Search results for: binary logistic regression
3323 Is Socio-Economic Characteristic is Associated with Health-Related Quality of Life among Elderly: Evidence from SAGE Data in India
Authors: Mili Dutta, Lokender Prashad
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Introduction: Population ageing is a phenomenon that can be observed around the globe. The health-related quality of life (HRQOL) is a measurement of health status of an individual, and it describes the effect of physical and mental health disorders on the well-being of a person. The present study is aimed to describe the influence of socio-economic characteristics of elderly on their health-related quality of life in India. Methods: EQ-5D instrument and population-based EQ-5D index score has been measured to access the HRQOL among elderly. Present study utilized the Study on Global Ageing and Adult Health (SAGE) data which was conducted in 2007 in India. Multiple Logistic Regression model and Multivariate Linear Regression model has been employed. Result: In the present study, it was found that the female are more likely to have problems in mobility (OR=1.41, 95% Cl: 1.14 to 1.74), self-care (OR=1.26, 95% Cl: 1.01 to 1.56) and pain or discomfort (OR=1.50, 95% Cl: 1.16 to 1.94). Elderly residing in rural area are more likely to have problems in pain/discomfort (OR=1.28, 95% Cl: 1.01 to 1.62). More older and non-working elderly are more likely whereas higher educated and highest wealth quintile elderly are less likely to have problems in all the dimensions of EQ-5D viz. mobility, self-care, usual activity, pain/discomfort and anxiety/depression. The present study has also shown that oldest old people, residing in rural area and currently not working elderly are more likely to report low EQ-5D index score whereas elderly with high education level and high wealth quintile are more likely to report high EQ-5D index score than their counterparts. Conclusion: The present study has found EQ-5D instrument as the valid measure for assessing the HRQOL of elderly in India. The study indicates socio-economic characteristics of elderly such as female, more older people, residing in rural area, non-educated, poor and currently non-working as the major risk groups of having poor HRQOL in India. Findings of the study will be helpful for the programmes and policy makers, researchers, academician and social workers who are working in the field of ageing.Keywords: ageing, HRQOL, India, EQ-5D, SAGE, socio-economic characteristics
Procedia PDF Downloads 4003322 Association of Work Pattern with the Well-Being and Happiness: Evidence from Married Women Working in Delhi, India
Authors: Kanchan Negi
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Background: Modern work culture has driven demands for people to work long hours and weekends and take work to home at times. Research on the health effects of these exhaustive temporal work patterns is scant or contradictory. This study examines the relationship between work patterns and well-being (including happiness) in a sample of working women. Method: Primary data of 360 currently married women working in the education, health, banking and IT sector in Delhi, India, were analysed. Logistic regression was used to estimate physical and psychological well-being and happiness across work characteristics. Results: Relative to 35–40 hours/week, working longer related to poor well-being (ß=0.75, 95% CI 0.12 to 1.39). Compared with not working weekends, working most or all weekends is related to poor physical (ß=0.34, 95% CI 0.08 to 0.61) and psychological well-being (ß=0.50, 95% CI 0.20 to 0.79). Rigid work patterns (ß=0.17, 95% CI −0.09 to 0.42) are also related to poor well-being. Conclusion: Decreased well-being and unhappiness are significantly linked to strenuous and rigid work patterns, suggesting that modern work culture may contribute to poor well-being. Flexible timings, compensatory holidays, work-from-home, and daycare facilities for young ones must be welcomed by companies to ease the dual burden of homemakers and career makers.Keywords: happiness, well-being, work pattern, working women
Procedia PDF Downloads 1843321 Association of Post-Traumatic Stress Disorder with Work Performance amongst Emergency Medical Service Personnel, Karachi, Pakistan
Authors: Salima Kerai, Muhammad Islam, Uzma Khan, Nargis Asad, Junaid Razzak, Omrana Pasha
