Search results for: atomic models

Authors: Ryspek Usubamatov, Tan San Chin, Sarken Kapaeva

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Productivity of the manufacturing systems depends on technological processes, a technical data of machines and a structure of systems. Technology is presented by the machining mode and data, a technical data presents reliability parameters and auxiliary time for discrete production processes. The term structure of manufacturing systems includes the number of serial and parallel production machines and links between them. Structures of manufacturing systems depend on the complexity of technological processes. Mathematical models of productivity rate for manufacturing systems are important attributes that enable to define best structure by criterion of a productivity rate. These models are important tool in evaluation of the economical efficiency for production systems.

Keywords: productivity, structure, manufacturing systems, structural design

Procedia PDF Downloads 580

Authors: Onur Karaman, Ceren Karaman

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In this study, the adsorption of Methylene Blue (MB), a cationic dye, onto Orange Peel Derived Activated Carbon (OPAC) and chitosan(OPAC/Chitosan composite) composite (a low-cost absorbent) was carried out using a batch system. The composite was characterised using IR spectra, XRD, FESEM and Pore size studies. The effects of initial pH, adsorbent dose rate and initial dye concentration on the initial adsorption rate, capacity and dye removal efficiency were investigated. The Langmuir and Freundlich adsorption models were used to define the adsorption equilibrium of dye-adsorbent system mathematically and it was decided that the Langmuir model was more suitable to describe the adsorption equilibrium for the system. In addition, first order, second order and saturation type kinetic models were applied to kinetic data of adsorption and kinetic constants were calculated. It was concluded that the second order and the saturation type kinetic models defined the adsorption data more accurately. Finally, the evaluated thermodynamic parameters of adsorption show a spontaneous and exothermic behavior. Overall, this study indicates OPAC/Chitosan composite as an effective and low-cost adsorbent for the removal of MB dye from aqueous solutions.

Keywords: activated carbon, adsorption, chitosan, methylene blue, orange peel

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Authors: Harold Vásquez, Cesar Hernández, Ingrid Páez

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This paper approach cognitive radio technic and applied pure proactive handoff Model to decrease interference between PU and SU and comparing it with reactive handoff model. Through the study and analysis of multivariate models SAW and TOPSIS join to 3 dynamic prediction techniques AR, MA ,and ARMA. To evaluate the best model is taken four metrics: number failed handoff, number handoff, number predictions, and number interference. The result presented the advantages using this type of pure proactive models to predict changes in the PU according to the selected channel and reduce interference. The model showed better performance was TOPSIS-MA, although TOPSIS-AR had a higher predictive ability this was not reflected in the interference reduction.

Keywords: cognitive radio, spectrum handoff, decision making, time series, wireless networks

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Authors: A. Marolleau, F. Salaun, D. Dupont, H. Gidik, S. Ducept.

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The goal of this study is to analyze the hydric behaviour of textiles which can impact significantly the comfort of the wearer. Indeed, fabrics can be adapted for different climate if hydric and thermal behaviors are known. In this study, fabrics are only submitted to hydric variations. Sorption and desorption isotherms obtained from the dynamic vapour sorption apparatus (DVS) are fitted with the parallel exponential kinetics (PEK), the Hailwood-Horrobin (HH) and the Brunauer-Emmett-Teller (BET) models. One of the major finding is the relationship existing between PEK and HH models. During slow and fast processes, the sorption of water molecules on the polymer can be in monolayer and multilayer form. According to the BET model, moisture regain, a physical property of textiles, show a linear correlation with the total amount of water taken in monolayer. This study provides potential information of the end uses of these fabrics according to the selected activity level.

Keywords: comfort, hydric properties, modelling, underwears

Procedia PDF Downloads 147

Authors: Mengxiao Li, Johnson Zhang

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Currently, topology optimization technique is widely used to define the layout design of structures that are presented as truss-like topologies. However, due to the difficulty in combining optimization technique with more realistic material models where their nonlinear properties should be considered, the achieved optimized topologies are commonly unable to apply straight towards the practical design problems. This study presented an optimization procedure of composite structures where different elastic stiffness, yield criteria, and hardening models are assumed for the candidate materials. From the results, it can be concluded that a more explicit modeling has the significant influence on the resulting topologies. Also, the isotropic or kinematic hardening is important for elastoplastic structural optimization design. The capability of the proposed optimization procedure is shown through several cases.

