Search results for: erosion rate prediction

Authors: M. F. Wu, F. T. Cheng

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Leather industry is the most important traditional industry to provide the leather products in the world for thousand years. The fierce global competitive environment and common awareness of global carbon reduction make livestock supply quantities falling, salt and wet blue leather material reduces and the price skyrockets significantly. Exchange rate fluctuation led sales revenue decreasing which due to the differences of export exchanges and compresses the overall profitability of leather industry. This paper applies activity-based benefit assessment approach to build up fitness fur input mixed model, fur is Wet Blue, which concerned with four key factors: the output rate of wet blue, unit cost of wet blue, yield rate and grade level of Wet Blue to achieve the low cost strategy under given unit price of leather product condition of the company. The research findings indicate that applying this model may improve the input cost structure, decrease numbers of leather product inventories and to raise the competitive advantages of the enterprise in the future.

Keywords: activity-based benefit assessment approach, input mixed, output rate, wet blue

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Authors: Lojain Suliman, Xinrong Liu, Xiaohan Zhou

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Since most tunnels are built in crowded metropolitan settings, the excavation process must take place in highly condensed locations, including high-density cities. In this way, the tunnels are typically located close together, which leads to more interaction between the parallel existing tunnels, and this, in turn, leads to more settlement. This research presents an examination of the impact of a large-scale tunnel excavation on two forms of settlement: surface settlement and settlement surrounding the tunnel. Additionally, research has been done on the properties of interactions between two and three parallel tunnels. The settlement has been evaluated using three primary techniques: theoretical modeling, numerical simulation, and data monitoring. Additionally, a parametric investigation on how distance affects the settlement characteristic for parallel tunnels with short distances has been completed. Additionally, it has been observed that the sequence of excavation has an impact on the behavior of settlements. Nevertheless, a comparison of the model test and numerical simulation yields significant agreement in terms of settlement trend and value. Additionally, when compared to the FEM study, the suggested analytical solution exhibits reduced sensitivity in the settlement prediction. For example, the settlement of the small tunnel diameter does not appear clearly on the settlement curve, while it is notable in the FEM analysis. It is advised, however, that additional studies be conducted in the future employing analytical solutions for settlement prediction for parallel tunnels.

Keywords: settlement, FEM, analytical solution, parallel tunnels

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Authors: Phil P. Wagner

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There is an overwhelming amount of athlete-centric information available for sport practitioners in this era of tech and big data, but protocols in athletic rehabilitation remain arbitrary. It is a common assumption that the rate at which tissue heals amongst individuals is the same; yielding protocols that are entirely time-based. Progressing athletes through rehab programs that lack individualization can potentially expose athletes to stimuli they are not prepared for or unnecessarily lengthen their recovery period. A 7-year aggregated and anonymous database was used to develop reliable and valid assessments to measure athletic resilience. Each assessment utilizes force plate technology with proprietary protocols and analysis to provide key thresholds for injury risk and recovery. Using a T score to analyze movement qualities, much like the Z score used for bone density from a Dexa scan, specific prescriptions are provided to mitigate the athlete’s inherent injury risk. In addition to obliging to surgical clearance, practitioners must put in place a clearance protocol guided by standardized assessments and achievement in strength thresholds. In order to truly hold individuals accountable (practitioners, athletic trainers, performance coaches, etc.), success in improving pre-defined key performance indicators must be frequently assessed and analyzed.

Keywords: analytics, athlete rehabilitation, athlete resilience, injury prediction, injury prevention

