Search results for: Gaussian Conditional Random Field

Authors: Kuan-Wei Chen, Chien-Wei Chen, Tai-Ling Chang, Nan-Cheng Chen, Ching-Lin Hsieh, Gong-Hong Lin

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Background and Purposes: Deficits in sustained attention are common in patients with schizophrenia. Such impairment can limit patients to effectively execute daily activities and affect the efficacy of rehabilitation. The aims of this study were to examine the test-retest agreement, random measurement error, and practice effect of the Continuous Performance Test-Identical Pairs (CPT-IP) (a commonly used sustained attention test) in patients with schizophrenia. The results can provide empirical evidence for clinicians and researchers to apply a sustained attention test with sound psychometric properties in schizophrenia patients. Methods: We recruited patients with chronic schizophrenia to be assessed twice with 1 week interval using CPT-IP. The intra-class correlation coefficient (ICC) was used to examine the test-retest agreement. The percentage of minimal detectable change (MDC%) was used to examine the random measurement error. Moreover, the standardized response mean (SRM) was used to examine the practice effect. Results: A total of 56 patients participated in this study. Our results showed that the ICC was 0.82, MDC% was 47.4%, and SRMs were 0.36 for the CPT-IP. Conclusion: Our results indicate that CPT-IP has acceptable test-retests agreement, substantial random measurement error, and small practice effect in patients with schizophrenia. Therefore, to avoid overestimating patients’ changes in sustained attention, we suggest that clinicians interpret the change scores of CPT-IP conservatively in their routine repeated assessments.

Keywords: schizophrenia, sustained attention, CPT-IP, reliability

Procedia PDF Downloads 279

Authors: Adi A. Maaita, Hamza A. A. Al Sewadi

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One of the crucial parameters of digital cryptographic systems is the selection of the keys used and their distribution. The randomness of the keys has a strong impact on the system’s security strength being difficult to be predicted, guessed, reproduced or discovered by a cryptanalyst. Therefore, adequate key randomness generation is still sought for the benefit of stronger cryptosystems. This paper suggests an algorithm designed to generate and test pseudo random number sequences intended for cryptographic applications. This algorithm is based on mathematically manipulating a publically agreed upon information between sender and receiver over a public channel. This information is used as a seed for performing some mathematical functions in order to generate a sequence of pseudorandom numbers that will be used for encryption/decryption purposes. This manipulation involves permutations and substitutions that fulfills Shannon’s principle of “confusion and diffusion”. ASCII code characters wereutilized in the generation process instead of using bit strings initially, which adds more flexibility in testing different seed values. Finally, the obtained results would indicate sound difficulty of guessing keys by attackers.

Keywords: cryptosystems, information security agreement, key distribution, random numbers

Procedia PDF Downloads 245

Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Arnab Roy, P. S. Anil Kumar

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Hall bar magnetic field sensors based on planar hall effect were fabricated from permalloy (Ni¬80Fe20) thin films grown by pulsed laser ablation. As large as 400% planar Hall voltage change was observed for a magnetic field sweep within ±4 Oe, a value comparable with present day TMR sensors at room temperature. A very large planar Hall sensitivity of 1200 Ω/T was measured close to switching fields, which was not obtained so far apart from 2DEG Hall sensors. In summary, a highly sensitive low magnetic field sensor has been constructed which has the added advantage of simple architecture, good signal to noise ratio and robustness.

Keywords: planar hall effect, permalloy, NiFe, pulsed laser ablation, low magnetic field sensor, high sensitivity magnetic field sensor

