Search results for: the linear regression model

Authors: Adan Valdez, Shawn Gallaher, James Morison, Jordan Aragon

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Global climate change has impacted atmospheric temperatures contributing to rising sea levels, decreasing sea ice, and increased freshening of high latitude oceans. This freshening has contributed to increased stratification inhibiting local mixing and nutrient transport and modifying regional circulations in polar oceans. In recent years, the Western Arctic has seen an increase in freshwater volume at an average rate of 397+-116 km3/year. The majority of the freshwater volume resides in the Beaufort Gyre surface lens driven by anticyclonic wind forcing, sea ice melt, and Arctic river runoff. The total climatological freshwater content is typically defined as water fresher than 34.8. The near-isothermal nature of Arctic seawater and non-linearities in the equation of state for near-freezing waters result in a salinity driven pycnocline as opposed to the temperature driven density structure seen in the lower latitudes. In this study, we investigate the relationship between freshwater content and remotely sensed dynamic ocean topography (DOT). In-situ measurements of freshwater content are useful in providing information on the freshening rate of the Beaufort Gyre; however, their collection is costly and time consuming. NASA’s Advanced Topographic Laser Altimeter System (ATLAS) derived dynamic ocean topography (DOT), and Air Expendable CTD (AXCTD) derived Freshwater Content are used to develop a linear regression model. In-situ data for the regression model is collected across the 150° West meridian, which typically defines the centerline of the Beaufort Gyre. Two freshwater content models are determined by integrating the freshwater volume between the surface and an isopycnal corresponding to reference salinities of 28.7 and 34.8. These salinities correspond to those of the winter pycnocline and total climatological freshwater content, respectively. Using each model, we determine the strength of the linear relationship between freshwater content and satellite derived DOT. The result of this modeling study could provide a future predictive capability of freshwater volume changes in the Beaufort-Chukchi Sea using non in-situ methods. Successful employment of the ICESat-2’s DOT approximation of freshwater content could potentially reduce reliance on field deployment platforms to characterize physical ocean properties.

Keywords: ICESat-2, dynamic ocean topography, freshwater content, beaufort gyre

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Liang-Hsuan Chen, Sheng-Shing Nien

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This study aims to develop approaches to formulate intuitionistic fuzzy regression (IFR) models for many decision-making applications in the fuzzy environments using intuitionistic fuzzy observations. Intuitionistic fuzzy numbers (IFNs) are used to characterize the fuzzy input and output variables in the IFR formulation processes. A mathematical programming problem (MPP) is built up to optimally determine the IFR parameters. Each parameter in the MPP is defined as a couple of alternative numerical variables with opposite signs, and an intuitionistic fuzzy error term is added to the MPP to characterize the uncertainty of the model. The IFR model is formulated based on the distance measure to minimize the total distance errors between estimated and observed intuitionistic fuzzy responses in the MPP resolution processes. The proposed approaches are simple/efficient in the formulation/resolution processes, in which the sign of parameters can be determined so that the problem to predetermine the sign of parameters is avoided. Furthermore, the proposed approach has the advantage that the spread of the predicted IFN response will not be over-increased, since the parameters in the established IFR model are crisp. The performance of the obtained models is evaluated and compared with the existing approaches.

Keywords: fuzzy sets, intuitionistic fuzzy number, intuitionistic fuzzy regression, mathematical programming method

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Authors: Sanchali Das, Swapan Debbarma

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Music mood classification is developing in both the areas of music information retrieval (MIR) and natural language processing (NLP). Some of the Indian languages like Hindi English etc. have considerable exposure in MIR. But research in mood classification in regional language is very less. In this paper, powerful audio based feature for Kokborok Christian song is identified and mood classification task has been performed. Kokborok is an Indo-Burman language especially spoken in the northeastern part of India and also some other countries like Bangladesh, Myanmar etc. For performing audio-based classification task, useful audio features are taken out by jMIR software. There are some standard audio parameters are there for the audio-based task but as known to all that every language has its unique characteristics. So here, the most significant features which are the best fit for the database of Kokborok song is analysed. The regression-based model is used to find out the independent parameters that act as a predictor and predicts the dependencies of parameters and shows how it will impact on overall classification result. For classification WEKA 3.5 is used, and selected parameters create a classification model. And another model is developed by using all the standard audio features that are used by most of the researcher. In this experiment, the essential parameters that are responsible for effective audio based mood classification and parameters that do not significantly change for each of the Christian Kokborok songs are analysed, and a comparison is also shown between the two above model.