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Background: Pre-hospital care providers are exposed to various kinds of stressors. Their daily exposure to diverse critical and traumatic incidents can lead to stress reactions like Post-Traumatic Stress Disorder (PTSD). Consequences of PTSD in terms of work loss can be catastrophic because of its compound effect on families, which affect them economically, socially and emotionally. Therefore, it is critical to assess the association between PTSD and Work performance in Emergency Medical Service (EMS) if exist any. Methods: This prospective observational study was carried out at AMAN EMS in Karachi, Pakistan. EMS personnel were screened for potential PTSD using impact of event scale-revised (IES-R). Work performance was assessed on basis of five variables; number of late arrivals to work, number of days absent, number of days sick, adherence to protocol and patient satisfaction survey over the period of 3 months. In order to model outcomes like number of late arrivals to work, days absent and days late; negative binomial regression was used whereas logistic regression was applied for adherence to protocol and linear for patient satisfaction scores. Results: Out of 536 EMS personnel, 525 were found to be eligible, of them 518 consented. However data on 507 were included because 7 left the job during study period. The mean score of PTSD was found to be 24.0 ± 12.2. However, weak and insignificant association was found between PTSD and work performance measures: number of late arrivals (RRadj 0.99; 95% CI 0.98-1.00), days absent (RRadj 0.98; 95% CI 0.96-0.99), days sick (Rradj 0.99; 95% CI 0.98 to 1.00), adherence to protocol (ORadj 1.01: 95% CI 0.99 to 1.04) and patient satisfaction (0.001% score; 95% CI -0.03% to 0.03%). Conclusion: No association was found between PTSD and Work performance in the selected EMS population in Karachi Pakistan. Further studies are needed to explore the phenomenon of resiliency in these populations. Moreover, qualitative work is required to explore perceptions and feelings like willingness to go to work, readiness to carry out job responsibilities.Keywords: trauma, emergency medical service, stress, pakistan
Procedia PDF Downloads 3383320 Plasmodium falciparum Infection and SARS-CoV-2 Immunoglobulin-G Positivity Rates Among Primary Healthcare Centre Attendees in Osogbo, Nigeria
Authors: Ojo Oo, Akinde S. B., Kiilani A. O., Jayeola Jo, Jogbodo T. M., Ajani Ka, Olaniyan So, Adeagbo Oy, Bolarinwa Ra, Durosomo Ha, Sule W. F.
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Lockdown imposed to control SARS-CoV-2 transmission hampered malaria control services in Nigeria. Considering COVID-19 vaccination, we assessed Plasmodium falciparum (Pf) antigen and SARS-CoV-2 immunoglobulin-G (IgG) positivity among adults in Osogbo, Osun State, Nigeria. Consenting attendees of four Healthcare Centres were consecutively enrolled for blood sampling; relevant socio-demographic/behavioral/clinical/environmental data were collected with a questionnaire. Samples were tested, using commercial rapid test kits, for Pf antigen and SARS-CoV-2 IgG and results were analyzed using logistic regression. Participants' mean age was 40.99 years (n=200), and they were predominantly females (84.5%), traders/businessmen/women (86.0%), with self-reported receipt of COVID-19 vaccine from 123 (61.5%). Pf antigen positivity was 17.5% (95% CI: 12.23–22.77%) with age (p=0.004), marital status (p=0.004), report of stagnant water around the workplace (p=0.041) and bush around homes (p=0.008) being associated. SARS-CoV-2 IgG positivity was 56.5% (95% CI: 49.63–63.37%) with age (p=0.012) and receipt of COVID-19 vaccination (p=0.001) being associated. Although the vaccinated had a 22.8 times higher likelihood of IgG positivity, no factor was predictive of COVID-19 vaccine receipt. We report 17.5% Pf antigen positivity with four predictors, and 56.5% SARS-CoV-2 IgG positivity with two predictors.Keywords: COVID-19, vaccine, IgG, Plasmodium falciparum, SARS-CoV-2
Procedia PDF Downloads 1413319 Prevalence of Microalbuminuria and Its Relation with Various Risk Factors in Type 1 Diabetes Mellitus
Authors: Singh Baljinder, Sharma Navneet
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Microalbuminuria is the earliest detectable marker of diabetic nephropathy. We planned to evaluate the prevalence of microalbuminuria in type 1 diabetics and correlate with various risk factor. We randomly selected 100 type 1 diabetic patients after inclusion and exclusion criteria from DCRC, S. P. Medical College, Bikaner. Clinical examinations for anthropometeric parameters, hypertension, retinopathy, glycaemic status, lipid profile were done and microalbuminuria was estimated by micral test. Microalbuminuria was seen in 38% patients. The mean urinary albumin concentration was 96.61 mg/l in microalbuminuria positive cases, 134 mg/L in hypertensive patients while 74.5 mg/L in normal patients. Mean diabetic duration was 6.43 years in microalbuminurics. Albumin excretion increased significantly with age at onset of 10-18 years and declined thereafter. Microalbuminuria cases exhibited mean cholesterol 181.63 mg%, TG 130.94 mg%, LDL 109.87 mg%, HDL 57.5 mg% and VLDL 30.64 mg%. Mean urinary albumin concentration in patients with retinopathy was 160.52 mg/L while 78.66 mg/L without retinopathy. In multiple stepwise logistic regression analysis, a strong positive association was seen between microalbuminuria and hypertension (OR=5.087, CI=2.1319-12.101), fasting blood sugar (OR=3. 491, CI=1.138-10.70), duration of diabetes (OR=3.41, CI=1.360-8.55) and HbA1c (OR=2.381, CI-=1.1-5.64). The present study indicates that microalbuminuria is a common complication of type 1 diabetes mellitus and can be prevented by careful management of risk factors.Keywords: type 1 diabetes, microalbuminuria, diabetic nephropathy, retinopathy, hypertension