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Authors: Huu Hue Van

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The Mekong Delta is the region in southwestern Vietnam where the Mekong River approaches and flow into the sea through a network of distributaries. The Climate Change Research Institute at University of Can Tho, in studying the possible consequences of climate change, has predicted that, many provinces in the Mekong Delta will be flooded by the year 2030. The Mekong Delta lacks fresh water in the dry season. Being served for daily life, industry and agriculture in the dry season, the water is mainly taken from layers of soil contained water under the ground (aquifers) depleted water; the water level in aquifers have decreased. Previously, the Mekong Delta can withstand two bad scenarios in the future: 1) The Mekong Delta will be submerged into the sea again: Due to subsidence of the ground (over-exploitation of groundwater), subsidence of constructions because of the low groundwater level (10 years ago, some of constructions were built on the foundation of Melaleuca poles planted in Mekong Delta, Melaleuca poles have to stay in saturated soil layer fully, if not, they decay easyly; due to the top of Melaleuca poles are higher than the groundwater level, the top of Melaleuca poles will decay and cause subsidence); erosion the river banks (because of the hydroelectric dams in the upstream of the Mekong River is blocking the flow, reducing the concentration of suspended substances in the flow caused erosion the river banks) and the delta will be flooded because of sea level rise (climate change). 2) The Mekong Delta will be deserted: People will migrate to other places to make a living because of no planting due to alum capillary (In Mekong Delta, there is a layer of alum soil under the ground, the elevation of groundwater level is lower than the the elevation of layer of alum soil, alum will be capillary to the arable soil layer); there is no fresh water for cultivation and daily life (because of saline intrusion and groundwater depletion in the aquifers below). Mekong Delta currently has about seven aquifers below with a total depth about 500 m. The water mainly has exploited in the middle - upper Pleistocene aquifer (qp2-3). The major cause of two bad scenarios in the future is over-exploitation of water in aquifers. Therefore, studying and building water reservoirs in seven aquifers will solve many pressing problems such as preventing subsidence, providing water for the whole delta, especially in coastal provinces, favorable to nature, saving land ( if we build the water lake on the surface of the delta, we will need a lot of land), pollution limitation (because when building some hydraulic structures for preventing the salt instrutions and for storing water in the lake on the surface, we cause polluted in the lake)..., It is necessary to build a reservoir under the ground in aquifers in the Mekong Delta. The super-sized reservoir will contribute to the existence and development of the Mekong Delta.

Keywords: aquifers, aquifers storage, groundwater, land subsidence, underground reservoir

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Ahmed Abdulrahman, Jalal Foroozesh

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CO₂ based enhanced oil recovery (EOR) techniques have gained massive attention from major oil firms since they resolve the industry's two main concerns of CO₂ contribution to the greenhouse effect and the declined oil production. Carbonated water injection (CWI) is a promising EOR technique that promotes safe and economic CO₂ storage; moreover, it mitigates the pitfalls of CO₂ injection, which include low sweep efficiency, early CO₂ breakthrough, and the risk of CO₂ leakage in fractured formations. One of the main challenges that hinder the wide adoption of this EOR technique is the complexity of accurate modeling of the kinetics of CO₂ mass transfer. The mechanisms of CO₂ mass transfer during CWI include the slow and gradual cross-phase CO₂ diffusion from carbonated water (CW) to the oil phase and the CO₂ dispersion (within phase diffusion and mechanical mixing), which affects the oil physical properties and the spatial spreading of CO₂ inside the reservoir. A 2D non-equilibrium compositional simulator has been developed using a fully implicit finite difference approximation. The material balance term (k) was added to the governing equation to account for the slow cross-phase diffusion of CO₂ from CW to the oil within the gird cell. Also, longitudinal and transverse dispersion coefficients have been added to account for CO₂ spatial distribution inside the oil phase. The CO₂-oil diffusion coefficient was calculated using the Sigmund correlation, while a scale-dependent dispersivity was used to calculate CO₂ mechanical mixing. It was found that the CO₂-oil diffusion mechanism has a minor impact on oil recovery, but it tends to increase the amount of CO₂ stored inside the formation and slightly alters the residual oil properties. On the other hand, the mechanical mixing mechanism has a huge impact on CO₂ spatial spreading (accurate prediction of CO₂ production) and the noticeable change in oil physical properties tends to increase the recovery factor. A sensitivity analysis has been done to investigate the effect of formation heterogeneity (porosity, permeability) and injection rate, it was found that the formation heterogeneity tends to increase CO₂ dispersion coefficients, and a low injection rate should be implemented during CWI.

