Search results for: numerical modeling

Authors: Mounir Soule, Hindatou Saidou, Razafimahefa, Mohamed Thani Ibouroi

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High levels of habitat fragmentation and loss are the main drivers of plant species extinction. They reduce the habitat quality, which is a determining factor for the reproduction of plant species, and generate strong selective pressures for habitat selection, with impacts on the reproduction and survival of individuals. The Comorian’s yam (Dioscorea comorensis) is one of the most threatened plant species of the Comoros archipelago. The species faces one of the highest rates of habitat loss worldwide (9.3 % per year) and is classified as Endangered in the IUCN red list. Despite the nutritional potential of this tuber, the Comorian’s yam cultivation remains neglected by local populations due probably to lack of knowledge on its nutritional importance and the factors driving its spatial distribution and development. In this study, we assessed the nutritional characteristics of Dioscorea comorensis and the drivers of spatial distribution and abundance to propose conservation measures and improve crop yields. To determine the nutritional characteristics, the Kjeldahl method, the Soxhlet method, and Atwater's specific calorific coefficients methods were applied for analyzing proteins, lipids, and caloric energy respectively. In addition, atomic absorption spectrometry was used to measure mineral particles. By combining species occurrences with ecological (habitat types), climatic (temperature, rainfall, etc.), and physicochemical (soil types and quality) variables, we assessed habitat suitability and spatial distribution of the species and the factors explaining the origin, persistence, distribution and competitive capacity of a species using a Species Distribution Modeling (SDM) method. The results showed that the species contains 83.37% carbohydrates, 6.37% protein, and 0.45% lipids. In 100 grams, the quantities of Calcium, Sodium, Zinc, Iron, Copper, Potassium, Phosphorus, Magnesium, and Manganese are respectively 422.70, 599.41, 223.11, 252.32, 332.20, 780.41, 444.17, 287.71 and 220.73 mg. Its PRAL index is negative (- 9.80 mEq/100 g), and its Ca/P (0.95) and Na/K (0.77) ratios are less than 1. This species provides an energy value of 357.46 Kcal per 100 g, thanks to its carbohydrates and minerals and is distinguished from others by its high protein content, offering benefits for cardiovascular health. According to our SDM, the species has a very limited distribution, restricted to forests with higher biomass, humidity, and clay. Our findings highlight how distribution patterns are related to ecological and environmental factors. They also emphasize how the Comoros yam is beneficial in terms of nutritional quality. Our results represent a basic knowledge that will help scientists and decision-makers to develop conservation strategies and to improve crop yields.

Keywords: Dioscorea comorensis, nutritional characteristics, species distribution modeling, conservation strategies, crop yields improvement

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Authors: Ali Masria , Abdelazim Negm, Moheb Iskander, Oliver C. Saavedra

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Estuaries play an important role in exchanging water and providing a navigational pathway for ships. These zones are very sensitive and vulnerable to any interventions in coastal dynamics. Almost major of these inlets experience coastal problems such as severe erosion, and accretion. Rosetta promontory, Egypt is an example of this environment. It suffers from many coastal problems as erosion problem along the coastline and siltation problem inside the inlet. It is due to lack of water and sediment resources as a side effect of constructing the Aswan High dam. The shoaling of the inlet leads to hindering the navigation process of fishing boats, negative impacts to estuarine and salt marsh habitat and decrease the efficiency of the cross section to transfer the flow during emergencies to the sea. This paper aims to reach a new condition of stability of Rosetta Promontory by using coastal measures to control the sediment entering, and causes shoaling inside the inlet. These coastal measures include modifying the inlet cross section by using centered jetties, eliminate the coastal dynamic in the entrance using boundary jetties. This target is achieved by using a hydrodynamic model Coastal Modeling System (CMS). Extensive field data collection (hydrographic surveys, wave data, tide data, and bed morphology) is used to build and calibrate the model. About 20 scenarios were tested to reach a suitable solution that mitigate the coastal problems at the inlet. The results show that 360 m jetty in the eastern bank with system of sand bypass from the leeside of the jetty can stabilize the estuary.

Keywords: Rosetta promontory, erosion, sedimentation, inlet stability

Procedia PDF Downloads 583

Authors: Bouanati Sidi Mohammed, N. E. Chabane Sari, Mostefa Kara Selma

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It is well-known that Organic light emitting diodes (OLEDs) are attracting great interest in the display technology industry due to their many advantages, such as low price of manufacturing, large-area of electroluminescent display, various colors of emission included white light. Recently, there has been much progress in understanding the device physics of OLEDs and their basic operating principles. In OLEDs, Light emitting is the result of the recombination of electron and hole in light emitting layer, which are injected from cathode and anode. For improve luminescence efficiency, it is needed that hole and electron pairs exist affluently and equally and recombine swiftly in the emitting layer. The aim of this paper is to modeling polymer LED and OLED made with small molecules for studying the electrical and optical characteristics. The first simulation structures used in this paper is a mono layer device; typically consisting of the poly (2-methoxy-5(2’-ethyl) hexoxy-phenylenevinylene) (MEH-PPV) polymer sandwiched between an anode usually an indium tin oxide (ITO) substrate, and a cathode, such as Al. In the second structure we replace MEH-PPV by tris (8-hydroxyquinolinato) aluminum (Alq3). We choose MEH-PPV because of it's solubility in common organic solvents, in conjunction with a low operating voltage for light emission and relatively high conversion efficiency and Alq3 because it is one of the most important host materials used in OLEDs. In this simulation, the Poole-Frenkel- like mobility model and the Langevin bimolecular recombination model have been used as the transport and recombination mechanism. These models are enabled in ATLAS -SILVACO software. The influence of doping and thickness on I(V) characteristics and luminescence, are reported.