Procedia PDF Downloads 4453318 Study of Seismic Damage Reinforced Concrete Frames in Variable Height with Logistic Statistic Function Distribution
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
Procedia PDF Downloads 2463317 Support Vector Regression for Retrieval of Soil Moisture Using Bistatic Scatterometer Data at X-Band
Authors: Dileep Kumar Gupta, Rajendra Prasad, Pradeep Kumar, Varun Narayan Mishra, Ajeet Kumar Vishwakarma, Prashant K. Srivastava
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An approach was evaluated for the retrieval of soil moisture of bare soil surface using bistatic scatterometer data in the angular range of 200 to 700 at VV- and HH- polarization. The microwave data was acquired by specially designed X-band (10 GHz) bistatic scatterometer. The linear regression analysis was done between scattering coefficients and soil moisture content to select the suitable incidence angle for retrieval of soil moisture content. The 250 incidence angle was found more suitable. The support vector regression analysis was used to approximate the function described by the input-output relationship between the scattering coefficient and corresponding measured values of the soil moisture content. The performance of support vector regression algorithm was evaluated by comparing the observed and the estimated soil moisture content by statistical performance indices %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE). The values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 2.9451, 1.0986, and 0.9214, respectively at HH-polarization. At VV- polarization, the values of %Bias, root mean squared error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were found 3.6186, 0.9373, and 0.9428, respectively.Keywords: bistatic scatterometer, soil moisture, support vector regression, RMSE, %Bias, NSE
Procedia PDF Downloads 4283316 A Novel Approach to Asynchronous State Machine Modeling on Multisim for Avoiding Function Hazards
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
Procedia PDF Downloads 4293315 Change of Endocrine and Exocrine Insufficiency on Non-Diabetes Patients after Distal Pancreatectomy: A Nationwide Database Study
Authors: Jin-Ming Wu, Te-Wei Ho, Yu-Wen Tien
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Background: The aim of this population-based study was to determine the occurrence of diabetes and exocrine pancreatic insufficiencies (EPI) on non-diabetes subjects receiving distal pancreatectomy (DP). Method: A nationwide cohort study between 2000 and 2010 was collected from the Taiwan National Health Insurance Research Database. Among 3264 DP patients, we identified 1410 non-diabetes and 966 non-diabetes non-EPI. Results. Of 1410 non-diabetes DP subjects, 312 patients (22.1%) developed newly-diagnosed diabetes after PD. On a multiple logistic regression model, co-morbid hyperlipidemia (odds ratio, 1.640; 95% CI, 1.362–2.763; P < 0.001) and pancreatitis (odds ratio, 2.428; 95% CI, 1.889–3.121; P < 0.001) significantly contributed to higher incidences of diabetes after DP. Moreover, 380 subjects (39.3%) developed EPI, and pancreatic cancer is the statistically significant risk factor (odds ratio, 4.663; 95% CI, 2.108–6.085; P < 0.001). Conclusion: The patients with co-morbid hyperlipidemia and chronic pancreatitis had higher rates of newly-diagnosed diabetes after DP, moreover, pancreatic cancer subjects had higher rates of pancreatic exocrine insufficiency after DP. The clinicians should be alert to follow up glucose metabolism and clinical symptoms of fat intolerance for DP patients.Keywords: distal pancreatectomy, National database, diabetes, exocrine insufficiency
Procedia PDF Downloads 1973314 A Comparative Analysis of Machine Learning Techniques for PM10 Forecasting in Vilnius
Authors: Mina Adel Shokry Fahim, Jūratė Sužiedelytė Visockienė
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With the growing concern over air pollution (AP), it is clear that this has gained more prominence than ever before. The level of consciousness has increased and a sense of knowledge now has to be forwarded as a duty by those enlightened enough to disseminate it to others. This realisation often comes after an understanding of how poor air quality indices (AQI) damage human health. The study focuses on assessing air pollution prediction models specifically for Lithuania, addressing a substantial need for empirical research within the region. Concentrating on Vilnius, it specifically examines particulate matter concentrations 10 micrometers or less in diameter (PM10). Utilizing Gaussian Process Regression (GPR) and Regression Tree Ensemble, and Regression Tree methodologies, predictive forecasting models are validated and tested using hourly data from January 2020 to December 2022. The study explores the classification of AP data into anthropogenic and natural sources, the impact of AP on human health, and its connection to cardiovascular diseases. The study revealed varying levels of accuracy among the models, with GPR achieving the highest accuracy, indicated by an RMSE of 4.14 in validation and 3.89 in testing.Keywords: air pollution, anthropogenic and natural sources, machine learning, Gaussian process regression, tree ensemble, forecasting models, particulate matter