Procedia PDF Downloads 496

Authors: Sheng-Bo Zhang, Huan-Hao Zhang, Zhi-Hua Chen, Chun Zheng

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In this paper, the Richtmyer-Meshkov instability has been studied numerically by using the high-resolution Roe scheme based on the two-dimensional unsteady Euler equation, which was caused by the interaction between shock wave and the helium circular light gas cylinder with different component distributions. The numerical results further discuss the deformation process of the gas cylinder, the wave structure of the flow field and quantitatively analyze the characteristic dimensions (length, height, and central axial width) of the gas cylinder, the volume compression ratio of the cylinder over time. In addition, the flow mechanism of shock-driven interface gas mixing is analyzed from multiple perspectives by combining it with the flow field pressure, velocity, circulation, and gas mixing rate. Then the effects of different initial component distribution conditions on interface instability are investigated. The results show when the diffusion interface transit to the sharp interface, the reflection coefficient gradually increases on both sides of the interface. When the incident shock wave interacts with the cylinder, the transmission of the shock wave will transit from conventional transmission to unconventional transmission. At the same time, the reflected shock wave is gradually strengthened, and the transmitted shock wave is gradually weakened, which leads to an increase in the Richtmyer-Meshkov instability. Moreover, the Atwood number on both sides of the interface also increases as the diffusion interface transit to the sharp interface, which leads to an increase in the Rayleigh-Taylor instability and the Kelvin-Helmholtz instability. Therefore, the increase in instability will lead to an increase the circulation, resulting in an increase in the growth rate of gas mixing rate.

Keywords: shock wave, He light cylinder, Richtmyer-Meshkov instability, Gaussian distribution

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Authors: Wesley Teskey, Vedran Glavas, Julian Wegener

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Generating battery cathode microstructures is an important area of research, given the proliferation of the use of automotive batteries. Currently, finite element analysis (FEA) is often used for simulations of battery cathode microstructures before physical batteries can be manufactured and tested to verify the simulation results. Unfortunately, a key drawback of using FEA is that this method of simulation is very slow in terms of computational runtime. Generative AI offers the key advantage of speed when compared to FEA, and because of this, generative AI is capable of evaluating very large numbers of candidate microstructures. Given AI generated candidate microstructures, a subset of the promising microstructures can be selected for further validation using FEA. Leveraging the speed advantage of AI allows for a better final microstructural selection because high speed allows for the evaluation of many more candidate microstructures. For the approach presented, battery cathode 3D candidate microstructures are generated using Gaussian Mixture Models (GMMs) and pix2pix. This approach first uses GMMs to generate a population of spheres (representing the “active material” of the cathode). Once spheres have been sampled from the GMM, they are placed within a microstructure. Subsequently, the pix2pix sweeps over the 3D microstructure (iteratively) slice by slice and adds details to the microstructure to determine what portions of the microstructure will become electrolyte and what part of the microstructure will become binder. In this manner, each subsequent slice of the microstructure is evaluated using pix2pix, where the inputs into pix2pix are the previously processed layers of the microstructure. By feeding into pix2pix previously fully processed layers of the microstructure, pix2pix can be used to ensure candidate microstructures represent a realistic physical reality. More specifically, in order for the microstructure to represent a realistic physical reality, the locations of electrolyte and binder in each layer of the microstructure must reasonably match the locations of electrolyte and binder in previous layers to ensure geometric continuity. Using the above outlined approach, a 10x to 100x speed increase was possible when generating candidate microstructures using AI when compared to using a FEA only approach for this task. A key metric for evaluating microstructures was the battery specific power value that the microstructures would be able to produce. The best generative AI result obtained was a 12% increase in specific power for a candidate microstructure when compared to what a FEA only approach was capable of producing. This 12% increase in specific power was verified by FEA simulation.

Keywords: finite element analysis, gaussian mixture models, generative design, Pix2Pix, structural design

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Authors: Beng Yew Low, Cher Liang Cha, Cheng Yong Teoh

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Due to a lack of continual assessment or grade related data, identifying first-year engineering students in a polytechnic education at risk of failing is challenging. Our experience over the years tells us that there is no strong correlation between having good entry grades in Mathematics and the Sciences and excelling in hardcore engineering subjects. Hence, identifying students at risk of failure cannot be on the basis of entry grades in Mathematics and the Sciences alone. These factors compound the difficulty of early identification and intervention. This paper describes the development of a predictive analytics model in the early detection of students at risk of failing and evaluates its effectiveness. Data from continual assessments conducted in term one, supplemented by data of student psychological profiles such as interests and study habits, were used. Three classification techniques, namely Logistic Regression, K Nearest Neighbour, and Random Forest, were used in our predictive model. Based on our findings, Random Forest was determined to be the strongest predictor with an Area Under the Curve (AUC) value of 0.994. Correspondingly, the Accuracy, Precision, Recall, and F-Score were also highest among these three classifiers. Using this Random Forest Classification technique, students at risk of failure could be identified at the end of term one. They could then be assigned to a Learning Support Programme at the beginning of term two. This paper gathers the results of our findings. It also proposes further improvements that can be made to the model.

