Search results for: structural kinetic model

Authors: Naima Bouslah, Leila Bounabi, Farid Ouazib, Nabila Haddadine

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Conventional release dosage forms are known to provide an immediate release of the drug. Controlling the rate of drug release from polymeric matrices is very important for a number of applications, particularly in the pharmaceutical area. Hydrogels are polymers in three-dimensional network arrangement, which can absorb and retain large amounts of water without dissolution. They have been frequently used to develop controlled released formulations for oral administration because they can extend the duration of drug release and thus reduce dose to be administrated improving patient compliance. Tramadol is an opioid pain medication used to treat moderate to moderately severe pain. When taken as an immediate-release oral formulation, the onset of pain relief usually occurs within about an hour. In the present work, we synthesized pH-responsive hydrogels of (hydroxyl ethyl methacrylate-co-acrylic acid), (HEMA-AA) for control drug delivery of tramadol in the gastro-intestinal tractus. The hydrogels with different acrylic acid content, were synthesized by free radical polymerization and characterized by FTIR spectroscopy, X ray diffraction analysis (XRD), differential scanning calorimetry (DSC) and thermo gravimetric analysis (TGA). FTIR spectroscopy has shown specific hydrogen bonding interactions between the carbonyl groups of the hydrogels and hydroxyl groups of tramadol. Both the XRD and DSC studies revealed that the introduction of tramadol in the hydrogel network induced the amorphization of the drug. The swelling behaviour, absorptive kinetics and the release kinetics of tramadol in simulated gastric fluid (pH 1.2) and in simulated intestinal fluid (pH 7.4) were also investigated. The hydrogels exhibited pH-responsive behavior in the swelling study. The (HEMA-AA) hydrogel swelling was much higher in pH =7.4 medium. The tramadol release was significantly increased when pH of the medium was changed from simulated gastric fluid (pH 1.2) to simulated intestinal fluid (pH 7.4). Using suitable mathematical models, the apparent diffusional coefficients and the corresponding kinetic parameters have been calculated.

Keywords: biopolymres, drug delivery, hydrogels, tramadol

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Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: Md. Kausar Alam, Mohammad Yazdi, Peiman Zogh, Ramin Motamed

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The liquefaction-induced ground settlement has caused severe damage to structures in the past decades. However, the amount of building settlement caused by liquefaction is directly proportional to the intensity of the ground shaking. To reduce this soil liquefaction effect, it is essential to examine the influence of peak ground acceleration (PGA). Unfortunately, limited studies have been carried out on this issue. In this study, a series of moderate scale 1g shake table experiments were conducted at the University of Nevada Reno to evaluate the influence of PGA with the same duration in liquefiable soil layers. The model is prepared based on a large-scale shake table with a scaling factor of N = 5, which has been conducted at the University of California, San Diego. The model ground has three soil layers with relative densities of 50% for crust, 30% for liquefiable, and 90% for dense layer, respectively. In addition, a shallow foundation is seated over an unsaturated crust layer. After preparing the model, the input motions having various peak ground accelerations (i.e., 0.16g, 0.25g, and 0.37g) for the same duration (10 sec) were applied. Based on the experimental results, when the PGA increased from 0.16g to 0.37g, the foundation increased from 20 mm to 100 mm. In addition, the expected foundation settlement based on the scaling factor was 25 mm, while the actual settlement for PGA 0.25g for 10 seconds was 50 mm.

Keywords: foundation settlement, liquefaction, peak ground acceleration, shake table test

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Authors: Ehsan Gholamalizadeh, Jae Dong Chung

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Solar chimney power plant is a feasible solar thermal system which produces electricity from the Sun. The objective of this study is to investigate buoyancy-driven flow and heat transfer through a built pilot solar chimney system called 'Kerman Project'. The system has a chimney with the height and diameter of 60 m and 3 m, respectively, and the average radius of its solar collector is about 20 m, and also its average collector height is about 2 m. A three-dimensional simulation was conducted to analyze the system, using computational fluid dynamics (CFD). In this model, radiative transfer equation was solved using the discrete ordinates (DO) radiation model taking into account a non-gray radiation behavior. In order to modelling solar irradiation from the sun’s rays, the solar ray tracing algorithm was coupled to the computation via a source term in the energy equation. The model was validated with comparing to the experimental data of the Manzanares prototype and also the performance of the built pilot system. Then, based on the numerical simulations, velocity and temperature distributions through the system, the temperature profile of the ground surface and the system performance were presented. The analysis accurately shows the flow and heat transfer characteristics through the pilot system and predicts its performance.

