Search results for: Cox regression model

Authors: Laidi Maamar, Hanini Salah

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The present work investigates the potential of artificial neural network (ANN) model to predict the horizontal global solar radiation (HGSR). The ANN is developed and optimized using three years meteorological database from 2011 to 2013 available at the meteorological station of Blida (Blida 1 university, Algeria, Latitude 36.5°, Longitude 2.81° and 163 m above mean sea level). Optimal configuration of the ANN model has been determined by minimizing the Root Means Square Error (RMSE) and maximizing the correlation coefficient (R2) between observed and predicted data with the ANN model. To select the best ANN architecture, we have conducted several tests by using different combinations of parameters. A two-layer ANN model with six hidden neurons has been found as an optimal topology with (RMSE=4.036 W/m²) and (R²=0.999). A graphical user interface (GUI), was designed based on the best network structure and training algorithm, to enhance the users’ friendliness application of the model.

Keywords: artificial neural network, global solar radiation, solar energy, prediction, Algeria

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Authors: Khadidja Rahmani, Nahla Bouaziz

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Algeria is known for its rich heritage. It owns an immense historical heritage with a universal reputation. Unfortunately, this wealth is withered because of abundance. This research focuses on the Ksourian model, which constitutes a large portion of this wealth. In fact, the Ksourian model is not just a witness to a great part of history or a vernacular culture, but also it includes a panoply of assets in terms of energetic efficiency. In this context, the purpose of our work is to evaluate the performance of the old techniques which are derived from the Ksourian model , and that using the simulation tools. The proposed method is decomposed in two steps; the first consists of isolate and reintroduce each device into a basic model, then run a simulation series on acquired models. And this in order to test the contribution of each of these dialectal processes. In another scale of development, the second step consists of aggregating all these processes in an aboriginal model, then we restart the simulation, to see what it will give this mosaic on the environmental and energetic plan .The model chosen for this study is one of the ksar units of Knadsa city of Bechar (Algeria). This study does not only show the ingenuity of our ancestors in their know-how, and their adapting power to the aridity of the climate, but also proves that their conceptions subscribe in the current concerns of energy efficiency, and respond to the requirements of sustainable development.

Keywords: dialectal processes, energy efficiency, evaluation, Ksourian model, simulation tools

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Authors: Krolo Paulina

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The seismic response of moment-resisting steel frames depends on the behavior of the joints, especially when they are considered as ductile zones. The aim of this research is to provide a realistic assessment of the moment-resisting steel frame behavior under seismic loading using nonlinear static pushover analysis (N2 method). The hysteresis behavior of the joints in the frame model was described using a new hysteresis envelope model. The obtained seismic response was compared with the results of the seismic analysis obtained for the same steel frame that takes into account the monotonic model of the joints.

Keywords: beam-to-column joints, hysteresis envelope model, moment-resisting frame, nonlinear static pushover analysis, N2 method

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Authors: B. Gepts, E. Meex, E. Nuyts, E. Knaepen, G. Verbeeck

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The importance of resource efficiency and environmental impact assessment has raised the interest in knowing the amount of materials used in buildings. If no BIM model or energy performance certificate is available, material quantities can be obtained through an estimation or time-consuming calculation. For the interior wall area, no validated estimation method exists. However, in the case of environmental impact assessment or evaluating the existing building stock as future material banks, knowledge of the material quantities used in interior walls is indispensable. This paper presents a validated method for the estimation of the interior wall area for dwellings based on easy-to-collect building characteristics. A database of 4963 residential buildings spread all over Belgium is used. The data are collected through onsite measurements of the buildings during the construction phase (between mid-2010 and mid-2017). The interior wall area refers to the area of all interior walls in the building, including the inner leaf of exterior (party) walls, minus the area of windows and doors, unless mentioned otherwise. The two predictive modelling techniques used are 1) a (stepwise) linear regression and 2) a decision tree. The best estimation method is selected based on the best R² k-fold (5) fit. The research shows that the building volume is by far the most important variable to estimate the interior wall area. A stepwise regression based on building volume per building, building typology, and type of house provides the best fit, with R² k-fold (5) = 0.88. Although the best R² k-fold value is obtained when the other parameters ‘building typology’ and ‘type of house’ are included, the contribution of these variables can be seen as statistically significant but practically irrelevant. Thus, if these parameters are not available, a simplified estimation method based on only the volume of the building can also be applied (R² k-fold = 0.87). The robustness and precision of the method (output) are validated three times. Firstly, the prediction of the interior wall area is checked by means of alternative calculations of the building volume and of the interior wall area; thus, other definitions are applied to the same data. Secondly, the output is tested on an extension of the database, so it has the same definitions but on other data. Thirdly, the output is checked on an unrelated database with other definitions and other data. The validation of the estimation methods demonstrates that the methods remain accurate when underlying data are changed. The method can support environmental as well as economic dimensions of impact assessment, as it can be used in early design. As it allows the prediction of the amount of interior wall materials to be produced in the future or that might become available after demolition, the presented estimation method can be part of material flow analyses on input and on output.

