Search results for: binary logistic model

Authors: K. Sekulová, M. Šimon

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This article focuses on upper-extremity musculoskeletal disorders risk assessment model at workplace. In this model are used risk factors that are responsible for musculoskeletal system damage. Based on statistic calculations the model is able to define what risk of MSD threatens workers who are under risk factors. The model is also able to say how MSD risk would decrease if these risk factors are eliminated.

Keywords: ergonomics, musculoskeletal disorders, occupational diseases, risk factors

Procedia PDF Downloads 535

Authors: Anthony Usoro

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In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.

Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model

Procedia PDF Downloads 391

Authors: M. D. D. Chathurangi, M. G. K. Nayanathara, K. M. H. M. M. Gunapala, G. M. R. G. Dayananda, Kavinga Yapa Abeywardena, Deemantha Siriwardana

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Due to the growing popularity of social media platforms at present, there are various concerns, mostly cyberbullying, spam, bot accounts, and the spread of incorrect information. To develop a risk score calculation system as a thorough method for deciphering and exposing unethical social media profiles, this research explores the most suitable algorithms to our best knowledge in detecting the mentioned concerns. Various multiple models, such as Naïve Bayes, CNN, KNN, Stochastic Gradient Descent, Gradient Boosting Classifier, etc., were examined, and the best results were taken into the development of the risk score system. For cyberbullying, the Logistic Regression algorithm achieved an accuracy of 84.9%, while the spam-detecting MLP model gained 98.02% accuracy. The bot accounts identifying the Random Forest algorithm obtained 91.06% accuracy, and 84% accuracy was acquired for fake news detection using SVM.

Keywords: cyberbullying, spam behavior, bot accounts, fake news, machine learning

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Authors: Biniam Seifu Debelo, Bheema Lingaiah Thamineni, Hanumesh Kumar Dasari, Ahmed Ali Dawud

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Background: One of the most frequent neurological conditions in newborns is neonatal seizures, which may indicate severe neurological dysfunction. They may be caused by a broad range of problems with the central nervous system during or after pregnancy, infections, brain injuries, and/or other health conditions. These seizures may have very subtle or very modest clinical indications because patterns like oscillatory (spike) trains begin with relatively low amplitude and gradually increase over time. This becomes very challenging and erroneous if clinical observation is the primary basis for identifying newborn seizures. Objectives: In this study, a diagnosis system using deep convolutional neural networks is proposed to determine and classify the severity level of neonatal seizures using multichannel neonatal EEG data. Methods: Clinical multichannel EEG datasets were compiled using datasets from publicly accessible online sources. Various preprocessing steps were taken, including converting 2D time series data to equivalent waveform pictures. The proposed models underwent training, and their performance was evaluated. Results: The proposed CNN was used to perform binary classification with an accuracy of 92.6%, F1-score of 92.7%, specificity of 92.8%, and precision of 92.6%. To detect newborn seizures, this model is utilized. Using the proposed CNN model, multiclassification was performed with accuracy rates of 88.6%, specificity rates of 92.18%, F1-score rates of 85.61%, and precision rates of 88.9%. A multiclassification model is used to classify the severity level of neonatal seizures. The results demonstrated that the suggested strategy can assist medical professionals in making accurate diagnoses close to healthcare institutions. Conclusion: The developed system was capable of detecting neonatal seizures and has the potential to be used as a decision-making tool in resource-limited areas with a scarcity of expert neurologists.

Keywords: CNN, multichannel EEG, neonatal seizure, severity identification

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Authors: Jyoti Srivastava, Sandhya S. Kaushik, Mallika Tewari, Hari S. Shukla