Keywords: CO₂ mass transfer, carbonated water injection, CO₂ dispersion, CO₂ diffusion, cross phase CO₂ diffusion, within phase CO2 diffusion, CO₂ mechanical mixing, non-equilibrium simulation

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Authors: Puji Sucia Sukmaningrum, Achsania Hendratmi, Noven Suprayogi, Muhammad Madyan

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Theoretically, Islamic banks in Indonesia and Malaysia not necessarily aware to the interest rate fluctuation, since they don’t use interest-based instruments. Both countries use dual banking system in which Islamic and conventional banking system are exist. This situation makes the profit-sharing level of the Islamic banks will be indirectly affected by the interest rate fluctuation from the conventional banks system. One of the risk management tools for anticipating the risk of interest rate fluctuation is gap management, which has purpose to narrow the difference between Rate Sensitive Asset (RSA) and Rate Sensitive Liability (RSL). This formed gap will give the information about the risk potential in Islamic banks which respect to the fluctuation on the interest rate. This study aims to determine the position of the gap formed at Islamic Maybank Indonesia and Islamic Maybank Malaysia, and analyze the difference in the formation of gap based on the period of sensitivity. This study is a quantitative research with comparative study using sensitivity gap analysis, independent sample t-test, and Mann-Whitney method. The data being used was secondary data from Maturity Profile contained in the Annual Financial Report of Islamic Maybank Indonesia and Islamic Maybank Malaysia from 2011 to 2015 period. The result shows that, cumulatively the formation of the gap was negative gap. From the results of independent sample t-test and Mann-Whitney, the formation of the gap in Islamic Maybank Indonesia and Islamic Maybank Malaysia for a period of sensitivity of ≤ 1 month and >1-3 months show a significant difference, while the period of sensitivity >3-12 months does not. The result shows, even though Indonesia and Malaysia using same dual banking systems, the gap values are different. The difference in debt policy between Indonesia and Malaysia also affecting the gap sensitivity in debt. In can be concluded that each country needs an appropriate gap management to support its Islamic banking performance specifically.

Keywords: assets and liability management, gap management, interest rate risk, Islamic bank

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Authors: Pinel Sebastien, Bourrin François, De Madron Du Rieu Xavier, Ludwig Wolfgang, Arnau Pedro

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Heavy precipitation constitutes a major meteorological threat in the western Mediterranean. Research has investigated the relationship between the states of the Mediterranean Sea and the atmosphere with the precipitation for short temporal windows. However, at a larger temporal scale, the precursor signals of heavy rainfall in the sea and atmosphere have drawn little attention. Moreover, despite ongoing improvements in numerical weather prediction, the medium-term forecasting of rainfall events remains a difficult task. Here, we aim to investigate the influence of early-spring environmental parameters on the following autumnal heavy precipitations. Hence, we develop a machine learning model to predict extreme autumnal rainfall with a 6-month lead time over the Spanish Catalan coastal area, based on i) the sea pattern (main current-LPC and Sea Surface Temperature-SST) at the mesoscale scale, ii) 4 European weather teleconnection patterns (NAO, WeMo, SCAND, MO) at synoptic scale, and iii) the hydrological regime of the main local river (Rhône River). The accuracy of the developed model classifier is evaluated via statistical analysis based on classification accuracy, logarithmic and confusion matrix by comparing with rainfall estimates from rain gauges and satellite observations (CHIRPS-2.0). Sensitivity tests are carried out by changing the model configuration, such as sea SST, sea LPC, river regime, and synoptic atmosphere configuration. The sensitivity analysis suggests a negligible influence from the hydrological regime, unlike SST, LPC, and specific teleconnection weather patterns. At last, this study illustrates how public datasets can be integrated into a machine learning model for heavy rainfall prediction and can interest local policies for management purposes.

Keywords: extreme hazards, sensitivity analysis, heavy rainfall, machine learning, sea-atmosphere modeling, precipitation forecasting

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Authors: Grienggrai Rajchakit

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As we all know that coronavirus is announced as a pandemic in the world by WHO. It is speeded all over the world with few days of time. To control this spreading, every citizen maintains social distance and self-preventive measures are the best strategies. As of now, many researchers and scientists are continuing their research in finding out the exact vaccine. The machine learning model finds that the coronavirus disease behaves in an exponential manner. To abolish the consequence of this pandemic, an efficient step should be taken to analyze this disease. In this paper, a recurrent neural network model is chosen to predict the number of active cases in a particular state. To make this prediction of active cases, we need a database. The database of COVID-19 is downloaded from the KAGGLE website and is analyzed by applying a recurrent LSTM neural network with univariant features to predict the number of active cases of patients suffering from the corona virus. The downloaded database is divided into training and testing the chosen neural network model. The model is trained with the training data set and tested with a testing dataset to predict the number of active cases in a particular state; here, we have concentrated on Andhra Pradesh state.