Keywords: organic light emitting diode, polymer lignt emitting diode, organic materials, hexoxy-phenylenevinylene

Procedia PDF Downloads 552

Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

Procedia PDF Downloads 344

Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

Procedia PDF Downloads 118

Authors: J. Novak, A. Kohoutkova, V. Kristek, J. Vodicka, M. Sramek

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While the use of cast-in-place concrete for an airfield and highway pavement overlay is very common, the application of precast concrete elements is very limited today. The main reasons consist of high production costs and complex structural behavior. Despite that, several precast concrete systems have been developed and tested with the aim to provide a system with rapid construction. The contribution deals with the reinforcement design of a hexagonal element developed for a proposed airfield pavement system. The sub-base course of the system is composed of compacted recycled concrete aggregates and fiber reinforced concrete with recycled aggregates place on top of it. The selected element belongs to a group of precast concrete elements which are being considered for the construction of a surface course. Both high costs of full-scale experiments and the need to investigate various elements force to simulate their behavior in a numerical analysis software by using finite element method instead of performing expensive experiments. The simulation of the selected element was conducted on a nonlinear model in order to obtain such results which could fully compensate results from experiments. The main objective was to design reinforcement of the precast concrete element subject to quasi-static loading from airplanes with respect to geometrical imperfections, manufacturing imperfections, tensile stress in reinforcement, compressive stress in concrete and crack width. The obtained findings demonstrate that the position and the presence of imperfection in a pavement highly affect the stress distribution in the precast concrete element. The precast concrete element should be heavily reinforced to fulfill all the demands. Using under-reinforced concrete elements would lead to the formation of wide cracks and cracks permanently open.

Keywords: imperfection, numerical simulation, pavement, precast concrete element, reinforcement design, stress analysis

Procedia PDF Downloads 157

Authors: Gelacio JuáRez-Luna, Daniel Enrique GonzáLez-RamíRez, Enrique Tenorio-Montero

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Frame elements coupled with springs elements are used for modelling the development of hinges in segmented tunnels, the spring elements modelled the rotational, transversal and axial failure. These spring elements are equipped with constitutive models to include independently the moment, shear force and axial force, respectively. These constitutive models are formulated based on damage mechanics and experimental test reported in the literature review. The mesh of the segmented tunnels was discretized in the software GID, and the nonlinear analyses were carried out in the finite element software ANSYS. These analyses provide the capacity curve of the primary and secondary lining of a segmented tunnel. Two numerical examples of segmented tunnels show the capability of the spring elements to release energy by the development of hinges. The first example is a segmental concrete lining discretized with frame elements loaded until hinges occurred in the lining. The second example is a tunnel with primary and secondary lining, discretized with a double ring frame model. The outer ring simulates the segmental concrete lining and the inner ring simulates the secondary cast-in-place concrete lining. Spring elements also modelled the joints between the segments in the circumferential direction and the ring joints, which connect parallel adjacent rings. The computed load vs displacement curves are congruent with numerical and experimental results reported in the literature review. It is shown that the modelling of a tunnel with primary and secondary lining with frame elements and springs provides reasonable results and save computational cost, comparing with 2D or 3D models equipped with smeared crack models.

Keywords: damage, hinges, lining, tunnel

Procedia PDF Downloads 385

Authors: Shu-Hui Jan, Hui-Ping Tserng, Shih-Ping Ho

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Construction project control attempts to obtain real-time as-built schedule information and to eliminate project delays by effectively enhancing dynamic schedule control and management. Suitable platforms for enhancing an as-built schedule visually during the construction phase are necessary and important for general contractors. As the application of building information modeling (BIM) becomes more common, schedule management integrated with the BIM approach becomes essential to enhance visual construction management implementation for the general contractor during the construction phase. To enhance visualization of the updated as-built schedule for the general contractor, this study presents a novel system called the Construction BIM-assisted Schedule Management (ConBIM-SM) system for general contractors in Taiwan. The primary purpose of this study is to develop a web ConBIM-SM system for the general contractor to enhance visual as-built schedule information sharing and efficiency in tracking construction as-built schedule. Finally, the ConBIM-SM system is applied to a case study of a commerce building project in Taiwan to verify its efficacy and demonstrate its effectiveness during the construction phase. The advantages of the ConBIM-SM system lie in improved project control and management efficiency for general contractors, and in providing BIM-assisted as-built schedule tracking and management, to access the most current as-built schedule information through a web browser. The case study results show that the ConBIM-SM system is an effective visual as-built schedule management platform integrated with the BIM approach for general contractors in a construction project.

Keywords: building information modeling (BIM), construction schedule management, as-built schedule management, BIM schedule updating mechanism