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Little information is available about the development of psychological well being over time among women who have been undergoing treatment for breast cancer. The aim of this study was to identify the trajectories of depression anxiety and stress among women with early-stage breast cancer. Of the 48 Indian women with newly diagnosed early-stage breast cancer recruited from surgical oncology unit, 39 completed an interview and were assessed for depression anxiety and stress (Depression Anxiety Stress Scale-DASS 21) before their first course of chemotherapy (baseline) and follow up interviews at 3, 6 and 9 months thereafter. Growth mixture modeling was used to identify distinct trajectories of Depression Anxiety and Stress symptoms. Logistic Regression analysis was used to evaluate the characteristics of women in distinct groups. Most women showed mild to moderate level of depression and anxiety (68%) while normal to mild level of stress (71%). But one in 11 women was chronically anxious (9%) and depressed (9%). Young age, having a partner, shorter education and receiving chemotherapy but not radiotherapy might characterize women whose psychological symptoms remain strong nine months after diagnosis. By looking beyond the mean, it was found that several socio-demographic and treatment factors characterized the women whose depression, anxiety and stress level remained severe even nine months after diagnosis. The results suggest that support provided to cancer patients should have a special focus on a relatively small group of patient most in need.

Keywords: psychological well being, growth mixture modeling, logistic regression analysis, socio-demographic factors

Procedia PDF Downloads 134

Authors: Massimo Miccoli, Luca Marangoni, Alberto Aniello Scaringi, Alessandro Marceddu, Alessandro Amicone

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The susceptibility of deep neural networks (DNNs) to adversarial manipulations is a recognized challenge within the computer vision domain. Adversarial examples, crafted by adding subtle yet malicious alterations to benign images, exploit this vulnerability. Various defense strategies have been proposed to safeguard DNNs against such attacks, stemming from diverse research hypotheses. Building upon prior work, our approach involves the utilization of autoencoder models. Autoencoders, a type of neural network, are trained to learn representations of training data and reconstruct inputs from these representations, typically minimizing reconstruction errors like mean squared error (MSE). Our autoencoder was trained on a dataset of benign examples; learning features specific to them. Consequently, when presented with significantly perturbed adversarial examples, the autoencoder exhibited high reconstruction errors. The architecture of the autoencoder was tailored to the dimensions of the images under evaluation. We considered various image sizes, constructing models differently for 256x256 and 512x512 images. Moreover, the choice of the computer vision model is crucial, as most adversarial attacks are designed with specific AI structures in mind. To mitigate this, we proposed a method to replace image-specific dimensions with a structure independent of both dimensions and neural network models, thereby enhancing robustness. Our multi-modal autoencoder reconstructs the spectral representation of images across the red-green-blue (RGB) color channels. To validate our approach, we conducted experiments using diverse datasets and subjected them to adversarial attacks using models such as ResNet50 and ViT_L_16 from the torch vision library. The autoencoder extracted features used in a classification model, resulting in an MSE (RGB) of 0.014, a classification accuracy of 97.33%, and a precision of 99%.

Keywords: adversarial attacks, malicious images detector, binary classifier, multimodal transformer autoencoder

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Authors: Shank Kulkarni, Alireza Tabarraei

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The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s.

Keywords: constitutive modelling, ogden model, polyurea, SLS model, uniaxial compression test

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Authors: Hussein Altartouri

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In this paper the kinematic and kinetic models of an omnidirectional holonomic mobile robot is presented. The kinematic and kinetic models form the OmniDrive model. Therefore, a mathematical model for the robot equipped with three- omnidirectional wheels is derived. This model which takes into consideration the kinematics and kinetics of the robot, is developed to state space representation. Relative analysis of the velocities and displacements is used for the kinematics of the robot. Lagrange’s approach is considered in this study for deriving the equation of motion. The drive train and the mechanical assembly only of the Festo Robotino® is considered in this model. Mainly the model is developed for motion control. Furthermore, the model can be used for simulation purposes in different virtual environments not only Robotino® View. Further use of the model is in the mechatronics research fields with the aim of teaching and learning the advanced control theories.