Keywords: COVID-19, coronavirus, KAGGLE, LSTM neural network, machine learning

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Authors: Achut Manandhar, Kenneth D. Morton, Peter A. Torrione, Leslie M. Collins

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The current trends in affect recognition research are to consider continuous observations from spontaneous natural interactions in people using multiple feature modalities, and to represent affect in terms of continuous dimensions, incorporate spatio-temporal correlation among affect dimensions, and provide fast affect predictions. These research efforts have been propelled by a growing effort to develop affect recognition system that can be implemented to enable seamless real-time human-computer interaction in a wide variety of applications. Motivated by these desired attributes of an affect recognition system, in this work a multi-dimensional affect prediction approach is proposed by integrating multivariate Relevance Vector Machine (MVRVM) with a recently developed Output-associative Relevance Vector Machine (OARVM) approach. The resulting approach can provide fast continuous affect predictions by jointly modeling the multiple affect dimensions and their correlations. Experiments on the RECOLA database show that the proposed approach performs competitively with the OARVM while providing faster predictions during testing.

Keywords: dimensional affect prediction, output-associative RVM, multivariate regression, fast testing

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Authors: Susanta Kumar Gachhayat, S. K. Dash

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Multi objective non-convex economic dispatch problems of a thermal power plant are of grave concern for deciding the cost of generation and reduction of emission level for diminishing the global warming level for improving green-house effect. This paper deals with ramp rate constraints for achieving better inequality constraints so as to incorporate valve point loading for cost of generation in thermal power plant through ramp rate biogeography based optimization involving mutation and migration. Through 50 out of 100 trials, the cost function and emission objective function were found to have outperformed other classical methods such as lambda iteration method, quadratic programming method and many heuristic methods like particle swarm optimization method, weight improved particle swarm optimization method, constriction factor based particle swarm optimization method, moderate random particle swarm optimization method etc. Ramp rate biogeography based optimization applications prove quite advantageous in solving non convex multi objective economic dispatch problems subjected to nonlinear loads that pollute the source giving rise to third harmonic distortions and other such disturbances.

Keywords: economic load dispatch, ELD, biogeography-based optimization, BBO, ramp rate biogeography-based optimization, RRBBO, valve-point loading, VPL

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Authors: Chulsang Yoo, Gildo Kim

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Dual polarization radar provides comprehensive information about rainfall by measuring multiple parameters. In Korea, for the rainfall estimation, JPOLE and CSU-HIDRO algorithms are generally used. This study evaluated the local applicability of JPOLE and CSU-HIDRO algorithms in Korea by using the observed rainfall data collected on August, 2014 by the Biseulsan dual polarization radar data and KMA AWS. A total of 11,372 pairs of radar-ground rain rate data were classified according to thresholds of synthetic algorithms into suitable and unsuitable data. Then, evaluation criteria were derived by comparing radar rain rate and ground rain rate, respectively, for entire, suitable, unsuitable data. The results are as follows: (1) The radar rain rate equation including KDP, was found better in the rainfall estimation than the other equations for both JPOLE and CSU-HIDRO algorithms. The thresholds were found to be adequately applied for both algorithms including specific differential phase. (2) The radar rain rate equation including horizontal reflectivity and differential reflectivity were found poor compared to the others. The result was not improved even when only the suitable data were applied. Acknowledgments: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2013R1A1A2011012).

Keywords: CSU-HIDRO algorithm, dual polarization radar, JPOLE algorithm, radar rainfall estimation algorithm

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Authors: Cristian Păuna

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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Sheena K. J., Jyoti Badge, Sayed Mohammed Zeeshan

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A Comparative Study of Exponential and Logistic Population Growth Models in India India is the second most populous city in the world, just behind China, and is going to be in the first place by next year. The Indian population has remarkably at higher rate than the other countries from the past 20 years. There were many scientists and demographers who has formulated various models of population growth in order to study and predict the future population. Some of the models are Fibonacci population growth model, Exponential growth model, Logistic growth model, Lotka-Volterra model, etc. These models have been effective in the past to an extent in predicting the population. However, it is essential to have a detailed comparative study between the population models to come out with a more accurate one. Having said that, this research study helps to analyze and compare the two population models under consideration - exponential and logistic growth models, thereby identifying the most effective one. Using the census data of 2011, the approximate population for 2016 to 2031 are calculated for 20 Indian states using both the models, compared and recorded the data with the actual population. On comparing the results of both models, it is found that logistic population model is more accurate than the exponential model, and using this model, we can predict the future population in a more effective way. This will give an insight to the researchers about the effective models of population and how effective these population models are in predicting the future population.

