Search results for: bit error probability

Authors: Jose Ruiz, Jose Velasquez, Holger Lovon

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Peruvian university buildings are critical structures for which very little research about its seismic vulnerability is available. This paper develops a probabilistic methodology that predicts seismic loss for university buildings with simulated fragility functions. Two university buildings located in the city of Cusco were analyzed. Fragility functions were developed considering seismic and structural parameters uncertainty. The fragility functions were generated with the Latin Hypercube technique, an improved Montecarlo-based method, which optimizes the sampling of structural parameters and provides at least 100 reliable samples for every level of seismic demand. Concrete compressive strength, maximum concrete strain and yield stress of the reinforcing steel were considered as the key structural parameters. The seismic demand is defined by synthetic records which are compatible with the elastic Peruvian design spectrum. Acceleration records are scaled based on the peak ground acceleration on rigid soil (PGA) which goes from 0.05g to 1.00g. A total of 2000 structural models were considered to account for both structural and seismic variability. These functions represent the overall building behavior because they give rational information regarding damage ratios for defined levels of seismic demand. The university buildings show an expected Mean Damage Factor of 8.80% and 19.05%, respectively, for the 0.22g-PGA scenario, which was amplified by the soil type coefficient and resulted in 0.26g-PGA. These ratios were computed considering a seismic demand related to 10% of probability of exceedance in 50 years which is a requirement in the Peruvian seismic code. These results show an acceptable seismic performance for both buildings.

Keywords: fragility functions, university buildings, loss assessment, Montecarlo simulation, latin hypercube

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Authors: O. S. Asaolu, A. Bolorunduro

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Background: Unwanted, mistimed and unintended pregnancy is an important public health issue and the most common cause of maternal mortality in developing countries. Unintended pregnancy is a potential hazard for every sexually active woman as it most times ends in unsafe abortion. The study aimed at assessing the pre-conception contraceptive use, prevalence of unintended pregnancies and the non-contraceptive factors associated with unintended pregnancy amongst currently married women in Osun state. Methodology: A descriptive cross-sectional study among randomly selected 341 currently married pregnant women attending antenatal clinics in Ilesa town of Osun state was conducted in 5 health facilities. A random selection of 5 of the 22 health facilities in the state was done. Data was collected through a self-administered questionnaire and all completed questionnaires were analyzed with SPSS. Result: About two-fifth of the currently pregnant women (40%) who has never used an FP method reported that their current pregnancy was unintended. The results indicate that age of women, age at first sex, substance use, total children ever born of children, religion, and extramarital affairs were key predictors of unintended pregnancy. Women who have higher parity are more likely to experience unintended pregnancy compared to women with lower parity (odds ratio, 0.25). Furthermore, those women who don’t engage in extra marital affairs were less likely to experience unintended pregnancy (odds ratio, 0.3) compared to those who do not. Contribution to knowledge: The predicted probability, using logistic regression, has shown that women who engage in extramarital affairs and women with high parity are more likely to have unintended pregnancy. Conclusion: Behaviour change programs should aim to reduce unintended pregnancy by focusing mostly on identified factors so that the need for abortion is decreased and the overall well-being of the family is maintained and enhanced.

Keywords: unintended pregnancy, factors, pregnant women, Nigeria

Procedia PDF Downloads 369

Authors: Olaiya Folorunsho, Comfort Y. Daramola, Joel N. Ugwu, Lawrence B. Adewole, Olufisayo S. Ekundayo

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In today world, there is a tremendous rise in the usage of internet due to the fact that almost all the communication and information sharing is done over the web. Conversely, there is a continuous growth of unauthorized access to confidential data. This has posed a challenge to information security expertise whose major goal is to curtail the menace. One of the approaches to secure the safety delivery of data/information to the rightful destination without any modification is steganography. Steganography is the art of hiding information inside an embedded information. This research paper aimed at designing a secured algorithm with the use of image steganographic technique that makes use of Least Significant Bit (LSB) algorithm for embedding the data into the bit map image (bmp) in order to enhance security and reliability. In the LSB approach, the basic idea is to replace the LSB of the pixels of the cover image with the Bits of the messages to be hidden without destroying the property of the cover image significantly. The system was implemented using C# programming language of Microsoft.NET framework. The performance evaluation of the proposed system was experimented by conducting a benchmarking test for analyzing the parameters like Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). The result showed that image steganography performed considerably in securing data hiding and information transmission over the networks.

