Search results for: binary logistic model

Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Vivian Correa

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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

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Authors: Naima Nur, Safa Islam, Saeema Islam, Md. Faridul Alam

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Background: The worldwide increase in pediatric presumptive tuberculosis (TB) is the most life-threatening challenge in effectively controlling TB. The objective of this study was to determine the proportion of presumptive TB and the factors associated with it. Methods: A cross-sectional study was conducted between March and November 2013 at ICDDR-Bangladesh. Two hundred twelve pulmonary and extra-pulmonary specimens were collected from 84 suspected pediatric patients diagnosed with TB based on their clinical symptoms/radiological findings. Presumptive TB and confirmed TB were considered presumptive TB and non-presumptive TB and were isolated by smear-microscopy, culture, and GeneXpert. Logistic regression was used to analyze associations between outcome and predictor variables. Results: The proportion of presumptive TB was 85.7%, and 14.3% of non-presumptive TB. In presumptive TB, vaccine scars, family TB history, and school-going children were 16.6%, 33.3%, and 56.9%, respectively. In contrast, vaccine scars and family TB history were 8.3%, and school-going children were 58.3% in non-presumptive TB. Significant factors did not appear in the logistic regression analysis. Conclusion: Despite the high proportion of presumptive TB, there was no statistically significant between presumptive TB and non-presumptive TB.

Keywords: presumptive tuberculosis, confirmed tuberculosis, patient's characteristics, diagnosis

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Authors: Rosa Tsegaye Aga, Xuan Jiang, Pavel Vazquez Faci, Siqing Liu, Simon Rayner, Endalkachew Alemu, Markos Abebe

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Tuberculosis (TB) is a major cause of disease globally. In most cases, TB is treatable and curable, but only with the proper treatment. There is a time when drug-resistant TB occurs when bacteria become resistant to the drugs that are used to treat TB. Current strategies to identify drug-resistant TB bacteria are laboratory-based, and it takes a longer time to identify the drug-resistant bacteria and treat the patient accordingly. But machine learning (ML) and data science approaches can offer new approaches to the problem. In this study, we propose to develop an ML-based model to predict the antibiotic resistance phenotypes of TB isolates in minutes and give the right treatment to the patient immediately. The study has been using the whole genome sequence (WGS) of TB isolates as training data that have been extracted from the NCBI repository and contain different countries’ samples to build the ML models. The reason that different countries’ samples have been included is to generalize the large group of TB isolates from different regions in the world. This supports the model to train different behaviors of the TB bacteria and makes the model robust. The model training has been considering three pieces of information that have been extracted from the WGS data to train the model. These are all variants that have been found within the candidate genes (F1), predetermined resistance-associated variants (F2), and only resistance-associated gene information for the particular drug. Two major datasets have been constructed using these three information. F1 and F2 information have been considered as two independent datasets, and the third information is used as a class to label the two datasets. Five machine learning algorithms have been considered to train the model. These are Support Vector Machine (SVM), Random forest (RF), Logistic regression (LR), Gradient Boosting, and Ada boost algorithms. The models have been trained on the datasets F1, F2, and F1F2 that is the F1 and the F2 dataset merged. Additionally, an ensemble approach has been used to train the model. The ensemble approach has been considered to run F1 and F2 datasets on gradient boosting algorithm and use the output as one dataset that is called F1F2 ensemble dataset and train a model using this dataset on the five algorithms. As the experiment shows, the ensemble approach model that has been trained on the Gradient Boosting algorithm outperformed the rest of the models. In conclusion, this study suggests the ensemble approach, that is, the RF + Gradient boosting model, to predict the antibiotic resistance phenotypes of TB isolates by outperforming the rest of the models.

Keywords: machine learning, MTB, WGS, drug resistant TB

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Authors: Abbul B. Khan, Neeraj Dohare, Rajan Patel

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The mixed micellization of adiphenine hydrochloride (ADP) with 1-decyl-3-methylimidazolium chloride (C10mim.Cl), was investigated at different mole fractions and temperatures by surface tension measurements. The synergistic behavior (i.e., non-ideal behavior) for binary mixtures was explained by the deviation of critical micelle concentration (cmc) from ideal critical micelle concentration (cmc*), micellar mole fraction (Xim) from ideal micellar mole fraction (Xiideal), the values of interaction parameter (β) and activity coefficients (fi) (for both mixed micelles and mixed monolayer). The excess free energy (∆Gex) for the ADP- C10mim.Cl binary mixtures explain the stability of mixed micelles in comparison to micelles of pure ADP and C10mim.Cl. Interfacial parameters, i.e., Gibbs surface excess (Гmax), minimum head group area at air/ water interface (Amin), and free energy of micellization (ΔG0m) were also evaluated for the systems.