Keywords: mobile robot, omni-direction wheel, mathematical model, holonomic mobile robot

Procedia PDF Downloads 578

Authors: Syrus Zimaze, George Nsimbe, Valley Mugwanya, Matiya Lule, Edgar Watson, Patrick Gwayambadde

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Physical inactivity is a growing global epidemic, also recognised as a major public health challenge. Globally, there are alarming rates of children reported with cardiovascular disease and obesity with limited interventions. In Sub Saharan Africa, there is limited information about involvement in physical activity especially among children aged 6 to 18 years. The aim of this study was to explore factors associated with involvement in physical activity among children in Uganda. Methods: We included all parents with children aged 6 to 18 years training with Excel Soccer Academy between January 2017 and June 2018. Physical activity definition was time spent participating in routine soccer training at the academy for more than 30 days. Each child's attendance was recorded, and parents provided demographic and social economic data. Data on predictors of physical activity involvement were collected using a standardized questionnaire. Descriptive statistics and frequency were used. Binary logistic regression was used at the multi variable level adjusting for education, residence, transport means and access to information technology. Results: Overall 356 parents were interviewed; Boys 318 (89.3%) engaged more in physical activity than girls. The median age for children was 13 years (IQR:6-18) and 42 years (IQR:37-49) among parents. The median time spent at the Excel soccer academy was 13.4 months (IQR: 4.6-35.7) Majority of the children attended formal education, p < 0.001). Factors associated with involvement in physical activity included: owning a permanent house compared to a rented house (odds ratio [OR] :2.84: 95% CI: 2.09-3.86, p < 0.0001), owning a car compared to using public transport (OR: 5.64 CI: 4.80-6.63, p < 0.0001), a parent having received formal education compared to non-formal education (OR: 2.93 CI: 2.47-3.46, p < 0.0001) and daily access to information technology (OR:0.40 CI:0.25-0.66, p < 0.001). Parent’s age and gender were not associated to involvement in physical activity. Conclusions: Socioeconomic factors were positively associated with involvement in physical activity with boys participating more than girls in soccer activities. More interventions are required geared towards increasing girl’s participation in physical activity and those targeting children from less privilege homes.

Keywords: physical activity, Sub-Saharan Africa, social economic factors, children

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Authors: T. N. Mac, B. Shahbodaghkhan, N. Khalili

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A new elastic-viscoplastic (EVP) constitutive model is proposed for the analysis of time-dependent behavior of clay. The proposed model is based on the bounding surface plasticity and the concept of viscoplastic consistency framework to establish continuous transition from plasticity to rate dependent viscoplasticity. Unlike the overstress based models, this model will meet the consistency condition in formulating the constitutive equation for EVP model. The procedure of deriving the constitutive relationship is also presented. Simulation results and comparisons with experimental data are then presented to demonstrate the performance of the model.

Keywords: bounding surface, consistency theory, constitutive model, viscosity

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Authors: Saem Lee, Thanh Nguyen, Hio-Jung Shin, Thomas Koellner

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Land-use management has an influence on the provision of ecosystem service in dynamic, agricultural landscapes. Agricultural land use is important for maintaining the productivity and sustainability of agricultural ecosystems. However, in Korea, intensive farming activities in this highland agricultural zone, the upper stream of Soyang has led to contaminated soil caused by over-use pesticides and fertilizers. This has led to decrease in water and soil quality, which has consequences for ecosystem services and human wellbeing. Conventional farming has still high percentage in this area and there is no special measure to prevent low water quality caused by farming activities. Therefore, the adoption of environmentally friendly farming has been considered one of the alternatives that lead to improved water quality and increase in biomass production. Concurrently, farm households with environmentally friendly farming have occupied still low rates. Therefore, our research involved a farm household survey spanning conventional farming, the farm in transition and organic farming in Soyang watershed. Another purpose of our research was to compare economic advantage of the farmers adopting environmentally friendly farming and non-adaptors and to investigate the different factors by logistic regression analysis with socio-economic and benefit-cost ratio variables. The results found that farmers with environmentally friendly farming tended to be younger than conventional farming and farmer in transition. They are similar in terms of gender which was predominately male. Farmers with environmentally friendly farming were more educated and had less farming experience than conventional farming and farmer in transition. Based on the benefit-cost analysis, total costs that farm in transition farmers spent for one year are about two times as much as the sum of costs in environmentally friendly farming. The benefit of organic farmers was assessed with 2,800 KRW per household per year. In logistic regression, the factors having statistical significance are subsidy and district, residence period and benefit-cost ratio. And district and residence period have the negative impact on the practice of environmentally friendly farming techniques. The results of our research make a valuable contribution to provide important information to describe Korean policy-making for agricultural and water management and to consider potential approaches to policy that would substantiate ways beneficial for sustainable resource management.