Keywords: buoyancy-driven flow, computational fluid dynamics, heat transfer, renewable energy, solar chimney power plant

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Authors: M. Stefanovska, B. Samakoski, S. Risteska, G. Maneski

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In this study was performed in situ consolidation of polypropylene matrix/glass reinforced roving by combining heating systems and roll pressing. The commingled roving during hoop winding was winded on a cylindrical mandrel. The work also presents the advances made in the processing of these materials into composites by conventional technique filament winding. Experimental studies were performed with changing parameters – temperature, pressure and speed. Finally, it describes the investigation of the optimal processing conditions that maximize the mechanical properties of the composites. These properties are good enough for composites to be used as engineering materials in many structural applications.

Keywords: commingled fiber, consolidation heat, filament winding, voids

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Authors: S. Belgherras Bekkouche, T. Benouaz, S. M. A. Bekkouche

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Tokamak plasma is far from having a stable background. The study of turbulent transport is an important part of the current research and advanced scenarios were devised to minimize it. To do this, we used a three-wave interaction model which allows to investigate the occurrence drift-wave turbulence driven by pressure gradients in the edge plasma of a tokamak. In order to simulate the energy redistribution among different modes, the growth/decay rates for the three waves was added. After a numerical simulation, we can determine certain aspects of the temporal dynamics exhibited by the model. Indeed for a wide range of the wave decay rate, an intermittent transition from periodic behavior to chaos is observed. Then, a control strategy of chaos was introduced with the aim of reducing or eliminating the weak turbulence.

Keywords: wave interaction, plasma drift waves, wave turbulence, tokamak, edge plasma, chaos

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Authors: Manish Kumar Pillai, Madhavi Pathak Pillai, Rajender Lal, Dinesh P. Sharma

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India has not yet been able to produce many weightlifters in the past years. Karnam Malleshwari is the only woman to win a medal for India in Olympics. When we try to introspect, there seem to be different reasons. One of the probable cause could be the lack of biomechanical analysis for technique improvements. The analysis of motion in sports has gained prime importance for technical improvement. It helps an athlete to develop a better understanding of his own skills and increasing the rate of technical learning process. Kinematics is concerned with describing and quantifying both the linear and angular position of bodies and their time derivatives. The techniques analysis of barbell movement is very important in weightlifting. But women weightlifting has a shorter history than men’s. Research on women weightlifting based on video analysis is less; there is a lack of scientific evidence based on kinematic analysis of especially on Indian weightlifters at national level are limited. Hence, the present investigation was aimed to analyze the barbell kinematics of women weightlifters in India. The study was delimited to the medal winners of 69-kilogram weight category in the All India Inter-University Competition, age ranging between 18 and 28 years. The variables selected for the mechanical analysis of Barbell kinematics included barbell trajectory, velocity, acceleration, potential energy, kinetic energy, mechanical energy, and average power output. The performance was captured during the competition by two DV PC-60 Digital cameras (Panasonic Company, Ltd). Two cameras were placed 6-meters perpendicular to the plane of the motion, 130 cm. above the ground to record/capture the frontal and lateral view of the lifters simultaneously. Video recordings were analyzed by using Dartfish software, and barbell kinematics were analyzed with the information derived with the help of software. The result documented on the basis of the finding of the study clearly states that there are differences in the selected kinematic variables in all three lifters in respect to their technique in five phases during snatch technique using by them.

Keywords: dartfish, digital camera, kinematic, snatch, weightlifting

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Authors: Muhammad Jahanzaib, Muhammad Z. A. Khan, Junaid Khayyam

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Islamabad is the capital of Pakistan with the population of 1.365 million people and with a vehicular fleet size of 0.75 million. The vehicular fleet size is growing annually by the rate of 11%. Vehicular emissions are major source of Black carbon (BC). In developing countries like Pakistan, most of the vehicles consume conventional fuels like Petrol, Diesel, and CNG. These fuels are the major emitters of pollutants like CO, CO2, NOx, CH4, VOCs, and particulate matter (PM10). Carbon dioxide and methane are the leading contributor to the global warming with a global share of 9-26% and 4-9% respectively. NOx is the precursor of nitrates which ultimately form aerosols that are noxious to human health. In this study, COPERT (Computer program to Calculate Emissions from Road Transport) was used for vehicular emission estimation in Islamabad. COPERT is a windows based program which is developed for the calculation of emissions from the road transport sector. The emissions were calculated for the year of 2016 include pollutants like CO, NOx, VOC, and PM and energy consumption. The different variable was input to the model for emission estimation including meteorological parameters, average vehicular trip length and respective time duration, fleet configuration, activity data, degradation factor, and fuel effect. The estimated emissions for CO, CH4, CO2, NOx, and PM10 were found to be 9814.2, 44.9, 279196.7, 3744.2 and 304.5 tons respectively.