Keywords: topology optimization, material composition, nonlinear modeling, hardening rules

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Authors: Janet Modu, Jean-Francois Georgin, Laurent Briancon, Eric Antoinet

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The current practice during the repowering phase of wind turbines is deconstruction of existing foundations and construction of new foundations to accept larger wind loads or once the foundations have reached the end of their service lives. The ongoing research project FUI25 FEDRE (Fondations d’Eoliennes Durables et REpowering) therefore serves to propose scalable wind turbine foundation designs to allow reuse of the existing foundations. To undertake this research, numerical models and laboratory-scale models are currently being utilized and implemented in the GEOMAS laboratory at INSA Lyon following instrumentation of a reference wind turbine situated in the Northern part of France. Sensors placed within both the foundation and the underlying soil monitor the evolution of stresses from the foundation’s early age to stresses during service. The results from the instrumentation form the basis of validation for both the laboratory and numerical works conducted throughout the project duration. The study currently focuses on the effect of coupled mechanisms (Thermal-Hydro-Mechanical-Chemical) that induce stress during the early age of the reinforced concrete foundation, and scale factor considerations in the replication of the reference wind turbine foundation at laboratory-scale. Using THMC 3D models on COMSOL Multi-physics software, the numerical analysis performed on both the laboratory-scale and the full-scale foundations simulate the thermal deformation, hydration, shrinkage (desiccation and autogenous) and creep so as to predict the initial damage caused by internal processes during concrete setting and hardening. Results show a prominent effect of early age properties on the damage potential in full-scale wind turbine foundations. However, a prediction of the damage potential at laboratory scale shows significant differences in early age stresses in comparison to the full-scale model depending on the spatial position in the foundation. In addition to the well-known size effect phenomenon, these differences may contribute to inaccuracies encountered when predicting ultimate deformations of the on-site foundation using laboratory scale models.

Keywords: cement hydration, early age behavior, reinforced concrete, shrinkage, THMC 3D models, wind turbines

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Authors: HyunSu Seo, HoYoung Son, Sungjin Kim, WooYoung Jung

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In order to understand the seismic behavior of steel frame structure with infill FRP composite panel, simple models for simulation on the steel frame with the panel systems were developed in this study. To achieve the simple design method of the steel framed structure with the damping panel system, 2-D finite element analysis with the springs and dashpots models was conducted in ABAQUS. Under various applied spring stiffness and dashpot coefficient, the expected hysteretic energy responses of the steel frame with damping panel systems we re investigated. Using the proposed simple design method which decides the stiffness and the damping, it is possible to decide the FRP and damping materials on a steel frame system.

Keywords: numerical analysis, FEM, infill, GFRP, damping

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Authors: Mahtab Assadian, Mohd Hasbullah Idris, Mostafa Rezazadeh Shirdar, Mohammad Mahdi Taheri, ‎S. Izman

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Corrosion behaviour and surface roughness of magnesium substrate were investigated after NaOH pretreatment in different concentrations (1, 5, and 10 molar) and duration of (10 min, 30 min, 1 h, 3 h, 6 h and 24 h). Creation of Mg(OH)2 barrier layer after pretreatment enhanced corrostion resistance as well as wettability of substrate surface. Characterization including Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) was conducted to detect the existence of this barrier layer. Surface roughness and wettability of substrate was evaluated using atomic force microscopy (AFM) and contact angle measurement respectively. It is found that magnesium treated by 1M NaOH for 30 min reveals higher corrosion resistance and lower water contact angle of substrate surface. In addition, this investigation indicates that pH value of SBF solution is strongly influenced by different time and concentration of alkaline pretreatment.

Keywords: magnesium, NaOH pretreatment, corrosion resistance, wettability

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Authors: Jan Stodt, Christoph Reich

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The usage of machine learning models for prediction is growing rapidly and proof that the intended requirements are met is essential. Audits are a proven method to determine whether requirements or guidelines are met. However, machine learning models have intrinsic characteristics, such as the quality of training data, that make it difficult to demonstrate the required behavior and make audits more challenging. This paper describes an ML audit framework that evaluates and reviews the risks of machine learning applications, the quality of the training data, and the machine learning model. We evaluate and demonstrate the functionality of the proposed framework by auditing an steel plate fault prediction model.

