Search results for: regression modeling of extremes

Authors: Ashkan Shafee, Ahamd Fahimifar, Sajjad V. Maghvan

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Promotion of the Fiber Reinforced Concrete (FRC) as a construction material for civil engineering projects has invoked numerous researchers to investigate their mechanical behavior. Even though there is satisfactory information about the effects of fiber type and length, concrete mixture, casting type and other variables on the strength and deformability parameters of FRC, the numerical modeling of such materials still needs research attention. The focus of this study is to investigate the feasibility of Concrete Damaged Plasticity (CDP) model in prediction of Macro-synthetic FRC structures behavior. CDP model requires the tensile behavior of concrete to be well characterized. For this purpose, a series of uniaxial direct tension and four point bending tests were conducted on the notched specimens to define bilinear tension softening (post-peak tension stress-strain) behavior. With these parameters obtained, the flexural behavior of macro-synthetic FRC beams were modeled and the results showed a good agreement with the experimental measurements.

Keywords: concrete damaged plasticity, fiber reinforced concrete, finite element modeling, macro-synthetic fibers, uniaxial tensile test

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Authors: Mohammad Moezzibadi, Isabelle Charpentier, Adrien Wanko, Robert Mosé

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The calibration of hydrodynamic parameters for subsurface constructed wetlands (CWs) is a sensitive process since highly non-linear equations are involved in unsaturated flow modeling. CW systems are engineered systems designed to favour natural treatment processes involving wetland vegetation, soil, and their microbial flora. Their significant efficiency at reducing the ecological impact of urban runoff has been recently proved in the field. Numerical flow modeling in a vertical variably saturated CW is here carried out by implementing the Richards model by means of a mixed hybrid finite element method (MHFEM), particularly well adapted to the simulation of heterogeneous media, and the van Genuchten-Mualem parametrization. For validation purposes, MHFEM results were compared to those of HYDRUS (a software based on a finite element discretization). As van Genuchten-Mualem soil hydrodynamic parameters depend on water content, their estimation is subject to considerable experimental and numerical studies. In particular, the sensitivity analysis performed with respect to the van Genuchten-Mualem parameters reveals a predominant influence of the shape parameters α, n and the saturated conductivity of the filter on the piezometric heads, during saturation and desaturation. Modeling issues arise when the soil reaches oven-dry conditions. A particular attention should also be brought to boundary condition modeling (surface ponding or evaporation) to be able to tackle different sequences of rainfall-runoff events. For proper parameter identification, large field datasets would be needed. As these are usually not available, notably due to the randomness of the storm events, we thus propose a simple, robust and low-cost numerical method for the inverse modeling of the soil hydrodynamic properties. Among the methods, the variational data assimilation technique introduced by Le Dimet and Talagrand is applied. To that end, a variational data assimilation technique is implemented by applying automatic differentiation (AD) to augment computer codes with derivative computations. Note that very little effort is needed to obtain the differentiated code using the on-line Tapenade AD engine. Field data are collected for a three-layered CW located in Strasbourg (Alsace, France) at the water edge of the urban water stream Ostwaldergraben, during several months. Identification experiments are conducted by comparing measured and computed piezometric head by means of the least square objective function. The temporal variability of hydrodynamic parameter is then assessed and analyzed.

Keywords: automatic differentiation, constructed wetland, inverse method, mixed hybrid FEM, sensitivity analysis

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Authors: Avdhesh K. Sharma

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Formulation for drying and pyrolysis process in packed beds at slow heating rates is presented. Drying of biomass particles bed is described by mass diffusion equation and local moisture-vapour-equilibrium relations. In gasifiers, volatilization rate during pyrolysis of biomass is modeled by using apparent kinetic rate expression, while product compositions at slow heating rates is modeled using empirical fitted mass ratios (i.e., CO/CO₂, ME/CO₂, H₂O/CO₂) in terms of pyrolysis temperature. The drying module is validated fairly with available chemical kinetics scheme and found that the testing zone in gasifier bed constituted of relatively smaller particles having high airflow with high isothermal temperature expedite the drying process. Further, volatile releases more quickly within the shorter zone height at high temperatures (isothermal). Both, moisture loss and volatile release profiles are found to be sensitive to temperature, although the influence of initial moisture content on volatile release profile is not so sensitive.

Keywords: modeling downdraft gasifier, drying, pyrolysis, moist woody biomass

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Authors: Hsu Yi-Chia, Hoy June-Hao

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FDM (Fused Deposition Modeling) is a rapid proofing method without mold, however, high material and time costs have always been a major disadvantage. Wire-printing is the next generation technology that can more flexible, and also easier to apply on a 3D printer and robotic arms printing. It can create its own construction methods. The research is mainly divided into three parts. The first is about the method of parameterizing the generated paths and the conversion of g-code to the wire-printing. The second is about material attempts and the application of effects. Third, is about the improvement of the operation of mechanical equipment and the design of robotic tool-head. The purpose of this study is to develop a new wire-print method that can efficiently generate line segments and paths in three- dimensions space. The parametric modeling software transforms the digital model into a 3D printer or robotic arms g-code, this article uses thermoplastics/ clay/composites materials for testing. The combination of materials and wire-print process makes architects and designers have the ability to research and develop works and construction in the future.