Keywords: steganography, image steganography, least significant bits, bit map image

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Authors: Lotfi Farah, Nadir Farah, Zaiem Kamar

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This paper achieves a maximum power point tracking (MPPT) controller using a high-efficiency reduced rules neuro-fuzzy inference system (HE2RNF) for a 100 kW stand-alone photovoltaic (PV) system connected to the grid. The suggested HE2RNF based MPPT seeks the optimal duty cycle for the boost DC-DC converter, making the designed PV system working at the maximum power point (MPP), then transferring this power to the grid via a three levels voltage source converter (VSC). PV current variation and voltage variation are chosen as HE2RNF-based MPPT controller inputs. By using these inputs with the duty cycle as the only single output, a six rules ANFIS is generated. The high performance of the proposed HE2RNF numerically in the MATLAB/Simulink environment is shown. The 0.006% steady-state error, 0.006s of tracking time, and 0.088s of starting time prove the robustness of this six reduced rules against the widely used twenty-five ones.

Keywords: PV, MPPT, ANFIS, HE2RNF-based MPPT controller, VSC, grid connection

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Authors: Jihad S. Daba, J. P. Dubois, Yvette Antar

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With the exponential growth of cellular users, a new generation of cellular networks is needed to enhance the required peak data rates. The co-channel interference between neighboring base stations inhibits peak data rate increase. To overcome this interference, multi-cell cooperation known as coordinated multipoint transmission is proposed. Such a solution makes use of multiple-input-multiple-output (MIMO) systems under two different structures: Micro- and macro-diversity. In this paper, we study the capacity and bit error rate in cellular networks using MIMO technology. We analyse both micro- and macro-diversity schemes and develop a hybrid model that switches between macro- and micro-diversity in the case of hard handoff based on a cut-off range of signal-to-noise ratio values. We conclude that our hybrid switched micro-macro MIMO system outperforms classical MIMO systems at the cost of increased hardware and software complexity.

Keywords: cooperative multipoint transmission, ergodic capacity, hard handoff, macro-diversity, micro-diversity, multiple-input-multiple output systems, orthogonal frequency division multiplexing

Procedia PDF Downloads 287

Authors: Narges Ebadi, Davod Ahmadi, Hiliary Monteith, Hugo Melgar-Quinonez

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Migration cannot necessarily relieve pressure on water resources in origin communities, and male out-migration can increase the water management burden of women. However, inflows of financial remittances seem to offer possibilities of investing in improving drinking-water access. Therefore, remittances may be an important pathway for migrants to support water security. This paper explores the association between water access and the receipt of remittances in households in sub-Saharan Africa. Data from round 6 of the 'Afrobarometer' surveys in 2016 were used (n= 49,137). Descriptive, bivariate and multivariate statistical analyses were carried out in this study. Regardless of country, findings from descriptive analyses showed that approximately 80% of the respondents never received remittance, and 52% had enough clean water. Only one-fifth of the respondents had piped water supply inside the house (19.9%), and approximately 25% had access to a toilet inside the house. Bivariate analyses revealed that even though receiving remittances was significantly associated with water supply, the strength of association was very weak. However, other factors such as the area of residence (rural vs. urban), cash income frequencies, electricity access, and asset ownership were strongly associated with water access. Results from unadjusted multinomial logistic regression revealed that the probability of having no access to piped water increased among remittance recipients who received financial support at least once a month (OR=1.324) (p < 0.001). In contrast, those not receiving remittances were more likely to regularly have a water access concern (OR=1.294) (p < 0.001), and not have access to a latrine (OR=1.665) (p < 0.001). In conclusion, receiving remittances is significantly related to water access as the strength of odds ratios for socio-demographic factors was stronger.