Keywords: continual assessment, predictive analytics, random forest, student psychological profile

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Authors: Halil Ibrahim Demir, Caner Erden, Mumtaz Ipek, Ozer Uygun

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Traditionally, the three important manufacturing functions, which are process planning, scheduling and due-date assignment, are performed separately and sequentially. For couple of decades, hundreds of studies are done on integrated process planning and scheduling problems and numerous researches are performed on scheduling with due date assignment problem, but unfortunately the integration of these three important functions are not adequately addressed. Here, the integration of these three important functions is studied by using genetic, random-genetic hybrid, simulated annealing, random-simulated annealing hybrid and random search techniques. As well, the importance of the integration of these three functions and the power of meta-heuristics and of hybrid heuristics are studied.

Keywords: process planning, weighted scheduling, weighted due-date assignment, genetic search, simulated annealing, hybrid meta-heuristics

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Authors: Yan Bachtiar Muslih, Hangga Wijaya, Trio Fani, Putri Agustin

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Demand of Energy in Indonesia always increases 5-6% a year, but production of conventional energy always decreases 3-5% a year, it means that conventional energy in 20-40 years ahead will not able to complete all energy demand in Indonesia, one of the solve way is using unconventional energy that is gas hydrate, gas hydrate is gas that form by biogenic process, gas hydrate stable in condition with extremely depth and low temperature, gas hydrate can form in two condition that is in pole condition and in deep-sea condition, wherein this research will focus in gas hydrate that association with methane form methane hydrate in deep-sea condition and usually form in depth between 150-2000 m, this research will focus in process of methane hydrate formation that is biogenic process and the important role of deep-sea sediment so can produce accumulation of methane hydrate, methane hydrate usually will be accumulated in find sediment in deep-sea environment with condition high-pressure and low-temperature this condition too usually make methane hydrate change into white nodule, methodology of this research is geology field work and laboratory analysis, from geology field work will get sample data consist of 10-15 samples from Kerek Formation outcrops as random for imagine the condition of deep-sea environment that influence the methane hydrate formation and also from geology field work will get data of measuring stratigraphy in outcrops Kerek Formation too from this data will help to imagine the process in deep-sea sediment like energy flow, supply sediment, and etc, and laboratory analysis is activity to analyze all data that get from geology field work, the result of this research can used to exploration activity of methane hydrate in another prospect deep-sea environment in Indonesia.

Keywords: methane hydrate, deep-sea sediment, kerek formation, sub-basin of kendeng, central java, Indonesia

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Authors: Atif Zafar, Fan Haijun

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A sequence of different Reservoir Engineering methods and tools in reservoir characterization and field development are presented in this paper. The real data of Jin Gas Field of L-Basin of Pakistan is used. The basic concept behind this work is to enlighten the importance of well test analysis in a broader way (i.e. reservoir characterization and field development) unlike to just determine the permeability and skin parameters. Normally in the case of reservoir characterization we rely on well test analysis to some extent but for field development plan, the well test analysis has become a forgotten tool specifically for locations of new development wells. This paper describes the successful implementation of well test analysis in Jin Gas Field where the main uncertainties are identified during initial stage of field development when location of new development well was marked only on the basis of G&G (Geologic and Geophysical) data. The seismic interpretation could not encounter one of the boundary (fault, sub-seismic fault, heterogeneity) near the main and only producing well of Jin Gas Field whereas the results of the model from the well test analysis played a very crucial rule in order to propose the location of second well of the newly discovered field. The results from different methods of well test analysis of Jin Gas Field are also integrated with and supported by other tools of Reservoir Engineering i.e. Material Balance Method and Volumetric Method. In this way, a comprehensive way out and algorithm is obtained in order to integrate the well test analyses with Geological and Geophysical analyses for reservoir characterization and field development. On the strong basis of this working and algorithm, it was successfully evaluated that the proposed location of new development well was not justified and it must be somewhere else except South direction.