Keywords: adiphenine hydrochloride, critical micelle concentration, interaction parameter, activity coefficient

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Authors: Szymon Misiek

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The genderqueer (or gender non-binary, both terms referring to those individuals who are identified as neither men nor women) community has been gaining greater visibility over the last few years. This includes legal recognition, representation in popular media, and inclusion of non-binary perspectives in research on transgender issues. Another important aspect of visibility is language. Gender-neutrality, often associated with genderqueer people, is relatively easy to achieve in natural-gender languages such as English. This can be observed in the growing popularity of the 'singular they' pronoun (used specifically with reference to genderqueer individuals) or the gender-neutral title 'Mx.' (as an alternative to 'Ms./Mr.'). 'Singular they' seems to have become a certain standard in the genderqueer community. Grammatical-gender languages, such as Polish, provide for a greater challenge to genderqueer speakers. In Polish, every noun is inherently gendered, while verbs, adjectives, and pronouns inflect for gender. Those who do not wish to settle for using only either masculine or feminine forms (which some genderqueer Polish speakers do choose) have to somehow mix the two, attempt to avoid gendered forms altogether, or turn to non-standard forms, such as neuter (not used for people in standard Polish), plurals (vaguely akin to English 'singular they'), or neologisms (such as verb forms using the '-u-' affix). The following paper presents the results of a survey conducted among genderqueer speakers of Polish regarding their choice of linguistic strategies. As no definitive standard such as 'singular they' has (yet) emerged, it rather seeks to emphasize the diversity of chosen strategies and their relation to a person's specific identity as well as the context an exchange takes place. The findings of the study may offer an insight into how heavily gendered languages deal with non-normatively gendered experiences, and to what extent English influences this process (e.g., the majority of genderqueer poles choose English terms to label their identity), as well as help design good practices aimed at achieving gender-equality in speech.

Keywords: genderqueer, grammatical gender in Polish, non-binary, transgender

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: Tuji Mohammed Sani

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Back ground: An open tibial fracture is an injury where the fractured bone directly communicates with the outside environment. Due to the specific anatomical features of the tibia (limited soft tissue coverage), more than quarter of its fractures are classified as open, representing the most common open long-bone injuries. Open tibial fractures frequently cause significant bone comminution, periosteal stripping, soft tissue loss, contamination and are prone to bacterial entry with biofilm formation, which increases the risk of deep bone infection. Objective: The main objective of the study was to determine Prevalence of infection and its associated factors in surgically treated open tibial fracture in Addis Ababa Burn Emergency and Trauma (AaBET) center. Method: A facility based retrospective cross-sectional study was conducted among patient treated for open tibial fracture at AaBET center from September 2018 to September 2021. The data was collected from patient’s chart using structured data collection form, and Data was entered and analyzed using SPSS version 26. Bivariable and multiple binary logistic regression were fitted. Multicollinearity was checked among candidate variables using variance inflation factor and tolerance, which were less than 5 and greater than 0.2, respectively. Model adequacy were tested using Hosmer-Lemeshow goodness of fitness test (P=0.711). AOR at 95% CI was reported, and P-value < 0.05 was considered statistically significant. Result: This study found that 33.9% of the study participants had an infection. Initial IV antibiotic time (AOR=2.924, 95% CI:1.160- 7.370) and time of wound closure from injury (AOR=3.524, 95% CI: 1.798-6.908), injury to admission time (AOR=2.895, 95% CI: 1.402 – 5.977). and definitive fixation method (AOR=0.244, 95% CI: 0.113 – 0.4508) were the factors found to have a statistically significant association with the occurrence of infection. Conclusion: The rate of infection in open tibial fractures indicates that there is a need to improve the management of open tibial fracture treated at AaBET center. Time from injury to admission, time from injury to first debridement, wound closure time, and initial Intra Venous antibiotic time from the injury are an important factor that can be readily amended to improve the infection rate. Whether wound closed before seven days or not were more important factor associated with occurrences of infection.

