupled with the Excel VBA were adopted to construct the Case Based Reasoning (CBR) system. The results through statistical tests show that the GALR and PSO produced no significant differences, and the accuracy of both models were above 97%. Moreover, the area under the curve of ROC for these two models also exceeded 0.87. This study shall serve as a reference for medical staff as an assistance for clinical assessment of infections in order to effectively enhance medical service quality and efficiency, avoid unnecessary medical waste, and substantially contribute to resource allocations in medical institutions.

Keywords: Case Based Reasoning, Cross Entropy Method, Genetic Algorithm Logistic Regression, Particle Swarm Optimization, Total Knee Replacement Surgery

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Authors: Islam Khalil, Amr El-Kadi

Abstract:

With the increased use of web technologies, microservices, and Application Programming Interface (API) for integration between systems, and with the development of containerization of services on the operating system level as a method of isolating system execution and for easing the deployment and scaling of systems, there is a growing need as well as opportunities for providing platforms that improve the security of such services. In our work, we propose an architecture for a containerization platform that utilizes various concepts derived from the human immune system. The goal of the proposed containerization platform is to introduce the concept of slowing down or throttling suspected malicious digital pathogens (intrusions) to reduce their damage footprint while providing more opportunities for forensic inspection of suspected pathogens in addition to the ability to snapshot, rollback, and recover from possible damage. The proposed platform also leverages existing intrusion detection algorithms by integrating and orchestrating their cooperative operation for more effective intrusion detection. We show how this model reduces the damage footprint of intrusions and gives a greater time window for forensic investigation. Moreover, during our experiments, our proposed platform was able to uncover unintentional system design flaws that resulted in internal DDoS-like attacks by submodules of the system itself rather than external intrusions.

Keywords: containers, human immunity, intrusion detection, security, web services

Procedia PDF Downloads 100

Authors: S. S. R. Shojaei, S. Kord Afshari

Abstract:

The study was initiated to compare the production and defecation of Eimerial oocysts of two internationally reputed broiler strains under the local environmental and management conditions. 40 one-day old male chickens of Arbor Acres strain and ROSS strain (20 chicks from each strain) used in this study and were divided randomly into four control and challenge groups. Feed and water were provided for ad libitum consumption. At 15 d of age, chickens of challenge groups (from each strain) were individually inoculated with a mixture of 50000 of sporulated oocysts of 4 species including of E. acervulina (20%), E. maxima (40%), E. tenella (25%) and E. necatrix (15%) and also from the fourth day after Eimerial challenge, faecal droppings (litter samples) were collected 10 days consecutively for counting oocyst per gram (OPG). The results indicated that in the challenge groups, there was an increasing process of OPG in days of 4 to 7 post challenging and the pick level of OPG was seen at seventh day after challenging. From day 8 to 9, decreasing of OPG was happened. This decreasing continues with mild, fast and mild process to day of 13. Respectively and totally the average of OPG in the Arbor Acres group was lower than the group Ross in all days post inoculation and this difference was significant according to t-test. According to the obtained results in this study and since oocyst index almost always is considered as one of the most important indicators for coccidiosis evaluation, it can be realized that in the same surveillance condition the regarding the severity evaluation of coccidiosis, Arbor Acres strain broilers shed less oocysts than Ross strain broilers.

Keywords: arbor acres, ross, coccidiosis, OPG

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Authors: Shreyas Srinivas Rangan, Jurgis Porins

Abstract:

The internet traffic has increased exponentially due to the high demand for data rates by the users, and the constantly increasing metro networks and access networks are focused on improving the maximum transmit distance of the long-reach optical networks. One of the common methods to improve the maximum transmit distance of the long-reach optical networks at the component level is to use broadband optical amplifiers. The Erbium Doped Fiber Amplifier (EDFA) provides high amplification with low noise figure but due to the characteristics of EDFA, its operation is limited to C-band and L-band. In contrast, the Raman amplifier exhibits a wide amplification spectrum, and negative noise figure values can be achieved. To obtain such results, high powered pumping sources are required. Operating Raman amplifiers with such high-powered optical sources may cause fire hazards and it may damage the optical system. In this paper, we implement a hybrid optical amplifier configuration. EDFA and Raman amplifiers are used in this hybrid setup to combine the advantages of both EDFA and Raman amplifiers to improve the reach of the system. Using this setup, we analyze the maximum transmit distance of the network by obtaining a correlation diagram between the length of the single-mode fiber (SMF) and the Bit Error Rate (BER). This hybrid amplifier configuration is implemented in a Wavelength Division Multiplexing (WDM) system with a BER of 10⁻⁹ by using NRZ modulation format, and the gain uniformity noise ratio (signal-to-noise ratio (SNR)), the efficiency of the pumping source, and the optical signal gain efficiency of the amplifier are studied experimentally in a mathematical modelling environment. Numerical simulations were implemented in RSoft OptSim simulation software based on the nonlinear Schrödinger equation using the Split-Step method, the Fourier transform, and the Monte Carlo method for estimating BER.

Keywords: Raman amplifier, erbium doped fibre amplifier, bit error rate, hybrid optical amplifiers

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Authors: Rakesh Choudhary, Ajay Kumar, Deepak Sharma

Abstract:

The main goal of this paper is to investigate the challenges and benefits of IoT integration in biogas production. This overview explains how the inclusion of IoT can enhance biogas production efficiency. Therefore, such collected data can be explored by advanced analytics, including Artificial intelligence (AI) and Machine Learning (ML) algorithms, consequently improving bio-energy processes. To boost biogas generation efficiency, this report examines the use of IoT devices for real-time data collection on key parameters, e.g., pH, temperature, gas composition, and microbial growth. Real-time monitoring through big data has made it possible to detect diverse, complex trends in the process of producing biogas. The Informed by advanced analytics can also help in improving bio-energy production as well as optimizing operational conditions. Moreover, IoT allows remote observation, control and management, which decreases manual intervention needed whilst increasing process effectiveness. Such a paradigm shift in the incorporation of IoT technologies into biogas production systems helps to achieve higher productivity levels as well as more practical biomass quality biomethane through real-time monitoring-based proactive decision-making, thus driving continuous performance improvement.

Keywords: internet of things, biogas, renewable energy, sustainability, anaerobic digestion, real-time monitoring, optimization

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