Keywords: Christian Kokborok song, mood classification, music information retrieval, regression

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Authors: A. Brouri, F. Giri, A. Mkhida, A. Elkarkri, M. L. Chhibat

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Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. Presently, the linear subsystem is allowed to be parametric or not, continuous- or discrete-time. The input and output nonlinearities are polynomial and may be noninvertible. A two-stage identification method is developed such the parameters of all nonlinear elements are estimated first using the Kozen-Landau polynomial decomposition algorithm. The obtained estimates are then based upon in the identification of the linear subsystem, making use of suitable pre-ad post-compensators.

Keywords: nonlinear system identification, Hammerstein-Wiener systems, frequency identification, polynomial decomposition

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Authors: Fatma Mohamed Magdy Badr El Dine, Amr Mohamed Abd Allah

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Introduction: Age estimation of individuals is one of the crucial steps in forensic practice. Different traditional methods rely on the length of the diaphysis of long bones of limbs, epiphyseal-diaphyseal union, fusion of the primary ossification centers as well as dental eruption. However, there is a growing need for the development of precise and reliable methods to estimate age, especially in cases where dismembered corpses, burnt bodies, purified or fragmented parts are recovered. Teeth are the hardest and indestructible structure in the human body. In recent years, assessment of pulp/tooth area ratio, as an indirect quantification of secondary dentine deposition has received a considerable attention. However, scanty work has been done in Egypt in terms of applicability of pulp/tooth ratio for age estimation. Aim of the Work: The present work was designed to assess the Cameriere’s method for age estimation from pulp/tooth ratio of maxillary canines, central and lateral incisors among a sample from Egyptian population. In addition, to formulate regression equations to be used as population-based standards for age determination. Material and Methods: The present study was conducted on 270 peri-apical X-rays of maxillary canines, central and lateral incisors (collected from 131 males and 139 females aged between 19 and 52 years). The pulp and tooth areas were measured using the Adobe Photoshop software program and the pulp/tooth area ratio was computed. Linear regression equations were determined separately for canines, central and lateral incisors. Results: A significant correlation was recorded between the pulp/tooth area ratio and the chronological age. The linear regression analysis revealed a coefficient of determination (R² = 0.824 for canine, 0.588 for central incisor and 0.737 for lateral incisor teeth). Three regression equations were derived. Conclusion: As a conclusion, the pulp/tooth ratio is a useful technique for estimating age among Egyptians. Additionally, the regression equation derived from canines gave better result than the incisors.

Keywords: age determination, canines, central incisors, Egypt, lateral incisors, pulp/tooth ratio

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Authors: Issam Aouari, Abdelmalek Abdelhamid

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For seismologists, the characterization of seismic demand should include the amplitude and duration of strong shaking in the system. The duration of ground shaking is one of the key parameters in earthquake resistant design of structures. This paper proposes a nonlinear statistical model to estimate earthquake ground motion duration in soft soils using multiple seismicity indicators. Three definitions of ground motion duration proposed by literature have been applied. With a comparative study, we select the most significant definition to use for predict the duration. A stochastic model is presented for the McCann and Shah Method using nonlinear regression analysis based on a data set for moment magnitude, source to site distance and site conditions. The data set applied is taken from PEER strong motion databank and contains shallow earthquakes from different regions in the world; America, Turkey, London, China, Italy, Chili, Mexico...etc. Main emphasis is placed on soft site condition. The predictive relationship has been developed based on 600 records and three input indicators. Results have been compared with others published models. It has been found that the proposed model can predict earthquake ground motion duration in soft soils for different regions and sites conditions.

Keywords: duration, earthquake, prediction, regression, soft soil

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Authors: Altayeb Abdalla Ahmed