Keywords: field development plan, reservoir characterization, reservoir engineering, well test analysis

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Authors: Thota Sai Prakash, B. Yaswanth, Jhade Bhuvaneswar, Marreddy Divakar Reddy, Shyam Ji Gupta

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Across the globe, there are a lot of chronic diseases, and heart disease stands out as one of the most perilous. Sadly, many lives are lost to this condition, even though early intervention could prevent such tragedies. However, identifying heart disease in its initial stages is not easy. To address this challenge, we propose an automated system aimed at predicting the presence of heart disease using advanced techniques. By doing so, we hope to empower individuals with the knowledge needed to take proactive measures against this potentially fatal illness. Our approach towards this problem involves meticulous data preprocessing and the development of predictive models utilizing classification algorithms such as Support Vector Machines (SVM), Decision Tree, and Random Forest. We assess the efficiency of every model based on metrics like accuracy, ensuring that we select the most reliable option. Additionally, we conduct thorough data analysis to reveal the importance of different attributes. Among the models considered, Random Forest emerges as the standout performer with an accuracy rate of 96.04% in our study.

Keywords: support vector machines, decision tree, random forest

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Authors: Matěj Kuc

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This paper compares different types of profitability measures of cooperative banks from two developed regions: the European Union and the United States of America together with Canada. We created balanced dataset of more than 200 cooperative banks covering 2011-2016 period. We made series of tests and run Random Effects estimation on panel data. We found that American and Canadian cooperatives are more profitable in terms of return on assets (ROA) and return on equity (ROE). There is no significant difference in net interest margin (NIM). Our results show that the North American cooperative banks accommodated better to the current market environment.

Keywords: cooperative banking, panel data, profitability measures, random effects

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Authors: Michail Milas, Aikaterini Dafni Tegiou, Nickolas Rigopoulos, Eustathios Giaouris, Zaharias Loannou

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Nanotechnology, a relatively new scientific field, addresses the manipulation of nanoscale materials and devices, which are governed by unique properties, and is applied in a wide range of industries, including food packaging. The incorporation of nanoparticles into polymer matrices used for food packaging is a field that is highly researched today. One such combination is silver nanoparticles with polypropylene. In the present study, the synthesis of the silver nanoparticles was carried out by a natural method. In particular, a ripe banana peel extract was used. This method is superior to others as it stands out for its environmental friendliness, high efficiency and low-cost requirement. In particular, a 1.75 mM AgNO₃ silver nitrate solution was used, as well as a BPE concentration of 1.7% v/v, an incubation period of 48 hours at 70°C and a pH of 4.3 and after its preparation, the polypropylene films were soaked in it. For the PP films, random PP spheres were melted at 170-190°C into molds with 0.8cm diameter. This polymer was chosen as it is suitable for plastic parts and reusable plastic containers of various types that are intended to come into contact with food without compromising its quality and safety. The antimicrobial test against Escherichia coli DFSNB1 and Staphylococcus epidermidis DFSNB4 was performed on the films. It appeared that the films with silver nanoparticles had a reduction, at least 100 times, compared to those without silver nanoparticles, in both strains. The limit of detection is the lower limit of the vertical error lines in the presence of nanoparticles, which is 3.11. The main reasons that led to the adsorption of nanoparticles are the porous nature of polypropylene and the adsorption capacity of nanoparticles on the surface of the films due to hydrophobic-hydrophilic forces. The most significant parameters that contributed to the results of the experiment include the following: the stage of ripening of the banana during the preparation of the plant extract, the temperature and residence time of the nanoparticle solution in the oven, the residence time of the polypropylene films in the nanoparticle solution, the number of nanoparticles inoculated on the films and, finally, the time these stayed in the refrigerator so that they could dry and be ready for antimicrobial treatment.

Keywords: antimicrobial control, banana peel extract, E. coli, natural synthesis, microbe, plant extract, polypropylene films, S.epidermidis, silver nano, random pp

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Samson Henry Dogo, Allan Clark, Elena Kulinskaya

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The objective in meta-analysis is to combine results from several independent studies in order to create generalization and provide evidence based for decision making. But recent studies show that the magnitude of effect size estimates reported in many areas of research finding changed with year publication and this can impair the results and conclusions of meta-analysis. A number of sequential methods have been proposed for monitoring the effect size estimates in meta-analysis. However they are based on statistical theory applicable to fixed effect model (FEM). For random-effects model (REM), the analysis incorporates the heterogeneity variance, tau-squared and its estimation create complications. In this paper proposed the use of Gombay and Serbian (2005) truncated CUSUM-type test with asymptotically valid critical values for sequential monitoring of REM. Simulation results show that the test does not control the Type I error well, and is not recommended. Further work required to derive an appropriate test in this important area of application.