Keywords: population growth, population models, exponential model, logistic model, fibonacci model, lotka-volterra model, future population prediction, demographers

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Authors: Ahmed A. Abdel-Khalek, Reham A. Mohamed

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The kinetics of oxidation of the cobalt (II) complexes, [CoII(ADA)(Gly)(H2O)2]-, (ADA = N-(2-acetamido) iminodi-acetic acid and (Gly = Glycine) by periodate in aqueous acetate medium to cobalt (III) have been studied spectrophotometrically at 530 nm over the 30–50°C and a variety pH 4.57-5.25 range and I = 0.50 mol dm-3 under pseudo first order condition by taking large excess of oxidant [IO4-] and it obeys the following rate law: Rate=[CoII(ADA)(Gly)(H2O)2]-[H5IO6]{k4K6+(k5K7K5/[H+])}. Also, the kinetics of oxidation of the cobalt(II) complexes, [CoII(ADA)(Val)(H2O)2]- (ADA = N-(2-acetamido) iminodi-acetic acid and (Val = valine) by periodate in aqueous medium to cobalt (III) have been studied spectrophotometrically at 580 nm over the 30–50°C and a variety pH 4.3-5.12 range and I = 0.50 mol dm-3 under pseudo first order condition by taking large excess of oxidant [IO4-] and it obeys the following rate law: Rate=[CoII(ADA)(Val)(H2O)2]-[H5IO6]{k4K6+(k5K7K5/[H+])}

Keywords: periodate, oxidation, cobalt (II), glycine, valine acid, n-(2-acetamido imino-diacetato)

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Authors: G. P. Gunasinghe, Lilani Ruhunage, N. P. Ratnayake, G. V. I. Samaradivakara, H. M. R. Premasiri, A. S. Ratnayake, Nimila Dushantha, W. A. P. Weerakoon, K. B. A. Silva

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Sri Lanka is affected by different natural disasters such as tsunami, landslides, lightning, and riverine flood. Out of them, riverine floods act as a major disaster in the country. Different strategies are applied to control the impacts of flood hazards, and the expansion of river mouth is considered as one of the main activities for flood mitigation and disaster reduction. However, due to this expansion process, natural sand barriers including sand spits, barrier islands, and tidal planes are destroyed or subjected to change. This, in turn, can change the hydrodynamics and sediment dynamics of the area leading to other damages to the natural coastal features. The removal of a considerable portion of naturally formed sand barrier at Kaluganga River outlet (Calido Beach), Sri Lanka to control flooding event at Kaluthara urban area on May 2017, has become a serious issue in the area causing complete collapse of river mouth barrier spit bar system leading to rapid coastal erosion Kaluganga river outlet area and saltwater intrusion into the Kaluganga River. The present investigation is focused on assessing effects due to the removal of a considerable portion of naturally formed sand barrier at Kaluganga river mouth. For this study, the beach profiles, the bathymetric surveys, and Google Earth historical satellite images, before and after the flood event were collected and analyzed. Furthermore, a beach boundary survey was also carried out in October 2018 to support the satellite image data. The results of Google Earth satellite images and beach boundary survey data analyzed show a chronological breakdown of the sand barrier at the river outlet. The comparisons of pre and post-disaster bathymetric maps and beach profiles analysis revealed a noticeable deepening of the sea bed at the nearshore zone as well. Such deepening in the nearshore zone can cause the sea waves to break very near to the coastline. This might also lead to generate new diffraction patterns resulting in differential coastal accretion and erosion scenarios. Unless immediate mitigatory measures were not taken, the impacts may cause severe problems to the sensitive Kaluganag river mouth system.