Keywords: infection, open tibia, fracture, magnitude

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Authors: Areej Al-Khabbaz, Swapna Goerge, Majedah Abdul-Rasoul

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Introduction: The most prevalent periodontal disease among children is gingivitis, and it usually becomes more severe in adolescence. A number of intervention studies suggested that resolution of periodontal inflammation can improve metabolic control in patients diagnosed with diabetes mellitus. Aim: to assess the effect of non-surgical periodontal therapy on glycemic control of children diagnosed with diabetes mellitus. Method: Twenty-eight children diagnosed with diabetes mellitus were recruited with established diagnosis diabetes for at least 1 year. Informed consent and child assent form were obtained from children and parents prior to enrolment. The dental examination for the participants was performed on the same week directly following their annual medical assessment. All patients had their glycosylated hemoglobin (HbA1c%) test one week prior to their annual medical and dental visit and 3 months following non-surgical periodontal therapy. All patients received a comprehensive periodontal examination The periodontal assessment included clinical attachment loss, bleeding on probing, plaque score, plaque index and gingival index. All patients were referred for non-surgical periodontal therapy, which included oral hygiene instruction and motivation followed by supra-gingival and subg-ingival scaling using ultrasonic and hand instruments. Statistical Analysis: Data were entered and analyzed using the Statistical Package for Social Science software (SPSS, Chicago, USA), version 18. Statistical analysis of clinical findings was performed to detect differences between the two groups in term of periodontal findings and HbA1c%. Binary logistic regression analysis was performed in order to examine which factors were significant in multivariate analysis after adjusting for confounding between effects. The regression model used the dependent variable ‘Improved glycemic control’, and the independent variables entered in the model were plaque index, gingival index, bleeding %, plaque Statistical significance was set at p < 0.05. Result: A total of 28 children. The mean age of the participants was 13.3±1.92 years. The study participants were divided into two groups; Compliant group (received dental scaling) and non-complaints group (received oral hygiene instructions only). No statistical difference was found between compliant and non-compliant group in age, gender distribution, oral hygiene practice and the level of diabetes control. There was a significant difference between compliant and non-compliant group in term of improvement of HBa1c before and after periodontal therapy. Mean gingival index was the only significant variable associated with improved glycemic control level. In conclusion, this study has demonstrated that non-surgical mechanical periodontal therapy can improve HbA1c% control. The result of this study confirmed that children with diabetes mellitus who are compliant to dental care and have routine professional scaling may have better metabolic control compared to diabetic children who are erratic with dental care.

Keywords: children, diabetes, metabolic control, periodontal therapy

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Authors: Mokrani Mohamed Amine

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In this paper, we have implemented successive interference cancellation algorithms in the 5G downlink. We have calculated the maximum throughput in Frequency Division Duplex (FDD) mode in the downlink, where we have obtained a value equal to 836932 b/ms. The transmitter is of type Multiple Input Multiple Output (MIMO) with eight transmitting and receiving antennas. Each antenna among eight transmits simultaneously a data rate of 104616 b/ms that contains the binary messages of the three users; in this case, the Cyclic Redundancy Check CRC is negligible, and the MIMO category is the spatial diversity. The technology used for this is called Non-Orthogonal Multiple Access (NOMA) with a Quadrature Phase Shift Keying (QPSK) modulation. The transmission is done in a Rayleigh fading channel with the presence of obstacles. The MIMO Successive Interference Cancellation (SIC) receiver with two transmitting and receiving antennas recovers its binary message without errors for certain values of transmission power such as 50 dBm, with 0.054485% errors when the transmitted power is 20dBm and with 0.00286763% errors for a transmitted power of 32 dBm(in the case of user 1) as well as with 0.0114705% errors when the transmitted power is 20 dBm also with 0.00286763% errors for a power of 24 dBm(in the case of user2) by applying the steps involved in SIC.

Keywords: 5G, NOMA, QPSK, TBS, LDPC, SIC, capacity

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Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

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In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: BER, DuoBinary, NRZ-OOK, TWDM-PON

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Authors: L. Cesari, L. Canabady-Rochelle, F. Mutelet

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The thermal conversion of lignin produces bio-oils that contain many compounds with high added-value such as phenolic compounds. In order to efficiently extract these compounds, the possible use of choline bis(trifluoromethylsulfonyl)imide [Choline][NTf2] ionic liquid was explored. To this end, a multistep approach was implemented. First, binary (phenolic compound and solvent) and ternary (phenolic compound and solvent and ionic liquid) solutions were investigated. Eight binary systems of phenolic compound and water were investigated at atmospheric pressure. These systems were quantified using the turbidity method and UV-spectroscopy. Ternary systems (phenolic compound and water and [Choline][NTf2]) were investigated at room temperature and atmospheric pressure. After stirring, the solutions were let to settle down, and a sample of each phase was collected. The analysis of the phases was performed using gas chromatography with an internal standard. These results were used to quantify the values of the interaction parameters of thermodynamic models. Then, extractions were performed on synthetic solutions to determine the influence of several operating conditions (temperature, kinetics, amount of [Choline][NTf2]). With this knowledge, it has been possible to design and simulate an extraction process composed of one extraction column and one flash. Finally, the extraction efficiency of [Choline][NTf2] was quantified with real bio-oils from lignin pyrolysis. Qualitative and quantitative analysis were performed using gas chromatographic connected to mass spectroscopy and flame ionization detector. The experimental measurements show that the extraction of phenolic compounds is efficient at room temperature, quick and does not require a high amount of [Choline][NTf2]. Moreover, the simulations of the extraction process demonstrate that [Choline][NTf2] process requires less energy than an organic one. Finally, the efficiency of [Choline][NTf2] was confirmed in real situations with the experiments on lignin pyrolysis bio-oils.