Keywords: meta-analysis, random-effects model, sequential test, temporal changes in effect sizes

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Authors: Constantin Z. Leshan

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Hole Vacuum theory is based on discontinuous spacetime that contains vacuum holes. Vacuum holes can explain gravitation, some laws of quantum mechanics and allow teleportation of matter. All massive bodies emit a flux of holes which curve the spacetime; if we increase the concentration of holes, it leads to length contraction and time dilation because the holes do not have the properties of extension and duration. In the limited case when space consists of holes only, the distance between every two points is equal to zero and time stops - outside of the Universe, the extension and duration properties do not exist. For this reason, the vacuum hole is the only particle in physics capable of describing gravitation using its own properties only. All microscopic particles must 'jump' continually and 'vibrate' due to the appearance of holes (impassable microscopic 'walls' in space), and it is the cause of the quantum behavior. Vacuum holes can explain the entanglement, non-locality, wave properties of matter, tunneling, uncertainty principle and so on. Particles do not have trajectories because spacetime is discontinuous and has impassable microscopic 'walls' due to the simple mechanical motion is impossible at small scale distances; it is impossible to 'trace' a straight line in the discontinuous spacetime because it contains the impassable holes. Spacetime 'boils' continually due to the appearance of the vacuum holes. For teleportation to be possible, we must send a body outside of the Universe by enveloping it with a closed surface consisting of vacuum holes. Since a material body cannot exist outside of the Universe, it reappears instantaneously in a random point of the Universe. Since a body disappears in one volume and reappears in another random volume without traversing the physical space between them, such a transportation method can be called teleportation (or Hole Teleportation). It is shown that Hole Teleportation does not violate causality and special relativity due to its random nature and other properties. Although Hole Teleportation has a random nature, it can be used for colonization of extrasolar planets by the help of the method called 'random jumps': after a large number of random teleportation jumps, there is a probability that the spaceship may appear near a habitable planet. We can create vacuum holes experimentally using the method proposed by Descartes: we must remove a body from the vessel without permitting another body to occupy this volume.

Keywords: border of the Universe, causality violation, perfect isolation, quantum jumps

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Authors: Mohd A. Azizan, Mohd Z. Zali

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It is well established that exposure to vibration has an adverse effect on human health, comfort, and performance. However, there is little quantitative knowledge on performance combined with drowsiness level during vibration exposure. This paper reports a study investigating the influence of vibration amplitude on seated occupant reaction time and drowsiness level. Eighteen male volunteers were recruited for this experiment. Before commencing the experiment, total transmitted acceleration measured at interfaces between the seat pan and seatback to human body was adjusted to become 0.2 ms-2 r.m.s and 0.4 ms-2 r.m.s for each volunteer. Seated volunteers were exposed to Gaussian random vibration with frequency band 1-15 Hz at two level of amplitude (low vibration amplitude and medium vibration amplitude) for 20-minutes in separate days. For the purpose of drowsiness measurement, volunteers were asked to complete 10-minutes PVT test before and after vibration exposure and rate their subjective drowsiness by giving score using Karolinska Sleepiness Scale (KSS) before vibration, every 5-minutes interval and following 20-minutes of vibration exposure. Strong evidence of drowsiness was found as there was a significant increase in reaction time and number of lapse following exposure to vibration in both conditions. However, the effect is more apparent in medium vibration amplitude. A steady increase of drowsiness level can also be observed in KSS in all volunteers. However, no significant differences were found in KSS between low vibration amplitude and medium vibration amplitude. It is concluded that exposure to vibration has an adverse effect on human alertness level and more pronounced at higher vibration amplitude. Taken together, these findings suggest a role of vibration in promoting drowsiness, especially at higher vibration amplitude.