Keywords: buildings as material banks, building stock, estimation method, interior wall area

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Authors: Jeremy Ritchey

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Undocumented immigrants in the U.S. face many challenges when looking to progress in society, especially when pursuing post-secondary education. The majority of research done on state-level policy adoption pertaining to undocumented higher-education pursuits, specifically in-state resident tuition and financial aid eligibility policies, have framed the discussion on the potential and actual impacts which implementation can and has achieved. What is missing is a model to view the social, political and demographic landscapes upon which such policies (in their various forms) find a route to legislative enactment. This research looks to address this gap in the field by investigating the correlations and significant state-level variables which can be operationalized to construct a framework for adoption of these specific policies. In the process, analysis will show that past unexamined conceptualizations of how such policies come to fruition may be limited or contradictory when compared to available data. Circling on the principles of Policy Innovation and Policy Diffusion theory, this study looks to use variables collected via Michigan State University’s Correlates of State Policy Project, a collectively and ongoing compiled database project centered around annual variables (1900-2016) collected from all 50 states relevant to policy research. Using established variable groupings (demographic, political, social capital measurements, and educational system measurements) from the time period of 2000 to 2014 (2001 being when such policies began), one can see how this data correlates with the adoption of policies related to undocumented students and in-state college tuition. After regression analysis, the results will illuminate which variables appears significant and to what effect, as to help formulate a model upon which to explain when adoption appears to occur and when it does not. Early results have shown that traditionally held conceptions on conservative and liberal identities of the state, as they relate to the likelihood of such policies being adopted, did not fall in line with the collected data. Democratic and liberally identified states were, overall, less likely to adopt pro-undocumented higher education policies than Republican and conservatively identified states and vis versa. While further analysis is needed as to improve the model’s explanatory power, preliminary findings are showing promise in widening our understanding of policy adoption factors in this realm of policies compared to the gap of such knowledge in the publications of the field as it currently exists. The model also looks to serve as an important tool for policymakers in framing such potential policies in a way that is congruent with the relevant state-level determining factors while being sensitive to the most apparent sources of potential friction. While additional variable groups and individual variables will ultimately need to be added and controlled for, this research has already begun to demonstrate how shallow or unexamined reasoning behind policy adoption in the realm of this topic needs to be addressed or else the risk is erroneous conceptions leaking into the foundation of this growing and ever important field.

Keywords: policy adoption, in-state tuition, higher education, undocumented immigrants

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Authors: A. G. Matveev

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The copyrights system is a combination of different elements. The number, content and the correlation of these elements are different for different legal orders. The models of copyrights systems display this system in terms of the interaction of economic and author's moral rights. Monistic and dualistic models are the most popular ones. The article deals with different points of view on the monism and dualism in copyright system. A specific model of the copyright in Switzerland in the XXth century is analyzed. The evolution of a French dualistic model of copyright is shown. The author believes that one should talk not about one, but rather about a number of dualism forms of copyright system.