Procedia PDF Downloads 533313 Forecasting Equity Premium Out-of-Sample with Sophisticated Regression Training Techniques
Authors: Jonathan Iworiso
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Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains
Procedia PDF Downloads 1073312 Image Analysis for Obturator Foramen Based on Marker-controlled Watershed Segmentation and Zernike Moments
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
Procedia PDF Downloads 4343311 Self-Image of Police Officers
Authors: Leo Carlo B. Rondina
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Self-image is an important factor to improve the self-esteem of the personnel. The purpose of the study is to determine the self-image of the police. The respondents were the 503 policemen assigned in different Police Station in Davao City, and they were chosen with the used of random sampling. With the used of Exploratory Factor Analysis (EFA), latent construct variables of police image were identified as follows; professionalism, obedience, morality and justice and fairness. Further, ordinal regression indicates statistical characteristics on ages 21-40 which means the age of the respondent statistically improves self-image.Keywords: police image, exploratory factor analysis, ordinal regression, Galatea effect
Procedia PDF Downloads 2883310 Regression Analysis of Travel Indicators and Public Transport Usage in Urban Areas
Authors: Mehdi Moeinaddini, Zohreh Asadi-Shekari, Muhammad Zaly Shah, Amran Hamzah
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Currently, planners try to have more green travel options to decrease economic, social and environmental problems. Therefore, this study tries to find significant urban travel factors to be used to increase the usage of alternative urban travel modes. This paper attempts to identify the relationship between prominent urban mobility indicators and daily trips by public transport in 30 cities from various parts of the world. Different travel modes, infrastructures and cost indicators were evaluated in this research as mobility indicators. The results of multi-linear regression analysis indicate that there is a significant relationship between mobility indicators and the daily usage of public transport.Keywords: green travel modes, urban travel indicators, daily trips by public transport, multi-linear regression analysis
Procedia PDF Downloads 5493309 Development of Generalized Correlation for Liquid Thermal Conductivity of N-Alkane and Olefin
Authors: A. Ishag Mohamed, A. A. Rabah
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The objective of this research is to develop a generalized correlation for the prediction of thermal conductivity of n-Alkanes and Alkenes. There is a minority of research and lack of correlation for thermal conductivity of liquids in the open literature. The available experimental data are collected covering the groups of n-Alkanes and Alkenes.The data were assumed to correlate to temperature using Filippov correlation. Nonparametric regression of Grace Algorithm was used to develop the generalized correlation model. A spread sheet program based on Microsoft Excel was used to plot and calculate the value of the coefficients. The results obtained were compared with the data that found in Perry's Chemical Engineering Hand Book. The experimental data correlated to the temperature ranged "between" 273.15 to 673.15 K, with R2 = 0.99.The developed correlation reproduced experimental data that which were not included in regression with absolute average percent deviation (AAPD) of less than 7 %. Thus the spread sheet was quite accurate which produces reliable data.Keywords: N-Alkanes, N-Alkenes, nonparametric, regression
Procedia PDF Downloads 6543308 Investigating the Motion of a Viscous Droplet in Natural Convection Using the Level Set Method
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
Procedia PDF Downloads 1203307 Spectral Analysis Approaches for Simultaneous Determination of Binary Mixtures with Overlapping Spectra: An Application on Pseudoephedrine Sulphate and Loratadine
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 3213306 Exercise Behavior of Infertile Women at Risk of Osteoporosis: Application of The Health Belief Model
Authors: Arezoo Fallahi