Keywords: parametric software, wire print, robotic arms fabrication, composite filament additive manufacturing

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Authors: Guilherme F. Guimaraes, Alfredo R. De Faria, Ronnie R. Rego, Andre L. R. D'Oliveira

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The development of methods for predicting manufacturing phenomena steadily grows due to their high potential to contribute to the component’s performance and durability. One of the most relevant phenomena in manufacturing is the residual stress state development through the manufacturing chain. In most cases, the residual stresses have their origin due to heterogenous plastifications produced by the processes. Although a few manufacturing processes have been successfully approached by numerical modeling, there is still a lack of understanding on how these processes' interactions will affect the final stress state. The objective of this work is to analyze the influence of previous stresses on the residual stress state induced by plastic deformation of a quasi-static indentation. The model consists of a simplified approach of shot peening, modeling four cases with variations in indenter size and force. This model was validated through topography, measured by optical 3D focus-variation, and residual stress, measured with the X-ray diffraction technique. The validated model was then exposed to several initial stress states, and the effect on the final residual stress was analyzed.

Keywords: plasticity, residual stress, finite element method, manufacturing

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Authors: Meera Shanker

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Excellent education and financial need have encouraged Indian women to go out and work in well-paid and high-status occupations. In the era of cutthroat competition, women are expected to work hard to produce the desired result; hence, workload and expectations haveincreased. At home, they are anticipated to take care of family members, children, and household work. Women are stretching themselves mechanically to remain in the job competition and try to give their best at home. Consequentially, they are under tremendous pressure, stressed, and having issues related to physical-mental wellness. Mental healthcare is often ignored and not accepted due to a lack of awareness and cultural barriers. These further compounds the problem, resulting in decreased productivity in economic terms and an increase in stress-related physical-mental ailments. The main objective of the study was to find out the impact of stress on the physical-mental wellbeing of working women in India, along with their awareness and acceptability related to mental health. Six hundred and one woman working at various levels took part in this study, responding to the items related to stress and physical-mental illness. Finally, 21 items were retained under four meaningful factors measuring stress dimensions along with 17 items with three factors measuring physical-mental wellbeing. Confirmatory Factor Analysis (CFA), path analysis, in Structural Equation Modeling (SEM), was used to get a relationship, validity of the instruments. The psychometric properties of items and Cronbach’s Alpha reliabilities calculated for the subscales were relatively acceptable. The subscale correlations, regression, and path analysis of stress dimensions with physical-mental illness were found to be positive, indicating the growing stress among working women in India, which is impacting their physical-mental health. Single item analysis revealed that 77 percent of women have never visited psychologists. However, 70 percent of working women were not ready to seek the help of a psychologist.

Keywords: working women, stress, physical-mental well-being, confirmatory factor analysis

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Authors: Yasmeen A. S. Essawy, Khaled Nassar

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With the rapid increase of complexity in the building industry, professionals in the A/E/C industry were forced to adopt Building Information Modeling (BIM) in order to enhance the communication between the different project stakeholders throughout the project life cycle and create a semantic object-oriented building model that can support geometric-topological analysis of building elements during design and construction. This paper presents a model that extracts topological relationships and geometrical properties of building elements from an existing fully designed BIM, and maps this information into a directed acyclic Elemental Graph Data Model (EGDM). The model incorporates BIM-based search algorithms for automatic deduction of geometrical data and topological relationships for each building element type. Using graph search algorithms, such as Depth First Search (DFS) and topological sortings, all possible construction sequences can be generated and compared against production and construction rules to generate an optimized construction sequence and its associated schedule. The model is implemented in a C# platform.

Keywords: building information modeling (BIM), elemental graph data model (EGDM), geometric and topological data models, graph theory

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Authors: Oluwatosin Olofintoye, Josiah Adeyemo, Gbemileke Shomade

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The relationship between rainfall and runoff is an important issue in surface water hydrology therefore the understanding and development of accurate rainfall-runoff models and their applications in water resources planning, management and operation are of paramount importance in hydrological studies. This paper reviews some of the previous works on the rainfall-runoff process modeling. The hydrologic applications of conceptual models and artificial neural networks (ANNs) for the precipitation-runoff process modeling were studied. Gradient training methods such as error back-propagation (BP) and evolutionary algorithms (EAs) are discussed in relation to the training of artificial neural networks and it is shown that application of EAs to artificial neural networks training could be an alternative to other training methods. Therefore, further research interest to exploit the abundant expert knowledge in the area of artificial intelligence for the solution of hydrologic and water resources planning and management problems is needed.