Keywords: COPERT Model, emission estimation, PM10, vehicular emission

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Authors: Hayder H. Kareem, Shunqi Pan

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Groundwater is a vital water resource in many areas in the world, particularly in the Middle-East region where the water resources become scarce and depleting. Sustainable management and planning of the groundwater resources become essential and urgent given the impact of the global climate change. In the recent years, numerical models have been widely used to predict the flow pattern and assess the water resources security, as well as the groundwater quality affected by the contaminants transported. In this study, MODFLOW is used to study the current status of groundwater resources and the risk of water resource security in the region centred at Al-Najaf City, which is located in the mid-west of Iraq and adjacent to the Euphrates River. In this study, a conceptual model is built using the geologic and hydrogeologic collected for the region, together with the Digital Elevation Model (DEM) data obtained from the "Global Land Cover Facility" (GLCF) and "United State Geological Survey" (USGS) for the study area. The computer model is also implemented with the distributions of 69 wells in the area with the steady pro-defined hydraulic head along its boundaries. The model is then applied with the recharge rate (from precipitation) of 7.55 mm/year, given from the analysis of the field data in the study area for the period of 1980-2014. The hydraulic conductivity from the measurements at the locations of wells is interpolated for model use. The model is calibrated with the measured hydraulic heads at the locations of 50 of 69 wells in the domain and results show a good agreement. The standard-error-of-estimate (SEE), root-mean-square errors (RMSE), Normalized RMSE and correlation coefficient are 0.297 m, 2.087 m, 6.899% and 0.971 respectively. Sensitivity analysis is also carried out, and it is found that the model is sensitive to recharge, particularly when the rate is greater than (15mm/year). Hydraulic conductivity is found to be another parameter which can affect the results significantly, therefore it requires high quality field data. The results show that there is a general flow pattern from the west to east of the study area, which agrees well with the observations and the gradient of the ground surface. It is found that with the current operational pumping rates of the wells in the area, a dry area is resulted in Al-Najaf City due to the large quantity of groundwater withdrawn. The computed water balance with the current operational pumping quantity shows that the Euphrates River supplies water into the groundwater of approximately 11759 m3/day, instead of gaining water of 11178 m3/day from the groundwater if no pumping from the wells. It is expected that the results obtained from the study can provide important information for the sustainable and effective planning and management of the regional groundwater resources for Al-Najaf City.

Keywords: Al-Najaf city, conceptual modelling, groundwater, unconfined aquifer, visual MODFLOW

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Authors: Naohiro Nakamura, Takuya Kinoshita, Hiroshi Fukuyama

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The 2011 off The Pacific Coast of Tohoku Earthquake caused considerable damage to wide areas of eastern Japan. A large number of earthquake observation records were obtained at various places. To design more earthquake-resistant buildings and improve earthquake disaster prevention, it is necessary to utilize these data to analyze and evaluate the behavior of a building during an earthquake. This paper presents an earthquake response simulation analysis (hereafter a seismic response analysis) that was conducted using data recorded during the main earthquake (hereafter the main shock) as well as the earthquakes before and after it. The data were obtained at a high-rise steel-reinforced concrete (SRC) building in the bay area of Tokyo. We first give an overview of the building, along with the characteristics of the earthquake motion and the building during the main shock. The data indicate that there was a change in the natural period before and after the earthquake. Next, we present the results of our seismic response analysis. First, the analysis model and conditions are shown, and then, the analysis result is compared with the observational records. Using the analysis result, we then study the effect of soil-structure interaction on the response of the building. By identifying the characteristics of the building during the earthquake (i.e., the 1st natural period and the 1st damping ratio) by the Auto-Regressive eXogenous (ARX) model, we compare the analysis result with the observational records so as to evaluate the accuracy of the response analysis. In this study, a lumped-mass system SR model was used to conduct a seismic response analysis using observational data as input waves. The main results of this study are as follows: 1) The observational records of the 3/11 main shock put it between a level 1 and level 2 earthquake. The result of the ground response analysis showed that the maximum shear strain in the ground was about 0.1% and that the possibility of liquefaction occurring was low. 2) During the 3/11 main shock, the observed wave showed that the eigenperiod of the building became longer; this behavior could be generally reproduced in the response analysis. This prolonged eigenperiod was due to the nonlinearity of the superstructure, and the effect of the nonlinearity of the ground seems to have been small. 3) As for the 4/11 aftershock, a continuous analysis in which the subject seismic wave was input after the 3/11 main shock was input was conducted. The analyzed values generally corresponded well with the observed values. This means that the effect of the nonlinearity of the main shock was retained by the building. It is important to consider this when conducting the response evaluation. 4) The first period and the damping ratio during a vibration were evaluated by an ARX model. Our results show that the response analysis model in this study is generally good at estimating a change in the response of the building during a vibration.

Keywords: ARX model, response analysis, SRC building, the 2011 off the Pacific Coast of Tohoku Earthquake

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Authors: Suk-Hyun Yu, Heeyong Kwon

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Recently PM-10 has become a social and global issue. It is one of major air pollutants which affect human health. Therefore, it needs to be forecasted rapidly and precisely. However, PM-10 comes from various emission sources, and its level of concentration is largely dependent on meteorological and geographical factors of local and global region, so the forecasting of PM-10 concentration is very difficult. Neural network model can be used in the case. But, there are few cases of high concentration PM-10. It makes the learning of the neural network model difficult. In this paper, we suggest a simple input balancing method when the data distribution is uneven. It is based on the probability of appearance of the data. Experimental results show that the input balancing makes the neural networks’ learning easy and improves the forecasting rates.