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We aimed at investigating the association between health beliefs and exercise behavior in infertile women who were at risk of developing osteoporosis. This cross-sectional study was conducted in Sanandaj city, west of Iran in 2018. From 35 comprehensive healthcare centers, 483 infertile women were included in the study through convenience sampling. Standardized face-to-face interviews were conducted using established, reliable instruments for the assessment of exercise behavior behavior and health beliefs. Logistic regression models were applied to assess the association between exercise behavior and health beliefs. Estimates were adjusted for age, job status, income, literacy, and duration and type of infertility. We reported estimated logits and Odds Ratios (OR) with corresponding 95% confidence intervals (95% CI). Employed women compared to housewives had substantially higher odds of adopting exercise behavior behaviors (OR=3.19, 95% CI=1.53-6.66, p<0.01). Moreover, the odds of exercise behavior adoption increased with self-efficacy (OR=1.35, 95% CI=1.20-1.52, p<0.01), and decreased with perceived barriers (OR=0.90, 95% CI=0.84-0.97, p<0.01). It is essential to increase perceived self-efficacy and reduce perceived barriers to promote EB in infertile women. Consequently, health professionals should develop or adopt appropriate strategies to decrease barriers and increase self-efficacy to enhance exercise behavior in this group of women.Keywords: infertility, women, exercise, osteoporosis
Procedia PDF Downloads 713305 An Improved Method on Static Binary Analysis to Enhance the Context-Sensitive CFI
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 3233304 Premature Menopause among Women in India: Evidence from National Family Health Survey-IV
Authors: Trupti Meher, Harihar Sahoo
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Premature menopause refers to the occurrence of menopause before the age of 40 years. Women who experience premature menopause either due to biological or induced reasons have a longer duration of exposure to severe symptoms and adverse health consequences when compared to those who undergo menopause at a later age, despite the fact that premature menopause has a profound effect on the health of women. This study attempted to determine the prevalence and predictors of premature menopause among women aged 25-39 years, using data from the National Family Health Survey (NFHS-4) conducted during 2015–16 in India. Descriptive statistics and multinomial logistic regression were used to carry out the result. The results revealed that the prevalence of premature menopause in India was 3.7 percent. Out of which, 2.1 percent of women had experienced natural premature menopause, whereas 1.7 percent had premature surgical menopause. The prevalence of premature menopause was highest in the southern region of India. Further, results of the multivariate model indicated that rural women, women with higher parity, early age at childbearing and women with smoking habits were at a greater risk of premature menopause. A sizeable proportion of women in India are attaining menopause prematurely. Unless due attention is given to this matter, it will emerge as a major problem in India in the future. The study also emphasized the need for further research to enhance knowledge on the problems of premature menopausal women in different socio-cultural settings in India.Keywords: India, natural menopause, premature menopause, surgical menopause
Procedia PDF Downloads 2073303 Response Surface Methodology for the Optimization of Paddy Husker by Medium Brown Rice Peeling Machine 6 Rubber Type
Authors: S. Bangphan, P. Bangphan, C. Ketsombun, T. Sammana
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Optimization of response surface methodology (RSM) was employed to study the effects of three factor (rubber of clearance, spindle of speed, and rice of moisture) in brown rice peeling machine of the optimal good rice yield (99.67, average of three repeats). The optimized composition derived from RSM regression was analyzed using Regression analysis and Analysis of Variance (ANOVA). At a significant level α=0.05, the values of Regression coefficient, R2 adjust were 96.55% and standard deviation were 1.05056. The independent variables are initial rubber of clearance, spindle of speed and rice of moisture parameters namely. The investigating responses are final rubber clearance, spindle of speed and moisture of rice.Keywords: brown rice, response surface methodology (RSM), peeling machine, optimization, paddy husker
Procedia PDF Downloads 5743302 Factors Influencing Disclosure and CSR Spending in Indian Companies: An Econometric Analysis
Authors: Shekar Babu, Amalendu Jyothishi