Procedia PDF Downloads 370

Authors: M. De Filippo, J. S. Kuang

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In construction industry, reinforced concrete (RC) slabs represent fundamental elements of buildings and bridges. Different methods are available for analysing the structural behaviour of slabs. In the early ages of last century, the yield-line method has been proposed to attempt to solve such problem. Simple geometry problems could easily be solved by using traditional hand analyses which include plasticity theories. Nowadays, advanced finite element (FE) analyses have mainly found their way into applications of many engineering fields due to the wide range of geometries to which they can be applied. In such cases, the application of an elastic or a plastic constitutive model would completely change the approach of the analysis itself. Elastic methods are popular due to their easy applicability to automated computations. However, elastic analyses are limited since they do not consider any aspect of the material behaviour beyond its yield limit, which turns to be an essential aspect of RC structural performance. Furthermore, their applicability to non-linear analysis for modeling plastic behaviour gives very reliable results. Per contra, this type of analysis is computationally quite expensive, i.e. not well suited for solving daily engineering problems. In the past years, many researchers have worked on filling this gap between easy-to-implement elastic methods and computationally complex plastic analyses. This paper aims at proposing a numerical procedure, through which a pseudo-lower bound solution, not violating the yield criterion, is achieved. The advantages of moment distribution are taken into account, hence the increase in strength provided by plastic behaviour is considered. The lower bound solution is improved by detecting over-yielded moments, which are used to artificially rule the moment distribution among the rest of the non-yielded elements. The proposed technique obeys Nielsen’s yield criterion. The outcome of this analysis provides a simple, yet accurate, and non-time-consuming tool of predicting the lower-bound solution of the collapse load of RC slabs. By using this method, structural engineers can find the fracture patterns and ultimate load bearing capacity. The collapse triggering mechanism is found by detecting yield-lines. An application to the simple case of a square clamped slab is shown, and a good match was found with the exact values of collapse load.

Keywords: computational mechanics, lower bound method, reinforced concrete slabs, yield-line

Procedia PDF Downloads 174

Authors: Gajanan M. Sonwane

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Nowadays, the entire pharmaceutical industry is facing the challenge of increasing efficiency and innovation. The major hurdles are the growing cost of research and development and a concurrent stagnating number of new chemical entities (NCEs). Hence, the challenge is to select the most druggable targets and to search the equivalent drug-like compounds, which also possess specific pharmacokinetic and toxicological properties that allow them to be developed as drugs. The present research work includes the studies of developing new anticancer heterocycles by using molecular modeling techniques. The heterocycles synthesized through such methodology are much effective as various physicochemical parameters have been already studied and the structure has been optimized for its best fit in the receptor. Hence, on the basis of the literature survey and considering the need to develop newer anticancer agents, new phenazinamine derivatives were designed by subjecting the nucleus to molecular modeling, viz., GQSAR analysis and docking studies. Simultaneously, these designed derivatives were subjected to in silico prediction of biological activity through PASS studies and then in silico toxicity risk assessment studies. In PASS studies, it was found that all the derivatives exhibited a good spectrum of biological activities confirming its anticancer potential. The toxicity risk assessment studies revealed that all the derivatives obey Lipinski’s rule. Amongst these series, compounds 4c, 5b and 6c were found to possess logP and drug-likeness values comparable with the standard Imatinib (used for anticancer activity studies) and also with the standard drug methotrexate (used for antimitotic activity studies). One of the most notable mutations is the threonine to isoleucine mutation at codon 315 (T315I), which is known to be resistant to all currently available TKI. Enzyme assay planned for confirmation of target selective activity.

Keywords: drug design, tyrosine kinases, anticancer, Phenazinamine

Procedia PDF Downloads 112

Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

Procedia PDF Downloads 83

Authors: Felipe M. de Freitas, Icaro V. Soares, Lucas L. L. Fortes, Sandro T. M. Gonçalves, Úrsula D. C. Resende

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The SPICE-based simulators are quite robust and widely used for simulation of electronic circuits, their algorithms support linear and non-linear lumped components and they can manipulate an expressive amount of encapsulated elements. Despite the great potential of these simulators based on SPICE in the analysis of quasi-static electromagnetic field interaction, that is, at low frequency, these simulators are limited when applied to microwave hybrid circuits in which there are both lumped and distributed elements. Usually the spatial discretization of the FDTD (Finite-Difference Time-Domain) method is done according to the actual size of the element under analysis. After spatial discretization, the Courant Stability Criterion calculates the maximum temporal discretization accepted for such spatial discretization and for the propagation velocity of the wave. This criterion guarantees the stability conditions for the leapfrogging of the Yee algorithm; however, it is known that for the field update, the stability of the complete FDTD procedure depends on factors other than just the stability of the Yee algorithm, because the FDTD program needs other algorithms in order to be useful in engineering problems. Examples of these algorithms are Absorbent Boundary Conditions (ABCs), excitation sources, subcellular techniques, grouped elements, and non-uniform or non-orthogonal meshes. In this work, the influence of the stability of the FDTD method in the modeling of concentrated elements such as resistive sources, resistors, capacitors, inductors and diode will be evaluated. In this paper is proposed, therefore, the electromagnetic modeling of electronic components in order to create models that satisfy the needs for simulations of circuits in ultra-wide frequencies. The models of the resistive source, the resistor, the capacitor, the inductor, and the diode will be evaluated, among the mathematical models for lumped components in the LE-FDTD method (Lumped-Element Finite-Difference Time-Domain), through the parametric analysis of Yee cells size which discretizes the lumped components. In this way, it is sought to find an ideal cell size so that the analysis in FDTD environment is in greater agreement with the expected circuit behavior, maintaining the stability conditions of this method. Based on the mathematical models and the theoretical basis of the required extensions of the FDTD method, the computational implementation of the models in Matlab® environment is carried out. The boundary condition Mur is used as the absorbing boundary of the FDTD method. The validation of the model is done through the comparison between the obtained results by the FDTD method through the electric field values and the currents in the components, and the analytical results using circuit parameters.