Keywords: remittances, water access, SSA, migration

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Authors: Soofiyan Atar, Adil Shaikh, Sahil Rajpurkar, Pragnesh Bhalala, Aniket Desai, Irfan Siddavatam

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Deep reinforcement learning (deep RL) algorithms leverage the symbolic power of complex controllers by automating it by mapping sensory inputs to low-level actions. Deep RL eliminates the complex robot dynamics with minimal engineering. Deep RL provides high-risk involvement by directly implementing it in real-world scenarios and also high sensitivity towards hyperparameters. Tuning of hyperparameters on a pneumatic quadruped robot becomes very expensive through trial-and-error learning. This paper presents an automated learning control for a pneumatic quadruped robot using sample efficient deep Q learning, enabling minimal tuning and very few trials to learn the neural network. Long training hours may degrade the pneumatic cylinder due to jerk actions originated through stochastic weights. We applied this method to the pneumatic quadruped robot, which resulted in a hopping gait. In our process, we eliminated the use of a simulator and acquired a stable gait. This approach evolves so that the resultant gait matures more sturdy towards any stochastic changes in the environment. We further show that our algorithm performed very well as compared to programmed gait using robot dynamics.

Keywords: model-based reinforcement learning, gait stability, supervised learning, pneumatic quadruped

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Authors: Qasem Qananwah, Ahmad Dagamseh, Hiam AlQuran, Khalid Shaker Ibrahim

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Blood pressure helps the physicians greatly to have a deep insight into the cardiovascular system. The determination of individual blood pressure is a standard clinical procedure considered for cardiovascular system problems. The conventional techniques to measure blood pressure (e.g. cuff method) allows a limited number of readings for a certain period (e.g. every 5-10 minutes). Additionally, these systems cause turbulence to blood flow; impeding continuous blood pressure monitoring, especially in emergency cases or critically ill persons. In this paper, the most important statistical features in the photoplethysmogram (PPG) signals were extracted to estimate the blood pressure noninvasively. PPG signals from more than 40 subjects were measured and analyzed and 12 features were extracted. The features were fed to principal component analysis (PCA) to find the most important independent features that have the highest correlation with blood pressure. The results show that the stiffness index means and standard deviation for the beat-to-beat heart rate were the most important features. A model representing both features for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) was obtained using a statistical regression technique. Surface fitting is used to best fit the series of data and the results show that the error value in estimating the SBP is 4.95% and in estimating the DBP is 3.99%.

Keywords: blood pressure, noninvasive optical system, principal component analysis, PCA, continuous monitoring

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Authors: Sara Mahesar, Saleem M. Chandio, Hira Soomro

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Nonlinear system of equations in two variables is a system which contains variables of degree greater or equal to two or that comprises of the transcendental functions. Mathematical modeling of numerous physical problems occurs as a system of nonlinear equations. In applied and pure mathematics it is the main dispute to solve a system of nonlinear equations. Numerical techniques mainly used for finding the solution to problems where analytical methods are failed, which leads to the inexact solutions. To find the exact roots or solutions in case of the system of non-linear equations there does not exist any analytical technique. Various methods have been proposed to solve such systems with an improved rate of convergence and accuracy. In this paper, a new scheme is developed for solving system of non-linear equation in two variables. The iterative scheme proposed here is modified form of the conventional Newton’s Method (CN) whose order of convergence is two whereas the order of convergence of the devised technique is three. Furthermore, the detailed error and convergence analysis of the proposed method is also examined. Additionally, various numerical test problems are compared with the results of its counterpart conventional Newton’s Method (CN) which confirms the theoretic consequences of the proposed method.