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The New Companies Bill-2013 in India has mandated all the companies with a certain profit to spend on Corporate Social Responsibility (CSR). Despite the Corporate Governance (CG) compliances at the strategic level the firms have to engage in social good. For both the Central Public Sector Enterprises (CPSE) and the private companies in India the need for strategic CSR focus through operational efficiency measures are mandated. In this paper the focus is to find out if the Indian companies understand their responsibility towards the society despite government making CSR mandatory. Analyzing both the CPSEs and Private companies the researchers find out which set of companies behave responsibly towards the society. Does any particular industry group(s) impact the society by disclosing their CSR spending activities. The key financial and non-financial parameters that influence CSR spending were identified and through econometric analysis methodologies (logistic regression and OLS models) the results were analyzed. The innovative methods were developed to identify if the firms operate efficiently and at the same time complying with the new CSR laws. An innovative matrix was developed to explain how companies could operate efficiently and be compliant in parallel how some of the companies can strategically realign their spending by operating efficiently.Keywords: corporate social responsibility(CSR), corporate governance(CG), India, logit function, ordinary least squares (OLS)
Procedia PDF Downloads 3553301 Alkali Activation of Fly Ash, Metakaolin and Slag Blends: Fresh and Hardened Properties
Authors: Weiliang Gong, Lissa Gomes, Lucile Raymond, Hui Xu, Werner Lutze, Ian L. Pegg
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Alkali-activated materials, particularly geopolymers, have attracted much interest in academia. Commercial applications are on the rise, as well. Geopolymers are produced typically by a reaction of one or two aluminosilicates with an alkaline solution at room temperature. Fly ash is an important aluminosilicate source. However, using low-Ca fly ash, the byproduct of burning hard or black coal reacts and sets slowly at room temperature. The development of mechanical durability, e.g., compressive strength, is slow as well. The use of fly ashes with relatively high contents ( > 6%) of unburned carbon, i.e., high loss on ignition (LOI), is particularly disadvantageous as well. This paper will show to what extent these impediments can be mitigated by mixing the fly ash with one or two more aluminosilicate sources. The fly ash used here is generated at the Orlando power plant (Florida, USA). It is low in Ca ( < 1.5% CaO) and has a high LOI of > 6%. The additional aluminosilicate sources are metakaolin and blast furnace slag. Binary fly ash-metakaolin and ternary fly ash-metakaolin-slag geopolymers were prepared. Properties of geopolymer pastes before and after setting have been measured. Fresh mixtures of aluminosilicates with an alkaline solution were studied by Vicat needle penetration, rheology, and isothermal calorimetry up to initial setting and beyond. The hardened geopolymers were investigated by SEM/EDS and the compressive strength was measured. Initial setting (fluid to solid transition) was indicated by a rapid increase in yield stress and plastic viscosity. The rheological times of setting were always smaller than the Vicat times of setting. Both times of setting decreased with increasing replacement of fly ash with blast furnace slag in a ternary fly ash-metakaolin-slag geopolymer system. As expected, setting with only Orlando fly ash was the slowest. Replacing 20% fly ash with metakaolin shortened the set time. Replacing increasing fractions of fly ash in the binary system by blast furnace slag (up to 30%) shortened the time of setting even further. The 28-day compressive strength increased drastically from < 20 MPa to 90 MPa. The most interesting finding relates to the calorimetric measurements. The use of two or three aluminosilicates generated significantly more heat (20 to 65%) than the calculated from the weighted sum of the individual aluminosilicates. This synergetic heat contributes or may be responsible for most of the increase of compressive strength of our binary and ternary geopolymers. The synergetic heat effect may be also related to increased incorporation of calcium in sodium aluminosilicate hydrate to form a hybrid (N,C)A-S-H) gel. The time of setting will be correlated with heat release and maximum heat flow.Keywords: alkali-activated materials, binary and ternary geopolymers, blends of fly ash, metakaolin and blast furnace slag, rheology, synergetic heats
Procedia PDF Downloads 1163300 Correlation of IFNL4 ss469415590 and IL28B rs12979860 with the Hepatitis C Virus Treatment Response among Tunisian Patients
Authors: Khaoula Azraiel, Mohamed Mehdi Abassi, Amel Sadraoui, Walid Hammami, Azouz Msaddek, Imed Cheikh, Maria Mancebo, Elisabet Perez-Navarro, Antonio Caruz, Henda Triki, Ahlem Djebbi