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Skeletal phenotype is a product of a balanced interaction between genetics and environmental factors throughout different life stages. Therefore, interlimb proportions are variable between populations. Although interlimb proportion indices have been used in anthropology in assessing the influence of various environmental factors on limbs, an extensive literature review revealed that there is a paucity of published research assessing interlimb part correlations and possibility of reconstruction. Hence, this study aims to assess the relationships between upper and lower limb parts and develop regression formulae to reconstruct the parts from one another. The left upper arm length, ulnar length, wrist breadth, hand length, hand breadth, tibial length, bimalleolar breadth, foot length, and foot breadth of 376 right-handed subjects, comprising 187 males and 189 females (aged 25-35 years), were measured. Initially, the data were analyzed using basic univariate analysis and independent t-tests; then sex-specific simple and multiple linear regression models were used to estimate upper limb parts from lower limb parts and vice-versa. The results of this study indicated significant sexual dimorphism for all variables. The results indicated a significant correlation between the upper and lower limbs parts (p < 0.01). Linear and multiple (stepwise) regression equations were developed to reconstruct the limb parts in the presence of a single or multiple dimension(s) from the other limb. Multiple stepwise regression equations generated better reconstructions than simple equations. These results are significant in forensics as it can aid in identification of multiple isolated limb parts particularly during mass disasters and criminal dismemberment. Although a DNA analysis is the most reliable tool for identification, its usage has multiple limitations in undeveloped countries, e.g., cost, facility availability, and trained personnel. Furthermore, it has important implication in plastic and orthopedic reconstructive surgeries. This study is the only reported study assessing the correlation and prediction capabilities between many of the upper and lower dimensions. The present study demonstrates a significant correlation between the interlimb parts in both sexes, which indicates a possibility to reconstruction using regression equations.

Keywords: anthropometry, correlation, limb, Sudanese

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Authors: Yun-Xuan Tang, Pei-Yuan Liu, Kun-Mu Lu, Min-Tsung Tseng, Liang-Kuang Chen, Yuh-Feng Tsai, Ching-Wen Lee, Jay Wu

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Breast cancer is predominant of malignant tumors in females. The increase in the glandular density increases the risk of breast cancer. BI-RADS is a frequently used density indicator in mammography; however, it significantly overestimates the glandularity. Therefore, it is very important to accurately and quantitatively assess the glandularity by mammography. In this study, 20%, 30% and 50% glandularity phantoms were exposed using a mammography machine at 28, 30 and 31 kVp, and 30, 55, 80 and 105 mAs, respectively. The regions of interest (ROIs) were drawn to assess the gray level. The relationship between the glandularity and gray level under various compression thicknesses, kVp, and mAs was established by the multivariable linear regression. A phantom verification was performed with automatic exposure control (AEC). The regression equation was obtained with an R-square value of 0.928. The average gray levels of the verification phantom were 8708, 8660 and 8434 for 0.952, 0.963 and 0.985 g/cm3, respectively. The percent differences of glandularity to the regression equation were 3.24%, 2.75% and 13.7%. We concluded that the proposed method could be clinically applied in mammography to improve the glandularity estimation and further increase the importance of breast cancer screening.

Keywords: mammography, glandularity, gray value, BI-RADS

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Authors: Shruti Motiwale, Xianlin Zhou, Reuben H. Kraft

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Military and sports personnel are often required to wear heavy helmets for extended periods of time. This leads to excessive cyclic loads on the neck and an increased chance of injury. Computational models offer one approach to understand and predict the time progression of disc degeneration under severe cyclic loading. In this paper, we have applied an analytic non-linear damage evolution model to estimate damage evolution in an intervertebral disc due to cyclic loads over decade-long time periods. We have also proposed a novel strategy for inclusion of recovery in the damage model. Our results show that damage only grows 20% in the initial 75% of the life, growing exponentially in the remaining 25% life. The analysis also shows that it is crucial to include recovery in a damage model.

Keywords: cervical spine, computational biomechanics, damage evolution, intervertebral disc, continuum damage mechanics

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Authors: Trevor C. Brown, David J. Miron