Keywords: hybrid circuits, LE-FDTD, lumped element, parametric analysis

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Authors: Shahram Naghizadeh Raeisi, Ali Alghooneh

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The application of whey protein in drink industry to enhance the nutritional value of the products is important. Furthermore, the gelification of protein during thermal treatment and shelf life makes some limitations in its application. So, the main goal of this research is manufacturing of high concentrate whey protein orange drink with appropriate shelf life. In this way, whey protein was 5 to 30% hydrolyzed ( in 5 percent intervals at six stages), then thermal stability of samples with 10% concentration of protein was tested in acidic condition (T= 90 °C, pH=4.2, 5 minutes ) and neutral condition (T=120° C, pH:6.7, 20 minutes.) Furthermore, to study the shelf life of heat treated samples in 4 months at 4 and 24 °C, the time sweep rheological test were done. At neutral conditions, 5 to 20% hydrolyzed sample showed gelling during thermal treatment, whereas at acidic condition, was happened only in 5 to 10 percent hydrolyzed samples. This phenomenon could be related to the difference in hydrodynamic radius and zeta potential of samples with different level of hydrolyzation at acidic and neutral conditions. To study the gelification of heat resistant protein solutions during shelf life, for 4 months with 7 days intervals, the time sweep analysis were performed. Cross over was observed for all heat resistant neutral samples at both storage temperature, while in heat resistant acidic samples with degree of hydrolysis, 25 and 30 percentage at 4 and 20 °C, it was not seen. It could be concluded that the former sample was stable during heat treatment and 4 months storage, which made them a good choice for manufacturing high protein drinks. The Scheffe polynomial model and numerical optimization were employed for modeling and high protein orange drink formula optimization. Scheffe model significantly predicted the overal acceptance index (Pvalue<0.05) of sensorial analysis. The coefficient of determination (R2) of 0.94, the adjusted coefficient of determination (R2Adj) of 0.90, insignificance of the lack-of-fit test and F value of 64.21 showed the accuracy of the model. Moreover, the coefficient of variable (C.V) was 6.8% which suggested the replicability of the experimental data. The desirability function had been achieved to be 0.89, which indicates the high accuracy of optimization. The optimum formulation was found as following: Modified whey protein solution (65.30%), natural orange juice (33.50%), stevia sweetener (0.05%), orange peel oil (0.15%) and citric acid (1 %), respectively. Its worth mentioning that this study made an appropriate model for application of whey protein in drink industry without bitter flavor and gelification during heat treatment and shelf life.

Keywords: croos over, orange beverage, protein modification, optimization

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Authors: Joel Podgorski, Manouchehr Amini, Annette Johnson, Michael Berg

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One-third of the world’s population relies on groundwater for its drinking water. Natural geogenic arsenic and fluoride contaminate ~10% of wells. Prolonged exposure to high levels of arsenic can result in various internal cancers, while high levels of fluoride are responsible for the development of dental and crippling skeletal fluorosis. In poor urban and rural settings, the provision of drinking water free of geogenic contamination can be a major challenge. In order to efficiently apply limited resources in the testing of wells, water resource managers need to know where geogenically contaminated groundwater is likely to occur. The Groundwater Assessment Platform (GAP) fulfills this need by providing state-of-the-art global arsenic and fluoride contamination hazard maps as well as enabling users to create their own groundwater quality models. The global risk models were produced by logistic regression of arsenic and fluoride measurements using predictor variables of various soil, geological and climate parameters. The maps display the probability of encountering concentrations of arsenic or fluoride exceeding the World Health Organization’s (WHO) stipulated concentration limits of 10 µg/L or 1.5 mg/L, respectively. In addition to a reconsideration of the relevant geochemical settings, these second-generation maps represent a great improvement over the previous risk maps due to a significant increase in data quantity and resolution. For example, there is a 10-fold increase in the number of measured data points, and the resolution of predictor variables is generally 60 times greater. These same predictor variable datasets are available on the GAP platform for visualization as well as for use with a modeling tool. The latter requires that users upload their own concentration measurements and select the predictor variables that they wish to incorporate in their models. In addition, users can upload additional predictor variable datasets either as features or coverages. Such models can represent an improvement over the global models already supplied, since (a) users may be able to use their own, more detailed datasets of measured concentrations and (b) the various processes leading to arsenic and fluoride groundwater contamination can be isolated more effectively on a smaller scale, thereby resulting in a more accurate model. All maps, including user-created risk models, can be downloaded as PDFs. There is also the option to share data in a secure environment as well as the possibility to collaborate in a secure environment through the creation of communities. In summary, GAP provides users with the means to reliably and efficiently produce models specific to their region of interest by making available the latest datasets of predictor variables along with the necessary modeling infrastructure.

Keywords: arsenic, fluoride, groundwater contamination, logistic regression

Procedia PDF Downloads 343

Authors: S. K. Hashemi, M. A. Bradford, H. R. Valipour

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The Recent collapse of bridges has raised the awareness about safety and robustness of bridges subjected to extreme loading scenarios such as intentional/unintentional blast loads. The air blast generated by the explosion of bombs or fuel tankers leads to high-magnitude short-duration loading scenarios that can cause severe structural damage and loss of critical structural members. Hence, more attentions need to put towards bridge structures to develop guidelines to increase the resistance of such structures against the probable blast. Recent advancements in numerical methods have brought about the viable and cost effective facilities to simulate complicated blast scenarios and subsequently provide useful reference for safeguarding design of critical infrastructures. In the previous studies common bridge responses to blast load, the traffic load is sometimes not included in the analysis. Including traffic load will increase the axial compression in bridge piers especially when the axial load is relatively small. Traffic load also can reduce the uplift of girders and deck when the bridge experiences under deck explosion. For more complicated structures like cable-stayed or suspension bridges, however, the effect of traffic loads can be completely different. The tension in the cables increase and progressive collapse is likely to happen while traffic loads exist. Accordingly, this study is an attempt to simulate the effect of traffic load cases on the maximum local and global response of an entire cable-stayed bridge subjected to blast loadings using LS-DYNA explicit finite element code. The blast loads ranged from small to large explosion placed at different positions above the deck. Furthermore, the variation of the traffic load factor in the load combination and its effect on the dynamic response of the bridge under blast load is investigated.