Keywords: bio-oils, extraction, lignin, phenolic compounds

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Authors: Surya R. Niraula, Devendra B Chhetry, Girish K. Singh, S. Nagesh, Frederick A. Connell

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Background: Although random sampling is generally considered to be the gold standard for population-based research, the majority of drug abuse research is based on non-random sampling despite the well-known limitations of this kind of sampling. Method: We compared the statistical properties of two surveys of drug abuse in the same community: one using snowball sampling of drug users who then identified “friend controls” and the other using a random sample of non-drug users (controls) who then identified “friend cases.” Models to predict drug abuse based on risk factors were developed for each data set using conditional logistic regression. We compared the precision of each model using bootstrapping method and the predictive properties of each model using receiver operating characteristics (ROC) curves. Results: Analysis of 100 random bootstrap samples drawn from the snowball-sample data set showed a wide variation in the standard errors of the beta coefficients of the predictive model, none of which achieved statistical significance. One the other hand, bootstrap analysis of the random-sample data set showed less variation, and did not change the significance of the predictors at the 5% level when compared to the non-bootstrap analysis. Comparison of the area under the ROC curves using the model derived from the random-sample data set was similar when fitted to either data set (0.93, for random-sample data vs. 0.91 for snowball-sample data, p=0.35); however, when the model derived from the snowball-sample data set was fitted to each of the data sets, the areas under the curve were significantly different (0.98 vs. 0.83, p < .001). Conclusion: The proposed method of random sampling of controls appears to be superior from a statistical perspective to snowball sampling and may represent a viable alternative to snowball sampling.

Keywords: drug abuse, matched case-control study, non-probability sampling, probability sampling

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Authors: Mahesh Balan U, Rohith Srinivaas Mohanakrishnan, Venkat Subramanian

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Positive and unlabeled learning (PU) has gained more attention in both academic and industry research literature recently because of its relevance to existing business problems today. Yet, there still seems to be some existing challenges in terms of validating the performance of PU learning, as the actual truth of unlabeled data points is still unknown in contrast to a binary classification where we know the truth. In this study, we propose a novel PU learning technique based on the Pseudo-F measure, where we address this research gap. In this approach, we train the PU model to discriminate the probability distribution of the positive and unlabeled in the validation and spy data. The predicted probabilities of the PU model have a two-fold validation – (a) the predicted probabilities of reliable positives and predicted positives should be from the same distribution; (b) the predicted probabilities of predicted positives and predicted unlabeled should be from a different distribution. We experimented with this approach on a credit marketing case study in one of the world’s biggest fintech platforms and found evidence for benchmarking performance and backtested using historical data. This study contributes to the existing literature on semi-supervised learning.

Keywords: PU learning, semi-supervised learning, pseudo f measure, classification

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Authors: Chaiyaporn Yuksen

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Backgroud: Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). Method: The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. Result: 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times Conclusion: The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: clinical prediction score, SVT, recurrence, emergency department

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Authors: N. Bourayou, A. -H. Meniai, A. Gouaoura

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The presence of salt can either raise or lower the distribution coefficient of a solute acetic acid in liquid- liquid equilibria. The coefficient of solute is defined as the ratio of the composition of solute in solvent rich phase to the composition of solute in diluents (water) rich phase. The phenomena are known as salting–out or salting-in, respectively. The effect of monovalent salt, sodium chloride and the bivalent salt, sodium sulfate on the distribution of acetic acid between 1-butanol and water at 298.15K were experimentally shown to be effective in modifying the liquid-liquid equilibrium of water/acetic acid/1-butanol system in favour of the solvent extraction of acetic acid from an aqueous solution with 1-butanol, particularly at high salt concentrations of both salts. All the two salts studied are found to have to salt out effect for acetic acid in varying degrees. The experimentally measured data were well correlated by Eisen-Joffe equation. NRTL model for solvent mixtures containing salts was able to provide good correlation of the present liquid-liquid equilibrium data. Using the regressed salt concentration coefficients for the salt-solvent interaction parameters and the solvent-solvent interaction parameters obtained from the same system without salt. The calculated phase equilibrium was in a quite good agreement with the experimental data, showing the ability of NRTL model to correlate salt effect on the liquid-liquid equilibrium.