Keywords: drowsiness, human vibration, karolinska sleepiness scale, psychomotor vigilance test

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Authors: Mohan L. Jayatiake, Judd Storrs, Jing-Huei Lee

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The optimal MRI resolution relies on a homogeneous magnetic field. However, local susceptibility variations can lead to field inhomogeneities that cause artifacts such as image distortion and signal loss. The effects of local susceptibility variation notoriously increase with magnetic field strength. Active shimming improves homogeneity by applying corrective fields generated from shim coils, but requires calculation of optimal current for each shim coil. FASTMAP (fast automatic shimming technique by mapping along projections) is an effective technique for finding optimal currents works well at high-field, but is restricted to shimming spherical regions of interest. The 3D gradient-echo pulse sequence was modified to reduce sensitivity to eddy currents and used to obtain susceptibility field maps at 4T. Measured fields were projected onto first-and second-order spherical harmonic functions corresponding to shim hardware. A spherical phantom was used to calibrate the shim currents. Susceptibility maps of a volunteer’s brain with and without FASTMAP shimming were obtained. Simulations indicate that optimal shim currents derived from the field map may provide better overall shimming of the human brain.

Keywords: shimming, high-field, active, passive

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Authors: Turuganti Venkateswarlu, Jagadeesh Anmala

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Excessive runoffs from various non-point source land uses, and other point sources are rapidly contaminating the water quality of streams in the Upper Green River watershed, Kentucky, USA. It is essential to maintain the stream water quality as the river basin is one of the major freshwater sources in this province. It is also important to understand the water quality parameters (WQPs) quantitatively and qualitatively along with their important features as stream water is sensitive to climatic events and land-use practices. In this paper, a model was developed for predicting one of the significant WQPs, Fecal Coliform (FC) from precipitation, temperature, urban land use factor (ULUF), agricultural land use factor (ALUF), and forest land-use factor (FLUF) using Random Forest (RF) algorithm. The RF model, a novel ensemble learning algorithm, can even find out advanced feature importance characteristics from the given model inputs for different combinations. This model’s outcomes showed a good correlation between FC and climate events and land use factors (R2 = 0.94) and precipitation and temperature are the primary influencing factors for FC.

Keywords: water quality, land use factors, random forest, fecal coliform

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Authors: C. F. Kumru, C. Kocatepe, O. Arikan

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In this study, electric field distribution analyses for three pylon models are carried out by a Finite Element Method (FEM) based software. Analyses are performed in both stationary and time domains to observe instantaneous values along with the effective ones. Considering the results of the study, different line geometries is considerably affecting the magnitude and distribution of electric field although the line voltages are the same. Furthermore, it is observed that maximum values of instantaneous electric field obtained in time domain analysis are quite higher than the effective ones in stationary mode. In consequence, electric field distribution analyses should be individually made for each different line model and the limit exposure values or distances to residential buildings should be defined according to the results obtained.

Keywords: electric field, energy transmission line, finite element method, pylon

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Authors: Jian Cao

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This paper describes a novel watermarking technique which we call the random direction embedding (RDE) watermarking. Unlike traditional watermarking techniques, the watermark energy after the RDE embedding does not focus on a fixed direction, leading to the security against the traditional unauthorized watermark removal attack. In addition, the experimental results show that when compared with the existing secure watermarking, namely natural watermarking (NW), the RDE watermarking gains significant improvement in terms of robustness. In fact, the security of the RDE watermarking is not at the cost of low robustness, and it can even achieve more robust than the traditional spread spectrum watermarking, which has been shown to be very insecure.

Keywords: robustness, spread spectrum watermarking, watermarking security, random direction embedding (RDE)

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Authors: Ayaz Ahmad

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In this work, we present the effect of noise on the solution of a partial differential equation (PDE) in three different setting. We shall first consider random initial condition for two nonlinear dispersive PDE the non linear Schrodinger equation and the Kortteweg –de vries equation and analyse their effect on some special solution , the soliton solutions.The second case considered a linear partial differential equation , the wave equation with random initial conditions allow to substantially decrease the computational and data storage costs of an algorithm to solve the inverse problem based on the boundary measurements of the solution of this equation. Finally, the third example considered is that of the linear transport equation with a singular drift term, when we shall show that the addition of a multiplicative noise term forbids the blow up of solutions under a very weak hypothesis for which we have finite time blow up of a solution in the deterministic case. Here we consider the problem of wave propagation, which is modelled by a nonlinear dispersive equation with noisy initial condition .As observed noise can also be introduced directly in the equations.