Keywords: artificial intelligence, artificial neural networks, evolutionary algorithms, gradient training method, rainfall-runoff model

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Authors: Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab

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Urban Heat Islands (UHIs) are distinctive urban areas characterized by densely populated central cores surrounded by less densely populated peripheral lands. These areas experience elevated temperatures, primarily due to impermeable surfaces and specific land use patterns. The consequences of these temperature variations are far-reaching, impacting the environment and society negatively, leading to increased energy consumption, air pollution, and public health concerns. This paper emphasizes the need for simplified approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. To illustrate this relationship, the study focuses on the Guilan Plain, utilizing techniques like principal component analysis and generalized additive models. The research centered on mapping land use and land surface temperature in the low-lying area of Guilan province. Satellite data from Landsat sensors for three different time periods (2002, 2012, and 2021) were employed. Using eCognition software, a spatial unit known as a "city block" was utilized through object-based analysis. The study also applied the normalized difference vegetation index (NDVI) method to estimate land surface radiance. Predictive variables for urban land surface temperature within residential city blocks were identified categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Principal Component Analysis (PCA) was used to select significant variables, and a Generalized Additive Model (GAM) approach, implemented using R's mgcv package, modeled the relationship between urban land surface temperature and predictor variables.Notable findings included variations in urban temperature across different years attributed to environmental and climatic factors. Block size, shared boundary, mother polygon area, and perimeter-to-area ratio were identified as main variables for the generalized additive regression model. This model showed non-linear relationships, with block size, shared boundary, and mother polygon area positively correlated with temperature, while the perimeter-to-area ratio displayed a negative trend. The discussion highlights the challenges of predicting urban surface temperature and the significance of block size in determining urban temperature patterns. It also underscores the importance of spatial configuration and unit structure in shaping urban temperature patterns. In conclusion, this study contributes to the growing body of research on the connection between land use patterns and urban surface temperature. Block size, along with block dispersion and aggregation, emerged as key factors influencing urban surface temperature in residential areas. The proposed methodology enhances our understanding of parameter significance in shaping urban temperature patterns across various regions, particularly in Iran.

Keywords: urban heat island, land surface temperature, LST modeling, GAM, Gilan province

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Authors: Nicolas R. Finkler, Wesley A. Saltarelli, Taison A. Bortolin, Vania E. Schneider, Davi G. F. Cunha

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Estimating the relative contribution of nonpoint or point sources of pollution in low-orders streams is an important tool for the water resources management. The location of headwaters in areas with anthropogenic impacts from urbanization and agriculture is a common scenario in developing countries. This condition can lead to conflicts among different water users and compromise ecosystem services. Water pollution also contributes to exporting organic loads to downstream areas, including higher order rivers. The purpose of this research is to preliminarily assess nutrients and solids loads exported by water bodies located in watersheds with different types of land uses in São Carlos - SP (Latitude. -22.0087; Longitude. -47.8909) and Caxias do Sul - RS (Latitude. -29.1634, Longitude. -51.1796), Brazil, using regression analysis. The variables analyzed in this study were Total Kjeldahl Nitrogen (TKN), Nitrate (NO3-), Total Phosphorus (TP) and Total Suspended Solids (TSS). Data were obtained in October and December 2015 for São Carlos (SC) and in November 2012 and March 2013 for Caxias do Sul (CXS). Such periods had similar weather patterns regarding precipitation and temperature. Altogether, 11 sites were divided into two groups, some classified as more pristine (SC1, SC4, SC5, SC6 and CXS2), with predominance of native forest; and others considered as impacted (SC2, SC3, CXS1, CXS3, CXS4 and CXS5), presenting larger urban and/or agricultural areas. Previous linear regression was applied for data on flow and drainage area of each site (R² = 0.9741), suggesting that the loads to be assessed had a significant relationship with the drainage areas. Thereafter, regression analysis was conducted between the drainage areas and the total loads for the two land use groups. The R² values were 0.070, 0.830, 0.752 e 0.455 respectively for SST, TKN, NO3- and TP loads in the more preserved areas, suggesting that the loads generated by runoff are significant in these locations. However, the respective R² values for sites located in impacted areas were respectively 0.488, 0.054, 0.519 e 0.059 for SST, TKN, NO3- and P loads, indicating a less important relationship between total loads and runoff as compared to the previous scenario. This study suggests three possible conclusions that will be further explored in the full-text article, with more sampling sites and periods: a) In preserved areas, nonpoint sources of pollution are more significant in determining water quality in relation to the studied variables; b) The nutrient (TKN and P) loads in impacted areas may be associated with point sources such as domestic wastewater discharges with inadequate treatment levels; and c) The presence of NO3- in impacted areas can be associated to the runoff, particularly in agricultural areas, where the application of fertilizers is common at certain times of the year.