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IL28B rs12979860 genotype is confirmed as an important predictor of response to peginterferon/ribavirin therapy in patients with chronic hepatitis C (CHC). IFNL4 ss469415590 is a newly discovered polymorphism that could also affect the sustained virological response (SVR). The aim of this study was to evaluate the association of IL28B and IFNL4 genotypes with peginterferon/ribavirin treatment response in Tunisians patients with CHC and to determine which of these SNPs, was the stronger marker. A total of 120 patients were genotyped for both rs12979860 and ss469415590 polymorphisms. The association of each genetic marker with SVR was analyzed and comparison between the two SNPs was calculated by logistic regression models. For rs12979860, 69.6% of patients with CC, 41.8% with CT and 42.8% with TT achieved SVR (p = 0.003). Regarding ss469415590, 70.4% of patients with TT/TT genotype achieved SVR compared to 42.8% with TT/ΔG and 37.5% with ΔG /ΔG (p = 0.002). The presence of CC and TT/TT genotypes was independently associated with treatment response with an OR of 3.86 for each. In conclusion, both IL28B rs12979860 and IFNL4 ss469415590 variants were associated with response to pegIFN/RBV in Tunisian patients, without any additional benefit in performance for IFNL4. Our results are different from those detected in Sub-Saharan Africa countries.Keywords: Hepatitis C virus, IFNL4, IL28B, Peginterferon/ribavirin, polymorphism
Procedia PDF Downloads 3383299 Merging and Comparing Ontologies Generically
Authors: Xiuzhan Guo, Arthur Berrill, Ajinkya Kulkarni, Kostya Belezko, Min Luo
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Ontology operations, e.g., aligning and merging, were studied and implemented extensively in different settings, such as categorical operations, relation algebras, and typed graph grammars, with different concerns. However, aligning and merging operations in the settings share some generic properties, e.g., idempotence, commutativity, associativity, and representativity, labeled by (I), (C), (A), and (R), respectively, which are defined on an ontology merging system (D~M), where D is a non-empty set of the ontologies concerned, ~ is a binary relation on D modeling ontology aligning and M is a partial binary operation on D modeling ontology merging. Given an ontology repository, a finite set O ⊆ D, its merging closure Ô is the smallest set of ontologies, which contains the repository and is closed with respect to merging. If (I), (C), (A), and (R) are satisfied, then both D and Ô are partially ordered naturally by merging, Ô is finite and can be computed, compared, and sorted efficiently, including sorting, selecting, and querying some specific elements, e.g., maximal ontologies and minimal ontologies. We also show that the ontology merging system, given by ontology V -alignment pairs and pushouts, satisfies the properties: (I), (C), (A), and (R) so that the merging system is partially ordered and the merging closure of a given repository with respect to pushouts can be computed efficiently.Keywords: ontology aligning, ontology merging, merging system, poset, merging closure, ontology V-alignment pair, ontology homomorphism, ontology V-alignment pair homomorphism, pushout
Procedia PDF Downloads 8933298 Influence of Causal beliefs on self-management in Korean patients with hypertension
Authors: Hyun-E Yeom
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Patients’ views about the cause of hypertension may influence their present and proactive behaviors to regulate high blood pressure. This study aimed to examine the internal structure underlying the causal beliefs about hypertension and the influence of causal beliefs on self-care intention and medical compliance in Korean patients with hypertension. The causal beliefs of 145 patients (M age = 57.7) were assessed using the Illness Perception Questionnaire-Revised. An exploratory factor analysis was used to identify the factor structure of the causal beliefs, and the factors’ influence on self-care intention and medication compliance was analyzed using multiple and logistic regression analyses. The four-factor structure including psychological, fate-related, risk and habitual factors was identified and the psychological factor was the most representative component of causal beliefs. The risk and fate-related factors were significant factors affecting lower intention to engage in self-care and poor compliance with medication regimens, respectively. The findings support the critical role of causal beliefs about hypertension in driving patients’ current and future self-care behaviors. This study highlights the importance of educational interventions corresponding to patients’ awareness of hypertension for improving their adherence to a healthy lifestyle and medication regimens.Keywords: hypertension, self-care, beliefs, medication compliance
Procedia PDF Downloads 3513297 Scalable Systolic Multiplier over Binary Extension Fields Based on Two-Level Karatsuba Decomposition
Authors: Chiou-Yng Lee, Wen-Yo Lee, Chieh-Tsai Wu, Cheng-Chen Yang
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Shifted polynomial basis (SPB) is a variation of polynomial basis representation. SPB has potential for efficient bit-level and digit-level implementations of multiplication over binary extension fields with subquadratic space complexity. For efficient implementation of pairing computation with large finite fields, this paper presents a new SPB multiplication algorithm based on Karatsuba schemes, and used that to derive a novel scalable multiplier architecture. Analytical results show that the proposed multiplier provides a trade-off between space and time complexities. Our proposed multiplier is modular, regular, and suitable for very-large-scale integration (VLSI) implementations. It involves less area complexity compared to the multipliers based on traditional decomposition methods. It is therefore, more suitable for efficient hardware implementation of pairing based cryptography and elliptic curve cryptography (ECC) in constraint driven applications.Keywords: digit-serial systolic multiplier, elliptic curve cryptography (ECC), Karatsuba algorithm (KA), shifted polynomial basis (SPB), pairing computation