Keywords: activity coefficient, Eisen-Joffe, NRTL model, sodium chloride

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Authors: Fu Lin

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We consider the worst-case load-shedding problem in electric power networks where a number of transmission lines are to be taken out of service. The objective is to identify a prespecified number of line outages that lead to the maximum interruption of power generation and load at the transmission level, subject to the active power-flow model, the load and generation capacity of the buses, and the phase-angle limit across the transmission lines. For this nonlinear model with binary constraints, we show that all decision variables are separable except for the nonlinear power-flow equations. We develop an iterative decomposition algorithm, which converts the worst-case load shedding problem into a sequence of small subproblems. We show that the subproblems are either convex problems that can be solved efficiently or nonconvex problems that have closed-form solutions. Consequently, our approach is scalable for large networks. Furthermore, we prove the convergence of our algorithm to a critical point, and the objective value is guaranteed to decrease throughout the iterations. Numerical experiments with IEEE test cases demonstrate the effectiveness of the developed approach.

Keywords: load shedding, power system, proximal alternating linearization method, vulnerability analysis

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Authors: P. Zarfam, M. Mansouri Baghbaderani

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In seismic design, the proper reaction to the earthquake and the correct and accurate prediction of its subsequent effects on the structure are critical. Choose a proper probability distribution, which gives a more realistic probability of the structure's damage rate, is essential in damage discussions. With the development of design based on performance, analytical method of modal push over as an inexpensive, efficacious, and quick one in the estimation of the structures' seismic response is broadly used in engineering contexts. In this research three concrete frames of 3, 6, and 13 stories are analyzed in non-linear modal push over by 30 different earthquake records by OpenSEES software, then the detriment indexes of roof's displacement and relative displacement ratio of the stories are calculated by two parameters: peak ground acceleration and spectra acceleration. These indexes are used to establish the value of damage relations with log-normal distribution and logistics distribution. Finally the value of these relations is compared and the effect of height on the mentioned damage relations is studied, too.

Keywords: modal pushover analysis, concrete structure, seismic damage, log-normal distribution, logistic distribution

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Authors: N. Harun, E. E. Masiren, W. H. W. Ibrahim, F. Adam

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Amine absorption process is an approach for mitigation of CO2 from flue gas that produces from power plant. This process is the most common system used in chemical and oil industries for gas purification to remove acid gases. On the challenges of this process is high energy requirement for solvent regeneration to release CO2. In the past few years, mixed alkanolamines have received increasing attention. In most cases, the mixtures contain N-methyldiethanolamine (MDEA) as the base amine with the addition of one or two more reactive amines such as PZ. The reason for the application of such blend amine is to take advantage of high reaction rate of CO2 with the activator combined with the advantages of the low heat of regeneration of MDEA. Several experimental and simulation studies have been undertaken to understand this process using blend MDEA/PZ solvent. Despite those studies, the mechanism of CO2 absorption into the aqueous MDEA is not well understood and available knowledge within the open literature is limited. The aim of this study is to investigate the intermolecular interaction of the blend MDEA/PZ using Molecular Dynamics (MD) simulation. MD simulation was run under condition 313K and 1 atm using NVE ensemble at 200ps and NVT ensemble at 1ns. The results were interpreted in term of Radial Distribution Function (RDF) analysis through two system of interest i.e binary and tertiary. The binary system will explain the interaction between amine and water molecule while tertiary system used to determine the interaction between the amine and CO2 molecule. For the binary system, it was observed that the –OH group of MDEA is more attracted to water molecule compared to –NH group of MDEA. The –OH group of MDEA can form the hydrogen bond with water that will assist the solubility of MDEA in water. The intermolecular interaction probability of –OH and –NH group of MDEA with CO2 in blended MDEA/PZ is higher than using single MDEA. This findings show that PZ molecule act as an activator to promote the intermolecular interaction between MDEA and CO2.Thus, blend of MDEA with PZ is expecting to increase the absorption rate of CO2 and reduce the heat regeneration requirement.