Keywords: land use, linear regression, point and non-point pollution sources, streams, water resources management

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Authors: Gamal F.Attia

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The baselines are the distances and lengths of the chords between projections of the positions of the laser stations on the reference ellipsoid. For the satellite geodesy, it is very important to determine the optimal length of orbital arc along which laser measurements are to be carried out. It is clear that for the dynamical methods long arcs (one month or more) are to be used. According to which more errors of modeling of different physical forces such as earth's gravitational field, air drag, solar radiation pressure, and others that may influence the accuracy of the estimation of the satellites position, at the same time the measured errors con be almost completely excluded and high stability in determination of relative coordinate system can be achieved. It is possible to diminish the influence of the errors of modeling by using short-arcs of the satellite orbit (several revolutions or days), but the station's coordinates estimated by different arcs con differ from each other by a larger quantity than statistical zero. Under the semidynamical ‘short arc’ method one or several passes of the satellite in one of simultaneous visibility from both ends of the chord is known and the estimated parameter in this case is the length of the chord. The comparison of the same baselines calculated with long and short arcs methods shows a good agreement and even speaks in favor of the last one. In this paper the Short Arc technique has been explained and 3 baselines have been determined using the ‘short arc’ method.

Keywords: baselines, short arc, dynamical, gravitational field

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Authors: Sanyka Banerjee, Saikat Nandi, P. K. Dan

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Synchronizers as an integral part of gearbox is a key element in the transmission system in automotive. The performance of synchronizer affects transmission efficiency and driving comfort. Synchronizing mechanism as a major component of transmission system must be capable of preventing vibration and noise in the gears. Gear shifting efficiency improvement with an aim to achieve smooth, quick and energy efficient power transmission remains a challenge for the automotive industry. Performance of the synchronizer is dependent on the features and characteristics of its sub-components and therefore analysis of the contribution of such characteristics is necessary. An important exercise involved is to identify all such characteristics or factors which are associated with the modeling and analysis and for this purpose the literature was reviewed, rather extensively, to study the mathematical models, formulated considering such. It has been observed that certain factors are rather common across models; however, there are few factors which have specifically been selected for individual models, as reported. In order to obtain a more realistic model, an attempt here has been made to identify and assimilate practically all possible factors which may be considered in formulating the model more comprehensively. A simulation study, formulated as a block model, for such analysis has been carried out in a reliable environment like MATLAB. Lower synchronization time is desirable and hence, it has been considered here as the output factors in the simulation modeling for evaluating transmission efficiency. An improved synchronizer model requires optimized values of sub-component design parameters. A parametric optimization utilizing Taguchi’s design of experiment based response data and their analysis has been carried out for this purpose. The effectiveness of the optimized parameters for the improved synchronizer performance has been validated by the simulation study of the synchronizer block model with improved parameter values as input parameters for better transmission efficiency and driver comfort.

Keywords: design of experiments, modeling, parametric optimization, simulation, synchronizer

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Authors: Amin Amani, Masomeh Reihany Shirvan, Mahla Nabizadeh Mashizi, Mohadese Khoshtinat, Mohammad Elyas Ansarinia

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The purpose of this study is the relationship between a healthy lifestyle and risky behavior in physical education students of Bojnourd schools. The study sample consisted of teenagers studying in second and third grade of Bojnourd's high schools. According to level sampling, 604 students studying in the second grade, and 600 students studying in third grade were tested from physical education schools in Bojnourd. For sample selection, populations were divided into 4 area including north, East, West and South. Then according to the number of students of each area, sample size of each level was determined. Two questionnaires were used to collect data in this study which were consisted of three parts: The demographic data, Iranian teenagers' risk taking (IARS) and prevention methods with emphasize on the importance of family role were examined. The Central and dispersion indices, such as standard deviation, multiple variance analysis, and multivariate regression analysis were used. Results showed that the observed F is significant (P ≤ 0.01) and 21% of variance related to risky behavior is explained by the lack of awareness. Given the significance of the regression, the coefficients of risky behavior in teenagers in prediction equation showed that each of teenagers' risky behavior can have an impact on healthy lifestyle.