Keywords: copyright, exclusive copyright, economic rights, author's moral rights, rights of personality, monistic model, dualistic model

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Authors: Ho Young Son, Bu Seog Ju, Woo Young Jung

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This study presents the seismic safety evaluation of weir structure subjected to strong earthquake ground motions, as a flood defense structure in civil engineering structures. The seismic safety analysis procedure was illustrated through development of Finite Element (FE) and InFinite Element (IFE) method in ABAQUS platform. The IFE model was generated by CINPS4, 4-node linear one-way infinite model as a sold continuum infinite element in foundation areas of the weir structure and then nonlinear FE model using friction model for soil-structure interactions was applied in this study. In order to understand the complex behavior of weir structures, nonlinear time history analysis was carried out. Consequently, it was interesting to note that the compressive stress gave more vulnerability to the weir structure, in comparison to the tensile stress, during an earthquake. The stress concentration of the weir structure was shown at the connection area between the weir body and stilling basin area. The stress both tension and compression was reduced in IFE model rather than FE model of weir structures.

Keywords: seismic, numerical analysis, FEM, weir, boundary condition

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Authors: Nouby M. Ghazaly, Ahmed O. Moaaz, Mostafa Makrahy

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The main purpose of a car suspension system is to reduce the vibrations resulting from road roughness. The main objective of this research paper is to decrease vibration and improve passenger comfort through controlling car suspension system using sliding mode control techniques. The mathematical model for passive and active suspensions systems for quarter car model which subject to excitation from different road profiles is obtained. The active suspension system is synthesized based on sliding mode control for a quarter car model. The performance of the sliding mode control is determined through computer simulations using MATLAB and SIMULINK toolbox. The simulated results plotted in time domain, and root mean square values. It is found that active suspension system using sliding mode control improves the ride comfort and decrease vibration.

Keywords: quarter car model, active suspension system, sliding mode control, road profile

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Authors: Liaqat Ali, Khalid Farooq, Shafieullah Khan, Nasir Orakzai, Qudratullah

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Objectives: To determine the risk factors for high resistance of ciprofloxacin in complicated urinary tract infections. Materials and Methods: It is an analytical study that was conducted in the department of Urology (Team ‘C’) at Institute of Kidney Diseases Hayatabad Peshawar from 1st June 2012 till 31st December 2012. Total numbers of 100 patients with complicated UTI was selected in the study. Multivariate analysis and linear regression were performed for the detection of risk factors. All the data was recorded on structured Proforma and was analyzed on SPSS version 17. Results: The mean age of the patient was 55.6 years (Range 3-82 years). 62 patients were male while 38 patients were female. 66 isolates of E-Coli were found sensitive to ciprofloxacin while 34 isolates were found Resistant for ciprofloxacin. Using multivariate analysis and linear regression, an increasing age above 50 (p=0.002) History of urinary catheterization especially for bladder outflow obstruction (p=0.001) and previous multiple use of ciprofloxacin (p=0.001) and poor brand of ciprofloxacin were found to be independent risk factors for high resistance of ciprofloxacin. Conclusion: UTI is common illness across the globe with increasing trend of antimicrobial resistance for ciprofloxacin against E Coli in complicated UTI. The risk factors for emerging resistance are increasing age, urinary catheterization and multiple use and poor brand of ciprofloxacin.

Keywords: urinary tract infection, ciprofloxacin, urethral catheterization, antimicrobial resistance

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Authors: Akanksha Lohmore, Devanshu Arya, Preeti Kapur

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Rarely does the human mind look at the positive fallout of highly negative events. Positive psychology attempts to emphasize on the strengths and positives for human well-being. The present study examines the underpinning socio-cognitive factors of the protest movements regarding the gang rape case of December 16th, 2012 through the lens of positive psychology. A gamut of negative emotions came to the forum globally: of anger, shame, hatred, violence, death penalty for the perpetrators, amongst other equally strong. In relation to this incident, a number of questions can be raised. Can such a heinous crime have some positive inputs for contemporary society? What is it that has held people to protests for long even when they see faded lines of success in view? This paper explains the constant feeding of protests and continuation of movements by the robust model of Collective Hope by Snyder, a phenomenon unexplored by social psychologists. In this paper, mixed method approach was undertaken. Results confirmed the interaction of various socio-psychological factors that imitated the Snyders model of collective hope. Emergence of major themes was: Sense of Agency, Sense of Worthiness, Social Sharing and Common Grievances and Hope of Collective Efficacy. Statistical analysis (correlation and regression) showed significant relationship between media usage and occurrence of these themes among participants. Media-communication processes and educational theories for development of citizenship behavior can find implications from these results. Theory development as indicated by theorists working in the area of Social Psychology of Protests can be furthered by the direction of research.