Keywords: organic farming, logistic regression, profitability, agricultural land-use

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

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Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

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Authors: Muna Khushaim

Abstract:

Aluminum-based alloys play a key role in modern engineering, especially in the aerospace industry. Introduction of solute atoms such as Li and Cu is the main approach to improve the strength in age-hardenable Al alloys via the precipitation hardening phenomenon. Knowledge of the decomposition process of the microstructure during the precipitation reaction is particularly important for future technical developments. The objective of this study is to investigate the nano-scale chemical composition in the Al-Cu, Al-Li and Al-Li-Cu during the early stage of the precipitation sequence and to describe whether this compositional difference correlates with variations in the observed precipitation kinetics. Comparing the random binomial frequency distribution and the experimental frequency distribution of concentrations in atom probe tomography data was used to investigate the early stage of decomposition in the different binary and ternary alloys which were experienced different heat treatments. The results show that an Al-1.7 at.% Cu alloy requires a long ageing time of approximately 8 h at 160 °C to allow the diffusion of Cu atoms into Al matrix. For the Al-8.2 at.% Li alloy, a combination of both the natural ageing condition (48 h at room temperature) and a short artificial ageing condition (5 min at 160 °C) induces increasing on the number density of the Li clusters and hence increase number of precipitated δ' particles. Applying this combination of natural ageing and short artificial ageing conditions onto the ternary Al-4 at.% Li-1.7 at.% Cu alloy induces the formation of a Cu-rich phase. Increasing the Li content in the ternary alloy up to 8 at.% and increasing the ageing time to 30 min resulted in the precipitation processes ending with δ' particles. Thus, the results contribute to the understanding of Al-alloy design.

Keywords: aluminum alloy, atom probe tomography, early stage, decomposition

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Authors: Anna Avramenko, Heikki Haario

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This paper reports the development and application of a 2D depth-averaged model. The main goal of this contribution is to apply the depth averaged equations to a wind park model in which the treatment of the geometry, introduced on the mathematical model by the mass and momentum source terms. The depth-averaged model will be used in future to find the optimal position of wind turbines in the wind park. K-E and 2D LES turbulence models were consider in this article. 2D CFD simulations for one hill was done to check the depth-averaged model in practise.

Keywords: depth-averaged equations, numerical modeling, CFD, wind park model

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Authors: Timothee Gidenne, Xia Pinqi

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In this paper, an investigation of the use of carbon nanotubes (CNTs) reinforced polymer as an actuator for an active flutter suppression to counter the flutter phenomena is conducted. The goal of this analysis is to establish a link between the behavior of the control surface and the actuators to demonstrate the veracity of using such a suppression system for the aeronautical field. A preliminary binary flutter model using simplified unsteady aerodynamics is developed to study the behavior of the wing while reaching the flutter speed and when the control system suppresses the flutter phenomena. The Timoshenko beam theory for bilayer materials is used to match the response of the control surface with the CNTs reinforced polymer (CNRP) actuators. According to Timoshenko theory, results show a good and realistic response for such a purpose. Even if the results are still preliminary, they show evidence of the potential use of CNRP for control surface actuation for the small-scale and lightweight system.

Keywords: actuators, aeroelastic, aeroservoelasticity, carbon nanotubes, flutter, flutter suppression