Keywords: conventional Newton’s method, modified Newton’s method, order of convergence, system of nonlinear equations

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Authors: Swapneel U. Naphade, Dushyant A. Patil, Satyabodh M. Kulkarni

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In this work, we propose the concept and geometrical design of a soil moisture control system (SMCS) module by following the product development approach to develop an inexpensive, easy to use and quick to install product targeted towards agriculture practitioners. The module delivers water to the agricultural land efficiently by sensing the soil moisture and activating the delivery valve. We start with identifying the general needs of the potential customer. Then, based on customer needs we establish product specifications and identify important measuring quantities to evaluate our product. Keeping in mind the specifications, we develop various conceptual solutions of the product and select the best solution through concept screening and selection matrices. Then, we develop the product architecture by integrating the systems into the final product. In the end, the geometric design is done using human factors engineering concepts like heuristic analysis, task analysis, and human error reduction analysis. The result of human factors analysis reveals the remedies which should be applied while designing the geometry and software components of the product. We find that to design the best grip in terms of comfort and applied force, for a power-type grip, a grip-diameter of 35 mm is the most ideal.

Keywords: agriculture, human factors, product design, soil moisture control

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Authors: Jianguo Chen, Fenggang Zang, Yu Yang, Liangang Zheng

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Reactor Pressure Vessel (RPV) is an important security barrier in nuclear power plant. Crack like defects may be produced on RPV during the whole operation lifetime due to the harsh operation condition and irradiation embrittlement. During the severe loss of coolant accident, thermal shock happened as the injection of emergency cooling water into RPV, which results in re-pressurization of the vessel and very high tension stress on the vessel wall, this event called Pressurized Thermal Shock (PTS). Crack on the vessel wall may propagate even penetrate the vessel, so the safety of the RPV would undergo great challenge. Many assumptions in structure integrity evaluation make the result of deterministic fracture mechanics very conservative, which affect the operation lifetime of the plant. Actually, many parameters in the evaluation process, such as fracture toughness and nil-ductility transition temperature, have statistical distribution characteristics. So it is necessary to assess the structural integrity of RPV subjected to PTS event by means of Probabilistic Fracture Mechanics (PFM). Structure integrity evaluation methods of RPV subjected to PTS event are summarized firstly, then evaluation method based on probabilistic fracture mechanics are presented by considering the probabilistic characteristics of material and structure parameters. A comprehensive analysis example is carried out at last. The results show that the probability of crack penetrates through wall increases gradually with the growth of fast neutron irradiation flux. The results give advice for reactor life extension.

Keywords: fracture toughness, integrity evaluation, pressurized thermal shock, probabilistic fracture mechanics, reactor pressure vessel

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Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Alcardo Alex Barakabitze, Tan Xiaoheng

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Orthogonal Frequency Division Multiplexing (OFDM) is a special case of Multi-Carrier Modulation (MCM) technique which transmits a stream of data over a number of lower data rate subcarriers. OFDM splits the total transmission bandwidth into a number of orthogonal and non-overlapping subcarriers and transmit the collection of bits called symbols in parallel using these subcarriers. This paper explores the Peak to Average Power Reduction (PAPR) using the Partial Transmit Sequence technique. We provide the distribution analysis and the basics of OFDM signals and then show how the PAPR increases as the number of subcarriers increases. We provide the performance analysis of CCDF and PAPR expressed in decibels through MATLAB simulations. The simulation results show that, in PTS technique, the performance of PAPR reduction in OFDM systems improves significantly as the number of sub-blocks increases. However, by keeping the same number of sub-blocks variation, oversampling factor and the number of OFDM blocks’ iteration for generating the CCDF, the OFDM systems with 128 subcarriers have an improved performance in PAPR reduction compared to OFDM systems with 256, 512 or >512 subcarriers.