Keywords: agency, collective, hope, positive psychology, protest, social media

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Authors: Ana B. Carrera-Aguilar , Rodrigo T. Sepulveda-Hirose, Diego A. Bernal-Gurrusquieta, Francisco A. Ramirez Casas

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In recent years, Mexico City has presented high levels of atmospheric pollution; the city is also an example of inequality and poverty that impact metropolitan areas around the world. This combination of social and economic exclusion, coupled with high levels of pollution evidence the loss of well-being among the population. The effect of air pollution on quality of life is an area of study that has been overlooked. The purpose of this study is to find relations between air quality and quality of life in Mexico City through statistical analysis of a regression model and principal component analysis of several atmospheric contaminants (CO, NO₂, ozone, particulate matter, SO₂) and well-being indexes (HDI, poverty, inequality, life expectancy and health care index). The data correspond to official information (INEGI, SEDEMA, and CEPAL) for 2000-2018. Preliminary results show that the Human Development Index (HDI) is affected by the impacts of pollution, and its indicators are reduced in the presence of contaminants. It is necessary to promote a strong interest in this issue in Mexico City. Otherwise, the problem will not only remain but will worsen affecting those who have less and the population well-being in a generalized way.

Keywords: air quality, Mexico City, quality of life, statistics

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Authors: Eddegdag Nasser, Naamane Azzeddine, Radouani Mohammed, Ensam Meknes

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In this study, we are interested in the asymptotic modeling of the two-dimensional stationary supersonic flow of a viscous compressible fluid around wing airfoil. The aim of this article is to solve the partial differential equations of the flow far from the leading edge and near the wall using the triple-deck technique is what brought again in precision according to the principle of least degeneration. In order to validate our theoretical model, these obtained results will be compared with the experimental results. The comparison of the results of our model with experimentation has shown that they are quantitatively acceptable compared to the obtained experimental results. The experimental study was conducted using the AF300 supersonic wind tunnel and a NACA Reduced airfoil model with two pressure Taps on extrados. In this experiment, we have considered the incident upstream supersonic Mach number over a dissymmetric NACA airfoil wing. The validation and the accuracy of the results support our model.

Keywords: supersonic, viscous, triple deck technique, asymptotic methods, AF300 supersonic wind tunnel, reduced airfoil model

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Authors: Beata Jackowska-Zduniak

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We formulate and analyze a mathematical model describing dynamics of the hypothalamus-pituitary-thyroid homoeostatic mechanism in endocrine system. We introduce to this system two types of couplings and delay. In our model, feedback controls the secretion of thyroid hormones and delay reflects time lags required for transportation of the hormones. The influence of delayed feedback on the stability behaviour of the system is discussed. Analytical results are illustrated by numerical examples of the model dynamics. This system of equations describes normal activity of the thyroid and also a couple of types of malfunctions (e.g. hyperthyroidism).

Keywords: mathematical modeling, ordinary differential equations, endocrine system, delay differential equation

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Authors: Almutasim Billa A. Alanazi, Hal S. Tharp

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Safe performance and efficient energy consumption are essential factors for designing a control system. This paper presents a reinforcement learning (RL) model that can be applied to control applications to improve safety and reduce energy consumption. As hardware constraints and environmental disturbances are imprecise and unpredictable, conventional control methods may not always be effective in optimizing control designs. However, RL has demonstrated its value in several artificial intelligence (AI) applications, especially in the field of control systems. The proposed model intelligently monitors a system's success by observing the rewards from the environment, with positive rewards counting as a success when the controlled reference is within the desired operating zone. Thus, the model can determine whether the system is safe to continue operating based on the designer/user specifications, which can be adjusted as needed. Additionally, the controller keeps track of energy consumption to improve energy efficiency by enabling the idle mode when the controlled reference is within the desired operating zone, thus reducing the system energy consumption during the controlling operation. Water temperature control for a hydroponic system is taken as a case study for the RL model, adjusting the variance of disturbances to show the model’s robustness and efficiency. On average, the model showed safety improvement by up to 15% and energy efficiency improvements by 35%- 40% compared to a traditional RL model.