Keywords: blast, cable-stayed bridge, LS-DYNA, numerical, traffic load

Procedia PDF Downloads 328

Authors: Keda Li, Hong Hu

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Auxetic materials, as an emerging artificial designed metamaterial has attracted growing attention due to their promising negative Poisson’s ratio behaviors and tunable properties. The conventional auxetic lattice structures for which the deformation process is governed by a bending-dominated mechanism have faced the limitation of poor mechanical performance for many potential engineering applications. Recently, both load-bearing and energy absorption capabilities have become a crucial consideration in auxetic structure design. This study reports the finite element analysis of a class of hexagonal double-arrowhead auxetic structures with enhanced stiffness and energy absorption performance. The structure design was developed by extending the traditional double-arrowhead honeycomb to a hexagon frame, the stretching-dominated deformation mechanism was determined according to Maxwell’s stability criterion. The finite element (FE) models of 2D lattice structures established with stainless steel material were analyzed in ABAQUS/Standard for predicting in-plane structural deformation mechanism, failure process, and compressive elastic properties. Based on the computational simulation, the parametric analysis was studied to investigate the effect of the structural parameters on Poisson’s ratio and mechanical properties. The geometrical optimization was then implemented to achieve the optimal Poisson’s ratio for the maximum specific energy absorption. In addition, the optimized 2D lattice structure was correspondingly converted into a 3D geometry configuration by using the orthogonally splicing method. The numerical results of 2D and 3D structures under compressive quasi-static loading conditions were compared separately with the traditional double-arrowhead re-entrant honeycomb in terms of specific Young's moduli, Poisson's ratios, and specified energy absorption. As a result, the energy absorption capability and stiffness are significantly reinforced with a wide range of Poisson’s ratio compared to traditional double-arrowhead re-entrant honeycomb. The auxetic behaviors, energy absorption capability, and yield strength of the proposed structure are adjustable with different combinations of joint angle, struts thickness, and the length-width ratio of the representative unit cell. The numerical prediction in this study suggests the proposed concept of hexagonal double-arrowhead structure could be a suitable candidate for the energy absorption applications with a constant request of load-bearing capacity. For future research, experimental analysis is required for the validation of the numerical simulation.

Keywords: auxetic, energy absorption capacity, finite element analysis, negative Poisson's ratio, re-entrant hexagonal honeycomb

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Authors: Bayonle Abiola Omoniyi

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In turbidite reservoirs with volumetrically significant thin-bedded turbidites (TBTs), thin-pay intervals may be underestimated during calculation of reserve volume due to poor vertical resolution of conventional well logs. This paper demonstrates the strong control of bed-scale sedimentary heterogeneity on oil recovery using six facies distribution scenarios that were generated from outcrop data from the Eocene Itzurun Formation, Basque Basin (northern Spain). The variable net sand volume in these scenarios serves as a primary source of sedimentary heterogeneity impacting sandstone-mudstone ratio, sand and shale geometry and dimensions, lateral and vertical variations in bed thickness, and attribute indices. The attributes provided input parameters for modeling the scenarios. The models are 20-m (65.6 ft) thick. Simulation of the scenarios reveals that oil production is markedly enhanced where degree of sedimentary heterogeneity and resultant permeability contrast are low, as exemplified by Scenarios 1, 2, and 3. In these scenarios, bed architecture encourages better apparent vertical connectivity across intervals of laterally continuous beds. By contrast, low net-to-gross Scenarios 4, 5, and 6, have rapidly declining oil production rates and higher water cut with more oil effectively trapped in low-permeability layers. These scenarios may possess enough lateral connectivity to enable injected water to sweep oil to production well; such sweep is achieved at a cost of high-water production. It is therefore imperative to consider not only net-to-gross threshold but also facies stack pattern and related attribute indices to better understand how to effectively manage water production for optimum oil recovery from basin-plain reservoirs.

Keywords: architecture, connectivity, modeling, turbidites

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Authors: Malay K. Pramanik

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Climate change is continuously affecting the ecosystem, species distribution as well as global biodiversity. The assessment of the species potential distribution and the spatial changes under various climate change scenarios is a significant step towards the conservation and mitigation of habitat shifts, and species' loss and vulnerability. In this context, the present study aimed to predict the influence of current and future climate on an ecologically vulnerable medicinal species, Garcinia gummi-gutta, of the southern Western Ghats using Maximum Entropy (MaxEnt) modeling. The future projections were made for the period of 2050 and 2070 with RCP (Representative Concentration Pathways) scenario of 4.5 and 8.5 using 84 species occurrence data, and climatic variables from three different models of Intergovernmental Panel for Climate Change (IPCC) fifth assessment. Climatic variables contributions were assessed using jackknife test and AOC value 0.888 indicates the model perform with high accuracy. The major influencing variables will be annual precipitation, precipitation of coldest quarter, precipitation seasonality, and precipitation of driest quarter. The model result shows that the current high potential distribution of the species is around 1.90% of the study area, 7.78% is good potential; about 90.32% is moderate to very low potential for species suitability. Finally, the results of all model represented that there will be a drastic decline in the suitable habitat distribution by 2050 and 2070 for all the RCP scenarios. The study signifies that MaxEnt model might be an efficient tool for ecosystem management, biodiversity protection, and species re-habitation planning under climate change.