Keywords: OFDM, peak to average power reduction (PAPR), bit error rate (BER), subcarriers, wireless communications

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Jihad S. Daba, Jean-Pierre Dubois, Georges El Soury

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Fifth generation wireless networks guarantee significant capacity enhancement to suit more clients and services at higher information rates with better reliability while consuming less power. The deployment of massive multiple-input-multiple-output technology guarantees broadband wireless networks with the use of base station antenna arrays to serve a large number of users on the same frequency and time-slot channels. In this work, we evaluate the performance of massive multiple-input-multiple-output systems (MIMO) systems in 5^th generation cellular networks in terms of capacity and bit error rate. Several cases were considered and analyzed to compare the performance of massive MIMO systems while varying the number of antennas at both transmitting and receiving ends. We found that, unlike classical MIMO systems, reducing the number of transmit antennas while increasing the number of antennas at the receiver end provides a better solution to performance enhancement. In addition, enhanced orthogonal frequency division multiplexing and beam division multiple access schemes further improve the performance of massive MIMO systems and make them more reliable.

Keywords: beam division multiple access, D2D communication, enhanced OFDM, fifth generation broadband, massive MIMO

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Authors: Y. Kobori, S. Tanaka, N. Tsukiji, N. Takai, H. Kobayashi

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This paper proposes two kinds of new single-inductor dual-output (SIDO) DC-DC switching converters with ripple-based hysteretic control. First SIDO converters of type 1 utilize the triangular signal generated by the CR-circuit connected across the inductor. This triangular signal is used for generating the PWM signal instead of the saw-tooth signal used in the conventional converters. Second SIDO converters of type 2 utilize the triangular signal generated by the CR-circuit connected across the voltage error amplifier. This paper describes circuit topologies, Operation principles, simulation results and experimental results of the proposed SIDO converters. In simulation results of both type of SIDO converters, static output voltage ripples are less than 5mVpp and over/under shoots of the dynamic load regulations for the output current step are less than +/- 10mV. In experimental results of single output converter of type 2, static output voltage ripples are about 20mVpp. Output ripples of SIDO type 1 converter are about 80mVpp.

Keywords: DC-DC converter, switching converter, SIDO converter, hysteretic control, ripple-based control

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

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Authors: Bidzina Matsaberidze

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It is known that, in emergency situations, multi-attribute group decision-making (MAGDM) models are characterized by insufficient objective data and a lack of time to respond to the task. Evidence theory is an effective tool for describing such incomplete information in decision-making models when the expert and his knowledge are involved in the estimations of the MAGDM parameters. We consider an emergency decision-making model, where expert assessments on humanitarian aid from distribution centers (HADC) are represented in q-rung ortho-pair fuzzy numbers, and the data structure is described within the data body theory. Based on focal probability construction and experts’ evaluations, an objective function-distribution centers’ selection ranking index is constructed. Our approach for solving the constructed bicriteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a matrix, the columns of which allow us to find all possible partitionings of the HADCs with the service centers. Some constraints are also taken into consideration while generating the matrix. In the second phase, based on the matrix and using our exact algorithm, we find the partitionings -allocations of the HADCs to the centers- which correspond to the Pareto-optimal solutions. For an illustration of the obtained results, a numerical example is given for the facility location-selection problem.

Keywords: emergency MAGDM, q-rung orthopair fuzzy sets, evidence theory, HADC, facility location problem, multi-objective combinatorial optimization problem, Pareto-optimal solutions

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Authors: V. Pourmostaghimi, M. Zadshakoyan

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Efficiency and productivity of the finish hard turning can be enhanced impressively by utilizing accurate predictive models for cutting tool wear. However, the ability of genetic programming in presenting an accurate analytical model is a notable characteristic which makes it more applicable than other predictive modeling methods. In this paper, the genetic equation for modeling of tool flank wear is developed with the use of the experimentally measured flank wear values and genetic programming during finish turning of hardened AISI D2. Series of tests were conducted over a range of cutting parameters and the values of tool flank wear were measured. On the basis of obtained results, genetic model presenting connection between cutting parameters and tool flank wear were extracted. The accuracy of the genetically obtained model was assessed by using two statistical measures, which were root mean square error (RMSE) and coefficient of determination (R²). Evaluation results revealed that presented genetic model predicted flank wear over the study area accurately (R² = 0.9902 and RMSE = 0.0102). These results allow concluding that the proposed genetic equation corresponds well with experimental data and can be implemented in real industrial applications.