Keywords: artificial intelligence, air quality prediction, neural networks, pattern recognition, PM-10

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Authors: Hatthaphone Silimanotham, Michael Henry

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The effective maintenance of road bridge infrastructure is becoming a widely researched topic in the civil engineering field. Deterioration is one of the main issues in bridge performance, and it is necessary to understand how bridges deteriorate to optimally plan budget allocation for bridge maintenance. In Laos, many bridges are in a deteriorated state, which may affect the performance of the bridge. Due to bridge deterioration, the Ministry of Public Works and Transport is interested in the deterioration model to allocate the budget efficiently and support the bridge maintenance planning. A deterioration model can be used to predict the bridge condition in the future based on the observed behavior in the past. This paper analyzes the available inspection data of road bridges on the road classifications network to build deterioration prediction models for the main bridge type found at the provincial level (concrete slab, concrete girder, and steel truss) using probabilistic deterioration modeling by linear regression method. The analysis targets there has three bridge types in the 18 provinces of Laos and estimates the bridge deterioration rating for evaluating the bridge's remaining life. This research thus considers the relationship between the service period and the bridge condition to represent the probability of bridge condition in the future. The results of the study can be used for a variety of bridge management tasks, including maintenance planning, budgeting, and evaluating bridge assets.

Keywords: deterioration model, bridge condition, bridge management, probabilistic modeling

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Zhen Cheng, Xinyu Dai, Shujian Huang, Jiajun Chen

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Recently, explanatory natural language inference has attracted much attention for the interpretability of logic relationship prediction, which is also known as explanation generation for Natural Language Inference (NLI). Existing explanation generators based on discriminative Encoder-Decoder architecture have achieved noticeable results. However, we find that these discriminative generators usually generate explanations with correct evidence but incorrect logic semantic. It is due to that logic information is implicitly encoded in the premise-hypothesis pairs and difficult to model. Actually, logic information identically exists between premise-hypothesis pair and explanation. And it is easy to extract logic information that is explicitly contained in the target explanation. Hence we assume that there exists a latent space of logic information while generating explanations. Specifically, we propose a generative model called Variational Explanation Generator (VariationalEG) with a latent variable to model this space. Training with the guide of explicit logic information in target explanations, latent variable in VariationalEG could capture the implicit logic information in premise-hypothesis pairs effectively. Additionally, to tackle the problem of posterior collapse while training VariaztionalEG, we propose a simple yet effective approach called Logic Supervision on the latent variable to force it to encode logic information. Experiments on explanation generation benchmark—explanation-Stanford Natural Language Inference (e-SNLI) demonstrate that the proposed VariationalEG achieves significant improvement compared to previous studies and yields a state-of-the-art result. Furthermore, we perform the analysis of generated explanations to demonstrate the effect of the latent variable.

Keywords: natural language inference, explanation generation, variational auto-encoder, generative model

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Authors: Farzana Sharmin, Mohammad Tipu Sultan

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Emerging new media technology such as social media and social networking sites have changed the family business dynamics in Eastern Asia. The family business trends in China has been developed at an exponential rate towards technology. In the last two decades, many of this family business has succeeded in becoming major players in the Chinese and world economy. But there are a very few availabilities of literature on Chinese context regarding social media acceptance in terms of the family business. Therefore, this study has tried to cover the gap between culture and new media technology to understand the attitude of Chinese young entrepreneurs’ towards the family business. This paper focused on two cultural dimensions (collectivism, long-term orientation), which are adopted from Greet Hofstede’s. Additionally perceived usefulness and ease of use adopted from the Technology Acceptance Model (TAM) to explore the actual behavior of technology acceptance for the family business. A quantitative survey method (n=275) used to collect data Chinese family business owners' in Shanghai. The inferential statistical analysis was applied to extract trait factors, and verification of the model, respectively. The research results found that using social media for family business promotion has highly influenced by cultural values (collectivism and long-term orientation). The theoretical contribution of this research may also assist policymakers and practitioners of other developing countries to advertise and promote the family business through social media.

Keywords: China, cultural dimensions, family business, technology acceptance model, TAM

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Authors: Jianwen Li, Hongfang Ma, Weixin Qian, Haitao Zhang, Weiyong Ying

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Methanol-to-propylene conversion was carried out in a continuous-flow fixed-bed reactor over nano-sized HZSM-5 zeolites. The HZSM-5 catalysts were synthesized with different Si/Al ratio and silicon sources, and treated with NaOH. The structural property, morphology, and acidity of catalysts were measured by XRD, N₂ adsorption, FE-SEM, TEM, and NH₃-TPD. The results indicate that the increment of Si/Al ratio decreased the acidity of catalysts and then improved propylene selectivity, while silicon sources had slight impact on the acidity but affected the product distribution. The desilication after alkali treatment could increase intracrystalline mesopores and enhance propylene selectivity.