Keywords: Garcinia gummi gutta, maximum entropy modeling, medicinal plants, climate change, western ghats, MaxEnt

Procedia PDF Downloads 387

Authors: Mattahias Maj, Fabio Tatzgern, Karl Adam, Damir Kahrimanovic, Markus Varga

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Torpedo ladles are necessary transport carriages in steel production to move the molten crude iron from the blast furnace to the steel refining plant. This requires the ladles to be high temperature resistant and insulate well to preserve the temperature and hold the risk of solidification at bay. Therefore, the involved refractories lining the inside of the torpedo ladles are chosen mostly according to their thermal properties, although wear of the materials by the liquid iron is also of major importance. In this work, we combined investigations of the thermal behaviour with wear studies of the lining over the whole lifetime of a torpedo ladle. Additional numerical simulations enabled a detailed model of the mechanical loads and temperature propagation at the various stations (heating, filling, emptying, cooling). The core of the investigation were detailed 3D measurements of the ladle’s cavity and thereby quantitative information of the wear progress at different time intervals during the lifetime of the ladles. The measurements allowed for a separation of different wear zones according to severity, namely the “splash zone” where the melt directly hits the ladle, the “melt zone” where during transport always liquid melt is present, and the “slag zone”, where the slag floats on the melt causing the most severe wear loss. Numerical simulations of the filling process were taken to calculate stress levels and temperature gradients, which led to the different onset of wear on those zones. Thermal imaging and punctual temperature measurements allowed for a study of the thermal consequences entailed by the wear onset. Additional “classical” damage analysis of the worn refractories complete the investigation. Thereby the wear mechanisms leading to the substantial wear loss were disclosed.

Keywords: high temperature, tribology, liquid-solid interaction, refractories, thermography

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Authors: Pedro Mota Veiga

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This study comprehensively evaluates the repercussions of innovation, digital advancements, and technological capabilities on the operational performance of companies across fifteen European Union countries following the initial wave of the COVID-19 pandemic. Drawing insights from longitudinal data sourced from the 2019 World Bank business surveys and subsequent 2020 World Bank COVID-19 follow-up business surveys, our extensive examination involves a diverse sample of 5763 family businesses. In exploring the relationships between these variables, we adopt a nuanced approach to assess the impact of innovation and digital and technological capabilities on performance. This analysis unfolds along two distinct perspectives: one rooted in necessity and the other insufficiency. The methodological framework employed integrates partial least squares structural equation modeling (PLS-SEM) with condition analysis (NCA), providing a robust foundation for drawing meaningful conclusions. The findings of the study underscore a positive influence on the performance of family firms stemming from both technological capabilities and digital advancements. Furthermore, it is pertinent to highlight the indirect contribution of innovation to enhanced performance, operating through its impact on digital capabilities. This research contributes valuable insights to the broader understanding of how innovation, coupled with digital and technological capabilities, can serve as pivotal factors in shaping the post-COVID-19 landscape for businesses across the European Union. The intricate analysis of family businesses, in particular adds depth to the comprehension of the dynamics at play in diverse economic contexts within the European Union.

Keywords: digital capabilities, technological capabilities, family firms performance, innovation, NCA, PLS-SEM

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Authors: Siva K. Bathina, Sudheer Siddapureddy

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Pool fires are formed when the flammable liquid accidentally spills on the ground or water and ignites. Pool fire is a kind of buoyancy-driven and diffusion flame. There have been many pool fire accidents caused during processing, handling and storing of liquid fuels in chemical and oil industries. Such kind of accidents causes enormous damage to property as well as the loss of lives. Pool fires are complex in nature due to the strong interaction among the combustion, heat and mass transfers and pyrolysis at the fuel surface. Moreover, the experimental study of such large complex fires involves fire safety issues and difficulties in performing experiments. In the present work, large eddy simulations are performed to study such complex fire scenarios using fire dynamic simulator. A 1 m diesel pool fire is considered for the studied cases, and diesel is chosen as it is most commonly involved fuel in fire accidents. Fire simulations are performed by specifying two different boundary conditions: one the fuel is in liquid state and pyrolysis model is invoked, and the other by assuming the fuel is initially in a vapor state and thereby prescribing the mass loss rate. A domain of size 11.2 m × 11.2 m × 7.28 m with uniform structured grid is chosen for the numerical simulations. Grid sensitivity analysis is performed, and a non-dimensional grid size of 12 corresponding to 8 cm grid size is considered. Flame properties like mass burning rate, irradiance, and time-averaged axial flame temperature profile are predicted. The predicted steady-state mass burning rate is 40 g/s and is within the uncertainty limits of the previously reported experimental data (39.4 g/s). Though the profile of the irradiance at a distance from the fire along the height is somewhat in line with the experimental data and the location of the maximum value of irradiance is shifted to a higher location. This may be due to the lack of sophisticated models for the species transportation along with combustion and radiation in the continuous zone. Furthermore, the axial temperatures are not predicted well (for any of the boundary conditions) in any of the zones. The present study shows that the existing models are not sufficient enough for modeling blended fuels like diesel. The predictions are strongly dependent on the experimental values of the soot yield. Future experiments are necessary for generalizing the soot yield for different fires.