Keywords: amine absorption process, blend MDEA/PZ, CO2 capture, molecular dynamic simulation, radial distribution function

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Mahmoud T. Alwidyan, Ahmad Alrawashdeh, Alaa O. Oteir

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Background: Emergency medical service (EMS) providers, oftentimes, use the lights and sirens (L&S) of their ambulances to warn road users, navigate through traffic, and expedite transport to save lives of ill and injured patients. Despite the contribution of road users in the effectiveness of reducing transport time of EMS ambulances using L&S, there is a lack of empirical assessments exploring the road user’s attitude and behavior in such situations. This study, therefore, aimed to assess the attitude and behavior of road users in response to EMS ambulances with warning L&S in use. Methods: This was a cross-sectional survey developed and distributed to adult road users in Northern Jordan. The questionnaire included 20 items addressing demographics, attitudes, and behavior toward emergency ambulances. We described the participants’ responses and assessed the association between demographics and attitude statements using logistic regression. Results: A total of 1302 questionnaires were complete and appropriate for analysis. The mean age was 34.2 (SD± 11.4) years, and the majority were males (72.6%). About half of road users (47.9%) in our sample would perform inappropriate action in response to EMS ambulances with L&S in use. The multivariate logistic regression model show that being female (OR, 0.63; 95% CI = 0.48-0.81), more educated (OR, 0.68; 95% CI = 0.53-0.86), or public transport driver (OR, 0.55; 95% CI = 0.34-0.90) is significantly associated with inappropriate response to EMS ambulances. Additionally, a significant proportion of road users may perform inappropriate and lawless driving practices such as crossing red traffic lights or following the passing by EMS ambulances, which would, in turn, increase the risk on ambulances and other road users. Conclusions: A large proportion of road users in Jordan may respond inappropriately to the EMS ambulances, and many engage in risky driving behaviors due perhaps to the lack of procedural knowledge. Policy-related interventions and educational programs are crucially needed to increase public awareness of the traffic law concerning EMS ambulances and to enhance appropriate driving behavior, which, in turn, improves the efficiency of ambulance services.

Keywords: EMS ambulances, lights and sirens, road users, attitude and behavior

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Authors: Muhammad Waseem Akram

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The current study attempts to analyze the impact of business constraints on the growth potential and performance of Small and Medium Enterprises (SMEs) in the electrical industry of Pakistan. Primary data have been utilized for the study collected from the electrical industry cluster in Sargodha, Pakistan. OLS regression is used to assess the impact of business constraints on the performance of SMEs by controlling the effect of Technology Level, Innovations, and Firm Size. To associate business constraints with the growth potential of SMEs, the study utilized Tetrachoric Correlation and Logistic Regression. Findings reveal that all the business constraints negatively affect the performance of SMEs in the electrical industry except Political Instability. Results of Tetrachoric Correlation show that all the business constraints are negatively correlated with the growth potential of SMEs. Logistic Regression results show that Energy Constraint, Inflation and Price Instability, and Bad Business Practices, all three business constraints cause to reduce the probability of income growth in sample SMEs.

Keywords: SMEs, business constraints, performance, growth potential

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Authors: Parisi L., Hamili D., Azlan N.

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The aim of this study was to design and simulate a particular type of Asynchronous State Machine (ASM), namely a ‘traffic light controller’ (TLC), operated at a frequency of 0.5 Hz. The design task involved two main stages: firstly, designing a 4-bit binary counter using J-K flip flops as the timing signal and subsequently, attaining the digital logic by deploying ASM design process. The TLC was designed such that it showed a sequence of three different colours, i.e. red, yellow and green, corresponding to set thresholds by deploying the least number of AND, OR and NOT gates possible. The software Multisim was deployed to design such circuit and simulate it for circuit troubleshooting in order for it to display the output sequence of the three different colours on the traffic light in the correct order. A clock signal, an asynchronous 4-bit binary counter that was designed through the use of J-K flip flops along with an ASM were used to complete this sequence, which was programmed to be repeated indefinitely. Eventually, the circuit was debugged and optimized, thus displaying the correct waveforms of the three outputs through the logic analyzer. However, hazards occurred when the frequency was increased to 10 MHz. This was attributed to delays in the feedback being too high.