Keywords: alkali treatment, HZSM-5, methanol-to-propylene, synthesis condition

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Authors: Keyi Wang

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Similar to the touchscreen, hand gesture based human-computer interaction (HCI) is a technology that could allow people to perform a variety of tasks faster and more conveniently. This paper proposes a training method of an image-based hand gesture image and video clip recognition system using a CNN (Convolutional Neural Network) with a dataset. A dataset containing 6 hand gesture images is used to train a 2D CNN model. ~98% accuracy is achieved. Furthermore, a 3D CNN model is trained on a dataset containing 4 hand gesture video clips resulting in ~83% accuracy. It is demonstrated that a Cozmo robot loaded with pre-trained models is able to recognize static and dynamic hand gestures.

Keywords: deep learning, hand gesture recognition, computer vision, image processing

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Anika Zafiah M. Rus, Nur Munirah Abdullah, M. F. L. Abdullah

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The fabrication and characterization of composite films of graphite- bioepoxy is described. Free-standing thin films of ~0.1 mm thick are prepared using a simple solution mixing with mass proportion of 7/3 (bioepoxy/graphite) and drop casting at room temperature. Fourier transform infra-red spectroscopy (FTIR) and Ultraviolet-visible (UV-vis) spectrophotometer are performed to evaluate the changes in chemical structure and adsorption spectra arising with the increasing of graphite weight loading (wt.%) into the biopolymer matrix. The morphologic study shows a homogeneously dispersed and strong particle bonding between the graphite and the bioepoxy, with conductivity of the film 103 S/m, confirming the efficiency of the processes.

Keywords: absorbance peak, biopolymer, graphite- bioepoxy composites, particle bonding

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Authors: Hossain A., Chowdhury S. I.

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Introduction: The use of a gamma camera is a standard procedure in nuclear medicine facilities or hospitals to diagnose chronic kidney disease (CKD), but the gamma camera does not precisely stage the disease. The authors sought to determine whether they could use an artificial neural network to determine whether CKD was in normal or abnormal stages based on GFR values (ANN). Method: The 250 kidney patients (Training 188, Testing 62) who underwent an ultrasonography test to diagnose a renal test in our nuclear medical center were scanned using a gamma camera. Before the scanning procedure, the patients received an injection of ⁹⁹ᵐTc-DTPA. The gamma camera computes the pre- and post-syringe radioactive counts after the injection has been pushed into the patient's vein. The artificial neural network uses the softmax function with cross-entropy loss to determine whether CKD is normal or abnormal based on the GFR value in the output layer. Results: The proposed ANN model had a 99.20 % accuracy according to K-fold cross-validation. The sensitivity and specificity were 99.10 and 99.20 %, respectively. AUC was 0.994. Conclusion: The proposed model can distinguish between normal and abnormal stages of CKD by using an artificial neural network. The gamma camera could be upgraded to diagnose normal or abnormal stages of CKD with an appropriate GFR value following the clinical application of the proposed model.

Keywords: artificial neural network, glomerular filtration rate, stages of the kidney, gamma camera

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Authors: Matheus Madureira Matos, Alexandre Rodrigues Farias

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Magnetic Resonance Imaging (MRI) is a vital imaging technique used in both clinical and pre-clinical areas to obtain detailed anatomical and functional information. However, MRI scans can be expensive, time-consuming, and often require the use of anesthetics to keep animals still during the imaging process. Anesthetics are commonly administered to animals undergoing MRI scans to ensure they remain still during the imaging process. However, prolonged or repeated exposure to anesthetics can have adverse effects on animals, including physiological alterations and potential toxicity. Minimizing the duration and frequency of anesthesia is, therefore, crucial for the well-being of research animals. In recent years, various sampling trajectories have been investigated to reduce the number of MRI measurements leading to shorter scanning time and minimizing the duration of animal exposure to the effects of anesthetics. Compressed sensing (CS) and sampling trajectories, such as cartesian, spiral, and radial, have emerged as powerful tools to reduce MRI data while preserving diagnostic quality. This work aims to apply CS and cartesian, spiral, and radial sampling trajectories for the reconstruction of MRI of the abdomen of mice sub-sampled at levels below that defined by the Nyquist theorem. The methodology of this work consists of using a fully sampled reference MRI of a female model C57B1/6 mouse acquired experimentally in a 4.7 Tesla MRI scanner for small animals using Spin Echo pulse sequences. The image is down-sampled by cartesian, radial, and spiral sampling paths and then reconstructed by CS. The quality of the reconstructed images is objectively assessed by three quality assessment techniques RMSE (Root mean square error), PSNR (Peak to Signal Noise Ratio), and SSIM (Structural similarity index measure). The utilization of optimized sampling trajectories and CS technique has demonstrated the potential for a significant reduction of up to 70% of image data acquisition. This result translates into shorter scan times, minimizing the duration and frequency of anesthesia administration and reducing the potential risks associated with it.