Keywords: control system, hydroponics, machine learning, reinforcement learning

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Authors: Beaudoin Marie-Joelle, Poirier Nathalie

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Introduction: Children with autism spectrum disorder (ASD) tend to lack socio-emotional skills (e.g., emotional regulation and theory of mind). Eisenberg’s theoretical model on emotion-related socialization behaviors suggests that mothers of children with ASD could play a central role in fostering the acquisition of socio-emotional skills by engaging in frequent educational conversations about emotions. Although, mothers’ perceptions of their own emotional skills and their child’s personality traits and social deficits could mitigate the benefit of their educative role. Objective: Our study aims to explore the association between mother-child conversations about emotions and the socio-emotional skills of their children when accounting for the moderating role of the mothers’ perceptions. Forty-nine mothers completed five questionnaires about emotionally related conversations, self-openness to emotions, and perceptions of personality and socio-emotional skills of their children with ASD. Results: Regression analyses showed that frequent mother-child conversations about emotions predicted better emotional regulation and theory of mind skills in children with ASD (p < 0.01). The children’s theory of mind was moderated by mothers’ perceptions of their own emotional openness (p < 0.05) and their perceptions of their children’s openness to experience (p < 0.01) and conscientiousness (p < 0.05). Conclusion: Mothers likely play an important role in the socio-emotional education of children with ASD. Further, mothers may be most helpful when they perceive that their interventions improve their child’s behaviors. Our findings corroborate those of the Eisenberg model, which claims that mother-child conversations about emotions predict socio-emotional development skills in children with ASD. Our results also help clarify the moderating role of mothers’ perceptions, which could mitigate their willingness to engage in educational conversations about emotions with their children. Therefore, in special needs' children education, school professionals could collaborate with mothers to increase the frequency of emotion-related conversations in ASD's students with emotion dysregulation or theory of mind problems.

Keywords: autism, parental socialization of emotion, emotional regulation, theory of mind

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Authors: Elda Maraj, Shkelqim Kuka

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Coronary heart disease causes many deaths in the world. Unfortunately, this problem will continue to increase in the future. In this paper, a fuzzy logic model to predict coronary heart disease is presented. This model has been developed with seven input variables and one output variable that was implemented for 30 patients in Albania. Here fuzzy logic toolbox of MATLAB is used. Fuzzy model inputs are considered as cholesterol, blood pressure, physical activity, age, BMI, smoking, and diabetes, whereas the output is the disease classification. The fuzzy sets and membership functions are chosen in an appropriate manner. Centroid method is used for defuzzification. The database is taken from University Hospital Center "Mother Teresa" in Tirana, Albania.

Keywords: coronary heart disease, fuzzy logic toolbox, membership function, prediction model

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Authors: Md. Fazlul Karim, Ahmad Izani Md. Ismail, Mohammed Ashaque Meah

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This paper focuses on the development of a 2-D Boundary Fitted and Nested Grid (BFNG) model to compute the tsunami propagation of Indonesian tsunami 2004 along the coastal region of Penang in Peninsular Malaysia. In the presence of a curvilinear coastline, boundary fitted grids are suitable to represent the model boundaries accurately. On the other hand, when large gradient of velocity within a confined area is expected, the use of a nested grid system is appropriate to improve the numerical accuracy with the least grid numbers. This paper constructs a shallow water nested and orthogonal boundary fitted grid model and presents computational results of the tsunami impact on the Penang coast due to the Indonesian tsunami of 2004. The results of the numerical simulations are compared with available data.