Procedia PDF Downloads 111

Authors: Mala Tankam Carine, Kekeunou Sévilor, Nukenine Elias

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Storage and preservation of agricultural products remain the only conditions ensuring the almost permanent availability of foodstuffs. However, infestations due to insects and microorganisms often occur. Callosobruchus maculatus is a pest that causes a lot of damage to cowpea stocks in the tropics. Several methods are adopted to limit their damage, but the use of synthetic chemical insecticides is the most widespread. Biopesticides in sustainable agriculture respond to several environmental, economic and social concerns while offering innovative opportunities that are ecologically and economically viable for producers, workers, consumers and ecosystems. Our main objective is to evaluate the insecticidal efficacy of binary combinations of Fossilshield with root-bark powder of Securidaca longepedunculata against Callosobruchus maculatus in stored cowpea Vigna unguiculata. Laboratory bioassays were conducted in stored grains to evaluate the toxicity of root-bark powder of Securidaca longepedunculata alone or combined with diatomaceous earth Fossil-Shield ® against C. maculatus. Twenty-hour-old adults of C. maculatus were exposed to 50g of cowpea seeds treated with four doses (10, 20, 30, and 40g/kg) of root-bark powder of S. longepedunculata, on the one hand, and (0.5, 1, 1.5, and 2 g/kg) on DE and binary combinations on the other hand. 0g/kg corresponded to untreated control. Adult mortality was recorded up to 7 days (d) post-treatment, whereas the number of F1 progeny was assessed after 30 d. Weight loss and germinative ability were conducted after 120 d. All treatments were arranged according to a completely randomized block with four replicates. The combined mixture of S. longepedunculata and DE controlled the beetle faster compared to the root-bark powder of S. longepedunculata alone. According to the Co-toxicity coefficient, additive effect of binary combinations was recorded at 3-day post-exposure time with the mixture 25% FossilShield + 75% S. longepedunculata. A synergistic action was observed after 3-d post-exposure at mixture 50% FossilShield + 50% S. longepedunculata and at 1-d and 3-d post-exposure periods at mixture 75% FossilShield + 25% S. longepedunculata. The mixture 25% FossilShield + 75% S. longepedunculata induced a decreased progeny of 6 times fewer individuals for 4.5 times less weight loss and 2, 9 times more sprouted grains than with root-bark powder of S. longepedunculata. The combination of FossilShield + S. longepedunculata was more potent than root-bark powder of S. longepedunculata alone, although the root-bark powder of S. longepedunculata caused significant reduction of F1 adults compared to the control. Combined action of botanical insecticides with FossilShield as a grain protectant in an integrated pest management approach is discussed.

Keywords: diatomaceous earth, cowpea, callosobruchus maculatus, securidaca longepedunculata, combined action, co-toxicity coefficient

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Authors: Jui-Ching Chou

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Numerical simulation is a popular method used to evaluate the effects of soil liquefaction on a structure or the effectiveness of a mitigation plan. Many constitutive models (UBCSAND model, PM4 model, SANISAND model, etc.) were presented to model the liquefaction phenomenon. In general, inputs of a constitutive model need to be calibrated against the soil cyclic resistance before being applied to the numerical simulation model. Then, simulation results can be compared with results from simplified liquefaction potential assessing methods. In this article, inputs of the UBCSAND model, a simple elastic-plastic stress-strain model, are calibrated against several popular generic liquefaction triggering curves of simplified liquefaction potential assessing methods via FLAC program. Calibrated inputs can provide engineers to perform a preliminary evaluation of an existing structure or a new design project.

Keywords: calibration, liquefaction, numerical simulation, UBCSAND Model

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Authors: Rudy Bahouth, Farouk Benmeddour, Emmanuel Moulin, Jamal Assaad

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Guided ultrasonic waves are used in Non-Destructive Testing (NDT) and Structural Health Monitoring (SHM) for inspection and damage detection. Recently, wireless data transmission using ultrasonic waves in solid metallic channels has gained popularity in some industrial applications such as nuclear, aerospace and smart vehicles. The idea is to find a good substitute for electromagnetic waves since they are highly attenuated near metallic components due to Faraday shielding. The proposed solution is to use ultrasonic guided waves such as Lamb waves as an information carrier due to their capability of propagation for long distances. In addition to this, valuable information about the health of the structure could be extracted simultaneously. In this work, the reliable frequency bandwidth for communication is extracted experimentally from dispersion curves at first. Then, an experimental platform for wireless communication using Lamb waves is described and built. After this, coherent demodulation algorithm used in telecommunications is tested for Amplitude Shift Keying, On-Off Keying and Binary Phase Shift Keying modulation techniques. Signal processing parameters such as threshold choice, number of cycles per bit and Bit Rate are optimized. Experimental results are compared based on the average Bit Error Rate. Results have shown high sensitivity to threshold selection for Amplitude Shift Keying and On-Off Keying techniques resulting a Bit Rate decrease. Binary Phase Shift Keying technique shows the highest stability and data rate between all tested modulation techniques.