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Gas-solid physical adsorption methods are central to the characterization and optimization of the effective surface area, pore size and porosity for applications such as heterogeneous catalysis, and gas separation and storage. Properties such as adsorption uptake, capacity, equilibrium constants and Gibbs free energy are dependent on the composition and structure of both the gas and the adsorbent. However, challenges remain, in accurately calculating these properties from experimental data. Gas adsorption experiments involve measuring the amounts of gas adsorbed over a range of pressures under isothermal conditions. Various constant-parameter models, such as Langmuir and Brunauer-Emmett-Teller (BET) theories are used to provide information on adsorbate and adsorbent properties from the isotherm data. These models typically do not provide accurate interpretations across the full range of pressures and temperatures. The Langmuir adsorption isotherm is a simple approximation for modelling equilibrium adsorption data and has been effective in estimating surface areas and catalytic rate laws, particularly for high surface area solids. The Langmuir isotherm assumes the systematic filling of identical adsorption sites to a monolayer coverage. The BET model is based on the Langmuir isotherm and allows for the formation of multiple layers. These additional layers do not interact with the first layer and the energetics are equal to the adsorbate as a bulk liquid. This BET method is widely used to measure the specific surface area of materials. Both Langmuir and BET models assume that the affinity of the gas for all adsorption sites are identical and so the calculated adsorbent uptake at the monolayer and equilibrium constant are independent of coverage and pressure. Accurate representations of adsorption data have been achieved by extending the Langmuir and BET models to include pressure-varying uptake capacities and equilibrium constants. These parameters are determined using a novel regression technique called flexible least squares for time-varying linear regression. For isothermal adsorption the adsorption parameters are assumed to vary slowly and smoothly with increasing pressure. The flexible least squares for pressure-varying linear regression (FLS-PVLR) approach assumes two distinct types of discrepancy terms, dynamic and measurement for all parameters in the linear equation used to simulate the data. Dynamic terms account for pressure variation in successive parameter vectors, and measurement terms account for differences between observed and theoretically predicted outcomes via linear regression. The resultant pressure-varying parameters are optimized by minimizing both dynamic and measurement residual squared errors. Validation of this methodology has been achieved by simulating adsorption data for n-butane and isobutane on activated carbon at 298 K, 323 K and 348 K and for nitrogen on mesoporous alumina at 77 K with pressure-varying Langmuir and BET adsorption parameters (equilibrium constants and uptake capacities). This modeling provides information on the adsorbent (accessible surface area and micropore volume), adsorbate (molecular areas and volumes) and thermodynamic (Gibbs free energies) variations of the adsorption sites.

Keywords: Langmuir adsorption isotherm, BET adsorption isotherm, pressure-varying adsorption parameters, adsorbate and adsorbent properties and energetics

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Authors: Mingyu Xie, Mietek Brdys

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The paper develops a non-linear model predictive control (NMPC) of water quality in drinking water distribution systems (DWDS) based on the advanced non-linear quality dynamics model including disinfections by-products (DBPs). A special attention is paid to the analysis of an impact of the flow trajectories prescribed by an upper control level of the recently developed two-time scale architecture of an integrated quality and quantity control in DWDS. The new quality controller is to operate within this architecture in the fast time scale as the lower level quality controller. The controller performance is validated by a comprehensive simulation study based on an example case study DWDS.

Keywords: model predictive control, hierarchical control structure, genetic algorithm, water quality with DBPs objectives

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Authors: Florin Leon, Silvia Curteanu

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Developing complete mechanistic models for polymerization reactors is not easy, because complex reactions occur simultaneously; there is a large number of kinetic parameters involved and sometimes the chemical and physical phenomena for mixtures involving polymers are poorly understood. To overcome these difficulties, empirical models based on sampled data can be used instead, namely regression methods typical of machine learning field. They have the ability to learn the trends of a process without any knowledge about its particular physical and chemical laws. Therefore, they are useful for modeling complex processes, such as the free radical polymerization of methyl methacrylate achieved in a batch bulk process. The goal is to generate accurate predictions of monomer conversion, numerical average molecular weight and gravimetrical average molecular weight. This process is associated with non-linear gel and glass effects. For this purpose, an adaptive sampling technique is presented, which can select more samples around the regions where the values have a higher variation. Several machine learning methods are used for the modeling and their performance is compared: support vector machines, k-nearest neighbor, k-nearest neighbor and random forest, as well as an original algorithm, large margin nearest neighbor regression. The suggested method provides very good results compared to the other well-known regression algorithms.

Keywords: batch bulk methyl methacrylate polymerization, adaptive sampling, machine learning, large margin nearest neighbor regression

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

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Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Aouiche Abdelaziz, Soudani Mouhamed Salah, Aouiche El Moundhe

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The present paper addresses the utilization of Artificial Neural Networks (ANNs) and Fuzzy Inference Systems (FISs) for the identification and control of dynamical systems with some degree of uncertainty. Because ANNs and FISs have an inherent ability to approximate functions and to adapt to changes in input and parameters, they can be used to control systems too complex for linear controllers. In this work, we show how ANNs and FISs can be put in order to form nets that can learn from external data. In sequence, it is presented structures of inputs that can be used along with ANNs and FISs to model non-linear systems. Four systems were used to test the identification and control of the structures proposed. The results show the ANNs and FISs (Back Propagation Algorithm) used were efficient in modeling and controlling the non-linear plants.