Keywords: healthy lifestyle, high-risk behavior, students, physical education

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Authors: Shu Tao, Rong Wang, Huizhong Shen, Ye Huang

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High-resolution mapping of fuel combustion is essential for reducing uncertainties in assessments of greenhouse gases and air pollutant emissions. Such inventories provide valuable information for inferring carbon sinks, modeling pollutant transport, and developing control strategies. Previous inventories included only a few fuel types and were derived using national population proxies which may distort the geographical variation within countries. In this study, a global 0.1 degree by 0.1 degree geo-referenced inventory of fuel combustion (PKU-FUEL-2007) was developed for 64 fuel sub-types along with uncertainty analysis for the year 2007. Sub-national fuel consumption of large countries and major power-station locations were used. The disaggregation error can be reduced significantly by using the sub-nationally energy data, because the uneven distribution of per-capita fuel consumption within countries is taken into consideration. The PKU-FUEL was used to generate global emission inventories of CO2 (PKU-CO2-2007), polycyclic aromatic hydrocarbons (PKU-PAHs-2007), and black carbons (PKU-BC-2007). Atmospheric transport modeling and expsoure assessment were conducted for BC and PAHs based on the inventory.

Keywords: fuel, emission, BC, PAHs, atmospheric transport, exposure

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Authors: M. Aruna, Uma Gunasilan

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Hospitals play an ample role in securing the health of a country. Nevertheless, they also have an unhealthy side. Ecological issues strengthen ill-health throughout the domain which subsequently puts pressure on hospital supply chains. Medical waste indeed is hazardous for environment and subsequently for human. The hospital waste management is of immense prominence due to its infectious and hazardous nature that can source many effects on human health and the environment. Government regulations and public cognizance regarding hospital waste issues have imposed hospital units to admit these strategies. The innovative technologies and instruments have been developed to handle hospital wastes. Green supply chain management practices are common in the United States. In India, Green Supply Chain management (GSCM) has just started to be recognized and practiced. GSCM are green, integrated and ecologically optimized. In Green supply chain management environmental sustainability is found to be an important driver. Eleven barriers are identified in this work. Interpretive Structural Modeling (ISM) technique is used for ranking the obstructions.

Keywords: green supply chain management (GSCM), hospital waste management (HWM), interpretive structural modeling (ISM), medical waste (MW)

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Authors: Azita Behbahaninia

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This research has been carried out to determine the water quality and physico-chemical properties Polour River, one of the most branch of Haraz River. Polour River was studied for a period of one year Samples were taken from different stations along the main branch of River polour. In water samples determined pH, DO, SO4, Cl, PO4, NO3, EC, BOD, COD, Temprature, color and number of Caliform per liter. ArcGIS was used for the zoning of phosphate concentration in the polour River basin. The results indicated that the river is polluted in polour village station, because of discharge domestic wastewater and also river is polluted in Ziar village station, because of agricultural wastewater and water is contaminated in aquaculture station, because of fish ponds wastewater. Statistical analysis shows that between independent traits and coliform regression relationship is significant at the 1% level. Coefficient explanation index indicated independent traits control 80% coliform and 20 % is for unknown parameters. The causality analysis showed Temperature (0.6) has the most positive and direct effect on coliform and sulfate has direct and negative effect on coliform. The results of causality analysis and the results of the regression analysis are matched and other forms direct and indirect effects were negligible and ignorable. Kruskal-Wallis test showed, there is different between sampling stations and studied characters. Between stations for temperature, DO, COD, EC, sulfate and coliform is at 1 % and for phosphate 5 % level of significance.

Keywords: coliform, GIS, pollution, phosphate, river

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Authors: I. Inthanongsone, C. Drebenstedt, J. C. Bongaerts, P. Sontamino

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The major concern in evaluating the value of mining projects related to the deficiency of the traditional discounted cash flow (DCF) method. This method does not take uncertainties into account and, hence it does not allow for an economic assessment of managerial flexibility and operational adaptability, which are increasingly determining long-term corporate success. Such an assessment can be performed with the real options valuation (ROV) approach, since it allows for a comparative evaluation of unforeseen uncertainties in a project life cycle. This paper presents an economic evaluation model for open pit mining projects based on real options valuation approach. Uncertainties in the model are caused by metal prices and cost uncertainties and the system dynamics (SD) modeling method is used to structure and solve the real options model. The model is applied to a case study. It can be shown that that managerial flexibility reacting to uncertainties may create additional value to a mining project in comparison to the outcomes of a DCF method. One important insight for management dealing with uncertainty is seen in choosing the optimal time to exercise strategic options.

Keywords: DCF methods, ROV approach, system dynamics modeling methods, uncertainty

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Authors: Jamila Garba Audu, Shehu Usman Hassan

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The widespread failure in the financial information quality has created the need to improve the financial information quality and to strengthen the control of managers by setting up good firms structures. Published accounting information in financial statements is required to provide various users - shareholders, employees, suppliers, creditors, financial analysts, stockbrokers and government agencies – with timely and reliable information useful for making prudent, effective and efficient decisions. The relationship between corporate governance and performance to financial reporting quality is imperative; this is because despite rapid researches in this area the findings obtained from these studies are constantly inconclusive. Data for the study were extracted from the firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences; the data was empirically tested. A multiple regression was employed to test the model as a technique for data analysis. The results from the analysis revealed a negative association between all the regressors and financial reporting quality except the performance of listed manufacturing firms in Nigeria. This indicates that corporate governance plays a significant role in mitigating earnings management and improving financial reporting quality while performance does not. The study recommended among others that the composition of audit committee should be made in accordance with the provision for code of corporate governance which is not more than six (6) members with at least one (1) financial expert.