Keywords: audit, machine learning, assessment, metrics

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Authors: Isik Cetintav, Cenk Misirli, Yilmaz Can

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This work investigates upsetting of the tri-metallic cylindrical billets both experimentally and analytically with a reduction ratio 30%. Steel, brass, and copper are used for the outer and outmost rings and aluminum for the inner core. Two different models have been designed to show material flow and the cavity took place over the two interfaces during forming after this reduction ratio. Each model has an outmost ring material as steel. Model 1 has an outer ring between the outmost ring and the solid core material as copper and Model 2 has a material as brass. Solid core is aluminum for each model. Billets were upset in press machine by using parallel flat dies. Upsetting load was recorded and compared for models and single billets. To extend the tests and compare with experimental procedure to a wider range of inner core and outer ring geometries, finite element model was performed. ABAQUS software was used for the simulations. The aim is to show how contact between outmost ring, outer ring and the inner core are carried on throughout the upsetting process. Results have shown that, with changing in height, between outmost ring, outer ring and inner core, the Model 1 and Model 2 had very good interaction, and the contact surfaces of models had various interface behaviour. It is also observed that tri-metallic materials have lower weight but better mechanical properties than single materials. This can give an idea for using and producing these new materials for different purposes.

Keywords: tri-metallic, upsetting, copper, brass, steel, aluminum

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Authors: Galal Elkobrosy, Amr M. Abdelrazek, Bassuny M. Elsouhily, Mohamed E. Khidr

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Crank shaft length, connecting rod length, crank angle, engine rpm, cylinder bore, mass of piston and compression ratio are the inputs that can control the performance of the slider crank mechanism and then its efficiency. Several combinations of these seven inputs are used and compared. The throughput engine torque predicted by the simulation is analyzed through two different regression models, with and without interaction terms, developed according to multi-linear regression using LU decomposition to solve system of algebraic equations. These models are validated. A regression model in seven inputs including their interaction terms lowered the polynomial degree from 3^rd degree to 1^st degree and suggested valid predictions and stable explanations.

Keywords: design of experiments, regression analysis, SI engine, statistical modeling

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Authors: Shih-Pin Chen, Shih-Syuan You

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Fuzzy regression models are useful for investigating the relationship between explanatory variables and responses in fuzzy environments. To overcome the deficiencies of previous models and increase the explanatory power of fuzzy data, the graded mean integration (GMI) representation is applied to determine representative crisp regression coefficients. A fuzzy regression model is constructed based on the modified dissemblance index (MDI), which can precisely measure the actual total error. Compared with previous studies based on the proposed MDI and distance criterion, the results from commonly used test examples show that the proposed fuzzy linear regression model has higher explanatory power and forecasting accuracy.

Keywords: dissemblance index, fuzzy linear regression, graded mean integration, mathematical programming

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Authors: Ashneel Ajay Singh, Naoki Suzuki, Kazumi Sakuramoto, Swastika Roshni, Paras Nath, Alok Kalla

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Albacore tuna (Thunnus alalunga) is one of the commercially important species of tuna in the North Pacific region. Despite the long history of albacore fisheries in the Pacific, its ecological characteristics are not sufficiently understood. The effects of changing climate on numerous commercially and ecologically important fish species including albacore tuna have been documented over the past decades. The objective of this study was to explore and elucidate the relationship of environmental variables with the stock parameters of albacore tuna. The relationship of the North Pacific albacore tuna recruitment (R), spawning stock biomass (SSB) and recruits per spawning biomass (RPS) from 1970 to 2012 with the environmental factors of sea surface temperature (SST), Pacific decadal oscillation (PDO), El Niño southern oscillation (ENSO) and Pacific warm pool index (PWI) was construed. SST and PDO were used as independent variables with SSB to construct stock reproduction models for R and RPS as they showed most significant relationship with the dependent variables. ENSO and PWI were excluded due to collinearity effects with SST and PDO. Model selections were based on R2 values, Akaike Information Criterion (AIC) and significant parameter estimates at p<0.05. Models with single independent variables of SST, PDO, ENSO and PWI were also constructed to illuminate their individual effect on albacore R and RPS. From the results it can be said that SST and PDO resulted in the most significant models for reproducing North Pacific albacore tuna R and RPS time series. SST has the highest impact on albacore R and RPS when comparing models with single environmental variables. It is important for fishery managers and decision makers to incorporate the findings into their albacore tuna management plans for the North Pacific Oceanic region.