Keywords: compressed sensing, magnetic resonance, sampling trajectories, small animals

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Authors: Liu-Chao Qiu

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The damage caused by surge waves generated in water bodies by flow-like landslides can be very high in terms of human lives and economic losses. The complicated phenomena occurred in this highly unsteady process are difficult to model because three interacting phases: air, water and sediment are involved. The problem therefore is challenging since the effects of non-Newtonian fluid describing the rheology of the flow-like landslides, multi-phase flow and free surface have to be included in the simulation. In this work, the commercial computational fluid dynamics (CFD) package FLUENT is used to model the surge waves due to flow-like landslides. The comparison between the numerical results and experimental data reported in the literature confirms the accuracy of the method.

Keywords: flow-like landslide, surge wave, VOF, non-Newtonian fluids, multi-phase flows, free surface flow

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Authors: K. V. Ramanaiah, R. Jagan, N. N. Murthy

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Trinuclear oxo-centered carboxylate-bridged iron complexes, [Fe3(µ3-O)(µ2-O2CR)L¬3]+/0 (where R = alkyl or aryl; L = H2O, ROH, Py, solvent) have attracted tremendous attention because of their interesting structural and magnetic properties, exhibit mixed-valent trapped and de-trapped states, and have bioinorganic relevance. The presence of a trinuclear iron binding center has been implicated in the formation of both bacterial and human iron storage protein, Ft. They are used as precursors for the synthesis of models for the active-site structures of non-heme proteins, hemerythrin (Hr), methane monooxygenase (MMO) and polyiron storage protein, ferritin (Ft). Used as important building blocks for the design and synthesis of supramolecules this can exhibit single molecular magnetism (SMM). Such studies have often employed simple and compact carboxylate ligands and the use of bulky carboxylates is scarce. In the present study, we employed two different type of sterically hindered carboxylates and synthesized a series of novel oxo-centered, carboxylate-bridged triiron complexes of general formula [Fe3(O)(O2CCPh3)6L3]X (L = H2O, 1; py, 2; 4-NMe2py, 3; X = ClO4; L = CH3CN, 4; X = FeCl4) and [Fe3(O)(O2C-anth)6L3]X (L = H2O, 5; X = ClO4; L = CH3OH, 6; X = Cl). Along with complex [Fe(OMe)2(O2CCPh3)]10, 7 was prepared by the self-assemble of anhydrous FeCl3, sodium triphenylacetate and sodium methoxide at ratio of 1:1:2 in CH3OH. The Electronic absorption spectra of these complexes 1-6, in CH2Cl2 display weak bands at near FTIR region (970-1135 nm, ε > 15M-1cm-1). For complex 7, one broad band centered at ~670nm and also an additional intense charge transfer (L→M or O→M) bands between 300 to 550nm observed for all the complexes. Paramagnetic 1H NMR is introduced as a good probe for the characterization of trinuclear oxo - cantered iron compounds in solution when the L ligand coordinated to iron varies as: H2O, py, 4-NMe2py, and CH3OH. The solution state magnetic moment values calculated by using Evans method for all the complexes and also solid state magnetic moment value of complex, 7 was calculated by VSM method, which is comparable with solution state value. These all magnetic moment values indicate there is a spin exchange process through oxo and carboxylate bridges in between two irons (d5). The ESI-mass data complement the data obtained from single crystal X-ray structure. Further purity of the compounds was confirmed by elemental analysis. Finally, structural determination of complexes 1, 3, 4, 5, 6 and 7 were unambiguously conformed by single crystal x-ray studies.

Keywords: decanuclear, paramagnetic NMR, trinuclear, uv-visible

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Authors: Tareg M. Elsunaki, Suleiman A. Arafa, Mohamed A. Abd-Alla

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New interesting thermal stable polyesters containing 1-methyl-4-piperidone moiety in the main chain have been synthesized. These polyesters were synthesized by interfacial polycondensation technique of 3,5-bis(4-hydroxybenzylidene)-1-methyl-4-piperidone (I) and 3,5-bis(4-hydroxy-3-methoxy benzyli-dene)-1-methyl-4-piperidone (II) with terphthaloyl, isophthaloyl, 4,4'-diphenic, adipoyl and sebacoyl dichlorides. The yield and the values of the reduced viscosity of the produced polyesters were found to be affected by the type of an organic phase. In order to characterize these polymers, the necessary model compounds (A), (B) were prepared from (I), (II) respectively and benzoyl chloride. The structure of monomers (I), (II), model compounds and resulting polyesters were confirmed by IR, elemental analysis and 1HNMR spectroscopy. The various characteristic of the resulting polymers including solubility, thermal properties, viscosity and X-ray analysis were also studied.