Keywords: non-linear systems, fuzzy set Models, neural network, control law

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Authors: Prashant Verma, Prafulla K. Swain, K. K. Singh, Mukti Khetan

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Objective: In India, around 20,000 women die every year due to abortion-related complications. In the modelling of count variables, there is sometimes a preponderance of zero counts. This article concerns the estimation of various count regression models to predict the average number of spontaneous abortion among women in the Punjab state of India. It also assesses the factors associated with the number of spontaneous abortions. Materials and methods: The study included 27,173 married women of Punjab obtained from the DLHS-4 survey (2012-13). Poisson regression (PR), Negative binomial (NB) regression, zero hurdle negative binomial (ZHNB), and zero-inflated negative binomial (ZINB) models were employed to predict the average number of spontaneous abortions and to identify the determinants affecting the number of spontaneous abortions. Results: Statistical comparisons among four estimation methods revealed that the ZINB model provides the best prediction for the number of spontaneous abortions. Antenatal care (ANC) place, place of residence, total children born to a woman, woman's education and economic status were found to be the most significant factors affecting the occurrence of spontaneous abortion. Conclusions: The study offers a practical demonstration of techniques designed to handle count variables. Statistical comparisons among four estimation models revealed that the ZINB model provided the best prediction for the number of spontaneous abortions and is recommended to be used to predict the number of spontaneous abortions. The study suggests that women receive institutional Antenatal care to attain limited parity. It also advocates promoting higher education among women in Punjab, India.

Keywords: count data, spontaneous abortion, Poisson model, negative binomial model, zero hurdle negative binomial, zero-inflated negative binomial, regression

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Authors: Kaushalendra K. Khadanga, Lee Hee Hyol

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Passenger comfort has been paramount in the design of suspension systems of high speed cars. To analyze the effect of vibration on vehicle ride quality, a vertical model of a six degree of freedom railway passenger vehicle, with front and rear suspension, is built. It includes car body flexible effects and vertical rigid modes. A second order linear shaping filter is constructed to model Gaussian white noise into random rail excitation. The temporal correlation between the front and rear wheels is given by a second order Pade approximation. The complete track and the vehicle model are then designed. An active secondary suspension system based on a Linear Quadratic Gaussian (LQG) optimal control method is designed. The results show that the LQG control method reduces the vertical acceleration, pitching acceleration and vertical bending vibration of the car body as compared to the passive system.

Keywords: active suspension, bending vibration, railway vehicle, vibration control

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Authors: Belayneh Yohannes

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In Ethiopia, onion production is increasing from time to time mainly due to its high profitability per unit area. Onion has a significant contribution to generating cash income for farmers in the Raya Azebo district. Therefore, enhancing onion producers’ access to the market and improving market linkage is an essential issue. Hence, this study aimed to analyze structure-conduct-performance of onion market and identifying factors affecting the market supply of onion producers. Data were collected from both primary and secondary sources. Primary data were collected from 150 farm households and 20 traders. Four onion marketing channels were identified in the study area. The highest total gross margin is 27.6 in channel IV. The highest gross marketing margin of producers of the onion market is 88% in channel II. The result from the analysis of market concentration indicated that the onion market is characterized by a strong oligopolistic market structure, with the buyers’ concentration ratio of 88.7 in Maichew town and 82.7 in Mekelle town. Lack of capital, licensing problems, and seasonal supply was identified as the major entry barrier to onion marketing. Market conduct shows that the price of onion is set by traders while producers are price takers. Multiple linear regression model results indicated that family size in adult equivalent, irrigated land size, access to information, frequency of extension contact, and ownership of transport significantly determined the quantity of onion supplied to the market. It is recommended that strengthening and diversifying extension services in information, marketing, post-harvest handling, irrigation application, and water harvest technology is highly important.