Keywords: asynchronous state machine, traffic light controller, circuit design, digital electronics

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Authors: Seda Sahin, Emin Akata

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Obturator foramen is a specific structure in pelvic bone images and recognition of it is a new concept in medical image processing. Moreover, segmentation of bone structures such as obturator foramen plays an essential role for clinical research in orthopedics. In this paper, we present a novel method to analyze the similarity between the substructures of the imaged region and a hand drawn template, on hip radiographs to detect obturator foramen accurately with integrated usage of Marker-controlled Watershed segmentation and Zernike moment feature descriptor. Marker-controlled Watershed segmentation is applied to seperate obturator foramen from the background effectively. Zernike moment feature descriptor is used to provide matching between binary template image and the segmented binary image for obturator foramens for final extraction. The proposed method is tested on randomly selected 100 hip radiographs. The experimental results represent that our method is able to segment obturator foramens with % 96 accuracy.

Keywords: medical image analysis, segmentation of bone structures on hip radiographs, marker-controlled watershed segmentation, zernike moment feature descriptor

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Authors: Shahadut Hossain

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Measurement errors in continuous covariates and/or misclassifications in categorical covariates are common in epidemiological studies. Regression analysis ignoring such mismeasurements seriously biases the estimated main and interaction effects of covariates on the outcome of interest. Thus, adjustments for such mismeasurements are necessary. In this research, we propose a Bayesian parametric framework for eliminating deleterious impacts of covariate mismeasurements in logistic regression. The proposed adjustment method is unified and thus can be applied to any generalized linear and non-linear regression models. Furthermore, adjustment for covariate mismeasurements requires validation data usually in the form of either gold standard measurements or replicates of the mismeasured covariates on a subset of the study population. Initial investigation shows that adequacy of such adjustment depends on the sizes of main and validation samples, especially when prevalences of the categorical covariates are low. Thus, we investigate the impact of main and validation sample sizes on the adjusted estimates, and provide a general guideline about these sample sizes based on simulation studies.

Keywords: measurement errors, misclassification, mismeasurement, validation sample, Bayesian adjustment

Procedia PDF Downloads 397

Authors: Isadora Bugarin, Taygoara F. de Oliveira

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Binary fluids and emulsions, in general, are present in a vast range of industrial, medical, and scientific applications, showing complex behaviors responsible for defining the flow dynamics and the system operation. However, the literature describing those highlighted fluids in non-isothermal models is currently still limited. The present work brings a detailed investigation on droplet migration due to natural convection in square enclosure, aiming to clarify the effects of drop viscosity on the flow dynamics by showing how distinct viscosity ratios (droplet/ambient fluid) influence the drop motion and the final movement pattern kept on stationary regimes. The analysis was taken by observing distinct combinations of Rayleigh number, drop initial position, and viscosity ratios. The Navier-Stokes and Energy equations were solved considering the Boussinesq approximation in a laminar flow using the finite differences method combined with the Level Set method for binary flow solution. Previous results collected by the authors showed that the Rayleigh number and the drop initial position affect drastically the motion pattern of the droplet. For Ra ≥ 10⁴, two very marked behaviors were observed accordingly with the initial position: the drop can travel either a helical path towards the center or a cyclic circular path resulting in a closed cycle on the stationary regime. The variation of viscosity ratio showed a significant alteration of pattern, exposing a large influence on the droplet path, capable of modifying the flow’s behavior. Analyses on viscosity effects on the flow’s unsteady Nusselt number were also performed. Among the relevant contributions proposed in this work is the potential use of the flow initial conditions as a mechanism to control the droplet migration inside the enclosure.

Keywords: binary fluids, droplet motion, level set method, natural convection, viscosity

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Authors: Yisau Adelaja Odusote, Olakanmi Felix Akinto

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The associative tendency between like atoms in molten Cd-Ga and Cd-In alloy systems has been studied by using the Quasi-Chemical Approximation Model (QCAM). The concentration dependence of the microscopic functions (the concentration-concentration fluctuations in the long-wavelength limits, Scc(0), the chemical short-range order (CSRO) parameter α1 as well as the chemical diffusion) and the mixing properties as the free energy of mixing, GM, enthalpy of mixing and entropy of mixing of the two molten alloys have been determined. Thermodynamic properties of both systems deviate positively from Raoult's law, while the systems are characterized by positive interaction energy. The role of atomic size ratio on the alloying properties was discussed.