Keywords: cutting parameters, flank wear, genetic programming, hard turning

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Authors: Michael Williams

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The ability to control a flowing well is of the utmost important. During the kill phase, heavy weight kill mud is circulated around the well. While increasing bottom hole pressure near wellbore formation, the damage is increased. The addition of high density spherical objects has the potential to minimise this near wellbore damage, increase bottom hole pressure and reduce operational time to kill the well. This operational time saving is seen in the rapid deployment of high density spherical objects instead of building high density drilling fluid. The research aims to model the well kill process using a Computational Fluid Dynamics software. A model has been created as a proof of concept to analyse the flow of micron sized spherical objects in the drilling fluid. Initial results show that this new methodology of spherical objects in drilling fluid agrees with traditional stream lines seen in non-particle flow. Additional models have been created to demonstrate that areas of higher flow rate around the bit can lead to increased probability of wash out of formations but do not affect the flow of micron sized spherical objects. Interestingly, areas that experience dimensional changes such as tool joints and various BHA components do not appear at this initial stage to experience increased velocity or create areas of turbulent flow, which could lead to further borehole stability. In conclusion, the initial models of this novel well control methodology have not demonstrated any adverse flow patterns, which would conclude that this model may be viable under field conditions.

Keywords: well control, fluid mechanics, safety, environment

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

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In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, triazoles, novel heterocycles

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Authors: O. O. Onafowokan, K. Anderson, M. R. Norton, R. G. Middleton

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Revision total hip arthroplasty (THA) is known to be a challenging procedure with potential for poor outcomes. Due to its lack of metaphyseal encroachment, hip resurfacing arthroplasty (HRA) is classified as a bone conserving procedure. Although the literature postulates that this is an advantage at time of revision surgery, there is no evidence to either support or refute this claim. We identified 129 hips that had undergone HRA and 129 controls undergoing first revision THA. We recorded the clinical assessment and survivorship of implants in a multi-surgeon, single centre, retrospective case control series for both arms. These were matched for age and sex. Data collected included demographics, indications for surgery, Oxford Hip Score (OHS), length of surgery, length of hospital stay, blood transfusion, implant complexity and further surgical procedures. Significance was taken as p < 0.05. Mean follow up was 7.5 years (1 to 15). There was a significant 6 point difference in postoperative OHS in favour of the revision resurfacing group (p=0.0001). The revision HRA group recorded 48 minutes less length of surgery (p<0.0001), 2 days less in length of hospital stay (p=0.018), a reduced need for blood transfusion (p=0.0001), a need for less complexity in revision implants (p=0.001) and a reduced probability of further surgery being required (P=0.003). Whilst we acknowledge the limitations of this study our results suggest that, in contrast to THA, the bone conservation element of HRA may make for a less traumatic revision procedure with better functional outcomes. Use of HRA has seen a dramatic decline as a result of concerns regarding metallosis. However, this information remains of relevance when counselling young active patients about their arthroplasty options and may become pertinent in the future if the promise of ceramic hip resurfacing is ever realized.

Keywords: hip resurfacing, metallosis, revision surgery, total hip arthroplasty

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Authors: Ayan Chakraborty, BV. Rathish Kumar

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Off-late many models in viscoelasticity, signal processing or anomalous diffusion equations are formulated in fractional calculus. Tempered fractional calculus is the generalization of fractional calculus and in the last few years several important partial differential equations occurring in the different field of science have been reconsidered in this term like diffusion wave equations, Schr$\ddot{o}$dinger equation and so on. In the present paper, a time-dependent tempered fractional diffusion equation of order $\gamma \in (0,1)$ with forcing function is considered. Existence, uniqueness, stability, and regularity of the solution has been proved. Crank-Nicolson discretization is used in the time direction. B spline finite element approximation is implemented. Generally, B-splines basis are useful for representing the geometry of a finite element model, interfacing a finite element analysis program. By utilizing this technique a priori space-time estimate in finite element analysis has been derived and we proved that the convergent order is $\mathcal{O}(h²+T²)$ where $h$ is the space step size and $T$ is the time. A couple of numerical examples have been presented to confirm the accuracy of theoretical results. Finally, we conclude that the studied method is useful for solving tempered fractional diffusion equations.