Keywords: lamb waves communication, wireless communication, coherent demodulation, bit error rate

Procedia PDF Downloads 231

Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

Abstract:

Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies.

Keywords: runoff, roughness coefficient, PAR, WRM model

Procedia PDF Downloads 361

Authors: Masahiro Ohmura, Koh Kakusho, Takeshi Okadome

Abstract:

This paper presents a regression model with autocorrelated errors in which the inputs are social moods obtained by analyzing the adjectives in Twitter posts using a document topic model. The regression model predicts Dow Jones Industrial Average (DJIA) more precisely than autoregressive moving-average models.

Keywords: stock market prediction, social moods, regression model, DJIA

Procedia PDF Downloads 534

Authors: Adji Achmad Rinaldo Fernandes

Abstract:

SEM analysis is a complex multivariate analysis because it involves a number of exogenous and endogenous variables that are interconnected to form a model. The measurement model is divided into two, namely, the reflective model (reflecting) and the formative model (forming). Before carrying out further tests on SEM, there are assumptions that must be met, namely the linearity assumption, to determine the form of the relationship. There are three modeling approaches to path analysis, including parametric, nonparametric and semiparametric approaches. The aim of this research is to develop semiparametric SEM and obtain the best model. The data used in the research is secondary data as the basis for the process of obtaining simulation data. Simulation data was generated with various sample sizes of 100, 300, and 500. In the semiparametric SEM analysis, the form of the relationship studied was determined, namely linear and quadratic and determined one and two knot points with various levels of error variance (EV=0.5; 1; 5). There are three levels of closeness of relationship for the analysis process in the measurement model consisting of low (0.1-0.3), medium (0.4-0.6) and high (0.7-0.9) levels of closeness. The best model lies in the form of the relationship X1Y1 linear, and. In the measurement model, a characteristic of the reflective model is obtained, namely that the higher the closeness of the relationship, the better the model obtained. The originality of this research is the development of semiparametric SEM, which has not been widely studied by researchers.

Keywords: semiparametric SEM, measurement model, structural model, reflective model, formative model

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Authors: Vaibhav Barve

Abstract:

Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest.

Keywords: data embedding, decryption, encryption, reversible data hiding, steganography

Procedia PDF Downloads 278

Authors: Lazaro E. Nnko, Japhet J. Kashaigili, Gerald C. Monela, Pantaleo K. T. Munishi

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Agroforestry systems and practices are perceived to improve livelihood and sustainable management of natural resources. However, their adoption in various regions differs with the biophysical conditions and societal characteristics. This study was conducted in Kilombero District to investigate the factors influencing the adoption of different agroforestry systems and practices in agro-ecosystems and farming systems. A household survey, key informant interviews, and focus group discussion was used for data collection in three villages. Descriptive statistics and multinomial logistic regression in SPSS were applied for analysis. Results show that Igima and Ngajengwa villages had home garden practices dominated, as revealed by 63.3% and 66.7%, respectively, while Mbingu village had mixed intercropping practice with 56.67%. Agrosilvopasture systems were dominant in Igima and Ngajengwa villages with 56.7% and 66.7%, respectively, while in Mbingu village, the dominant system was agrosilviculture with 66.7%. The results from multinomial logistic regression show that different explanatory variable was statistical significance as predictors of the adoption of agroforestry systems and practices. Residence type and sex were the most dominant factor influencing the adoption of agroforestry systems. Duration of stay in the village, availability of extension education, residence, and sex were the dominant factor influencing the adoption of agroforestry practices. The most important and statistically significant factors among these were residence type and sex. The study concludes that agroforestry will be more successful if the local priorities, which include social-economic need characteristics of the society, will be considered in designing systems and practices. The socio-economic need of the community should be addressed in the process of expanding the adoption of agroforestry systems and practices.