Keywords: synthesis, characterization, new polyesters, chemistry

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Authors: Martha E. Aguiar-Barrera, Humberto Gutierrez-Pulido, Veronica Vargas-Alejo

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Identifying and modeling random phenomena is a fundamental cognitive process to understand and transform reality. Recognizing situations governed by chance and giving them a scientific interpretation, without being carried away by beliefs or intuitions, is a basic training for citizens. Hence the importance of generating teaching-learning processes, supported using technology, paying attention to model creation rather than only executing mathematical calculations. In order to develop the student's knowledge about basic probability distributions and decision making; in this work a model eliciting activity (MEA) is reported. The intention was applying the Model and Modeling Perspective to design an activity related to civil engineering that would be understandable for students, while involving them in its solution. Furthermore, the activity should imply a decision-making challenge based on sample data, and the use of the computer should be considered. The activity was designed considering the six design principles for MEA proposed by Lesh and collaborators. These are model construction, reality, self-evaluation, model documentation, shareable and reusable, and prototype. The application and refinement of the activity was carried out during three school cycles in the Probability and Statistics class for Civil Engineering students at the University of Guadalajara. The analysis of the way in which the students sought to solve the activity was made using audio and video recordings, as well as with the individual and team reports of the students. The information obtained was categorized according to the activity phase (individual or team) and the category of analysis (sample, linearity, probability, distributions, mechanization, and decision-making). With the results obtained through the MEA, four obstacles have been identified to understand and apply the binomial distribution: the first one was the resistance of the student to move from the linear to the probabilistic model; the second one, the difficulty of visualizing (infering) the behavior of the population through the sample data; the third one, viewing the sample as an isolated event and not as part of a random process that must be viewed in the context of a probability distribution; and the fourth one, the difficulty of decision-making with the support of probabilistic calculations. These obstacles have also been identified in literature on the teaching of probability and statistics. Recognizing these concepts as obstacles to understanding probability distributions, and that these do not change after an intervention, allows for the modification of these interventions and the MEA. In such a way, the students may identify themselves the erroneous solutions when they carrying out the MEA. The MEA also showed to be democratic since several students who had little participation and low grades in the first units, improved their participation. Regarding the use of the computer, the RStudio software was useful in several tasks, for example in such as plotting the probability distributions and to exploring different sample sizes. In conclusion, with the models created to solve the MEA, the Civil Engineering students improved their probabilistic knowledge and understanding of fundamental concepts such as sample, population, and probability distribution.

Keywords: linear model, models and modeling, probability, randomness, sample

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Authors: M. N. Ervina Efzan, H. J. Kong, C. K. Kok

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The present work deals with a study on the influences of hybrid modifier on LM 6 added through ladle metallurgy. In this study, LM 6 served as the reference alloy while Na2CO3 and Ti powders were used as the hybrid modifier. The effects of hybrid modifier on the micro structural enhancement of LM 6 were investigated using optical microscope (OM) and Scanning Electron Microscope (SEM). The results showed fragmented Si-rich needles and strength enhanced petal/ globular-like structures without obvious formation of soft primary α-Al and β-Fe-rich inter metallic compound (IMC) after the hybrid modification. Hardness test was conducted to examine the mechanical improvement of hybrid modified LM 6. 10% of hardness improvement was recorded in the hybrid modified LM 6 through ladle metallurgy.

Keywords: Al-Si, hybrid modifier, ladle metallurgy, hardness

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Authors: Wenxin Mei, Jinhua Sun, Qingsong Wang

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The volume expansion of lithium-ion batteries is mainly induced by intercalation induced stress within the negative electrode, resulting in capacity degradation and even battery failure. Stress generation due to lithium intercalation into graphite particles is investigated based on an electrochemical-mechanical model in this work. The two-dimensional model presented is fully coupled, inclusive of the impacts of intercalation-induced stress, stress-induced intercalation, to evaluate the lithium concentration, stress generation, and displacement intra and inter-particle. The results show that the distribution of lithium concentration and stress exhibits an analogous pattern, which reflects the relation between lithium diffusion and stress. The results of inter-particle stress indicate that larger Von-Mises stress is displayed where the two particles are in contact with each other, and deformation at the edge of particles is also observed, predicting fracture. Additionally, the maximum inter-particle stress at the end of lithium intercalation is nearly ten times the intraparticle stress. And the maximum inter-particle displacement is increased by 24% compared to the single-particle. Finally, the effect of graphite particle arrangement on inter-particle stress is studied. It is found that inter-particle stress with tighter arrangement exhibits lower stress. This work can provide guidance for predicting the intra and inter-particle stress to take measures to avoid cracking of electrode material.