Keywords: Albacore tuna, El Niño southern oscillation, Pacific decadal oscillation, sea surface temperature

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: Cristina da Silva Torres, José Luis Duarte Ribeiro, Maria Auxiliadora Cannarozzo Tinoco

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As a result of the rapid growth of the Brazilian Air Transport, many airports are at the limit of their capacities and have a reduction in the quality of services provided. Thus, there is a need of models for assessing the quality of airport services. Because of this, the main objective of this work is to propose a model for the evaluation of quality attributes in airport services. To this end, we used the method composed by literature review and interview. Structured a working method composed by 5 steps, which resulted in a model to evaluate the quality of airport services, consisting of 8 dimensions and 45 attributes. Was used as base for model definition the process mapping of boarding and landing processes of passengers and luggage. As a contribution of this work is the integration of management process with structuring models to assess the quality of services in airport environments.

Keywords: quality airport services, model for identification of attributes quality, air transport, passenger

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Authors: Qisheng Hu, Jianglei Han, Yue Yang, My Hoa Ha

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When a company is considering purchasing software for commercial use, contract risk assessment is critical to identify risks to mitigate the potential adverse business impact, e.g., security, financial and regulatory risks. Contract risk assessment requires reviewers with specialized knowledge and time to evaluate the legal documents manually. Specifically, validating contracts for a software vendor requires the following steps: manual screening, interpreting legal documents, and extracting risk-prone segments. To automate the process, we proposed a framework to assist legal contract document risk identification, leveraging pre-trained deep learning models and natural language processing techniques. Given a set of pre-defined risk evaluation problems, our framework utilizes the pre-trained transformer-based models for question-answering to identify risk-prone sections in a contract. Furthermore, the question-answering model encodes the concatenated question-contract text and predicts the start and end position for clause extraction. Due to the limited labelled dataset for training, we leveraged transfer learning by fine-tuning the models with the CUAD dataset to enhance the model. On a dataset comprising 287 contract documents and 2000 labelled samples, our best model achieved an F1 score of 0.687.

Keywords: contract risk assessment, NLP, transfer learning, question answering

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Authors: Abdullah A. AlShaher

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In this paper, we demonstrate how regression curves can be used to recognize 2D non-rigid handwritten shapes. Each shape is represented by a set of non-overlapping uniformly distributed landmarks. The underlying models utilize 2^nd order of polynomials to model shapes within a training set. To estimate the regression models, we need to extract the required coefficients which describe the variations for a set of shape class. Hence, a least square method is used to estimate such modes. We then proceed by training these coefficients using the apparatus Expectation Maximization algorithm. Recognition is carried out by finding the least error landmarks displacement with respect to the model curves. Handwritten isolated Arabic characters are used to evaluate our approach.

Keywords: character recognition, regression curves, handwritten Arabic letters, expectation maximization algorithm

Procedia PDF Downloads 143

Authors: Godwin Chigozie Okpara

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This paper using standard GARCH, EGARCH, and TARCH models on day of the week return series (of 246 days) from the Nigerian Stock market estimated the model variants’ VaR. An asymmetric return distribution and fat-tail phenomenon in financial time series were considered by estimating the models with normal, student t and generalized error distributions. The analysis based on Akaike Information Criterion suggests that the EGARCH model with student t innovation distribution can furnish more accurate estimate of VaR. In the light of this, we apply the likelihood ratio tests of proportional failure rates to VaR derived from EGARCH model in order to determine the short and long positions VaR performances. The result shows that as alpha ranges from 0.05 to 0.005 for short positions, the failure rate significantly exceeds the prescribed quintiles while it however shows no significant difference between the failure rate and the prescribed quantiles for long positions. This suggests that investors and portfolio managers in the Nigeria stock market have long trading position or can buy assets with concern on when the asset prices will fall. Precisely, the VaR estimates for the long position range from -4.7% for 95 percent confidence level to -10.3% for 99.5 percent confidence level.

Keywords: downside risk, value-at-risk, failure rate, kupiec LR tests, GARCH models

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Authors: P. K. Singhal, R. Naresh, V. Sharma

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This paper presents the three optimization models, namely New Binary Artificial Bee Colony (NBABC) algorithm, NBABC with Local Search (NBABC-LS), and NBABC with Genetic Crossover (NBABC-GC) for solving the Wind-Thermal Unit Commitment (WTUC) problem. The uncertain nature of the wind power is incorporated using the Weibull probability density function, which is used to calculate the overestimation and underestimation costs associated with the wind power fluctuation. The NBABC algorithm utilizes a mechanism based on the dissimilarity measure between binary strings for generating the binary solutions in WTUC problem. In NBABC algorithm, an intelligent scout bee phase is proposed that replaces the abandoned solution with the global best solution. The local search operator exploits the neighboring region of the current solutions, whereas the integration of genetic crossover with the NBABC algorithm increases the diversity in the search space and thus avoids the problem of local trappings encountered with the NBABC algorithm. These models are then used to decide the units on/off status, whereas the lambda iteration method is used to dispatch the hourly load demand among the committed units. The effectiveness of the proposed models is validated on an IEEE 10-unit thermal system combined with a wind farm over the planning period of 24 hours.