Keywords: bathymetry, beach profiles, coastal features, river outlet, sand barrier, Sri Lanka

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Authors: Yves Lacroix, Van Van Than, Didier Léandri, Pierre Liardet

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The tombolo of Giens is located in the town of Hyères (France). We recall the history of coastal erosion, and prominent factors affecting the evolution of the western tombolo. We then discuss the possibility of stabilizing the western tombolo. Our argumentation relies on a coupled model integrating swells, currents, water levels and sediment transport. We present the conclusions of the simulations of various scenarios, including pre-existing propositions from coastal engineering offices. We conclude that beach replenishment seems to be necessary but not sufficient for the stabilization of the beach. Breakwaters reveal effective particularly in the most exposed northern area. Some solutions fulfill conditions so as to be elected as satisfactory. We give a comparative analysis of the efficiency of 14 alternatives for the protection of the tombolo.

Keywords: breakwaters, coupled models, replenishment, silting

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Authors: Shank Kulkarni, Alireza Tabarraei

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The fantastic properties of polyurea such as flexibility, durability, and chemical resistance have brought it a wide range of application in various industries. Effective prediction of the response of polyurea under different loading and environmental conditions necessitates the development of an accurate constitutive model. Similar to most polymers, the behavior of polyurea depends on both strain and strain rate. Therefore, the constitutive model should be able to capture both these effects on the response of polyurea. To achieve this objective, in this paper, a nonlinear hyper-viscoelastic constitutive model is developed by the superposition of a hyperelastic and a viscoelastic model. The proposed constitutive model can capture the behavior of polyurea under compressive loading conditions at various strain rates. Four parameter Ogden model and Mooney Rivlin model are used to modeling the hyperelastic behavior of polyurea. The viscoelastic behavior is modeled using both a three-parameter standard linear solid (SLS) model and a K-BKZ model. Comparison of the modeling results with experiments shows that Odgen and SLS model can more accurately predict the behavior of polyurea. The material parameters of the model are found by curve fitting of the proposed model to the uniaxial compression test data. The proposed model can closely reproduce the stress-strain behavior of polyurea for strain rates up to 6500 /s.

Keywords: constitutive modelling, ogden model, polyurea, SLS model, uniaxial compression test

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Authors: Riya Mishra, Trayambak Tiwari, Anju Lata Singh, I. L. Singh, Tara Singh

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Decrement in vigilance task performance echoes impediment in effortful attention; here attention fluctuated in the realm of external and internal milieu of a person. To examine this fluctuation across time period, we employed two experiments of vigilance task with variation in thought probing rate, which was embedded in the task. The thought probe varies in terms of <2 minute per thought probe and <4 minute per thought probe during vigilance task. A 2x4 repeated measure factorial design was used. 15 individuals participated in this study with an age range of 20-26 years. It was found that thought probing rate has a negative trend with vigilance task performance whereas the subjective measures of mind-wandering have a positive relation with thought probe rate.

Keywords: criterion response, mental status, mind-wandering, thought probe, vigilance

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Authors: Horea-Serban Raluca-Ioana, Istrate Marinela

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The first part of the paper analyzes the dynamics of the total fertility rate both at the national and regional level, pointing out the regional disparities in the distribution of this indicator. At the same time, we also focus on the collapse of the number of live births, on the changes in the fertility rate by birth rank, as well as on the failure of acquiring the desired number of children. The second part of the study centres upon a survey applied to urban families with 3 and more than 3 offspring. The preliminary analysis highlights the fact that an increased fertility (more than 3rd rank) is triggered by the parents’ above the average material condition and superior education. The current situation of Romania, which is still passing through a period of relatively rapid demographic changes, marked by numerous convulsions, requires a new approach, in compliance with the recent interpretations appropriate to a new post-transitional demographic regime.

Keywords: fertility rate, family size intention, third birth rank, regional disparities