Keywords: boundary fitted nested model, tsunami, Penang Island, 2004 Indonesian Tsunami

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Authors: Wooyoung Jung, Ghang Lee

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This paper paper reports the worldwide status of building information modeling (BIM) adoption from the perspectives of the engagement level, the Hype Cycle model, the technology diffusion model, and BIM-uses. An online survey was distributed, and 156 experts from six continents responded. Overall, North America was the most advanced continent, followed by Oceania and Europe. Countries in Asia perceived their phase mainly as slope of enlightenment (mature) in the Hype Cycle model. In the technology diffusion model, the main BIM-users worldwide were “early majority” (third phase), but those in the Middle East/Africa and South America were “early adopters” (second phase). In addition, the more advanced the country, the more number of BIM services employed in general. In summary, North America, Europe, Oceania, and Asia were advancing rapidly toward the mature stage of BIM, whereas the Middle East/Africa and South America were still in the early phase. The simple indexes used in this study may be used to track the worldwide status of BIM adoption in long-term surveys.

Keywords: BIM adoption, BIM services, hype cycle model, technology diffusion model

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Authors: Hicham Gourgue, Ahmed Aharoune, Ahmed Ihlal

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We present in this work our model of road traffic emissions (line sources) and dispersion of these emissions, named DISPOLSPEM (Dispersion of Poly Sources and Pollutants Emission Model). In its emission part, this model was designed to keep the consistent bottom-up and top-down approaches. It also allows to generate emission inventories from reduced input parameters being adapted to existing conditions in Morocco and in the other developing countries. While several simplifications are made, all the performance of the model results are kept. A further important advantage of the model is that it allows the uncertainty calculation and emission rate uncertainty according to each of the input parameters. In the dispersion part of the model, an improved line source model has been developed, implemented and tested against a reference solution. It provides improvement in accuracy over previous formulas of line source Gaussian plume model, without being too demanding in terms of computational resources. In the case study presented here, the biggest errors were associated with the ends of line source sections; these errors will be canceled by adjacent sections of line sources during the simulation of a road network. In cases where the wind is parallel to the source line, the use of the combination discretized source and analytical line source formulas minimizes remarkably the error. Because this combination is applied only for a small number of wind directions, it should not excessively increase the calculation time.

Keywords: air pollution, dispersion, emissions, line sources, road traffic, urban transport

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Authors: Lynndee Kemmet

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The U.S. military plays an increasing role in supporting political stability efforts, and this includes efforts to prevent the food insecurity that can trigger political and social instability. This paper presents a model that assists military commanders in identifying variables that impact food production and distribution in their areas of operation (AO), in identifying connections between variables and in assessing the impacts of those variables on food production and distribution. Through use of the model, military units can better target their data collection efforts and can categorize and analyze data within the data categorization framework most widely-used by military forces—PMESII-PT (Political, Military, Economic, Infrastructure, Information, Physical Environment and Time). The model provides flexibility of analysis in that commanders can target analysis to be highly focused on a specific PMESII-PT domain or variable or conduct analysis across multiple PMESII-PT domains. The model is also designed to assist commanders in mapping food systems in their AOs and then identifying components of those systems that must be strengthened or protected.

Keywords: food security, food system model, political stability, US Military

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Authors: Suparman

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This paper addresses the problem of the signal segmentation within a Bayesian framework by using reversible jump MCMC algorithm. The signal is modelled by piecewise constant Moving-Average (MA) model where the numbers of segments, the position of change-point, the order and the coefficient of the MA model for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow calculating some interesting features of the posterior distribution. The performance of the methodology is illustrated via several simulation results.