Keywords: corporate governance, financial reporting quality, manufacturing firms, Nigeria, performance

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Authors: Golayeh Yousefi, Mehdi Homaee, Ali Akbar Norouzi

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Ongoing monitoring of soil contamination by heavy metals is critical for ecosystem stability and environmental protection, and food security. The conventional methods of determining these soil contaminants are time-consuming and costly. Spectroscopy in the visible near-infrared (VNIR) - short wave infrared (SWIR) region is a rapid, non-destructive, noninvasive, and cost-effective method for assessment of soil heavy metals concentration by studying the spectral properties of soil constituents. The aim of this study is to derive spectral bands and important ranges that are sensitive to heavy metals and can be used to estimate the concentration of these soil contaminants. In other words, the change in the spectral properties of spectrally active constituents of soil can lead to the accurate identification and estimation of the concentration of these compounds in soil. For this purpose, 325 soil samples were collected, and their spectral reflectance curves were evaluated at a range of 350-2500 nm. After spectral preprocessing operations, the partial least-squares regression (PLSR) model was fitted on spectral data to predict the concentration of Cu and Ni. Based on the results, the spectral range of Cu- sensitive spectra were 480, 580-610, 1370, 1425, 1850, 1920, 2145, and 2200 nm, and Ni-sensitive ranges were 543, 655, 761, 1003, 1271, 1415, 1903, 2199 nm. Finally, the results of this study indicated that the spectral data contains a lot of information that can be applied to identify the soil properties, such as the concentration of heavy metals, with more detail.

Keywords: heavy metals, spectroscopy, spectral bands, PLS regression

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Authors: Ebrahim Alamatian, Seyed Mehdi Afzalnia

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One of the most vital hydraulic structures is the dam. Regarding the unrecoverable damages which may occur after a dam break phenomenon, analyzing dams’ break is absolutely essential. GOLESTAN dam is located in the western South of Mashhad city in Iran. GOLESTAN dam break might lead to severe problems due to adjacent tourist and entertainment areas. In this paper, a numerical code based on the finite volume method was applied for assessing the risk of GOLESTAN dam break. As to this issue, first, a canal with a triangular barrier was modeled so as to verify the capability of the concerned code. Comparing analytical, experimental and numerical results showed that water level in the model results is in a good agreement with the similar water level in the analytical solutions and experimental data. The results of dam break modeling are revealed that two of the bridges, that are PARTOIE and NAMAYESHGAH, located downstream in the flow direction, are at risk following the potential GOLESTAN dam break. Therefore, the required times to conduct the precautionary measures at bridges were calculated at about 12 and 21 minutes, respectively. Thus, it is crucial to announce people about the possible risks of the dam break in order to decrease likely losses.

Keywords: numerical model, shallow water equations, GOLESTAN dam break, dry and wet beds modeling

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Predicting the risk of diabetic retinopathy (DR) in Indian type 2 diabetes patients is immensely necessary. India, being the second largest country after China in terms of a number of diabetic patients, to the best of our knowledge not a single risk score for complications has ever been investigated. Diabetic retinopathy is a serious complication and is the topmost reason for visual impairment across countries. Any type or form of DR has been taken as the event of interest, be it mild, back, grade I, II, III, and IV DR. A sample was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of DR. Cox proportional hazard regression is used to design risk scores for the prediction of retinopathy. Model calibration and discrimination are assessed from Hosmer Lemeshow and area under receiver operating characteristic curve (ROC). Overfitting and underfitting of the model are checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Optimal cut off point is chosen by Youden’s index. Five-year probability of DR is predicted by both survival function, and Markov chain two state model and the better technique is concluded. The risk scores developed can be applied by doctors and patients themselves for self evaluation. Furthermore, the five-year probabilities can be applied as well to forecast and maintain the condition of patients. This provides immense benefit in real application of DR prediction in T2DM.