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Authors: Hwang Kwon

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Objective: To assess the efficacy of embryo transfer (ET) of accumulated vitrified embryos and compare pregnancy outcomes between ET of thawed embryos following accumulation of vitrified embryos (frozen ET) and ET of fresh and thawed frozen embryos following accumulation of vitrified embryos (fresh ET + frozen ET). Study design: Patients were poor ovarian responders defined according to the Bologna criteria as well as a subgroup of women whose previous IVF-ET cycle through controlled ovarian stimulation (COS) yielded one or no embryos. Sixty-four frozen ETs were performed following accumulation of vitrified embryos (ACCE )(ACCE Frozen) and 51 fresh + frozen ETs were performed following accumulation of vitrified embryos (ACCE Fresh + Frozen). Positive βhCG rate, clinical pregnancy rate, ongoing pregnancy rate, and good quality embryos (%, ±SD) were compared between two groups. Results: There were more good quality embryos in the ACCE Fresh + Frozen group than in the ACCE Frozen group: 60±34.7 versus 42.9±28.9, respectively (p=0.03). Positive βhCG rate [18/64(28.2%) vs. 13/51(25.5%); p=0.75] and clinical pregnancy rate [12/64 (18.8%) vs. 11/51 (10.9%); p=0.71] were comparable between the two groups. Conclusion: Accumulation of vitrified embryos is an effective method in patients with poor ovarian response who fulfill the Bologna criteria. Pregnancy outcomes were comparable between the two groups.

Keywords: accumulation of embryos, frozen embryo transfer, poor responder, Bologna criteria

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Authors: Nikoo Soleimani

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Background: Reducing torque during bone drilling is one of the effective factors in reaching to an optimal drilling process. Methods: 15 bovine femurs were drilled in vitro with a drill bit with a diameter of 4 mm using two methods of torsional ultrasonic assisted drilling (T-UAD) and convent conventional drilling (CD) and the effects of changing the feed rate and rotational speed on the torque were compared in both methods. Results: There was no significant difference in the thrust force measured in both methods due to the direction of vibrations. Results showed that using T-UAD method for bone drilling at feed rates of 0.16, 0.24 and 0.32 mm/rev led for all rotational speeds to a decrease of at least 16.3% in torque compared to the CD method. Further, using T-UAD at rotational speeds of 355~1000 rpm with various feed rates resulted in a torque reduction of 16.3~50.5% compared to CD method. Conclusions: Reducing the feed rate and increasing the rotational speed, except for the rotational speed of 500 rpm and a feed rate of 0.32 mm/rev, resulted generally in torque reduction in both methods. However, T-UAD is a more effective and desirable option for bone drilling considering its significant torque reduction.

Keywords: torsional ultrasonic assisted drilling, torque, bone drilling, rotational speed, feed rate

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Authors: Aynur Aker, Hasan Kaya

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Al-12.6wt.%Si-%2wt.Co alloy was prepared in a graphite crucible under vacuum atmosphere. The samples were directionally solidified upwards with different growth rate at constant temperature gradient using by Bridgman–type growth apparatus. The values of microstructures (λ) was measured from transverse sections of the samples. The microhardness (HV), ultimate tensile strength (σ) and electrical resistivity (ρ) of the directional solidification samples were also measured. Influence of the growth rate and spacings on microhardness, ultimate tensile strength and electrical resistivity were investigated and the relationships between them were experimentally obtained by using regression analysis. The results obtained in present work were compared with the previous similar experimental results obtained for binary and ternary alloys.

Keywords: directional solidification, Al-Si-Co alloy, mechanical properties, electrical properties

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Authors: Ishapathik Das

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The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

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Authors: Tang Wei, Yang Xiaofeng, Gui Yewei, Du Yanxia

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Complex lifting entry was selected for precise landing performance during the Mars Science Laboratory entry. This study aims to develop the three-dimensional numerical method for precise computation and the surface panel method for rapid engineering prediction. Detailed flow field analysis for Mars exploration mission was performed by carrying on a series of fully three-dimensional Navier-Stokes computations. The static aerodynamic performance was then discussed, including the surface pressure, lift and drag coefficient, lift-to-drag ratio with the numerical and engineering method. Computation results shown that the shock layer is thin because of lower effective specific heat ratio, and that calculated results from both methods agree well with each other, and is consistent with the reference data. Aerodynamic performance analysis shows that CG location determines trim characteristics and pitch stability, and certain radially and axially shift of the CG location can alter the capsule lifting entry performance, which is of vital significance for the aerodynamic configuration des0ign and inner instrument layout of the Mars entry capsule.