Keywords: piecewise, moving-average model, reversible jump MCMC, signal segmentation

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Authors: Mian Muhammad Kamal

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Accurate detection of small objects is extremely essential in critical applications like military reconnaissance and emergency rescue. However, owing to the low resolution, occlusion, and background interference, small object detection is a tedious process. One of the most appropriate approaches is to combine the data available in multimodal images to enhance the detection ability. This paper proposes a small object detection technique using three kinds of multimodal images, such as Hyperspectral-Multispectral (HS-MS), HS-Synthetic Aperture Radar (HS-SAR), and HS-SAR-Digital Surface Model (HS-SAR-DSM). The detection is accomplished by utilizing the Jaccard Deep Q-Net (JDQN) that is created by the incorporation of the Jaccard similarity measure and Deep Q-Network (DQN) using Regression modeling. Further, a Deep Maxout Network (DMN) is used for fusing the detected outputs obtained from each modality so as to generate the final output. Moreover, the supremacy of the proposed JDQN in detecting small objects is established by the utilization of metrics, like accuracy, Mean Squared Error (MSE), precision, and Root MSE (RMSE), and experimentation reveals that the JDQN recorded superior accuracy of 0.907, normalized MSE of 0.448, precision of 0.904, and normalized RMSE of 0.670.

Keywords: small object detection, Multimodality, deep learning, Jaccard deep q-net, deep maxout network

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Authors: Se-Han Lee, Kwang-Woo Go, Gwang-Hyun Ahn, Hee-Sung Park, Cheol-Kyu Han, Jun-Bo Shim, Geun-Chul Kang, Hyun-Jung Lee

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In recent years, with the advent of smart cars and the expansion of the market, the announcement of 'Adventures in Automotive Networks and Control Units' at the DEFCON21 conference in 2013 revealed that cars are not safe from hacking. As a result, the HEAVENS model considering not only the functional safety of the vehicle but also the security has been suggested. However, the HEAVENS model only presents a simple process, and there are no detailed procedures and activities for each process, making it difficult to apply it to the actual vehicle security vulnerability check. In this paper, we propose an automated attack database that systematically summarizes attack vectors, attack types, and vulnerable vehicle models to prepare for various car hacking attacks, and data flow diagrams that can detect various vulnerabilities and suggest a way to materialize the HEAVENS model.

Keywords: automotive security, HEAVENS, car hacking, security model, information security

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Nora Gavira Duron, Cinthya Moreda Gonzalez-Ortega, Reyna Susana Garcia Ruiz

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This paper objective is to analyze the mathematics performance in the Learning Evaluation National Plan (PLANEA for its Spanish initials: Plan Nacional para la Evaluación de los Aprendizajes), applied to Mexican students who are enrolled in the last elementary-school year over the 2017-2018 academic year. Such test was conducted nationwide in 3,573 schools, using a sample of 108,083 students, whose average in mathematics, on a scale of 0 to 100, was 45.6 points. 75% of the sample analyzed did not reach the sufficiency level (60 points). It should be noted that only 2% got a 90 or higher score result. The performance is analyzed while considering whether there are differences in gender, marginalization level, public or private school enrollment, parents’ academic background, and living-with-parents situation. Likewise, this variable impact (among other variables) on school performance by gender is evaluated, considering multivariate logistic (Logit) regression analysis. The results show there are no significant differences in mathematics performance regarding gender in elementary school; nevertheless, the impact exerted by mothers who studied at least high school is of great relevance for students, particularly for girls. Other determining variables are students’ resilience, their parents’ economic status, and the fact they attend private schools, strengthened by the mother's education.

Keywords: multivariate regression analysis, academic performance, learning evaluation, mathematics result per gender

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Authors: Suganya Chandrababu, Dhundy Bastola

Abstract:

Like in the gut, the subgingival microbiota plays a crucial role in oral hygiene, health, and cariogenic diseases. Human activities like diet, antibiotics, and periodontal treatments alter the bacterial communities, metabolism, and functions in the oral cavity, leading to a dysbiotic state and changes in the plaques of orthodontic patients. Fixed periodontal appliances hinder oral hygiene and cause changes in the dental plaques influencing the subgingival microbiota. However, the microbial species’ diversity and complexity pose a great challenge in understanding the taxa’s community distribution patterns and their role in oral health. In this research, we analyze the subgingival microbial samples from individuals with fixed dental appliances (metal/clear) using an in silico approach. We employ exploratory hypothesis-driven multivariate and regression analysis to shed light on the microbial community and its functional fluctuations due to dental appliances used and identify risks associated with complex disease phenotypes. Our findings confirm the changes in oral microbiota composition due to the presence and type of fixed orthodontal devices. We identified seven main periodontic pathogens, including Bacteroidetes, Actinobacteria, Proteobacteria, Fusobacteria, and Firmicutes, whose abundances were significantly altered due to the presence and type of fixed appliances used. In the case of metal braces, the abundances of Bacteroidetes, Proteobacteria, Fusobacteria, Candidatus saccharibacteria, and Spirochaetes significantly increased, while the abundance of Firmicutes and Actinobacteria decreased. However, in individuals With clear braces, the abundance of Bacteroidetes and Candidatus saccharibacteria increased. The highest abundance value (P-value=0.004 < 0.05) was observed with Bacteroidetes in individuals with the metal appliance, which is associated with gingivitis, periodontitis, endodontic infections, and odontogenic abscesses. Overall, the bacterial abundances decrease with clear type and increase with metal type of braces. Regression analysis further validated the multivariate analysis of variance (MANOVA) results, supporting the hypothesis that the presence and type of the fixed oral appliances significantly alter the bacterial abundance and composition.

Keywords: oral microbiota, statistical analysis, fixed or-thodontal appliances, bacterial abundance, multivariate analysis, regression analysis

Procedia PDF Downloads 188

Authors: Samson Davoyan, Ashot Davoyan, Ani Khachatryan

Abstract:

The indexes (Global Competitiveness Index, Economic Freedom Index, Human Development Index, etc.) developed by different international and non-government organizations in time and space express the quantitative and qualitative features of different fields of various reforms implemented in different countries. The main objective of our research is to develop new methodology that we will use to create integral index based on many indexes and that will include many areas of reforms. To achieve our aim we have used econometric methods (regression model for panel data method). The basis of our methodology is the development of the new integral index based on quantitative assessment of the change of two main parameters: the score of the countries by different indexes and the change of the ranks of countries for following two periods of time. As a result of the usage of methods for analyzes we have defined the indexes that are used to create the new integral index and the scales for each of them. Analyzing quantitatively and qualitatively analysis through the integral index for more than 100 countries for 2009-2014, we have defined comparative efficiency that helps to conclude in which directions countries have implemented reforms more effectively compared to others and in which direction reforms have implemented less efficiently.

Keywords: development, rank, reforms, comparative, index, economic, corruption, social, program

Procedia PDF Downloads 322

Authors: J. Ritonja, B. Grcar

Abstract:

For the synchronous generator simulation and analysis and for the power system stabilizer design and synthesis a mathematical model of synchronous generator is needed. The model has to accurately describe dynamics of oscillations, while at the same time has to be transparent enough for an analysis and sufficiently simplified for design of control system. To study the oscillations of the synchronous generator against to the rest of the power system, the model of the synchronous machine connected to an infinite bus through a transmission line having resistance and inductance is needed. In this paper, the linearized reduced order dynamic model of the synchronous generator connected to the infinite bus is presented and analysed in details. This model accurately describes dynamics of the synchronous generator only in a small vicinity of an equilibrium state. With the digression from the selected equilibrium point the accuracy of this model is decreasing considerably. In this paper, the equations’ descriptions and the parameters’ determinations for the linearized reduced order mathematical model of the synchronous generator are explained and summarized and represent the useful origin for works in the areas of synchronous generators’ dynamic behaviour analysis and synchronous generator’s control systems design and synthesis. The main contribution of this paper represents the detailed analysis of the accuracy of the linearized reduced order dynamic model in the entire synchronous generator’s operating range. Borders of the areas where the linearized reduced order mathematical model represents accurate description of the synchronous generator’s dynamics are determined with the systemic numerical analysis. The thorough eigenvalue analysis of the linearized models in the entire operating range is performed. In the paper, the parameters of the linearized reduced order dynamic model of the laboratory salient poles synchronous generator were determined and used for the analysis. The theoretical conclusions were confirmed with the agreement of experimental and simulation results.

Keywords: eigenvalue analysis, mathematical model, power system stability, synchronous generator

Procedia PDF Downloads 240

Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

Procedia PDF Downloads 298