Keywords: artificial bee colony algorithm, economic dispatch, unit commitment, wind power

Procedia PDF Downloads 374

Authors: Nor Sayzwani Sukri, Siti Hajar Ya’acob, Musfiroh Jani, Farah Khaliz Kedri, Noor Syuhadah Subki, Zulhazman Hamzah

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In mid-December 2014, the biggest flood event occurred in East Coast of Peninsular Malaysia especially at Dabong area, Kelantan. As a consequent of flood disaster, the heavy metals concentration in soil may changes and become harmful to the environment due to the pollution that deposited in soil. This study was carried out to determine the heavy metal concentration from flood affected area. Sample have been collected and analysed by using Atomic Absorption Spectroscopy (AAS). Lead (Pb), Cadmium (Cd), Mercury (Hg), and Arsenic (As) were chosen for the heavy metals concentration. The result indicated that the heavy metal concentration did not exceed the limit. In-situ parameters also were carried out, were the results showed the range of soil pH (6.5-6.8), temperature (25°C – 26.5°C), and moisture content (1-2), respectively. The results from this study can be used as a base data to improve the soil quality and for consideration of future land use activities.

Keywords: flood, soil, heavy metal, AAS

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Authors: Dehni Abdellatif, Lounis Mourad

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The new approach of applied spatial geostatistics in materials sciences, agriculture accuracy, agricultural statistics, permitted an apprehension of managing and monitoring the water and groundwater qualities in a relationship with salt-affected soil. The anterior experiences concerning data acquisition, spatial-preparation studies on optical and multispectral data has facilitated the integration of correction models of electrical conductivity related with soils temperature (horizons of soils). For tomography apprehension, this physical parameter has been extracted from calibration of the thermal band (LANDSAT ETM+6) with a radiometric correction. Our study area is Oran region (Northern West of Algeria). Different spectral indices are determined such as salinity and sodicity index, the Combined Spectral Reflectance Index (CSRI), Normalized Difference Vegetation Index (NDVI), emissivity, Albedo, and Sodium Adsorption Ratio (SAR). The approach of geostatistical modeling of electrical conductivity (salinity), appears to be a useful decision support system for estimating corrected electrical resistivity related to the temperature of surface soils, according to the conversion models by substitution, the reference temperature at 25°C (where hydrochemical data are collected with this constraint). The Brightness temperatures extracted from satellite reflectance (LANDSAT ETM+) are used in consistency models to estimate electrical resistivity. The confusions that arise from the effects of salt stress and water stress removed followed by seasonal application of the geostatistical analysis in Geographic Information System (GIS) techniques investigation and monitoring the variation of the electrical conductivity in the alluvial aquifer of Es-Sénia for the salt-affected soil.

Keywords: geostatistical modelling, landsat, brightness temperature, conductivity

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Authors: Rahul Jain, Pradhir Parmar, Dhruvesh Patel

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Planning and drawing are the important aspects of civil engineering. For testing theories about spatial location and interaction between land uses and related activities the computer based solution of urban models are used. The planner’s primary interest is in creation of 3D models of building and to obtain the terrain surface so that he can do urban morphological mappings, virtual reality, disaster management, fly through generation, visualization etc. 3D city models have a variety of applications in urban studies. Gujarat International Finance Tec-City (GIFT) is an ongoing construction site between Ahmedabad and Gandhinagar, Gujarat, India. It will be built on 3590000 m2 having a geographical coordinates of North Latitude 23°9’5’’N to 23°10’55’’ and East Longitude 72°42’2’’E to 72°42’16’’E. Therefore to develop 3D city models of GIFT city, the base map of the city is collected from GIFT office. Differential Geographical Positioning System (DGPS) is used to collect the Ground Control Points (GCP) from the field. The GCP points are used for the registration of base map in QGIS. The registered map is projected in WGS 84/UTM zone 43N grid and digitized with the help of various shapefile tools in QGIS. The approximate height of the buildings that are going to build is collected from the GIFT office and placed on the attribute table of each layer created using shapefile tools. The Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global (30 m X 30 m) grid data is used to generate the terrain of GIFT city. The Google Satellite Map is used to place on the background to get the exact location of the GIFT city. Various plugins and tools in QGIS are used to convert the raster layer of the base map of GIFT city into 3D model. The fly through tool is used for capturing and viewing the entire area in 3D of the city. This paper discusses all techniques and their usefulness in 3D city model creation from the GCP, base map, SRTM and QGIS.