Keywords: Cox proportional hazard regression, diabetic retinopathy, ROC curve, type 2 diabetes mellitus

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Shashank. S. Bagane, H. N. Rajendra Prasad

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Building Information Modeling (BIM) has currently developed into a potent solution. The consistent development of BIM technology in the sphere of Architecture, Engineering, and Construction (AEC) industry has enhanced the effectiveness of construction and decision making. However, aggrandized global warming and energy crisis has impacted on building energy analysis. It is now becoming an important factor to be considered in the AEC industry. Amalgamating energy analysis in the planning and design phase of a structure has become a necessity. In the current construction industry, estimating energy usage and reducing its footprint is of high priority. The construction industry is giving more prominence to sustainability alongside energy efficiency. This demand is compelling the designers, planners, and engineers to inspect the sustainable performance throughout the building's life cycle. The current study primarily focuses on energy consumption, space arrangement, and spatial geometry of a residential building. Most commonly residential structures in India are constructed considering Vastu Shastra. Vastu designs are intended to integrate architecture with nature and utilizing geometric patterns, symmetry, and directional alignments. In the current study, a residential brick masonry structure is considered for BIM analysis, Architectural model of the structure will be created using Revit software, later the orientation and spatial arrangement will be finalized based on Vastu principles. Furthermore, the structure will be investigated for the impact of building orientation and spatial arrangements on energy using Green Building Studio software. Based on the BIM analysis of the structure, energy consumption of subsequent building orientations will be understood. A well-orientated building having good spatial arrangement can save a considerable amount of energy throughout its life cycle and reduces the need for heating and lighting which will prove to diminish energy usage and improve the energy efficiency of the residential building.

Keywords: building information modeling, energy impact, spatial geometry, vastu

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Authors: Imene Atek, Abed M. Affoune, Hubert Girault, Pekka Peljo

Abstract:

Due to the need for a rigorous mathematical model that can help to estimate kinetic properties for soluble-insoluble systems, through voltammetric experiments, a Nicholson Semi Analytical Approach was used in this work for modeling and prediction of theoretical linear sweep voltammetry responses for reversible, quasi reversible or irreversible electron transfer reactions. The redox system of interest is a one-step metal electrodeposition process. A rigorous analysis of simulated linear scan voltammetric responses following variation of dimensionless factors, the rate constant and charge transfer coefficients in a broad range was studied and presented in the form of the so called kinetic zones diagrams. These kinetic diagrams were divided into three kinetics zones. Interpreting these zones leads to empirical mathematical models which can allow the experimenter to determine electrodeposition reactions kinetics whatever the degree of reversibility. The validity of the obtained results was tested and an excellent experiment–theory agreement has been showed.

Keywords: electrodeposition, kinetics diagrams, modeling, voltammetry

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Authors: Krishang Surapaneni

Abstract:

The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Ali Aghazadegan, Ali Shokria, Julia Mullarneya, Jon Tunnicliffe

Abstract:

River channel confluences are dynamic systems with branching structures that exhibit a high degree of complexity both in natural and man-made open channel networks. Recent and past fields and modeling have investigated the river dynamics modeling of confluent based on a series of over-simplified assumptions (i.e. straight tributary channel with a bend with a 90° junction angle). Accurate assessment of such systems is important to the design and management of hydraulic structures and river engineering processes. Despite their importance, there has been little study of the hydrodynamics characteristics of river confluences, and the link between flow hydrodynamics and confluence morphodynamics in the confluence is still incompletely understood. This paper studies flow structures in confluences, morphodynamics and deposition patterns in 30 and 90 degrees confluences with different flow conditions. The results show that the junction angle is primarily the key factor for the determination of the confluence bed morphology and sediment pattern, while the discharge ratio is a secondary factor. It also shows that super elevation created by mixing flows is a key function of the morphodynamics patterns.

Keywords: helical flow, river confluence, bed morphology , secondary flows, shear layer

Procedia PDF Downloads 128

Authors: Nassim Bessaad, Qilian Bao, Zhao Jiangkang

Abstract:

The fiber optics gyroscope in the strap-down inertial navigation system (FOG-SINS) suffers from precision degradation due to the influence of random errors. In this work, an enhanced Allan variance (AV) stochastic modeling method combined with discrete wavelet transform (DWT) for signal denoising is implemented to estimate the random process in the FOG signal. Furthermore, we devise a measurement-based iterative adaptive Sage-Husa nonlinear filter with augmented states to integrate a star tracker sensor with SINS. The proposed filter adapts the measurement noise covariance matrix based on the available data. Moreover, the enhanced stochastic modeling scheme is invested in tuning the process noise covariance matrix and the augmented state Gauss-Markov process parameters. Finally, the effectiveness of the proposed filter is investigated by employing the collected data in laboratory conditions. The result shows the filter's improved accuracy in comparison with the conventional Kalman filter (CKF).

Keywords: inertial navigation, adaptive filtering, star tracker, FOG

Procedia PDF Downloads 66

Authors: Atef Ahmad Bleibel

Abstract:

Most codes of practice, like ACI 318-14, require the use of strut-and-tie modeling to analyze and design reinforced concrete deep beams. Though, investigations that conducted on deep beams do not include ring deep beams of influential parameters. This work presents an analytical parametric study using strut-and-tie modeling stated by ACI 318-14 to predict load capacity of 20 reinforced concrete ring deep beam specimens with different parameters. The parameters that were under consideration in the current work are ring diameter (Dc), number of supports (NS), width of ring beam (bw), concrete compressive strength (f'c) and width of bearing plate (Bp). It is found that the load capacity decreases by about 14-36% when ring diameter increases by about 25-75%. It is also found that load capacity increases by about 62-189% when number of supports increases by about 33-100%, while the load capacity increases by about 25-75% when the beam ring width increases by about 25-75%. Finally, it is found that load capacity increases by about 24-76% when compressive strength increases by about 24-76%, while the load capacity increases by about 5-16% when Bp increases by about 25-75%.