Keywords: Mars entry capsule, static aerodynamics, computational fluid dynamics, hypersonic

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Authors: Abhishek Kumar, Nilam

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As we know that, time delay exists almost in every biological phenomenon. Therefore, in the present study, we propose a susceptible–infected–recovered (SIR) epidemic model along with time delay, modulated incidence rate of infection, and Holling Type II nonlinear treatment rate. The present model aims to provide a strategy to control the spread of epidemics. In the mathematical study of the model, it has been shown that the model has two equilibriums which are named as disease-free equilibrium (DFE) and endemic equilibrium (EE). Further, stability analysis of the model is discussed. To prove the stability of the model at DFE, we derived basic reproduction number, denoted by (R₀). With the help of basic reproduction number (R₀), we showed that the model is locally asymptotically stable at DFE when the basic reproduction number (R₀) less than unity and unstable when the basic reproduction number (R₀) is greater than unity. Furthermore, stability analysis of the model at endemic equilibrium has also been discussed. Finally, numerical simulations have been done using MATLAB 2012b to exemplify the theoretical results.

Keywords: time delayed SIR epidemic model, modulated incidence rate, Holling type II nonlinear treatment rate, stability

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Authors: Numair A. Siddiqui, Usman Muhammad, Manoj J. Mathew, Ramkumar M., Benjamin Sautter, Muhammad A. K. El-Ghali, David Menier, Shiqi Zhang

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The sedimentological parameterization of shallow-marine siliciclastic reservoirs in terms of reservoir quality and heterogeneity from outcrop study can help improve the subsurface reservoir prediction. An architectural analysis has documented variations in sandstone geometry and rock properties within shallow-marine sandstone exposed in the Miocene Sandakan Formation of Sabah, Borneo. This study demonstrates reservoir sandstone quality assessment for subsurface rock evaluation, from well-exposed successions of the Sandakan Formation, Borneo, with which applicable analogues can be identified. The analyses were based on traditional conventional field investigation of outcrops, grain-size and petrographic studies of hand specimens of different sandstone facies and gamma-ray and permeability measurements. On the bases of these evaluations, the studied sandstone was grouped into three qualitative reservoir rock classes; high (Ø=18.10 – 43.60%; k=1265.20 – 5986.25 mD), moderate (Ø=17.60 – 37%; k=21.36 – 568 mD) and low quality (Ø=3.4 – 15.7%; k=3.21 – 201.30 mD) for visualization and prediction of subsurface reservoir quality. These results provided analogy for shallow marine sandstone reservoir complexity that can be utilized in the evaluation of reservoir quality of regional and subsurface analogues.

Keywords: architecture and sedimentology, subsurface rock evaluation, reservoir quality, borneo

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Authors: Arsalan Ghaderi

Abstract:

Introduction: Inactivity is one of the most important risk factors for cardiovascular disease. According to the study prevalence of inactivity in Iran, about 67.5% and in the staff, and especially nurses, are similar. The inflammatory index (HS-CRP) is highly predictive of the progression of these diseases. Physical activity education is very important in preventing these diseases. One of the modern educational methods is web-based theory-based education. Methods: This is a semi-experimental interventional study which was conducted in Isfahan and Kurdistan universities of medical sciences in two stages. A cross-sectional study was done to determine the status of physical activity and its predictive factors. Then, intervention was performed, and six months later the data were retrieved. The data was collected using a demographic questionnaire, an integrative model of behavior prediction constructs, a standard physical activity questionnaire and (HS-CRP) test. Data were analyzed by SPSS software. Results: Physical activity was low in 66.6% of nurses, 25.4% were moderate and 8% severe. According to Pearson correlation matrix, the highest correlation was found between behavioral intention and skill structures (0.553**), subjective norms (0.222**) and self-efficacy (0.198**). The relationship between age and physical activity in the first study was reverse and significant. After intervention, there was a significant change in attitudes, self-efficacy, skill and behavioral intention in the intervention group. This change was significant in attitudes, self-efficacy and environmental conditions of the control group. HS-CRP index decreased significantly after intervention in both groups, but there was not a significant relationship between inflammatory index and physical activity score. The change in physical activity level was significant only in the control group. Conclusion: Despite the effect of educational intervention on attitude, self-efficacy, skill, and behavioral intention, the results showed that if factors such as environmental factors are not corrected, training and changing structures cannot lead to physical activity behavior. On the other hand, no correlation between physical activity and HS-CRP showed that this index can be influenced by other factors, and this should be considered in any intervention to reduce the HS-CRP index.

Keywords: HS-CRP, integrative model of behavior prediction, physical activity, nurses, web-based education

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Authors: Ali Kazemi

Abstract:

Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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