Keywords: burning rate, fire accidents, fire dynamic simulator, pyrolysis

Procedia PDF Downloads 192

Authors: Mateusz Frydrych

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The construction of diaphragm walls as excavation shoring as well as part of deep foundations is widely used in geotechnical engineering. Today's design challenges lie in the optimal dimensioning of the cross-section, which is demanded by technological considerations. Also in force is the issue of optimization and sustainable use of construction materials, including reduction of carbon footprint, which is currently a relevant challenge for the construction industry. The author presents the concept of an approach to achieving increased efficiency of diaphragm wall excavation shoring by using structural compression technology. The author proposes to implement prestressed tendons in a non-linear manner in the reinforcement cage. As a result bending moment is reduced, which translates into a reduction in the amount of steel needed in the section, a reduction in displacements, and a reduction in the scratching of the casing, including the achievement of better tightness. This task is rarely seen and has not yet been described in a scientific way in the literature. The author has developed a dynamic numerical model that allows the dimensioning of the cross-section of a prestressed shear wall, as well as the study of casing displacements and cross-sectional forces in any defined computational situation. Numerical software from the Sofistik - open source development environment - was used for the study, and models were validated in Plaxis software . This is an interesting idea that allows for optimizing the execution of construction works and reducing the required resources by using fewer materials and saving time. The author presents the possibilities of a prestressed diaphragm wall, among others, using. The example of a diaphragm wall working as a cantilever at the height of two underground floors without additional strutting or stability protection by using ground anchors. This makes the execution of the work more criminal for the contractor and, as a result, cheaper for the investor.

Keywords: prestressed diaphragm wall, Plaxis, Sofistik, innovation, FEM, optimisation

Procedia PDF Downloads 71

Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

Procedia PDF Downloads 61

Authors: Mohammadreza Mojtahedi

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Rapid urbanization presents multifaceted challenges, including environmental degradation and public health concerns. As the inevitability of urban sprawl continues, it becomes essential to devise strategies to alleviate its pressures on natural ecosystems and elevate socio-economic benchmarks within cities. This research investigates urban agriculture's economic contributions, emphasizing its pivotal role in food provisioning and export potential. Adopting a descriptive-analytical approach, field survey data was primarily collected via questionnaires. The tool's validity was affirmed by expert opinions, and its reliability secured by achieving a Cronbach's alpha score over 0.70 from 30 preliminary questionnaires. The research encompasses Neishabour's populace of 264,375, extracting a sample size of 384 via Cochran's formula. Findings reveal the significance of urban agriculture in food supply and its potential for exports, underlined by a p-value < 0.05. Neishabour's urban farming can augment the export of organic commodities, fruits, vegetables, ornamental plants, and foster product branding. Moreover, it supports the provision of fresh produce, bolstering dietary quality. Urban agriculture further impacts urban development metrics—enhancing environmental quality, job opportunities, income levels, and aesthetics, while promoting rainwater utilization. Popular cultivations include peaches, Damask roses, and poultry, tailored to available spaces. Structural equation modeling indicates urban agriculture's overarching influence, accounting for a 56% variance, predominantly in food sufficiency and export proficiency.

Keywords: urban agriculture, food supply, export potential, urban development, environmental health, structural equation modeling

Procedia PDF Downloads 51

Authors: M. T. Alodat

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In this paper, we propose a mechanism for skewing any symmetric distribution. The new distribution is called the deflation-inflation distribution (DID). We discuss some statistical properties of the DID such moments, stochastic representation, log-concavity. Also we fit the distribution to real data and we compare it to normal distribution and Azzlaini's skew normal distribution. Numerical results show that the DID fits the the tree ring data better than the other two distributions.

Keywords: normal distribution, moments, Fisher information, symmetric distributions

Procedia PDF Downloads 651

Authors: Samuel Asiedu-Addo, Jonathan Annan, Yarhands Dissou Arthur

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The study aims to explore the relational effect of lecturers’ personal attribute on student’s interest in statistics. In this study personal attributes of lecturers’ such as lecturer’s dynamism, communication strategies and rapport in the classroom as well as applied knowledge during lecture were examined. Here, exploratory research design was used to establish the effect of lecturer’s personal attributes on student’s interest. Data were analyzed by means of confirmatory factor analysis and structural equation modeling (SEM) using the SmartPLS 3 program. The study recruited 376 students from the faculty of technical and vocational education of the University of Education Winneba Kumasi campus, and Ghana Technology University College as well as Kwame Nkrumah University of science and Technology. The results revealed that personal attributes of an effective lecturer were lecturer’s dynamism, rapport, communication and applied knowledge contribute (52.9%) in explaining students interest in statistics. Our regression analysis and structural equation modeling confirm that lecturers personal attribute contribute effectively by predicting student’s interest of 52.9% and 53.7% respectively. The paper concludes that the total effect of a lecturer’s attribute on student’s interest is moderate and significant. While a lecturer’s communication and dynamism were found to contribute positively to students’ interest, they were insignificant in predicting students’ interest. We further showed that a lecturer’s personal attributes such as applied knowledge and rapport have positive and significant effect on tertiary student’s interest in statistic, whilst lecturers’ communication and dynamism do not significantly affect student interest in statistics; though positively related.