Keywords: 3D model, DGPS, GIFT City, QGIS, SRTM

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Authors: Mohammad Hosein Panahi, Naser Yazdani

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we present a technical trading strategy that leverages the FinBERT language model and financial news analysis with a focus on news related to a subset of Nasdaq 100 stocks. Our approach surpasses the baseline Range Break-out strategy in the Bitcoin market, yielding a remarkable 24.8% increase in the win ratio for all Friday trades and an impressive 48.9% surge in short trades specifically on Fridays. Moreover, we conduct rigorous hypothesis testing to establish the statistical significance of these improvements. Our findings underscore considerable potential of our NLP-driven approach in enhancing trading strategies and achieving greater profitability within financial markets.

Keywords: quantitative finance, technical analysis, bitcoin market, NLP, language models, FinBERT, technical trading

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Authors: Dalia Rimawi, Walid Salameh, Amal Al-Omari, Hadeel AbdelKhaleq

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Predication of Survival time of patients with cancer, is a core factor that influences oncologist decisions in different aspects; such as offered treatment plans, patients’ quality of life and medications development. For a long time proportional hazards Cox regression (ph. Cox) was and still the most well-known statistical method to predict survival outcome. But due to the revolution of data sciences; new predication models were employed and proved to be more flexible and provided higher accuracy in that type of studies. Artificial neural network is one of those models that is suitable to handle time to event predication. In this study we aim to compare ph Cox regression with artificial neural network method according to data handling and Accuracy of each model.

Keywords: Cox regression, neural networks, survival, cancer.

Procedia PDF Downloads 198

Authors: M. Khazam, M. Altawil, A. Eljabri

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Fluid properties in states near a vapor-liquid critical region are the most difficult to measure and to predict with EoS models. The principal model difficulty is that near-critical property variations do not follow the same mathematics as at conditions far away from the critical region. Libyan NC98 field in Sirte basin is a typical example of near critical fluid characterized by high initial condensate gas ratio (CGR) greater than 160 bbl/MMscf and maximum liquid drop-out of 25%. The objective of this paper is to model NC98 phase behavior with the proper selection of EoS parameters and also to model reservoir depletion versus gas cycling option using measured PVT data and EoS Models. The outcomes of our study revealed that, for accurate gas and condensate recovery forecast during depletion, the most important PVT data to match are the gas phase Z-factor and C7+ fraction as functions of pressure. Reasonable match, within -3% error, was achieved for ultimate condensate recovery at abandonment pressure of 1500 psia. The smooth transition from gas-condensate to volatile oil was fairly simulated by the tuned PR-EoS. The predicted GOC was approximately at 14,380 ftss. The optimum gas cycling scheme, in order to maximize condensate recovery, should not be performed at pressures less than 5700 psia. The contribution of condensate vaporization for such field is marginal, within 8% to 14%, compared to gas-gas miscible displacement. Therefore, it is always recommended, if gas recycle scheme to be considered for this field, to start it at the early stage of field development.

Keywords: EoS models, gas-condensate, gas cycling, near critical fluid

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Rainer Goetz, Zahra Ahaliabadeh, Princess S. Llanos, Aliaksandr S. Bandarenka, Tanja Kallio

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In order to understand the electrochemistry of all-solid-state batteries (ASSBs), the use of electrochemical equivalent circuits with a physical meaning is essential. A model battery is needed whose characterization is independent of the influence of the complex battery assembly. Lithium-Ion Conducting Glass-Ceramic (LICGC), a model solid electrolyte, is chosen for its stability in the air, but on the other hand, it is also well-known for its instability against metallic lithium upon direct contact. Hence, as a first step towards a model ASSB, the interface between lithium and the solid electrolyte (SE) is stabilized with thin (5 nm and 10 nm) coatings of titanium oxide (TO) and lithium titanium oxide (LTO). Impedance data shows that both materials are able to protect the SE surface from rapid degradation due to reducing lithium and, therefore, can serve as a protective interlayer on the anode side of a model ASSB.