Keywords: drift term, finite time blow up, inverse problem, soliton solution

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Authors: E. G. Karvelas, C. Liosis, A. Theodorakakos, T. E. Karakasidis

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Magnetic navigation of the drug inside the human vessels is a very important concept since the drug is delivered to the desired area. Consequently, the quantity of the drug required to reach therapeutic levels is being reduced while the drug concentration at targeted sites is increased. Magnetic navigation of drug agents can be achieved with the use of magnetic nanoparticles where anti-tumor agents are loaded on the surface of the nanoparticles. The magnetic field that is required to navigate the particles inside the human arteries is produced by a magnetic resonance imaging (MRI) device. The main factors which influence the efficiency of the usage of magnetic nanoparticles for biomedical applications in magnetic driving are the size and the magnetization of the biocompatible nanoparticles. In this study, a computational platform for the simulation of the optimal gradient magnetic fields for the navigation of magnetic nanoparticles inside a carotid artery is presented. For the propulsion model of the particles, seven major forces are considered, i.e., the magnetic force from MRIs main magnet static field as well as the magnetic field gradient force from the special propulsion gradient coils. The static field is responsible for the aggregation of nanoparticles, while the magnetic gradient contributes to the navigation of the agglomerates that are formed. Moreover, the contact forces among the aggregated nanoparticles and the wall and the Stokes drag force for each particle are considered, while only spherical particles are used in this study. In addition, gravitational forces due to gravity and the force due to buoyancy are included. Finally, Van der Walls force and Brownian motion are taken into account in the simulation. The OpenFoam platform is used for the calculation of the flow field and the uncoupled equations of particles' motion. To verify the optimal gradient magnetic fields, a covariance matrix adaptation evolution strategy (CMAES) is used in order to navigate the particles into the desired area. A desired trajectory is inserted into the computational geometry, which the particles are going to be navigated in. Initially, the CMAES optimization strategy provides the OpenFOAM program with random values of the gradient magnetic field. At the end of each simulation, the computational platform evaluates the distance between the particles and the desired trajectory. The present model can simulate the motion of particles when they are navigated by the magnetic field that is produced by the MRI device. Under the influence of fluid flow, the model investigates the effect of different gradient magnetic fields in order to minimize the distance of particles from the desired trajectory. In addition, the platform can navigate the particles into the desired trajectory with an efficiency between 80-90%. On the other hand, a small number of particles are stuck to the walls and remains there for the rest of the simulation.

Keywords: artery, drug, nanoparticles, navigation

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Authors: H. R. Goshayeshi, M. Mansori, M. Ahmady, M. Zhaloyi

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This paper presents the result of an experimental investigation regarding the use of Fe2O3 nanoparticles added to Kerosene as a working fluid, under magnetic field for Copper Oscillating Heat pipe with inclination angle of 0°(horizontal), 15°, 30°, 45°, 60°, 75°, and 90° (vertical). The following were examined; measure the temperature distribution and heat transfer rate on Oscillating Heat Pipe (OHP), with magnetic field under different angles. Results showed that the addition of Fe2O3 nanoparticles under magnetic field improved thermal performance of OHP especially in 75°.

Keywords: copper oscillating heat pipe, Fe2O3, magnetic field, inclination angles

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Authors: Nordila Ahmad, Thamer Mohammad, Bruce W. Melville, Zuliziana Suif

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Current methods for predicting local scour at wide bridge piers, were developed on the basis of laboratory studies and very limited scour prediction were tested with field data. Laboratory wide pier scour equation from previous findings with field data were presented. A wide range of field data were used and it consists of both live-bed and clear-water scour. A method for assessing the quality of the data was developed and applied to the data set. Three other wide pier-scour equations from the literature were used to compare the performance of each predictive method. The best-performing scour equation were analyzed using statistical analysis. Comparisons of computed and observed scour depths indicate that the equation from the previous publication produced the smallest discrepancy ratio and RMSE value when compared with the large amount of laboratory and field data.