Keywords: oligopoly, onion, market chain, multiple linear regression

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Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

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This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

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Authors: Sajana Suwal, Ganesh R. Nhemafuki

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Evaluation of ground response under earthquake shaking is crucial in geotechnical earthquake engineering. Damage due to seismic excitation is mainly correlated to local geological and geotechnical conditions. It is evident from the past earthquakes (e.g. 1906 San Francisco, USA, 1923 Kanto, Japan) that the local geology has strong influence on amplitude and duration of ground motions. Since then significant studies has been conducted on ground motion amplification revealing the importance of influence of local geology on ground. Observations from the damaging earthquakes (e.g. Nigata and San Francisco, 1964; Irpinia, 1980; Mexico, 1985; Kobe, 1995; L’Aquila, 2009) divulged that non-uniform damage pattern, particularly in soft fluvio-lacustrine deposit is due to the local amplification of seismic ground motion. Non-uniform damage patterns are also observed in Kathmandu Valley during 1934 Bihar Nepal earthquake and recent 2015 Gorkha earthquake seemingly due to the modification of earthquake ground motion parameters. In this study, site effects resulting from amplification of soft soil in Kathmandu are presented. A large amount of subsoil data was collected and used for defining the appropriate subsoil model for the Kathamandu valley. A comparative study of one-dimensional total-stress equivalent linear and non-linear site response is performed using four strong ground motions for six sites of Kathmandu valley. In general, one-dimensional (1D) site-response analysis involves the excitation of a soil profile using the horizontal component and calculating the response at individual soil layers. In the present study, both equivalent linear and non-linear site response analyses were conducted using the computer program DEEPSOIL. The results show that there is no significant deviation between equivalent linear and non-linear site response models until the maximum strain reaches to 0.06-0.1%. Overall, it is clearly observed from the results that non-linear site response model perform better as compared to equivalent linear model. However, the significant deviation between two models is resulted from other influencing factors such as assumptions made in 1D site response, lack of accurate values of shear wave velocity and nonlinear properties of the soil deposit. The results are also presented in terms of amplification factors which are predicted to be around four times more in case of non-linear analysis as compared to equivalent linear analysis. Hence, the nonlinear behavior of soil prevails the urgent need of study of dynamic characteristics of the soft soil deposit that can specifically represent the site-specific design spectra for the Kathmandu valley for building resilient structures from future damaging earthquakes.

Keywords: deep soil, equivalent linear analysis, non-linear analysis, site response

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Authors: Kien-Trinh Thi Bui, Cuong Manh Nguyen

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In order to simulate and reproduce the operational characteristics of a dam visually, it is necessary to capture the displacement at different measurement points and analyze the observed movement data promptly to forecast the dam safety. The accuracy of forecasts is further improved by applying machine learning methods to data analysis progress. In this study, the horizontal displacement monitoring data of the Ialy hydroelectric dam was applied to machine learning algorithms: Gaussian processes, multi-layer perceptron neural networks, and the M5-rules algorithm for modelling and forecasting of horizontal displacement of the Ialy hydropower dam (Vietnam), respectively, for analysing. The database which used in this research was built by collecting time series of data from 2006 to 2021 and divided into two parts: training dataset and validating dataset. The final results show all three algorithms have high performance for both training and model validation, but the MLPs is the best model. The usability of them are further investigated by comparison with a benchmark models created by multi-linear regression. The result show the performance which obtained from all the GP model, the MLPs model and the M5-Rules model are much better, therefore these three models should be used to analyze and predict the horizontal displacement of the dam.

Keywords: Gaussian processes, horizontal displacement, hydropower dam, Ialy dam, M5-Rules, multi-layer perception neural networks

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Authors: Ignatio Madanhire, Charles Mbohwa

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This study looks at the best model of aggregate planning activity in an industrial entity and uses the trial and error method on spreadsheets to solve aggregate production planning problems. Also linear programming model is introduced to optimize the aggregate production planning problem. Application of the models in a furniture production firm is evaluated to demonstrate that practical and beneficial solutions can be obtained from the models. Finally some benchmarking of other furniture manufacturing industries was undertaken to assess relevance and level of use in other furniture firms

Keywords: aggregate production planning, trial and error, linear programming, furniture industry

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Authors: Yongjian Du, Jinhua Sun, Kim M. Liew, Huahua Xiao

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Optimal allocation of emergency resources before a disaster is of great importance for emergency response. In reality, the pre-protection for a single critical node where accidents may occur is common. In this study, a model is developed to determine location and inventory decisions of multiple emergency resources among a set of candidate stations to minimize the total cost based on the constraints of budgetary and capacity. The total cost includes the economic accident loss which is accorded with probability distribution of time and the warehousing cost of resources which is increasing over time. A ratio is set to measure the degree of a storage station only serving the target node that becomes larger with the decrease of the distance between them. For the application of linear program, it is assumed that the length of travel time to the accident scene of emergency resources has a linear relationship with the economic accident loss. A computational experiment is conducted to illustrate how the proposed model works, and the results indicate its effectiveness and practicability.