Keywords: all-solid-state battery, lithium anode, solid electrolytes, interlayers

Procedia PDF Downloads 113

Authors: Gabriel Legorreta Paulin, Marcus I. Bursik, Lilia Arana Salinas, Fernando Aceves Quesada

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In volcanic environments, landslides and debris flows occur continually along stream systems of large stratovolcanoes. This is the case on Pico de Orizaba volcano, the highest mountain in Mexico. The volcano has a great potential to impact and damage human settlements and economic activities by landslides. People living along the lower valleys of Pico de Orizaba volcano are in continuous hazard by the coalescence of upstream landslide sediments that increased the destructive power of debris flows. These debris flows not only produce floods, but also cause the loss of lives and property. Although the importance of assessing such process, there is few landslide inventory maps and landslide susceptibility assessment. As a result in México, no landslide susceptibility models assessment has been conducted to evaluate advantage and disadvantage of models. In this study, a comprehensive study of landslide susceptibility models assessment using GIS technology is carried out on the SE flank of Pico de Orizaba volcano. A detailed multi-temporal landslide inventory map in the watershed is used as framework for the quantitative comparison of two landslide susceptibility maps. The maps are created based on 1) the Stability Index MAPping (SINMAP) model by using default geotechnical parameters and 2) by using findings of volcanic soils geotechnical proprieties obtained in the field. SINMAP combines the factor of safety derived from the infinite slope stability model with the theory of a hydrologic model to produce the susceptibility map. It has been claimed that SINMAP analysis is reasonably successful in defining areas that intuitively appear to be susceptible to landsliding in regions with sparse information. The validations of the resulting susceptibility maps are performed by comparing them with the inventory map under LOGISNET system which provides tools to compare by using a histogram and a contingency table. Results of the experiment allow for establishing how the individual models predict the landslide location, advantages, and limitations. The results also show that although the model tends to improve with the use of calibrated field data, the landslide susceptibility map does not perfectly represent existing landslides.

Keywords: GIS, landslide, modeling, LOGISNET, SINMAP

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Authors: Georgia Pozoukidou

Abstract:

TELUM software is a land use model designed specifically to help metropolitan planning organizations (MPOs) prepare their transportation improvement programs and fulfill their numerous planning responsibilities. In this context obtaining, preparing, and validating socioeconomic forecasts are becoming fundamental tasks for an MPO in order to ensure that consistent population and employment data are provided to travel demand models. Chittenden County Metropolitan Planning Organization of Vermont State was used as a case study to test the applicability of TELUM land use model. The technical insights and lessons learned from the land use model application have transferable value for all MPOs faced with land use forecasting development and transportation modelling.

Keywords: calibration data requirements, land use models, land use planning, metropolitan planning organizations

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Authors: Strahinja Kovačević, Lidija Jevrić, Miloš Kuzmanović, Sanja Podunavac-Kuzmanović

Abstract:

In this paper, various derivatives of benzimidazole have been evaluated against Gram-negative bacteria Escherichia coli. For all investigated compounds the minimum inhibitory concentration (MIC) was determined. Quantitative structure-activity relationships (QSAR) attempts to find consistent relationships between the variations in the values of molecular properties and the biological activity for a series of compounds so that these rules can be used to evaluate new chemical entities. The correlation between MIC and some absorption, distribution, metabolism and excretion (ADME) parameters was investigated, and the mathematical models for predicting the antibacterial activity of this class of compounds were developed. The quality of the multiple linear regression (MLR) models was validated by the leave-one-out (LOO) technique, as well as by the calculation of the statistical parameters for the developed models and the results are discussed on the basis of the statistical data. The results of this study indicate that ADME parameters have a significant effect on the antibacterial activity of this class of compounds. Principal component analysis (PCA) and agglomerative hierarchical clustering algorithms (HCA) confirmed that the investigated molecules can be classified into groups on the basis of the ADME parameters: Madin-Darby Canine Kidney cell permeability (MDCK), Plasma protein binding (PPB%), human intestinal absorption (HIA%) and human colon carcinoma cell permeability (Caco-2).

Keywords: benzimidazoles, QSAR, ADME, in silico

Procedia PDF Downloads 373