Keywords: agroforestry adoption, agroforestry systems, agroforestry practices, agroforestry, Kilombero

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Authors: Leonard Simoni, Ilir Alimehmeti, Ervina Shirka, Endri Hasimi, Ndricim Kallashi, Verona Beka, Suerta Kabili, Artan Goda

Abstract:

Background: Diabetes Mellitus type 2, small vessel calibre, stented length of vessel, complex lesion morphology, and prior bypass surgery have resulted risk factors for In-Stent Restenosis (ISR). However, there are some contradictory results about body mass index (BMI) as a risk factor for ISR. Purpose: We want to identify clinical, lesional and procedural factors that can predict clinical ISR in our patients. Methods: Were enrolled 759 patients who underwent first-time elective PCI with Bare Metal Stents (BMS) from September 2011 to December 2013 in our Department of Cardiology and followed them for at least 1.5 years with a median of 862 days (2 years and 4 months). Only the patients re-admitted with ischemic heart disease underwent control coronary angiography but no routine angiographic control was performed. Patients were categorized in ISR and non-ISR groups and compared between them. Multivariate analysis - Binary Logistic Regression: Forward Conditional Method was used to identify independent predictive risk factors. P was considered statistically significant when <0.05. Results: ISR compared to non-ISR individuals had a significantly lower BMI (25.7±3.3 vs. 26.9±3.7, p=0.004), higher risk anatomy (LM + 3-vessel CAD) (23% vs. 14%, p=0.03), higher number of stents/person used (2.1±1.1 vs. 1.75±0.96, p=0.004), greater length of stents/person used (39.3±21.6 vs. 33.3±18.5, p=0.01), and a lower use of clopidogrel and ASA (together) (95% vs. 99%, p=0.012). They also had a higher, although not statistically significant, prevalence of Diabetes Mellitus (42% vs. 32%, p=0.072) and a greater number of treated vessels (1.36±0.5 vs. 1.26±0.5, p=0.08). In the multivariate analysis, Diabetes Mellitus type 2 and multiple stents used were independent predictors risk factors for In-Stent Restenosis, OR 1.66 [1.03-2.68], p=0.039, and OR 1.44 [1.16-1.78,] p=0.001, respectively. On the other side higher BMI and use of clopidogrel and ASA together resulted protective factors OR 0.88 [0.81-0.95], p=0.001 and OR 0.2 [0.06-0.72] p=0.013, respectively. Conclusion: Diabetes Mellitus and multiple stents are strong predictive risk factors, whereas the use of clopidogrel and ASA together are protective factors for clinical In-Stent Restenosis. Paradoxically High BMI is a protective factor for In-stent Restenosis, probably related to a larger diameter of vessels and consequently a larger diameter of stents implanted in these patients. Further studies are needed to clarify this finding.

Keywords: body mass index, diabetes mellitus, in-stent restenosis, percutaneous coronary intervention

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Authors: Eunji Choi, Borang Park, Youngjae Choi, Jinwoo Moon

Abstract:

In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.

Keywords: deep learning, indoor quality, metabolism, predictive model

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Authors: Dana Cassandra Winkler, Delia Leiding, Rene Bergs, Franziska Kaiser, Ramona Kirchhart, Ute Habel

Abstract:

Background: Violence is a multidimensional phenomenon, affecting people of every age, socio-economic status and gender. Nevertheless, most studies primarily focus on men perpetrating women. Aim of the present study is to identify the likelihood of mental and somatic disorders and risk-taking behavior in male violence affected. In addition, the relationship between age of violence experience and the risk for health-related problems was analyzed. Method: On the basis of current evidence, a questionnaire was developed focusing on demographic background, health status, risk-taking behavior, and active and passive violence exposure. In total, 5221 males (Mean: 56,1 years, SD: 17,6) were consulted. To account for the time of violence experience in an efficient way, age clusters ‘0-12 years’, ‘13-20 years’, ‘21-35 years’, ‘36-65 years’ and ‘over 65 years’ were defined. A binary logistic regression was calculated to reveal differences in violence-affected and non-violence affected males regarding health and risk-taking factors. Males who experienced violence on a daily/ almost daily basis vs. males who reported violence occurrence once/ several times a month/ year were compared with respect to health factors and risk-taking behavior. Data of males, who indicated active and passive violence exposure, were analyzed by a chi²-analysis, to investigate a possible relation between the age of victimization and violence perpetration. Findings: Results imply that general violence experience, independent of active and passive violence exposure increases the likelihood in favor of somatic-, psychosomatic- and mental disorders as well as risk-taking behavior in males. Experiencing violence on a daily or almost daily basis in childhood and adolescence may serve as a predictor for increased health problems and risk-taking behavior. Furthermore, the violence experience and perpetration occur significantly within the same age cluster. This underlines the importance of a near-term intervention to minimize the risk, that victims become perpetrators later. Conclusion: The present study reveals predictors concerning health risk factors as well as risk-taking behavior in males with violence exposure. The results of this study may underscore the benefit of intervention and regular health care approaches in violence-affected males and underline the importance of acknowledging the overlap of violence experience and perpetration for further research.