Procedia PDF Downloads 3633296 Clustering Categorical Data Using the K-Means Algorithm and the Attribute’s Relative Frequency
Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami
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Clustering is a well known data mining technique used in pattern recognition and information retrieval. The initial dataset to be clustered can either contain categorical or numeric data. Each type of data has its own specific clustering algorithm. In this context, two algorithms are proposed: the k-means for clustering numeric datasets and the k-modes for categorical datasets. The main encountered problem in data mining applications is clustering categorical dataset so relevant in the datasets. One main issue to achieve the clustering process on categorical values is to transform the categorical attributes into numeric measures and directly apply the k-means algorithm instead the k-modes. In this paper, it is proposed to experiment an approach based on the previous issue by transforming the categorical values into numeric ones using the relative frequency of each modality in the attributes. The proposed approach is compared with a previously method based on transforming the categorical datasets into binary values. The scalability and accuracy of the two methods are experimented. The obtained results show that our proposed method outperforms the binary method in all cases.Keywords: clustering, unsupervised learning, pattern recognition, categorical datasets, knowledge discovery, k-means
Procedia PDF Downloads 2603295 On the Performance of Improvised Generalized M-Estimator in the Presence of High Leverage Collinearity Enhancing Observations
Authors: Habshah Midi, Mohammed A. Mohammed, Sohel Rana
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Multicollinearity occurs when two or more independent variables in a multiple linear regression model are highly correlated. The ridge regression is the commonly used method to rectify this problem. However, the ridge regression cannot handle the problem of multicollinearity which is caused by high leverage collinearity enhancing observation (HLCEO). Since high leverage points (HLPs) are responsible for inducing multicollinearity, the effect of HLPs needs to be reduced by using Generalized M estimator. The existing GM6 estimator is based on the Minimum Volume Ellipsoid (MVE) which tends to swamp some low leverage points. Hence an improvised GM (MGM) estimator is presented to improve the precision of the GM6 estimator. Numerical example and simulation study are presented to show how HLPs can cause multicollinearity. The numerical results show that our MGM estimator is the most efficient method compared to some existing methods.Keywords: identification, high leverage points, multicollinearity, GM-estimator, DRGP, DFFITS
Procedia PDF Downloads 2623294 Vaccination Coverage and Its Associated Factors in India: An ML Approach to Understand the Hierarchy and Inter-Connections
Authors: Anandita Mitro, Archana Srivastava, Bidisha Banerjee
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The present paper attempts to analyze the hierarchy and interconnection of factors responsible for the uptake of BCG vaccination in India. The study uses National Family Health Survey (NFHS-5) data which was conducted during 2019-21. The univariate logistic regression method is used to understand the univariate effects while the interconnection effects have been studied using the Categorical Inference Tree (CIT) which is a non-parametric Machine Learning (ML) model. The hierarchy of the factors is further established using Conditional Inference Forest which is an extension of the CIT approach. The results suggest that BCG vaccination coverage was influenced more by system-level factors and awareness than education or socio-economic status. Factors such as place of delivery, antenatal care, and postnatal care were crucial, with variations based on delivery location. Region-specific differences were also observed which could be explained by the factors. Awareness of the disease was less impactful along with the factor of wealth and urban or rural residence, although awareness did appear to substitute for inadequate ANC. Thus, from the policy point of view, it is revealed that certain subpopulations have less prevalence of vaccination which implies that there is a need for population-specific policy action to achieve a hundred percent coverage.Keywords: vaccination, NFHS, machine learning, public health
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