Keywords: electrochemical-mechanical model, graphite particle, lithium concentration, lithium ion battery, stress

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Authors: Adeola Adenikinju, Olusanya E. Olubusoye, Lateef O. Akinpelu, Dilinna L. Nwobi

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Since the enactment of the Fiscal Responsibility Act (2007), the Federal Government of Nigeria (FGN) has made public its fiscal budget and the subsequent implementation report. A critical review of these documents shows significant variations in the five macroeconomic variables which are inputs in each Presidential budget; oil Production target (mbpd), oil price ($), Foreign exchange rate(N/$), and Gross Domestic Product growth rate (%) and inflation rate (%). This results in underperformance of the Federal budget expected output in terms of non-oil and oil revenue aggregates. This paper evaluates first the existing variance between budgeted and actuals, then the relationship and causality between the determinants of Federal fiscal budget assumptions, and finally the determinants of FGN’s Gross Oil Revenue. The paper employed the use of descriptive statistics, the Autoregressive distributed lag (ARDL) model, and a Profit oil probabilistic model to achieve these objectives. This model permits for both the static and dynamic effect(s) of the independent variable(s) on the dependent variable, unlike a static model that accounts for static or fixed effect(s) only. It offers a technique for checking the existence of a long-run relationship between variables, unlike other tests of cointegration, such as the Engle-Granger and Johansen tests, which consider only non-stationary series that are integrated of the same order. Finally, even with small sample size, the ARDL model is known to generate a valid result, for it is the dependent variable and is the explanatory variable. The results showed that there is a long-run relationship between oil revenue as a proxy for budget performance and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a short-run relationship between oil revenue and its determinants; oil price, produced oil quantity, and foreign exchange rate. There is a long-run relationship between non-oil revenue and its determinants; inflation rate, GDP growth rate, and foreign exchange rate. The grangers’ causality test results show that there is a mono-directional causality between oil revenue and its determinants. The Federal budget assumptions only explain 68% of oil revenue and 62% of non-oil revenue. There is a mono-directional causality between non-oil revenue and its determinants. The Profit oil Model describes production sharing contracts, joint ventures, and modified carrying arrangements as the greatest contributors to FGN’s gross oil revenue. This provides empirical justification for the selected macroeconomic variables used in the Federal budget design and performance evaluation. The research recommends other variables, debt and money supply, be included in the Federal budget design to explain the Federal budget revenue performance further.

Keywords: ARDL, budget performance, oil price, oil quantity, oil revenue

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Authors: K. Julia Rose Mary, Victor Arokia Doss

Abstract:

Electrical and chemical stimulations up-regulate phosphorylaion of CREB, a transcriptional factor that induces its target gene production for memory consolidation and Late Long-Term Potentiation (L-LTP) in CA1 region of the hippocampus. L-LTP requires complex interactions among second-messenger signaling cascade molecules such as cAMP, CAMKII, CAMKIV, MAPK, RSK, PKA, all of which converge to phosphorylate CREB which along with CBP induces the transcription of target genes involved in memory consolidation. A differential equation based model for L-LTP representing stimulus-mediated activation of downstream mediators which confirms the steep, supralinear stimulus-response effects of activation and inhibition was used. The same was extended to accommodate the inhibitory effect of the Inducible cAMP Early Repressor (ICER). ICER is the natural inducible CREB antagonist represses CRE-Mediated gene transcription involved in long-term plasticity for learning and memory. After verifying the sensitivity and robustness of the model, we had simulated it with various empirical levels of repressor concentration to analyse their effect on the gene induction. The model appears to predict the regulatory dynamics of repression on the L-LTP and agrees with the experimental values. The flux data obtained in the simulations demonstrate various aspects of equilibrium between the gene induction and repression.

Keywords: CREB, L-LTP, mathematical modeling, simulation

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Authors: B. Paygozar, S.A. Dizaji

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Adhesive bonding is a highly valued type of fastening mechanical parts in complex structures, where joining some simple components is always needed. This method is of several merits, such as uniform stress distribution, appropriate bonding strength, and fatigue performance, and lightness, thereby outweighing other sorts of bonding methods. This study is to investigate the failure load of adhesive single-strap joints, including adherends of different sizes and materials. This kind of adhesive joint is very practical in different industries, especially when repairing the existing joints or attaching substrates of dissimilar materials. In this research, experimentally validated numerical analyses carried out in a commercial finite element package, ABAQUS, are utilized to extract the failure loads of the joints, based on the cohesive zone model. In addition, the stress analyses of the substrates are performed in order to acquire the effects of lowering the thickness of the substrates on the stress distribution inside them to avoid designs suffering from the necking or failure of the adherends. It was found out that this method of bonding is really feasible in joining dissimilar materials which can be utilized in a variety of applications. Moreover, the stress analyses indicated the minimum thickness for the adherends so as to avoid the failure of them.

Keywords: cohesive zone model, dissimilar materials, failure load, single strap joint

Procedia PDF Downloads 108