Keywords: health disease, male, mental health, prevalence, risk-taking behavior, violence

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Authors: Alan Wan

Abstract:

In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

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Authors: Iyanujesu Adereti, Tajudeen Basiru, Ayodamola Olanipekun

Abstract:

Background: Substance abuse among adolescents is of public health concern globally. Despite being the most abused by adolescents, there are limited studies on the prevalence of alcohol use and cigarette smoking among adolescents in Mauritius. Objectives: To determine the prevalence of cigarette smoking, alcohol use and associated correlates among school-going adolescents in Mauritius. Methodology: Data obtained from 2017 WHO Global School-based Student Health Survey (GSHS) survey of 3,012 school-going adolescents in Mauritius was analyzed using STATA. Descriptive statistics were used to obtain prevalence. Bivariate and multivariate logistic regression analysis was used to evaluate predictors of cigarette smoking and alcohol use. Results: Prevalence of alcohol consumption and cigarette smoking were 26.0% and 17.1%, respectively. Smoking and alcohol use was more prevalent among males, younger adolescents, and those in higher school grades (p-value <.000). In multivariable logistic regression, male gender was associated with a higher risk of cigarette smoking (adjusted Odds Ratio (aOR) [95%Confidence Interval (CI)]= 1.51[1.06-2.14]) but lower risk of alcohol use (aOR[95%CI]= 0.69[0.53-0.90]) while older age (mid and late adolescence) and parental smoking were found to be associated with increased risk of alcohol use (aOR[95%CI]= 1.94[1.34-2.99] and 1.36[1.05-1.78] respectively). Marijuana use, truancy, being in a fight and suicide ideation were associated with increased odds of alcohol use (aOR[95%CI]= 3.82[3.39-6.09]; 2.15[1.62-2.87]; 1.83[1.34-2.49] and 1.93[1.38-2.69] respectively) and cigarette smoking (aOR[95%CI]= 17.28[10.4 - 28.51]; 1.73[1.21-2. 49]; 1.67[1.14-2.45] and 2.17[1.43-3.28] respectively) while involvement in sexual activity was associated with reduced risk of alcohol use (aOR[95%CI]= 0.50[0.37-0.68]) and cigarette smoking (aOR[95%CI]= 0.47[0.33-0.69]). Parental support and parental monitoring were uniquely associated with lower risk of cigarette smoking (aOR[95%CI]= 0.69[0.47-0.99] and 0.62[0.43-0.91] respectively). Conclusion: The high prevalence of alcohol use and cigarette smoking in this study shows the need for the government of Mauritius to enhance policies that will help address this issue putting into accounts the various risk and protective factors.

Keywords: adolescent health, alcohol use, cigarette smoking, global school-based student health survey

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Authors: Myung Hwan Na, Ho- Chun Song, EunHee Hong

Abstract:

We widely use normal linear mixed-effects model to analysis data in repeated measurement. In case of detecting heteroscedasticity and the non-normality of the population distribution at the same time, normal linear mixed-effects model can give improper result of analysis. To achieve more robust estimation, we use heavy tailed linear mixed-effects model which gives more exact and reliable analysis conclusion than standard normal linear mixed-effects model.

Keywords: reliability, tires, field data, linear mixed-effects model

Procedia PDF Downloads 552