Keywords: emergency response, integer linear program, multiple emergency resources, pre-allocation decisions, single potential accident node

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Authors: F. Hamidi, N. Amiri, H. Mishmast Nehi

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The Bilevel Programming (BP) model has been presented for a decision making process that consists of two decision makers in a hierarchical structure. In fact, BP is a model for a static two person game (the leader player in the upper level and the follower player in the lower level) wherein each player tries to optimize his/her personal objective function under dependent constraints; this game is sequential and non-cooperative. The decision making variables are divided between the two players and one’s choice affects the other’s benefit and choices. In other words, BP consists of two nested optimization problems with two objective functions (upper and lower) where the constraint region of the upper level problem is implicitly determined by the lower level problem. In real cases, the coefficients of an optimization problem may not be precise, i.e. they may be interval. In this paper we develop an algorithm for solving interval bilevel linear fractional programming problems. That is to say, bilevel problems in which both objective functions are linear fractional, the coefficients are interval and the common constraint region is a polyhedron. From the original problem, the best and the worst bilevel linear fractional problems have been derived and then, using the extended Charnes and Cooper transformation, each fractional problem can be reduced to a linear problem. Then we can find the best and the worst optimal values of the leader objective function by two algorithms.

Keywords: best and worst optimal solutions, bilevel programming, fractional, interval coefficients

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Authors: N. Alhazmi

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Knowing the thermodynamics properties of hydrocarbons is vital when it comes to analyzing the related chemical reaction outcomes and understanding the reaction process, especially in terms of petrochemical industrial applications, combustions, and catalytic reactions. However, measuring the thermodynamics properties experimentally is time-consuming and costly. In this paper, Gaussian process regression (GPR) has been used to directly predict the main thermodynamic properties - standard enthalpy of formation, standard entropy, and heat capacity -for more than 360 cyclic and non-cyclic alkanes, alkenes, and alkynes. A simple workflow has been proposed that can be applied to directly predict the main properties of any hydrocarbon by knowing its descriptors and chemical structure and can be generalized to predict the main properties of any material. The model was evaluated by calculating the statistical error R², which was more than 0.9794 for all the predicted properties.

Keywords: thermodynamic, Gaussian process regression, hydrocarbons, regression, supervised learning, entropy, enthalpy, heat capacity

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Authors: Phanida Phukoetphim, Asaad Y. Shamseldin

Abstract:

In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand.

Keywords: multi-model combination, rainfall-runoff modeling, stochastic gradient boosting, bioinformatics

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Authors: Chaimongkol Limpianchob

Abstract:

This work addresses the problem of production planning that arises in the production of aromatic coconuts from Samudsakhorn province in Thailand. The planning involves the forwarding of aromatic coconuts from the harvest areas to the factory, which is classified into two groups; self-owned areas and contracted areas, the decisions of aromatic coconuts flow in the plant, and addressing a question of which warehouse will be in use. The problem is formulated as a mixed-integer linear programming model within supply chain management framework. The objective function seeks to minimize the total cost including the harvesting, labor and inventory costs. Constraints on the system include the production activities in the company and demand requirements. Numerical results are presented to demonstrate the feasibility of coconuts supply chain model compared with base case.

Keywords: aromatic coconut, supply chain management, production planning, mixed-integer linear programming

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

Abstract:

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

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

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Authors: Wanatchapong Kongkaew

Abstract:

This paper proposes an application of probabilistic technique, namely Gaussian process regression, for estimating an optimal sequence of the single machine with total weighted tardiness (SMTWT) scheduling problem. In this work, the Gaussian process regression (GPR) model is utilized to predict an optimal sequence of the SMTWT problem, and its solution is improved by using an iterated local search based on simulated annealing scheme, called GPRISA algorithm. The results show that the proposed GPRISA method achieves a very good performance and a reasonable trade-off between solution quality and time consumption. Moreover, in the comparison of deviation from the best-known solution, the proposed mechanism noticeably outperforms the recently existing approaches.

Keywords: Gaussian process regression, iterated local search, simulated annealing, single machine total weighted tardiness

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Authors: Tayyab Hassan, Talha Rehman, Qasim Abdul Aziz, Ahmad Salman

Abstract:

The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.

Keywords: blood glucose level, breath acetone concentration, diabetes, linear regression

Procedia PDF Downloads 162