Keywords: student interest, effective teacher, personal attributes, regression and SEM

Procedia PDF Downloads 354

Authors: Devin Simmons

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At the USDOT’s Bureau of Transportation Statistics, a biannual census of ferry operators in the U.S. is conducted, with results such as route mileage used to determine federal funding levels for operators. AIS data allows for the possibility of using GIS software and geographical methods to confirm operator-reported mileage for individual ferry routes. As part of the USDOT’s work on the ferry census, an algorithm was developed that uses AIS data for ferry vessels in conjunction with known ferry terminal locations to model the average route travelled for use as both a cartographic product and confirmation of operator-reported mileage. AIS data from each vessel is first analyzed to determine individual journeys based on the vessel’s velocity, and changes in velocity over time. These trips are then converted to geographic linestring objects. Using the terminal locations, the algorithm then determines whether the trip represented a known ferry route. Given a large enough dataset, routes will be represented by multiple trip linestrings, which are then filtered by DBSCAN spatial clustering to remove outliers. Finally, these remaining trips are ready to be averaged into one route. The algorithm interpolates the point on each trip linestring that represents the start point. From these start points, a centroid is calculated, and the first point of the average route is determined. Each trip is interpolated again to find the point that represents one percent of the journey’s completion, and the centroid of those points is used as the next point in the average route, and so on until 100 points have been calculated. Routes created using this algorithm have shown demonstrable improvement over previous methods, which included the implementation of a LOESS model. Additionally, the algorithm greatly reduces the amount of manual digitizing needed to visualize ferry activity.

Keywords: ferry vessels, transportation, modeling, AIS data

Procedia PDF Downloads 167

Authors: Etienne Bonnaud, David Lindell

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Correlations between microstructure, deformation mechanisms, and mechanical properties in additively manufactured 316L steel components have been investigated. Mechanical properties in the vertical direction (building direction) and in the horizontal direction (in plane directions) are markedly different. Vertically built specimens show lower yield stress but higher elongation than their horizontally built counterparts. Microscopic observations by electron back scattered diffraction (EBSD) for both build orientations reveal a strong [110] fiber texture in the build direction but different grain morphologies. These microstructures are used as input in subsequent crystal plasticity numerical simulations to understand their influence on the deformation mechanisms and the mechanical properties. Mean field simulations using a visco plastic self consistent (VPSC) model were carried out first but did not give results consistent with the tensile test experiments. A more detailed full-field model had to be used based on the Visco Plastic Fast Fourier Transform (VPFTT) method. A more accurate microstructure description was then input to the simulation model, where thin vertical regions of smaller grains were also taken into account. It turned out that these small grain clusters were responsible for the discrepancies in yield stress and hardening. Texture and morphology have a strong effect on mechanical properties. The different mechanical behaviors between vertically and horizontally printed specimens could be explained by means of numerical full-field crystal plasticity simulations, and the presence of thin clusters of smaller grains was shown to play a central role in the deformation mechanisms.

Keywords: additive manufacturing, crystal plasticity, full-field simulations, mean-field simulations, texture

Procedia PDF Downloads 68

Authors: Yafit Gabay

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Attention deficit hyperactivity disorder (ADHD) has been associated with both suboptimal functioning in the striatum and prefrontal cortex. Such abnormalities may impede the acquisition of perceptual categories, which are important for fundamental abilities such as object recognition and speech perception. Indeed, prior research has supported this possibility, demonstrating that children with ADHD have similar visual category learning performance as their neurotypical peers but use suboptimal learning strategies. However, much less is known about category learning processes in the auditory domain or among adults with ADHD in which prefrontal functions are more mature compared to children. Here, we investigated auditory and visual perceptual category learning in adults with ADHD and neurotypical individuals. Specifically, we examined learning of rule-based categories – presumed to be optimally learned by a frontal cortex-mediated hypothesis testing – and information-integration categories – hypothesized to be optimally learned by a striatally-mediated reinforcement learning system. Consistent with striatal and prefrontal cortical impairments observed in ADHD, our results show that across sensory modalities, both rule-based and information-integration category learning is impaired in adults with ADHD. Computational modeling analyses revealed that individuals with ADHD were slower to shift to optimal strategies than neurotypicals, regardless of category type or modality. Taken together, these results suggest that both explicit, frontally mediated and implicit, striatally mediated category learning are impaired in ADHD. These results suggest impairments across multiple learning systems in young adults with ADHD that extend across sensory modalities and likely arise from domain-general mechanisms.

Keywords: ADHD, category learning, modality, computational modeling

Procedia PDF Downloads 40

Authors: Alexander Kospach, Fabian Benezeder, Jürgen Abraham

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E-mobility poses new challenges for inverters (e.g., higher switching frequencies) in terms of thermal behavior and thermal management. Due to even higher switching frequencies, thermal losses become greater, and the cooling of critical components (like insulated gate bipolar transistor and diodes) comes into focus. New manufacturing methods, such as 3D printing, enable completely new pin-fin structures that can handle higher waste heat to meet the new thermal requirements. Based on the geometrical specifications of the industrial partner regarding the manufacturing possibilities for 3D printing, different and completely new pin-fin structures were numerically investigated for their hydraulic and thermal behavior in fundamental studies assuming an indirect liquid cooling. For the 3D computational fluid dynamics (CFD) thermal simulations OpenFOAM was used, which has as numerical method the finite volume method for solving the conjugate heat transfer problem. A steady-state solver for turbulent fluid flow and solid heat conduction with conjugate heat transfer between solid and fluid regions was used for the simulations. In total, up to fifty pinfin structures and arrangements, some of them completely new, were numerically investigated. On the basis of the results of the principal investigations, the best two pin-fin structures and arrangements for the complete module cooling of an automotive inverter were numerically investigated and compared. There are clear differences in the maximum temperatures for the critical components, such as IGTBs and diodes. In summary, it was shown that 3D pin fin structures can significantly contribute to the improvement of heat transfer and cooling of an automotive inverter. This enables in the future smaller cooling designs and a better lifetime of automotive inverter modules. The new pin fin structures and arrangements can also be applied to other cooling applications where 3D printing can be used.

Keywords: pin fin heat sink optimization, 3D printed pin fins, CFD simulation, power electronic cooling, thermal management

Procedia PDF Downloads 95