Keywords: homo-pairs, interchange energy, enthalpy, entropy, Cd-Ga, Cd-In

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Authors: Sara El-Hanboushy, Hayam Lotfy, Yasmin Fayez, Engy Shokry, Mohammed Abdelkawy

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Simple, specific, accurate and precise spectrophotometric methods are developed and validated for simultaneous determination of pseudoephedrine sulphate (PSE) and loratadine (LOR) in combined dosage form based on spectral analysis technique. Pseudoephedrine (PSE) in binary mixture could be analyzed either by using its resolved zero order absorption spectrum at its λ max 256.8 nm after subtraction of LOR spectrum or in presence of LOR spectrum by absorption correction method at 256.8 nm, dual wavelength (DWL) method at 254nm and 273nm, induced dual wavelength (IDWL) method at 256nm and 272nm and ratio difference (RD) method at 256nm and 262 nm. Loratadine (LOR) in the mixture could be analyzed directly at 280nm without any interference of PSE spectrum or at 250 nm using its recovered zero order absorption spectrum using constant multiplication(CM).In addition, simultaneous determination for PSE and LOR in their mixture could be applied by induced amplitude modulation method (IAM) coupled with amplitude multiplication (PM).

Keywords: dual wavelength (DW), induced amplitude modulation method (IAM) coupled with amplitude multiplication (PM), loratadine, pseudoephedrine sulphate, ratio difference (RD)

Procedia PDF Downloads 302

Authors: N. E. Gallagher, C. E. Neville, N. Lyner, J. Yarnell, C. C. Patterson, J. E. Gallacher, Y. Ben-Shlomo, A. Fehily, J. V. Woodside

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Hearing loss is highly prevalent in older people and can reduce quality of life substantially. Emerging research suggests that potentially modifiable risk factors, including risk factors previously related to cardiovascular disease risk, may be associated with a decreased or increased incidence of hearing loss. This has prompted investigation into the possibility that certain nutrients, foods or dietary patterns may also be associated with incidence of hearing loss. The aim of this study was to determine any associations between dietary patterns and hearing loss in men enrolled in the Caerphilly study. The Caerphilly prospective cohort study began in 1979-1983 with recruitment of 2512 men aged 45-59 years. Dietary data was collected using a self-administered, semi-quantitative, 56-item food frequency questionnaire (FFQ) at baseline (1979-1983), and 7-day weighed food intake (WI) in a 30% sub-sample, while pure-tone unaided audiometric threshold was assessed at 0.5, 1, 2 and 4 kHz, between 1984 and 1988. Principal components analysis (PCA) was carried out to determine a posteriori dietary patterns and multivariate linear and logistic regression models were used to examine associations with hearing level (pure tone average (PTA) of frequencies 0.5, 1, 2 and 4 kHz in decibels (dB)) for linear regression and with hearing loss (PTA>25dB) for logistic regression. Three dietary patterns were determined using PCA on the FFQ data- Traditional, Healthy, High sugar/Alcohol avoider. After adjustment for potential confounding factors, both linear and logistic regression analyses showed a significant and inverse association between the Healthy pattern and hearing loss (P<0.001) and linear regression analysis showed a significant association between the High sugar/Alcohol avoider pattern and hearing loss (P=0.04). Three similar dietary patterns were determined using PCA on the WI data- Traditional, Healthy, High sugar/Alcohol avoider. After adjustment for potential confounding factors, logistic regression analyses showed a significant and inverse association between the Healthy pattern and hearing loss (P=0.02) and a significant association between the Traditional pattern and hearing loss (P=0.04). A Healthy dietary pattern was found to be significantly inversely associated with hearing loss in middle-aged men in the Caerphilly study. Furthermore, a High sugar/Alcohol avoider pattern (FFQ) and a Traditional pattern (WI) were associated with poorer hearing levels. Consequently, the role of dietary factors in hearing loss remains to be fully established and warrants further investigation.

Keywords: ageing, diet, dietary patterns, hearing loss

Procedia PDF Downloads 218

Authors: Qintao Shen, Lei Luo, Jun Ma, Jie Yu, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Control Flow Integrity (CFI) is one of the most promising technique to defend Code-Reuse Attacks (CRAs). Traditional CFI Systems and recent Context-Sensitive CFI use coarse control flow graphs (CFGs) to analyze whether the control flow hijack occurs, left vast space for attackers at indirect call-sites. Coarse CFGs make it difficult to decide which target to execute at indirect control-flow transfers, and weaken the existing CFI systems actually. It is an unsolved problem to extract CFGs precisely and perfectly from binaries now. In this paper, we present an algorithm to get a more precise CFG from binaries. Parameters are analyzed at indirect call-sites and functions firstly. By comparing counts of parameters prepared before call-sites and consumed by functions, targets of indirect calls are reduced. Then the control flow would be more constrained at indirect call-sites in runtime. Combined with CCFI, we implement our policy. Experimental results on some popular programs show that our approach is efficient. Further analysis show that it can mitigate COOP and other advanced attacks.

Keywords: contex-sensitive, CFI, binary analysis, code reuse attack

Procedia PDF Downloads 307