Keywords: field data, local scour, scour equation, wide piers

Procedia PDF Downloads 385

Authors: Ahmed Nour El Islam Ayad, Tahar Rouibah, Wafa Krika, Houari Boudjella, Larab Moulay, Farid Benhamida, Selma Benmoussa

Abstract:

The purpose of this study is to evaluate the electromagnetic field of an underground cable of very high voltage perforated by nail. The aim of this work shows a numerical simulation of the electromagnetic field of 400 kV line after perforation through a ferrous nail in four positions for the pinch pin at different distances. From results for a longitudinal section, we observe and evaluate the distribution and the variation of the electromagnetic field in the cable and the earth. When the nail approaches the underground power cable, the distribution of the magnetic field changes and takes several forms, the magnetic field increase and become very important when the nail breaks the metal screen and will produce a significant leak of the electric field, characterized by a large electric arc and or electric discharge to earth and then a fault in the electrical network. These electromagnetic analysis results help to detect defects in underground cables.

Keywords: underground, electromagnetic, nail, defect

Procedia PDF Downloads 204

Authors: Adel Kara, Abdelhafid Bayadi, Hocine Terrab

Abstract:

This paper presents a study of electric field distribution along of 72 kV polymeric outdoor insulators with broken sheds. Different cases of damaged insulators are modeled and both of clean and polluted cases. By 3D finite element analysis using the software package COMSOL Multiphysics 4.3b. The obtained results of potential and the electrical field distribution around insulators by 3D simulation proved that finite element computations is useful tool for studying insulation electrical field distribution.

Keywords: electric field distributions, insulator, broken sheds, potential distributions

Procedia PDF Downloads 489

Authors: Jayaprakash Kar, Daniyal M. Alghazzawi

Abstract:

The article proposes a novel construction of signcryption scheme with provable security which is most suited to implement on smart card. It is secure in random oracle model and the security relies on Decisional Bilinear Diffie-Hellmann Problem. The proposed scheme is secure against adaptive chosen ciphertext attack (indistiguishbility) and adaptive chosen message attack (unforgebility). Also, it is inspired by zero-knowledge proof. The two most important security goals for smart card are Confidentiality and authenticity. These functions are performed in one logical step in low computational cost.

Keywords: random oracle, provable security, unforgebility, smart card

Procedia PDF Downloads 579

Authors: Brahim Mahfoud

Abstract:

A three-dimensional (3D) numerical technique is used to investigate the possibility of reducing the price of manufacturing some silicon-based devices, particularly those in which minor temperature gradients can significantly reduce performance. The silicon melt under the magnetic field produces Lorentz force, which can effectively suppress the flow which is caused by temperature gradients. This might allow some silicon-based products, such as solar cells, to be manufactured using a less pure, and hence less expensive. The thermocapillary effect of the silicon melt flow in thin annular pools subjected to an externally induced magnetic field was observed. The results reveal that with a strong enough magnetic field, isothermal lines change form and become concentric circles. As the amplitude of the magnetic field (Ha) grows, the azimuthal velocity and temperature at the free surface reduce, and the asymmetric 3D flow becomes axisymmetric steady when Ha surpasses a threshold value.

Keywords: magnetic field, manufacturing, silicon melt, thermocapillary

Procedia PDF Downloads 58

Authors: B. Raghu, Shashi Vemuri, Ch. Sreenivasa Rao

Abstract:

The survey on pesticide use pattern was carried out by interviewing farmers growing chill in open fields and poly houses based on the questionnaire prepared to assess their knowledge and practices on crop cultivation, general awareness on pesticide recommendations and use. Education levels of poly house farmers are high compared to open field farmers, where 57.14% poly house farmers are high school educated, whereas 35% open field farmers are illiterates. Majority farmers use nursery of 35 days and grow in <0.5 acre poly house in summer and rabi and < 1 acre in open field during kharif. Awareness on pesticide related issues is varying among poly house and open field farmers with some commonality, where 28.57% poly house farmers know about recommended pesticides while only 10% open field farmers are aware of this issue. However, in general, all farmers contact pesticide dealer for recommendations, poly house farmers prefer to contact scientists (35.71%) and open field farmers prefer to contact agricultural officers (33.33). Most farmers are unaware about pesticide classification and toxicity symbols on packing. Farmers are aware about endosulfan ban, but only 21.42% poly house and 11.66% open field farmers know about ban of monocrotofos on vegetables. Very few farmers know about pesticide residues and related issues, but know washing helps to reduce contamination.

Keywords: open field, pesticide usage, polyhouses, residues survey

Procedia PDF Downloads 446