Keywords: B-spline finite element, error estimates, Gronwall's lemma, stability, tempered fractional

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Authors: Charles Klein O. Gorit, Mark Tristan J. Quimque Jr., M. Cecilia V. Almeda, Concepcion M. Salvana

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Chitosan is a promising biopolymer commonly found in crustacean shells that has plausible effects in water purification and wastewater treatment. It is a primary derivative of chitin and considered second of the most abundant biopolymer prior to cellulose. Morphological analysis had been done using Scanning Electron Microscopy with Energy Dispersive Microscopy (SEM/EDS), and due to its porous nature, it showcases a certain degree of porosity, hence, larger adsorption site of heavy metal. The Energy Dispersive Spectroscopy of the chitosan and ‘lead-bound’ chitosan, shows a relative increase of percent abundance of lead cation from 1.44% to 2.08% hence, adsorption occurs. Chitosan, as a nitrogenous polysaccharide, subjected to Fourier transform infrared spectroscopy (FTIR) analysis shows amide bands ranging from 1635.36 cm⁻¹ for amide 1 band and 1558.40 cm-1 for amide 2 band with NH stretching. For ‘lead-bound’ chitosan, the FT-IR analysis shows a change in peaks upon adsorption of Pb(II) cation. The spectrum shows broadening of OH and NH stretching band. Such observation can be attributed to the probability that the attachment of Pb(II) ions is in these functional groups. A column filter was devised with lead-bound chitosan to determine the zero point charge (pHzpc) of the biopolymer. The results show that at pH 8.34, below than the zpc level of literatures cited for lead which ranges from pH 4 to 7, favors the adsorption site of chitosan and its capability to adsorb traces amount of aqueous lead.

Keywords: chitosan, biopolymer, FT-IR, SEM, zero-point charge, heavy metal, lead ions

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Authors: K. P. Shinde, M. V. Tien, H. Lin, H.-R. Park, S.-C.Yu, K. C. Chung, D.-H. Kim

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Magnetic refrigeration provides an attractive alternative cooling technology due to its potential advantages such as high cooling efficiency, environmental friendliness, low noise, and compactness over the conventional cooling techniques based on gas compression. Magnetocaloric effect (MCE) occurs by changes in entropy (ΔS) and temperature (ΔT) under external magnetic fields. We have been focused on identifying materials with large MCE in two temperature regimes, not only room temperature but also at cryogenic temperature for specific technological applications, such as space science and liquefaction of hydrogen in fuel industry. To date, the commonly used materials for cryogenic refrigeration are based on hydrated salts. In the present work, we report giant MCE in rare earth Ho2O3 nanopowder at cryogenic temperature. HoN nanoparticles with average size of 30 nm were prepared by using plasma arc discharge method with gas composition of N2/H2 (80%/20%). The prepared HoN was sintered in air atmosphere at 1200 oC for 24 hrs to convert it into oxide. Structural and morphological properties were studied by XRD and SEM. XRD confirms the pure phase and cubic crystal structure of Ho2O3 without any impurity within error range. It has been discovered that Holmium oxide exhibits giant MCE at low temperature without magnetic hysteresis loss with the second-order antiferromagnetic phase transition with Néels temperature around 2 K. The maximum entropy change was found to be 25.2 J/kgK at an applied field of 6 T.