Keywords: load parameters, reinforced concrete, ring deep beam, strut and tie

Procedia PDF Downloads 88

Authors: Bader Eddin Al Asali, N. Srinivasa Reddy

Abstract:

Cities are dynamic in nature and are characterized by concentration of people, infrastructure, services and markets, which offer opportunities for production and consumption. Often growth and development in urban areas is not systematic, and is directed by number of factors like natural growth, land prices, housing availability, job locations-the central business district (CBD’s), transportation routes, distribution of resources, geographical boundaries, administrative policies, etc. One sided spatial and geographical development in cities leads to the unequal spatial distribution of population and jobs, resulting in high transportation activity. City development can be measured by the parameters such as urban size, urban form, urban shape, and urban structure. Urban Size is the city size and defined by the population of the city, and urban form is the location and size of the economic activity (CBD) over the geographical space. Urban shape is the geometrical shape of the city over which the distribution of population and economic activity occupied. And Urban Structure is the transport network within which the population and activity centers are connected by hierarchy of roads. Among the urban land-use systems transportation plays significant role and is one of the largest energy consuming sector. Transportation interaction among the land uses is measured in Passenger-Km and mean trip length, and is often used as a proxy for measurement of energy consumption in transportation sector. Among the trips generated in cities, work trips constitute more than 70 percent. Work trips are originated from the place of residence and destination to the place of employment. To understand the role of urban parameters on transportation interaction, theoretical cities of different size and urban specifications are generated through building block exercise using a specially developed interactive C++ programme and land use transportation modeling is carried. The land-use transportation modeling exercise helps in understanding the role of urban parameters and also to classify the cities for their urban form, structure, and shape. Muscat the capital city of Oman underwent rapid urbanization over the last four decades is taken as a case study for its classification. Also, a pilot survey is carried to capture urban travel characteristics. Analysis of land-use transportation modeling with field data classified Muscat as a linear city with polycentric CBD. Conclusions are drawn suggestion are given for policy making for the sustainability of Muscat City.

Keywords: land-use transportation, transportation modeling urban form, urban structure, urban rule parameters

Procedia PDF Downloads 254

Authors: Thoetchai Peeraphatdit, Peter A. Brady, Suraj Kapa, Samuel J. Asirvatham, Niyada Naksuk

Abstract:

Background: Serum electrolyte abnormalities are a common cause of an acquired prolonged QT syndrome, especially, in the coronary care unit (CCU) setting. Optimal electrolyte ranges among the CCU patients have not been sufficiently investigated. Methods: We identified 8,498 consecutive CCU patients who were admitted to the CCU at Mayo Clinic, Rochester, the USA, from 2004 through 2013. Association between first serum electrolytes and baseline corrected QT intervals (QTc), as well as in-hospital mortality, was tested using multivariate linear regression and logistic regression, respectively. Serum potassium 4.0- < 4.5 mEq/L, ionized calcium (iCa) 4.6-4.8 mg/dL, and magnesium 2.0- < 2.2 mg/dL were used as the reference levels. Results: There was a modest level-dependent relationship between hypokalemia ( < 4.0 mEq/L), hypocalcemia ( < 4.4 mg/dL), and a prolonged QTc interval; serum magnesium did not affect the QTc interval. Association between the serum electrolytes and in-hospital mortality included a U-shaped relationship for serum potassium (adjusted odds ratio (OR) 1.53 and OR 1.91for serum potassium 4.5- < 5.0 and ≥ 5.0 mEq/L, respectively) and an inverted J-shaped relationship for iCa (adjusted OR 2.79 and OR 2.03 for calcium < 4.4 and 4.4- < 4.6 mg/dL, respectively). For serum magnesium, the mortality was greater only among patients with levels ≥ 2.4 mg/dL (adjusted OR 1.40), compared to the reference level. Findings were similar in sensitivity analyses examining the association between mean serum electrolytes and mean QTc intervals, as well as in-hospital mortality. Conclusions: Serum potassium 4.0- < 4.5 mEq/L, iCa ≥ 4.6 mg/dL, and magnesium < 2.4 mg/dL had a neutral effect on QTc intervals and were associated with the lowest in-hospital mortality among the CCU patients.

Keywords: calcium, electrocardiography, long-QT syndrome, magnesium, mortality, potassium

Procedia PDF Downloads 378