Keywords: magnetocaloric effect, Ho₂O₃, magnetic entropy change, nanopowder

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Authors: M. Kislu Noman, Syed Mohammed Shamsul Islam, Shahriar Hassan, Raihana Pervin

Abstract:

With the increasing rate of wireless devices and high bandwidth operations, wireless networking and communications are becoming over crowded. To cope with such crowdy and messy situation, massive MIMO is designed to work with hundreds of low costs serving antennas at a time as well as improve the spectral efficiency at the same time. TDD has been used for gaining beamforming which is a major part of massive MIMO, to gain its best improvement to transmit and receive pilot sequences. All the benefits are only possible if the channel state information or channel estimation is gained properly. The common methods to estimate channel matrix used so far is LS, MMSE and a linear version of MMSE also proposed in many research works. We have optimized these methods using genetic algorithm to minimize the mean squared error and finding the best channel matrix from existing algorithms with less computational complexity. Our simulation result has shown that the use of GA worked beautifully on existing algorithms in a Rayleigh slow fading channel and existence of Additive White Gaussian Noise. We found that the GA optimized LS is better than existing algorithms as GA provides optimal result in some few iterations in terms of MSE with respect to SNR and computational complexity.

Keywords: channel estimation, LMMSE, LS, MIMO, MMSE

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Authors: Vivian Correa

Abstract:

Hypothesis There are typical patterns - phenotypes - of sleep behaviors by age and biological sex groups of parasomnia patients where wearable devices could avoid injuries. Materials and methods We analyzed public video records on sleep-related behaviors likely representing parasomnias, looking for phenotypes in different groups. We searched public internet databases using the keywords “sleepwalking”, “sleep eating,” “sleep sex”, and “aggression in sleep” in six languages. Poor-quality vide-records and those showing apparently faked sleep behaviors were excluded. We classified the videos into estimated sex and age (children, adults, elderly) groups; scored the activity types by a self-made scoring scale; and applied binary logistic regression for analyzing the association between sleep behaviors versus the groups by STATA package providing 95% confidence interval and the probability of statistical significance. Results 224 videos (102 women) were analyzed. The odds of sleepwalking and related dangerous behaviors were lower in the elderly than in adults (P<0.025). Females performed complex risky behaviors during sleepwalking more often than males (P<0.012). Elderly people presented emotional behaviors less frequently than adults (P<0.004), and females showed them twice often as males. Elderly males had 40-fold odds compared to adults and children to perform aggressive movements and 70-fold odds of complex movements in the bed compared to adults. Conclusion Unlike other groups, the high chances of adults being sleepwalkers and elderly males performing intense and violent movements in bed showed us the importance of developing wearable parasomnia devices to prevent injuries.

Keywords: parasomnia, wearable devices, sleepwalking, RBD

Procedia PDF Downloads 90

Authors: Parisa Mansour

Abstract:

Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: Gyula I. Tóth, Shaho Abdalla

Abstract:

Even though numerical simulations indeed have a significant precursory/supportive role in exploring the disordered phase displaying no long-range order in pattern formation models, studying the stability properties of this phase and determining the order of the ordered-disordered phase transition in these models necessitate an analytical description of the disordered phase. First, we will present the results of a comprehensive statistical analysis of a large number (1,000-10,000) of numerical simulations in the Swift-Hohenberg model, where the bulk disordered (or amorphous) phase is stable. We will show that the average free energy density (over configurations) converges, while the variance of the energy density vanishes with increasing system size in numerical simulations, which suggest that the disordered phase is a thermodynamic phase (i.e., its properties are independent of the configuration in the macroscopic limit). Furthermore, the structural analysis of this phase in the Fourier space suggests that the phase can be modeled by a colored isotropic Gaussian noise, where any instant of the noise describes a possible configuration. Based on these results, we developed the general mathematical framework of finding a pool of solutions to partial differential equations in the sense of continuous probability measure, which we will present briefly. Applying the general idea to the Swift-Hohenberg model we show, that the amorphous phase can be found, and its properties can be determined analytically. As the general mathematical framework is not restricted to continuum theories, we hope that the proposed methodology will open a new chapter in studying disordered phases.

Keywords: fundamental theory, mathematical physics, continuum models, analytical description

Procedia PDF Downloads 109

Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

Abstract:

The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

Procedia PDF Downloads 282